About this Author
Derek Lowe, an Arkansan by birth, got his BA from Hendrix College and his PhD in organic chemistry from Duke before spending time in Germany on a Humboldt Fellowship on his post-doc. He's worked for several major pharmaceutical companies since 1989 on drug discovery projects against schizophrenia, Alzheimer's, diabetes, osteoporosis and other diseases.
To contact Derek email him directly: derekb.lowe@gmail.com
Twitter: Dereklowe
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Category Archives
February 10, 2012
Posted by Derek
Everyone who's done drug discovery has encountered this situation: you get what looks like a hit in a screening assay, but when you re-check it with fresh material, it turns out to be inactive. So you go back to the original batch, but it's still active. There are several possibilities: if that original batch was a DMSO solution, perhaps the compound has done something funny on standing, and you don't have what you thought you had. Maybe the DMSO stock was made from the wrong compound, or was mislabeled somehow - in which case, good luck figuring out what's really in there. If the original batch was a solid, the first thing to do is a head-to-head analysis (NMR, LC-mass spec) between the two. (That sort of purity check is actually the first thing you should do with interesting screening hits in general, as experienced chemists will have had several chances to learn).
But if those assay numbers repeat for both batches, you're in the realm of the Infinitely Active Impurity. The thinking is, and it's hard to find fault with it, that there must be something in Batch One that's causing the assay to light up, something that's not present in Batch Two. I found myself in this situation one time where the problem turned out to be that Batch One had the right structure, except it was a zinc complex, a fact the original submitters apparently hadn't appreciated. (We had to send out for metals analysis to confirm that one). In that case, the assay could be made to show a hit by adding zinc to most any compound you wanted, which wasn't too useful.
Most of the time, chasing after these things proves futile, which is frustrating for everyone involved. But not always. There's a recent example of a successful impurity hunt in ACS Medicinal Chemistry Letters, from a group at Pfizer searching for inhibitors of kynurenine aminotransferase II.
One of the hits was that compound 6 shown in the figure, but a second batch of it showed no activity at all. They dug into the original sample, and found that there was a touch of the N-hydroxy compound in it, and that was the reason for all the activity. Interestingly, it turns out that the amino group was involved in a covalent interaction with the enzyme's cofactor, pyridoxal-5′-phosphate (PLP). That's one of the things you probably want to suspect when you find such tiny amounts of a compound having such a large effect.
It's not a deal-breaker, but it's something to keep in mind. The whole topic of irreversible inhibitors has come up around here before, but it's worth another post soon, in light of the recent acquisition of Avila Pharmaceuticals, who specialized in this field. In this case, the compound isn't covalently attached to the protein, but rather to its bound cofactor, which would make people breath a bit easier. (And the group responsible for the covalency, an amine, isn't something to worry about, either).
Still, it's interesting to see this part of the paper:
"Although irreversible inhibition was not one of our lead criteria at the outset of the program, maintaining this attribute of 7 was a high priority through our optimization efforts. The potential advantages of irreversible inhibitors include low dose requirements and reduced off-target toxicity."
I say that because increased off-target toxicity has always been the worry with covalent drugs. But there's been a real revival of interest in the last few years - more on this next week.
Comments (12)
+ TrackBacks (0) | Category: Drug Assays | The Central Nervous System
February 2, 2012
Posted by Derek
Fluorine NMR is underused in chemistry. Well, then again, maybe it's not, but it's one of those thing that just seems like it should have more uses than it does. (Here's a recent book on the subject). Fluorine is a great NMR nucleus - all the F in the world is the same isotope, unless you're right next to a PET scanning facility - and the different compound show up over a very wide range of chemical shifts. You've got that going for you, coupling information, NOE, basically all your friends from proton NMR.
There's a pretty recent paper showing a good use of all these qualities (blogged about here at Practical Fragments as well). A group at Amgen reports on their work using fluorine NMR as a fragment-screening tool. They can take mixtures of 10 or 12 compounds at a time (because of all those different chemical shifts) and run the spectra with and without a target protein in the vial. If a fragment binds, its F peak broadens out (you can even get binding constants if you run at a few different concentrations). A simple overlay of the two spectra tells you immediately if you have hits. You don't need to have any special form of the protein, and you don't even need to run in deuterated solvents, since you're just ignoring protons altogether.
Interestingly, when they go on to try other assay techniques as follow-up, they find that the fluorines themselves aren't always a key part of the binding. Sometimes switching to the non-fluorinated version of the fragment gives you a better compound; sometimes it doesn't. The binding constants you get from the NMR, though, do compare very well to the ones from other assays.
The part I found most interesting was the intra-ligand NOE example. (That's also something that's been done in proton NMR, although it's not easy). They show a case where 19F ligands do get close enough to show the effect, and that a linked version of the two fragments does, as you'd hope, make a much more potent compound. That's the sort of thing that fragment people are always wanting to know - what fits next door to my hit? Can they be linked together? Fragment linking has its ups and downs, going back to the Abbott SAR-by-NMR days. That was a technique that never really panned out, as far as can be seen, but this is at least an experimentally easy way to give it a shot. (Of course, the chances of the fluorines on your ligands actually being pointed at each other is probably small, so that does cancel things out a bit).
Overall, it's a fun paper to read - well, allowing for my geeky interests, it is - and perhaps it'll lead a few more people to think of things that could be done with fluorine NMR in general. It's just sitting there, waiting to be used. . .
Comments (9)
+ TrackBacks (0) | Category: Analytical Chemistry | Drug Assays
January 6, 2012
Posted by Derek
Some of the discussions that come up here around clinical attrition rates and compound properties prompts me to see how much we can agree on. So, are these propositions controversial, or not?
1. Too many drugs fail in clinical trials. We are having a great deal of trouble going on with these failure rates, given the expense involved.
2. A significant number of these failures are due to lack of efficacy - either none at all, or not enough.
2a. Fixing efficacy failures is hard, since it seems to require deeper knowledge, case-by-case, of disease mechanisms. As it stands, we get a significant amount of this knowledge from our drug failures themselves.
2b. Better target selection without such detailed knowledge is hard to come by. Good phenotypic assays are perhaps the only shortcut, but a good phenotypic assays are not easy to develop and validate.
3. Outside of efficacy, a significant number of clinical failures are also due to side effects/toxicity. These two factors (efficacy and tox) account for the great majority of compounds that drop out of the clinic.
3a. Fixing tox/side effect failures through detailed knowledge is perhaps hardest of all, since there are a huge number of possible mechanisms. There are far more ways for things to go wrong than there are for them to work correctly.
3b. But there are broad correlations between molecular structures and properties and the likelihood of toxicity. While not infallible, these correlations are strong enough to be useful, and we should be grateful for anything we can get that might diminish the possibility of later failure.
Example of such structural features are redox-active groups like nitros and quinones, which really are associated with trouble - not invariably, but enough to make you very cautious. More broadly, high logP values are also associated with trouble in development - not as strongly, but strong enough to be worth considering.
So, is everyone pretty much in agreement with these things? What I'm saying is that if you take a hundred aryl nitro compounds into development, versus a hundred that don't have such a group, the latter cohort of compounds will surely have a higher success rate. And if you take a hundred compounds with logP values of 1 to 3 into development, these will have a higher success rate than a hundred compounds, against the same targets, with logP of 4 to 6. Do we believe this, or not?
Comments (34)
+ TrackBacks (0) | Category: Drug Assays | Drug Development | Toxicology
January 5, 2012
Posted by Derek
A new paper in Angewandte Chemie tries to open another front in relations between academic and drug industry chemists. It's from several authors at GSK-Stevenage, and it proposes something they're calling "Lead-Oriented Synthesis". So what's that?
Well, the paper itself starts out as a quick tutorial on the state and practice of medicinal chemistry. That's a good plan, since Angewandte Chemie is not primarily a med-chem journal (he said with a straight face). Actually, it has the opposite reputation, a forum where high-end academic chemistry gets showplaced. So the authors start off by reminded the readership what drug discovery entails. And although we've had plenty of discussions around here about these topics, I think that most people can agree on the main points laid out:
1. Physical properties influence a drug's behavior.
2. Among those properties, logP may well be the most important single descriptor,
3. Most successful drugs have logP values between 1 and perhaps 4 or 5. Pushing the lipophilicity end of things is, generally speaking, asking for trouble.
4. Since optimization of lead compounds almost always adds molecular weight, and very frequently adds lipophilicity, lead compounds are better found in (and past) the low ends of these property ranges, to reduce the risk of making an unwieldy final compound.
As the authors take pains to say, though, there are many successful drugs that fall outside these ranges. But many of those turn out to have some special features - antibacterial compounds (for example) tend to be more polar outliers, for reasons that are still being debated. There is, though, no similar class of successful less polar than usual drugs, to my knowledge. If you're starting a program against a target that you have no reason to think is an outlier, and assuming you want an oral drug for it, then your chances for success do seem to be higher within the known property ranges.
So, overall, the GSK folks maintain that lead compounds for drug discovery are most desirable with logP values between -1 and 3, molecular weights from around 200 to 350, and no problematic functional groups (redox-active and so on). And I have to agree; given the choice, that's where I'd like to start, too. So why are they telling all this to the readers of Angewandte Chemie? Because these aren't the sorts of compounds that academic chemists are interested in making.
For example, a survey of the 2009 issues of the Journal of Organic Chemistry found about 32,700 compounds indexed with the word "preparation" in Chemical Abstracts, after organometallics, isotopically labeled compounds, and commercially available ones were stripped out. 60% of those are outside the molecular weight criteria for lead-like compounds. Over half the remainder fail cLogP, and most of the remaining ones fail the internal GSK structural filters for problematic functional groups. Overall, only about 2% of the JOC compounds from that year would be called "lead-like". A similar analysis across seven other synthetic organic journals led to almost the same results.
Looking at array/library synthesis, as reported in the Journal of Combinatorial Chemistry and from inside GSK's own labs, the authors quantify something else that most chemists suspected: the more polar structures tend to drop out as the work goes on. This "cLogP drift" seems to be due to incompatible chemistries or difficulties in isolation and purification, and this could also illustrate why many new synthetic methods aren't applied in lead-like chemical space: they don't work as well there.
So that's what underlies the call for "lead-oriented synthesis". This paper is asking for the development of robust reactions which will work across a variety of structural types, will be tolerant of polar functionalities, and will generate compounds without such potentially problematic groups as Michael acceptors, nitros, and the like. That's not so easy, when you actually try to do it, and the hope is that it's enough of a challenge to attract people who are trying to develop new chemistry.
Just getting a high-profile paper of this sort out into the literature could help, because it's something to reference in (say) grant applications, to show that the proposed research is really filling a need. Academic chemists tend, broadly, to work on what will advance or maintain their positions and careers, and if coming up with new reactions of this kind can be seen as doing that, then people will step up and try it. And the converse applies, too, and how: if there's no perceived need for it, no one will bother. That's especially true when you're talking about making molecules that are smaller than the usual big-and-complex synthetic targets, and made via harder-than-it-looks chemistry.
Thoughts from the industrial end of things? I'd be happy to see more work like this being done, although I think it' going to take more than one paper like this to get it going. That said, the intersection with popular fragment-based drug design ideas, which are already having an effect in the purely academic world of diversity-oriented synthesis, might give an extra impetus to all this.
Comments (34)
+ TrackBacks (0) | Category: Chemical News | Drug Assays | Drug Development | The Scientific Literature
December 6, 2011
Posted by Derek
There's a new paper coming to the defense of rhodanines, a class of compound that has been described as "polluting the scientific literature". Industrial drug discovery people tend to look down on them, but they show up a lot, for sure.
This new paper starts off sounding like a call to arms for rhodanine fans, but when you actually read it, I don't think that there's much grounds for disagreement. (That's a phenomenon that's worth writing about sometime by itself - the disconnects between title/abstract and actual body text that occur in the scientific literature). As I see it, the people with a low opinion of rhodanines are saying "Look out! These things hit in a lot of assays, and they're very hard to develop into drugs!". And this paper, when you read the whole thing, is saying something like "Don't throw away all the rhodanines yet! They hit a lot of things, but once in a while one of them can be developed into a drug!" The argument is between people who say that elephants are big and people who say that they have trunks.
The authors prepared a good-sized assortment of rhodanines and similar heterocycles (thiohydantoins, hydantoins, thiazolidinediones) and assayed them across several enzymes. Only the ones with double-bonded sulfur (rhodanines and thiohydantoins) showed a lot of cross-enzyme potency - that group has rather unusual electronic properties, which could be a lot of the story. Here's the conclusion, which is what makes me think that we're all talking about the same thing:
We therefore think that rhodanines and related scaffolds should not be regarded as problematic or promiscuous binders per se. However, it is important to note that the intermolecular interaction profile of these scaffolds makes them prone to bind to a large number of targets with weak or moderate affinity. It may be that the observed moderate affinities of rhodanines and related compounds, e.g. in screening campaigns, has been overinterpreted in the past, and that these compounds have too easily been put forward as lead compounds for further development. We suggest that particularly strong requirements, i.e. affinity in the lower nanomolar range and proven selectivity for the target, are applied in the further assessment of rhodanines and related compounds. A generalized "condemnation" of these chemotypes, however, appears inadequate and would deprive medicinal chemists from attractive building blocks that possess a remarkably high density of intermolecular interaction points.
That's it, right there: the tendency to bind off-target, as noted by these authors, is one of the main reasons that these compounds are regarded with suspicion in the drug industry. We know that we can't test for everything, so when you have one of these structures, you're always fearful of what else it can do once it gets into an animal (or a human). Those downstream factors - stability, pharmacokinetics, toxicity - aren't even addressed in this paper, which is all about screening hits. And that's another source of the bad reputation, for industry people: too many times, people who aren't so worried about those qualities have screening commercial compound collections, come up with rhodanines, and published them as potential drug leads, when (as this paper illustrates), you have to be careful even using them as tool compounds. Given a choice, we'd just rather work on something else. . .
Comments (7)
+ TrackBacks (0) | Category: Drug Assays | Drug Development | The Scientific Literature
November 7, 2011
Posted by Derek
Here's an interesting exercise carried out in the medicinal chemistry departments at J&J. The computational folks took all the molecules in the company's files, and then all the commercially available ones (over five million compounds), minus natural products, which were saved for another effort, and minus the obviously-nondruglike stuff (multiple nitro groups, solid hydrocarbons with no functionality, acid chlorides, etc.) They then clustered things down into (merely!) about 20,000 similarity clusters, and asked the chemists to rate them with up, down, or neutral votes.
What they found was that the opinions of the med-chem staff seemed to match known drug-like properties very closely. Molecular weights in the 300 to 400 range were most favorably received, while the likelihood of a downvote increased below 250 or above 425 or so. Similar trends held for rotatable bonds, hydrogen bond donors and acceptors, clogP, and other classic physical property descriptors. Even the ones that are hard to eyeball, like polar surface area, fell into line.
It's worth asking if that's a good thing, a bad thing, or nothing surprising at all. The authors themselves waffle a bit on that point:
The results of our experiment are fully consistent with prior literature on what confers drug- or lead-like characteristics to a chemical substance. Whether the strategy will yield the desired results in the long term with respect to quality, novelty, and number of hits/leads remains to be seen. It is also unclear whether this strategy can lead to sufficient differentiation from a competitive stand-point. In the meantime, the only undeniable benefits we can point to is that we harnessed our chemists’ opinions to select lead-like molecules that are totally within reasonable property ranges, that fill diversity holes, and that have been purchased for screening, and that we did so in a way that promoted greater transparency, greater awareness, greater collaboration, and a renewed sense of involvement and engagement of our employees.
I'll certainly give them the diversity-of-the-screening-deck point. But I'm not so sure about that renewed sense of involvement stuff. Apparently 145 chemists participated in total (this effort was open to everyone), but no mention is made of what fraction of the total staff that might be. People were advised to try to vote on at least 2,000 clusters (!), but fewer than half the participants even made it that far. Ten people made it halfway through the lot, and 6 lunatics actually voted on every single one of the 22,015 clusters, which makes me think that they had way too much time on their hands and/or have interesting and unusual personality features. A colleague's reaction to that figure was "Wow, they'll have to track those people down", to which my uncharitable reply was "Yeah, with a net".
So while this paper is interesting to read, I can't say that I would have been all that happy participating in it (although I've certainly had smaller-scale experiences of this type). And I'd like to know what the authors thought when they finally assembled all the votes and realized that they'd recapitulated a set of filters that they could have run in a few seconds, since they're surely already built into their software. And we all should reflect on how thoroughly we seem to have incorporated Lipinski's rules into our own software, between our ears. On balance, it's probably a good thing, but it's not without a price.
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+ TrackBacks (0) | Category: Drug Assays | Life in the Drug Labs
October 26, 2011
Posted by Derek
With all the recent talk about the NIH's translational research efforts, and the controversy about their drug screening efforts, this seems like a good time to note this interview with Francis Collins over at BioCentury TV. (It's currently the lead video, but you'll be able to find it in their "Show Guide" afterwards as well).
Collins says that they're not trying to compete with the private sector, but taking a look at the drug development process "the way an engineer would", which takes me back to this morning's post re: Andy Grove. One thing he emphasizes is that he believes that the failure rate is too high because the wrong targets are being picked, and that target validation would be a good thing to improve.
He's also beating the drum for new targets to come out of more sequencing of human genomes, but that's something I'll reserve judgment on. The second clip has some discussion of the DARPA-backed toxicology chip and some questions on repurposing existing drugs. The third clip talks about the FDA's role in all this, and tries to clarify what NIH's role would be in outlicensing any discoveries. (Collins also admits along the way that the whole NCATS proposal has needed some clarifying as well, and doesn't sound happy with some of the press coverage).
Part 5 (part 4 is just a short wrap-up) discusses the current funding environment, and then moves into ethics and conflicts of interest - other people's conflicts, I should note. Worth a lunchtime look!
Comments (16)
+ TrackBacks (0) | Category: Academia (vs. Industry) | Drug Assays | Drug Development
October 21, 2011
Posted by Derek
Science is reporting some problems with the NIH's drug screening efforts:
A $70-million-a-year program launched 7 years ago at the National Institutes of Health (NIH) to help academic researchers move into industry-style drug discovery may soon be forced to scale back sharply. NIH Director Francis Collins has been one of its biggest champions. But the NIH Molecular Libraries, according to plan, must be weaned starting next year from the NIH director's office Common Fund and find support at other NIH institutes. In a time of tight budgets, nobody wants it.
The fate of the Molecular Libraries program became “an extremely sensitive political issue” earlier this year when NIH realized it would not be easy to find a new home for the program, said one NIH official speaking on background. . .
. . .John Reed, head of the Sanford-Burnham Medical Research Institute screening center in San Diego, which receives about $16 million a year from the Common Fund, says his center has so far attracted only modest funding from drug companies. He expressed frustration with the Common Fund process. “NIH has put a huge investment into [the Molecular Libraries], and it's running very well,” he says. “If there's not a long-term commitment to keep it available to the academic community, why did we make this hundreds of millions of dollars investment?”
Good question! This all grew out of the 2003 "NIH Roadmap" initiative - here's a press release from better days. But it looks partly to be a victim of sheer bad timing. There's not a lot of extra money sloshing around the drug industry these days, and there sure isn't a lot in NIH's budget, either. You wouldn't know that there's a problem at all from looking at the program's web site, would you?
Since I know there are readers out there from both sides of this particular fence, I'd be interesting in hearing some comments. Has the screening initiative been worthwhile? Should it be kept up - and if so, how?
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+ TrackBacks (0) | Category: Academia (vs. Industry) | Drug Assays
October 7, 2011
Posted by Derek
Now here's a paper, packed to the edges with data, on what kinds of drug candidate compounds different companies produce. The authors assembled their list via the best method available to outsiders: they looked at what compounds are exemplified in patent filings
What they find is that over the 2000-2010 period that not much change has taken place, on average, in the properties of the molecules that are showing up. Note that we're assuming, for purposes of discussion, that these properties - things like molecular weight, logP, polar surface area, amount of aromaticity - are relevant. I'd have to say that they are. They're not the end of the discussion, because there are plenty of drugs that violate one or more of these criteria. But there are even more that don't, and given the finite amount of time and money we have to work with, you're probably better off approaching a new target with five hundred thousand compounds that are well within the drug-like properties boxes rather than five hundred thousand that aren't. And at the other end of things, you're probably better off with ten clinical candidates that mostly fit versus ten that mostly don't.
But even if overall properties don't seem to be changing much, that doesn't mean that there aren't differences between companies. That's actually the main thrust of the paper: the authors compare Abbott, Amgen, AstraZeneca, Bayer-Schering, Boehringer, Bristol-Myers Squibb, GlaxoSmithKline, J&J, Lilly, Merck, Novartis, Pfizer, Roche, Sanofi, Schering-Plough, Takeda, Wyeth, and Vertex. Of course, these organizations filed different numbers of patents, on different targets, with different numbers of compounds. For the record, Merck and GSK filed the most patents during those ten years (over 1500), while Amgen and Takeda filed the fewest (under 300). Merck and BMS had the largest number of unique compounds (over 70,000), and Takeda and Bayer-Schering had the fewest (in the low 20,000s). I should note that AstraZeneca just missed the top two in both patents and compounds.
If you just look at the raw numbers, ignoring targeting and therapeutic areas, Wyeth, Bayer-Schering, and Novartis come out looking the worst for properties, while Vertex and Pfizer look the best. But what's interesting is that even after you correct for targets and the like, that organizations still differ quite a bit in the sorts of compounds that they turn out. Takeda, Lilly, and Wyeth, for example, were at the top of the cLogP rankings (numberically, "top" meaning the greasiest). Meanwhile, Vertex, Pfizer, and AstraZeneca were at the other end of the scale in cLogP. In molecular weight, Novartis, Boehringer, and Schering-Plough were at the high end (up around 475), while Vertex was at the low end (around 425). I'm showing a radar-style plot from the paper where they cover several different target-unbiased properties (which have been normalized for scale), and you can see that different companies do cover very different sorts of space. (The numbers next to the company names are the total number of shared targets found and the total number of shared-target observations used - see the paper if you need more details on how they compiled the numbers).
Now, it's fair to ask how relevant the whole sweep of patented compounds might be, since only a few ever make it deep into the clinic. And some companies just have different IP approaches, patenting more broadly or narrowly. But there's an interesting comparison near the end of the paper, where the authors take a look at the set of patents that cover only single compounds. Now, those are things that someone has truly found interesting and worth extra layers of IP protection, and they average to significantly lower molecular weights, cLogP values, and number of rotatable bonds than the general run of patented compounds. Which just gets back to the points I was making in the first paragraph - other things being equal, that's where you'd want to spend more of your time and money.
What's odd is that the trends over the last ten years haven't been more pronounced. As the paper puts it:
blockquote>Over the past decade, the mean overall physico-chemical space used by many pharmaceutical companies has not changed substantially, and the overall output remains worryingly at the periphery of historical oral drug chemical space. This is despite the fact that potential candidate drugs, identified in patents protecting single compounds, seem to reflect physiological and developmental pressures, as they have improved drug-like properties relative to the full industry patent portfolio. Given these facts, and the established influence of molecular properties on ADMET risks and pipeline progression, it remains surprising that many organizations are not adjusting their strategies.
The big question that this paper leaves unanswered, because there's no way for them to answer it, is how these inter-organizational differences get going and how they continue. I'll add my speculations in another post - but speculations they will be.
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+ TrackBacks (0) | Category: Drug Assays | Drug Development | Drug Industry History
September 14, 2011
Posted by Derek
I'm still at the RSC/SCI symposium in Cambridge, and a talk yesterday by Marta Pineiro-Nuñez gives me a chance to update this post about Eli Lilly's foray into opening up its screening to outside collaboration. That effort has been working away for the last two or three years, and the company is now revealing some details about how it's been going.
The original plan was to allow people to put compounds into a set of Lilly phenotypic screens. No structures would be revealed, and the company would have "first rights of negotiated access" for any interesting hits. They have a new web gateway for the whole thing, since now they've added several target-based screens to the process. As mentioned in the earlier post, they've come up with a universal Material Transfer Agreement to bring the compounds in, but Pineiro-Nuñez said that this was still a bit of a struggle at first. Small companies were pretty open to the idea, she said, but there were some suspicious responses from academia, with a lot of careful digging through the MTA to make sure that they wouldn't be giving away too much.
But things seem to have gotten going pretty well. According to the presentation, Lilly has 252 affiliations in 27 countries. That breaks down as 174 academic partners and 78 small companies. About 42,000 compounds have been accepted for screening - that's after a firewalled computational screen of the structures to eliminate nasty functional groups and the like. About 40% of the submissions fail the suitability screens, but the single biggest reason is lack of structural novelty - too close to marketed drugs, too close to controlled substances, or too close to things that are already in Lilly's files.
Here's a recent overview of the screening results. In the end, 115 structures were requested for disclosure, and 97 of those ended up being shared with Lilly, who still wanted 13 of them after looking them over. And those have (so far) led to two recent signed collaborations, with one more set to go and two others still in negotiations. The compounds certainly aren't instant clinical candidates, but have been interesting enough to put money on. And so far, the initiative is seen as successful, enough to expand it to more assays.
It'll be interesting to see if more companies try this out. It would seem especially suited for unusual proprietary assays that might be hiding behind industrial walls. Having Lilly demonstrate that a model of this sort can actually work in practice should help - congratulations to them for putting the work in to make it happen.
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+ TrackBacks (0) | Category: Drug Assays
September 7, 2011
Posted by Derek
We've talked here before about the structural class known as rhodanines - the phrase "polluting the scientific literature" has been used to describe them, since they rather promiscuously light up a lot of drug target assays, and almost never to any useful effect.
Well, guess what? Now there's an even easier way to make them! And says this new paper in the Journal of Organic Chemistry:
5-(Z)-Alkylidene-2-thioxo-1,3-thiazolidin-4-ones (rhodanine derivatives) were prepared by reaction of in situ generated dithiocarbamates with recently reported racemic α-chloro-β,γ-alkenoate esters. This multicomponent sequential transformation performed in one reaction flask represents a general route to this medicinally valuable class of sulfur/nitrogen heterocycles. Using this convergent procedure, we prepared an analogue of the drug epalrestat, an aldose reductase inhibitory rhodanine.
Sequentially linking several different components in one reaction vessel has been studied intensively as a rapid way to increase molecular complexity while avoiding costly and environmentally unfriendly isolation and purification of intermediates.(1-4) Such efficient multicomponent reactions, such as the Ugi reaction, often produce privileged scaffolds of considerable medicinal value. Rhodanines (2-thioxo-1,3-thiazolidin-4-ones) are five-membered ring sulfur/nitrogen heterocycles some of which have antimalarial, antibacterial, antifungal, antiviral, antitumor, anti-inflammatory, or herbicidal activities. . .In conclusion, convergent syntheses of N-alkyl 5-(Z)-alkylidene rhodanine derivatives have been achieved using recently reported racemic α-chloro-β,γ-alkenoate ester building blocks. The formation of these rhodanine derivatives involves a three-step, one-flask protocol that provides quick access to biologically valuable sulfur–nitrogen heterocycles.
Just what we needed. Now it's only going to be a matter of time before someone makes and sells a library of these things, and we can all get to see them again as screening hits in the literature.
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+ TrackBacks (0) | Category: Chemical News | Drug Assays
September 2, 2011
Posted by Derek
So, are half the interesting new results in the medical/biology/med-chem literature impossible to reproduce? I linked earlier this year to an informal estimate from venture capitalist Bruce Booth, who said that this was his (and others') experience in the business. Now comes a new study from Bayer Pharmaceuticals that helps put some backing behind those numbers.
To mitigate some of the risks of such investments ultimately being wasted, most pharmaceutical companies run in-house target validation programmes. However, validation projects that were started in our company based on exciting published data have often resulted in disillusionment when key data could not be reproduced. Talking to scientists, both in academia and in industry, there seems to be a general impression that many results that are published are hard to reproduce. However, there is an imbalance between this apparently widespread impression and its public recognition. . .
Yes, indeed. The authors looked back at the last four years worth of oncology, women's health, and cardiovascular target validation efforts inside Bayer (this would put it right after they combined with Schering AG of Berlin). They surveyed all the scientists involved in early drug discovery in those areas, and had them tally up the literature results they'd acted on and whether they'd panned out or not. I should note that this is the perfect place to generate such numbers, since the industry scientists are not in it for publication glory, grant applications, or tenure reviews: they're interested in finding drug targets that look like they can be prosecuted, in order to find drugs that could make them money. You may or may not find those to be pure or admirable motives (I have no problem at all with them, personally!), but I think we can all agree that they're direct and understandable ones. And they may be a bit orthogonal to the motives that led to the initial publications. . .so, are they? The results:
"We received input from 23 scientists (heads of laboratories) and collected data from 67 projects, most of them (47) from the field of oncology. This analysis revealed that only in ~20–25% of the projects were the relevant published data completely in line with our in-house findings. In almost two-thirds of the projects, there were inconsistencies between published data and in-house data that either considerably prolonged the duration of the target validation process or, in most cases, resulted in termination of the projects. . ."
So Booth's estimate may actually have been too generous. How does this gap get so wide? The authors suggest a number of plausible reasons: small sample sizes in the original papers, leading to statistical problems, for one. The pressure to publish in academia has to be a huge part of the problem - you get something good, something hot, and you write that stuff up for the best journal you can get it into - right? And it's really only the positive results that you hear about in the literature in general, which can extend so far as (consciously or unconsciously) publishing just on the parts that worked. Or looked like they worked.
But the Bayer team is not alleging fraud - just irreproducibility. And it seems clear that irreproducibility is a bigger problem than a lot of people realize. But that's the way that science works, or is supposed to. When you see some neat new result, your first thought should be "I wonder if that's true?" You may have no particular reason to doubt it, but in an area with as many potential problems as discovery of new drug targets, you don't need any particular reasons. Not all this stuff is real. You have to make every new idea perform the same tricks in front of your own audience, on your own stage under bright lights, before you get too excited.
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August 26, 2011
Posted by Derek
We're going to need new antibiotics. Everyone knows this, and it's not like no one's been trying to do anything about it, either, but. . .we're still going to need more of them than we have. I'm not predicting that we're going to go all the way back to a world where young, healthy people with access to the best medical care die because they decided to play tennis without their socks on, but we're certainly in danger of a much nastier world than we have.
So I'm always interested to hear of new antibiotic discovery programs, and Merck is out with an interesting paper on theirs. They've been digging through the natural products, which have been the fount from which almost all antibiotics have sprung, and they have a new one called kibdelomycin to report. This one was dug out from an organism in a sample from the Central African Republic by a complicated but useful screening protocol, the S. aureus fitness test. This relies on 245 different engineered strains of the bacterium, each with an inducible RNAi pathway to downregulate some essential gene. When you pool these into mixed groups and grow them in the presence of test compounds (or natural product extracts) for 20 generations or so, a check of what strains have moved ahead (and fallen behind) can tell you what pathways you seem to be targeting. A key feature is that you can compare the profile you get with those of known antibiotics, so you don't end up rediscovering something (or discovering something that only duplicates what we already have anyway).
Now, that's no one's idea of a beautiful structure, although (to be fair) a lot of antibiotics have very weird structures themselves. But it's safe to say that there are some features there that could be trouble in a whole animal, such as that central keto-enol-pyrrolidone ring and the funky unsaturated system next to it. (The dichloropyrrole, though, is interestingly reminiscent of these AstraZeneca gyrase/topoisomerase antibiotic candidates, while both celestramycin and pyoluteorin have a different dichloropyrrole in them).
What kind of activity does kibdelomycin have? Well, this is where my enthusiasm cools off just a bit more. It showed up in screening with a profile similar to the coumarin antibiotics novobiocin and chlorobiocin, and sure enough, it's a topoisomerase II inhibitor. It appears to be active almost entirely on gram-positive organisms. And while there are certainly nasty gram-positive infections that have to be dealt with, I'm more encouraged when I see something that hits gram-negatives as well. They've got more complicated defenses, those guys, and they're harder to kill. It's not easy to get broad-spectrum activity when you're going after gyrase/Topo II, but the fluoroquinolones definitely manage it.
The Merck team makes much out of kibdelomycin being "the first truly novel bacterial type II topoisomerase inhibitor with potent antibacterial activity discovered from natural product sources in more than six decades". And they're right that this is an accomplishment. But the kicker in that sentence is "from natural product sources". Getting gram-positive Topo II inhibitors has actually been one of the areas where synthetic compounds have had the most success. Building off the quinolones themselves has been a reasonably fruitful strategy, and a look through the literature turns up a number of other structural classes with this sort of activity (including some pretty wild ones). Not all of these are going places, but there are certainly a number of possibilities out there.
In short, if kibdelomycin weren't an odd-looking natural product, I wonder how much attention another high-molecular-weight gram-positive-only topoisomerase inhibitor would be getting, especially with only in vitro data behind it. Every little bit helps, and having a new structural class to work from is a worthwhile discovery. But one could still want (and hope) for more.
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August 18, 2011
Posted by Derek
A couple of years ago, I wrote here about an initiative from Pfizer. They were proposing letting other (smaller) companies screen their compound collection, with rights to be worked out if something interesting turned up.
The thing is, I haven't heard about anyone taking them up on it. Does anyone know if this ever got off the ground, or did it get lost in the trackless Pfizer territories somewhere? It sounded like a reasonable idea in some ways, and I'm curious if it ever went anywhere. . .
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July 27, 2011
Posted by Derek
You hear often about how many marketed drugs target G-protein coupled receptors (GPCRs). And it's true, but not all GPCRs are created equal. There's a family of them (the Class B receptors) that has a number of important drug targets in it, but getting small-molecule drugs to hit them has been a real chore. There's Glucagon, CRF, GHRH, GLP-1, PACAP and plenty more, but they all recognize good-sized peptides as ligands, not friendly little small molecules. Drug-sized things have been found that affect a few of these receptors, but it has not been easy, and pretty much all of them have been antagonists. (That makes sense, because it's almost always easier to block some binding event rather than hitting the switch just the right way to turn a receptor on).
That peptide-to-receptor binding also means that we don't know nearly as much about what's going on in the receptor as we do for the small-molecule GPCRs, either (and there are still plenty of mysteries around even those). The generally accepted model is a two-step process: there's an extra section of the receptor protein that sticks out and recognizes the C-terminal end of the peptide ligand first. Once that's bound, the N-terminal part of the peptide ligand binds into the seven-transmembrane-domain part of the receptor. The first part of that process is a lot more well-worked-out than the second.
Now a German team has reported an interesting approach that might help to clear some things up. They synthesized a C-terminal peptide that was expected to bind to the extracellular domain of the CRF receptor, and made it with an azide coming off its N-terminal end. (Many of you will now have guessed where this is going!) Then they took a weak peptide agonist piece and decorated its end with an acetylene. Doing the triazole-forming "click" reaction between the two gave a nanomolar agonist for the receptor, revving up the activity of the second peptide by at least 10,000x.
This confirms the general feeling that the middle parts of the peptide ligands in this class are just spacers to hold the two business ends together in the right places. But it's a lot easier to run the "click" reaction than it is to make long peptides, so you can mix and match pieces more quickly. That's what this group did next, settling on a 12-amino-acid sequence as their starting point for the agonist peptide and running variations on it.
Out of 89 successful couplings to the carrier protein, 70 of the new combinations lowered the activity (or got rid of it completely). 15 were about the same as the original sequence, but 11 of them were actually more potent. Combining those single-point changes into "greatest-hit" sequences led to some really potent compounds, down to picomolar levels. And by that time, they found that they could get rid of the tethered carrier protein part, ending up with a nanomolar agonist peptide that only does the GPCR-binding part and bypasses the extracellular domain completely. (Interestingly, this one had five non-natural amino acid substitutions).
Now that's a surprise. Part of the generally accepted model for binding had the receptor changing shape during that first extracellular binding event, but in the case of these new peptides, that's clearly not happening. These things are acting more like the small-molecule GPCR agonists and just going directly into the receptor to do their thing. The authors suggest that this "carrier-conjugate" approach should speed up screening of new ligands for the other receptors in this category, and should be adaptable to molecules that aren't peptides at all. That would be quite interesting indeed: leave the carrier on until you have enough potency to get rid of it.
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July 7, 2011
Posted by Derek
Here's another new article in Nature Reviews Drug Discovery that (for once) isn't titled something like "The Productivity Crisis in Drug Research: Hire Us And We'll Consult Your Problems Away". This one is a look back at where drugs have come from.
Looking over drug approvals (259 of them) between 1999 and 2008, the authors find that phenotypic screens account for a surprising number of the winners. (For those not in the business, a phenotypic screen is one where you give compounds to some cell- or animal-based assay and look for effects. That's in contrast to the target-based approach, where you identify some sort of target as being likely important in a given disease state and set out to find a molecule to affect it. Phenotypic screens were the only kinds around in the old days (before, say, the mid-1970s or thereabouts), but they've been making a comeback - see below!)
Out of the 259 approvals, there were 75 first-in-class drugs and 164 followers (the rest were imaging agents and the like). 100 of the total were discovered using target-based approaches, 58 through phenotypic approaches, and 18 through modifying natural substances. There were also 56 biologics, which were all assigned to the target-based category. But out of the first-in-class small molecules, 28 of them could be assigned to phenotypic assays and only 17 to target-based approaches. Considering how strongly tilted the industry has been toward target-based drug discovery, that's really disproportionate. CNS and infectious disease were the therapeutic areas that benefited the most from phenotypic screening, which makes sense. We really don't understand the targets and mechanisms in the former, and the latter provide what are probably the most straightforward and meaningful phenotypic assays in the whole business. The authors' conclusion:
(this) leads us to propose that a focus on target-based drug discovery, without accounting sufficiently for the MMOA (molecular mechanism of action) of small-molecule first-in-class medicines, could be a technical reason contributing to high attrition rates. Our reasoning for this proposal is that the MMOA is a key factor for the success of all approaches, but is addressed in different ways and at different points in the various approaches. . .
. . .The increased reliance on hypothesis-driven target-based approaches in drug discovery has coincided with the sequencing of the human genome and an apparent belief by some that every target can provide the basis for a drug. As such, research across the pharmaceutical industry as well as academic institutions has increasingly focused on targets, arguably at the expense of the development of preclinical assays that translate more effectively into clinical effects in patients with a specific disease.
I have to say, I agree (and have said so here on the blog before). It's good to see some numbers put to that belief, though. This, in fact, was the reason why I thought that the NIH funding for translational research might be partly spent on new phenotypic approaches. Will we look back on the late 20th century/early 21st as a target-based detour in drug discovery?
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May 6, 2011
Posted by Derek
PNAS recently came out with a special concentration of chemistry papers, and they're worth a look. The theme is the synthesis of chemical probes, which makes me think that maybe Stuart Schrieber can guest-edit an issue of Vogue next. Today I'm going to highlight one from the Broad Institute on diversity-oriented synthesis (DOS), and next week I'll get to some more.
OK, that was something of a come-on for regular readers of this site, who now will be listening for the sound of grinding wheels coming up to speed, the better to sharpen the Sword of Justice. I've said unfriendly things in the past about DOS and some of the claims made for it. The point of much of this work has been lost on me, and I'm a pretty broad-minded guy. (That word, in this case, rhymes with "sawed", not with "load"). The first flush (no aspersions meant) of papers in the field might just as well have been titled "Check It Out: A Bunch of Huge Compounds No One's Ever Made Before", and were followed up, in my mind, by landmark publications such as "A Raving Heapload of Structures You Didn't Want in the First Place" and "Dang, There Are Even More Compounds With Molecular Weight 850 Than We Thought". But does it have to be this way?
Maybe not. As I mentioned earlier this year, people are starting to compare DOS and fragment-based approaches. (I think that Nature dialog could have been more useful than it was, but it was a start). And this latest paper continues that process. It's using DOS approaches to generate smaller molecular weight compounds - fragments, actually. They're not tiny ones, more medium-to-large size by fragment-based standards, but they're under 300 MW.
And, importantly, they're deliberately designed to be three-dimensional - lots of pyrrolidines and fused-ring compounds thereof, homopiperidines, spiro-lactams, and so on. Many of the early fragment libraries (and many of the commercial ones that you can still buy) are too invested in small, flat, heterocycles. It's not that you can't get good leads from those things, but there's a lot more to life (and to molecular property space). This paper's collection is still a bit heavy on the alkenes to my taste (all those ring-closing metathesis reactions), but they've also reduced those for part of the library, which means that a screen of this collection will tell you if the olefin is a key structural feature or not. The alkenes themselves could serve as useful handles to build out from as well; a fragment hit with no ways to elaborate its structure isn't too useful.
As I said back in February, "I'd prefer that DOS collections not get quite so carried away, and explore new structural motifs more in the range of druglike space." That's exactly what this paper does, and I think its direction should be encouraged. This plays to the strengths of both approaches, rather than pushing either of them to the point where they break down.
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May 3, 2011
Posted by Derek
Now here's a comparison that you don't get to see very often: how much do two large pharma compound collections overlap? There's a paper going into just that question in the wake of the 2006-2007 merger between Bayer and Schering AG. (By two coincidences, this paper is in the same feed as the one that I highlighted yesterday, and that merger is the one that closed my former research site out from under me).
Pre-merger, Bayer had over two million structures in its corporate collection, and Schering AG had just under 900,000. Both companies had undertaken recent library clean-up programs, clearing out undesirable compounds and adding both purchased and in-house diversity structures. Interestingly, it turns out that just under 50,000 structures were duplicated across both collections, about 1.5% of the total. Almost all of these duplicates were purchased compounds; only 2,000 of them had been synthesized in-house. And even most of those turned out to be from combichem programs or were synthetic intermediates - there was almost no overlap at all in submitted med-chem compounds.
Various measures of structural complexity and similarity backed up those numbers. The two collections were surprisingly different, which might well have something to do with the different therapeutic areas the two companies had focused on over the years. The Bayer compounds tended to run higher in molecular weight, rotatable bonds, and clogP, but then, a higher percentage of the Schering AG compounds were purchased with such filters already in place. As for undesirable structures, only about 2% of the Bayer collection and 1% of the Schering AG compounds were considered to be real offenders. I hope none of those were mine; I contributed quite a few compounds to the collection over the years, but they were, for the most part, relatively sane.
The paper's conclusion can be read in more than one way:
Furthermore, an argument that might support mergers and acquisitions (M&A) in the pharmaceutical sector can be harvested from this analysis. Currently, M&As in this industry are driven by product portfolios rather than by drug discovery competencies. With the current need for innovative drugs, R&D skills of pharmaceutical companies might again become more important. The technological complementarity of two companies is often quoted as an important factor for successful M&As in the long term. If compound libraries are regarded as a kind of company knowledge-base, then a high degree of complementarity is clearly desirable and would improve drug discovery skills. Based on our data, the libraries of BHC and SAG are structurally complementary and fit together well in terms of their physico-chemical properties. However, it remains to be proven if this leads to additional innovative products.
Not so sure about that, myself. I don't know how good a proxy the compound collections are, since the represent an historical record as much as they do the current state of a company. And that paragraph glosses over the effect of mergers on R&D itself - it's not like just adding pieces together, that's for sure. The track record for mergers generating "additional innovative products" is not good. We'll see how the Bayer-Schering one holds up. . .
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February 7, 2011
Posted by Derek
Nature has side-by-side editorial pieces about fragment-based drug discovery versus diversity-oriented synthesis (DOS). I've written about both topics here before (DOS here and here, fragments here and here), and it should be fairly clear that I favor the former. But both ideas deserve a hearing.
Background, for those who aren't having to think about this stuff: the fragment-based approach is to screen a reasonable set (hundreds to low thousands) of small (MW 150 to 300) molecules. You won't find any nanomolar hits that way, but you will find things that (for that molecular weight) are binding extremely efficiently. If you can get a structure (X-ray, most of the time), you can then use that piece as a starting point and build out, trying to keep the binding efficiency high as you go. Diversity-oriented synthesis, on the other hand, tries to make larger molecules that are in structural spaces not found in nature (or in other screening collections, either). It's a deliberate attempt to make wild-blue-yonder compounds in untried areas, and is often used to screen against similarly untried targets that haven't shown much in conventional screening.
The two articles make their cases, but spend some time talking past each other. Abbott's Phil Hajduk takes the following shots at DOS: that it's tended to produce compounds whose molecular weights are too high (and whose other properties are also undesirable), and that it needs (in order to cover any meaningful amount of chemical space at those molecular weights) to produce millions of compounds, all of which must then be screened. Meanwhile, Warren Galloway and David Spring of Cambridge make the following charges about fragment work: that it only works when you have a specific molecular target in mind (and that only then when you have high-quality structural information), that it tends to perform poorly against the less tractable targets (such as protein-protein interactions), and that fragments (and the molecules derived from them) tend not to be three-dimensional enough.
Here's my take: I like phenotypic screening, where you run compound collections across cells/tissues/small animal models and see what works. And fragment are indeed next to useless for that purpose. But I agree with Hajduk that most of the DOS compound libraries I've seen are far too large and ugly to furnish anything more than a new probe compound from such screens. There are many academic labs for whom that's a perfectly good end point, and they publish a paper saying, in short, We Found the First Compound That Makes X Cells Do Y. Which is interesting, and can even be important, but there's often no path whatsoever from that compound to an actual drug. I'd prefer that DOS collections not get quite so carried away, and explore new structural motifs more in the range of druglike space. But that's not easy - new structures are a lot easier to come by if you're willing to make compounds with molecular weights of 500 to 1000, since (a) not so many people have made such beasts before, and (b) there are a lot more possible structures up there.
Now, if I have a defined target, and can get structures, I'd much prefer to do things the fragment way. But this is where the two editorial talk past each other - they both beat the drum for what they do well, but they do different things well. It's the parts where they overlap that I find most interesting. One of those is, as just mentioned, the problem that DOS compounds tend to be too large and undevelopable (with one solution being to go back and make them more tractable to start with). The other overlap is whether fragment collections can hit well against tough targets like protein-protein interactions. I don't know the answer to that one myself - I'd be glad to hear of examples both pro and con.
So we'll call this a struggle still in progress. With any luck, both techniques will keep each other's partisans on their toes and force them to keep improving.
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February 1, 2011
Posted by Derek
I've been meaning to comment on the NIH's new venture into drug discovery, the National Center for Advancing Translational Sciences. Curious Wavefunction already has some thoughts here, and I share his concerns. We're both worried about the gene-o-centric views of Francis Collins, for example:
Creating the center is a signature effort of Dr. Collins, who once directed the agency’s Human Genome Project. Dr. Collins has been predicting for years that gene sequencing will lead to a vast array of new treatments, but years of effort and tens of billions of dollars in financing by drug makers in gene-related research has largely been a bust.
As a result, industry has become far less willing to follow the latest genetic advances with expensive clinical trials. Rather than wait longer, Dr. Collins has decided that the government can start the work itself.
“I am a little frustrated to see how many of the discoveries that do look as though they have therapeutic implications are waiting for the pharmaceutical industry to follow through with them,” he said.
Odd how the loss of tens of billions of dollars - and vast heaps of opportunity cost along the way - will make people reluctant to keep going. And where does this new center want to focus in particular? The black box that is the central nervous system:
Both the need for and the risks of this strategy are clear in mental health. There have been only two major drug discoveries in the field in the past century; lithium for the treatment of bipolar disorder in 1949 and Thorazine for the treatment of psychosis in 1950.
Both discoveries were utter strokes of luck, and almost every major psychiatric drug introduced since has resulted from small changes to Thorazine. Scientists still do not know why any of these drugs actually work, and hundreds of genes have been shown to play roles in mental illness — far too many for focused efforts. So many drug makers have dropped out of the field.
So if there are far too many genes for focused efforts (a sentiment with which I agree), what, exactly, is this new work going to focus on? Wavefunction, for his part, suggests not spending so much time on the genetic side of things and working, for example, on one specific problem, such as Why Does Lithium Work for Depression? Figuring that out in detail would have to tell us a lot about the brain along the way, and boy, is there a lot to learn.
Meanwhile, Pharmalot links to a statement from the industry trade group (PhRMA) which is remarkably vapid. It boils down to "research heap good", while beating the drum a bit for the industry's own efforts. And as an industrial researcher myself, it would be easy for me to continue heaping scorn on the whole NIH-does-drug-discovery idea.
But I actually wish them well. There really are a tremendous number of important things that we don't know about this business, and the more people working on them, the better. You'd think. What worries me, though, is that I can't help but believe that a good amount of the work that's going to be done at this new center will be misapplied. I'm really not so sure that the gene-to-disease-target paradigm just needs more time and money thrown at it, for example. And although there will be some ex-industry people around, the details of drug discovery are still likely to come as a shock to the more academically oriented people.
Put simply, the sorts of discoveries and project that make stellar academic careers, that get into Science and Nature and all the rest of them, are still nowhere near what you need to make an actual drug. It's an odd combination of inventiveness and sheer grunt work, and not everyone's ready for it. One likely result is that some people will just avoid the stuff as much as possible and spend their time and money doing something else that pleases them more.
What do I think that they should be doing, then? One possibility is the Pick One Big Problem option that Wavefunction suggests. What I'd recommend would also go against the genetic tracery stuff: I'd put money into developing new phenotypic assays in cells, tissues, and whole animals. Instead of chasing into finer and finer biochemical details in search of individual targets, I'd try to make the most realistic testbeds of disease states possible, and let the screening rip on that. Targets can be chased down once something works.
But it doesn't sound like that's what's going to happen. So, reluctantly, I'll make a prediction: if years of effort and billions of dollars thrown after genetic target-based drug discovery hasn't worked out, when done by people strongly motivated to make money off their work, then an NIH center focused on the same stuff will, in all likelihood, add very little more. It's not like they won't stay busy. That sort of work can soak up all the time and money that you can throw at it. And it will.
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November 11, 2010
Posted by Derek
I've been reading an interesting new paper from Stuart Schreiber's research group(s) in PNAS. But I'm not sure if the authors and I would agree on the reasons that it's interesting.
This is another in the series that Schreiber has been writing on high-throughput screening and diversity-oriented synthesis (DOS). As mentioned here before, I have trouble getting my head around the whole DOS concept, so perhaps that's the root of my problems with this latest paper. In many ways, it's a companion to one that was published earlier this year in JACS. In that paper, he made the case that natural products aren't quite the right fit for drug screening, which fit with an earlier paper that made a similar claim for small-molecule collections. Natural products, the JACS paper said, were too optimized by evolution to hit targets that we don't want, while small molecules are too simple to hit a lot of the targets that we do. Now comes the latest pitch.
In this PNAS paper, Schreiber's crew takes three compound collections: 6,152 small commercial molecules, 2,477 natural products, and 6,623 from academic synthetic chemistry (with a preponderance of DOS compounds), for a total of 15, 252. They run all of these past a set of 100 proteins using their small-molecule microarray screening method, and look for trends in coverage and specificity. What they found, after getting rid of various artifacts, was that about 3400 compounds hit at least one protein (and if you're screening 100 proteins, that's a perfectly reasonable result). But, naturally, these hits weren't distributed evenly among the three compound collections. 26% of the academic compounds were hits, and 23% of the commercial set, but only 13% of the natural products.
Looking at specificity, it appears that the commercial compounds were more likely, when they hit, to hit six or more different proteins in the set, and the natural products the least. Looking at it in terms of compounds that hit only one or two targets gave a similar distribution - in each case, the DOS compounds were intermediate, and that turns out to be a theme of the whole paper. They analyzed the three compound collections for structural features, specifically their stereochemical complexity (chiral carbons as a per cent of all carbons) and shape complexity (sp3 carbons as a percent of the whole). And that showed that the commercial set was biased towards the flat, achiral side of things, while the natural products were the other way around, tilted toward the complex, multiple-chiral-center end. The DOS-centric screening set was right in the middle.
The take-home, then, is similar to the other papers mentioned above: small molecule collections are inadequate, natural product collections are inadequate: therefore, you need diversity-oriented synthesis compounds, which are just right. I'll let Schreiber sum up his own case:
. . .Both protein-binding frequencies and selectivities are increased among compounds having: (i) increased content of sp3-hybridized atoms relative to commercial compounds, and (ii) intermediate frequency of stereogenic elements relative to commercial (low frequency) and natural (high frequency) compounds. Encouragingly, these favorable structural features are increasingly accessible using modern advances in the methods of organic synthesis and commonly targeted by academic organic chemists as judged by the compounds used in this study that were contributed by members of this community. On the other hand, these features are notably deficient in members of compound collections currently widely used in probe- and drug-discovery efforts.
But something struck me while reading all this. The two metrics used to characterize these compound collections are fine, but they're also two that would be expected to distinguish them thoroughly - after all, natural products do indeed have a lot of chiral carbons, and run-of-the-mill commercial screening sets do indeed have a lot of aryl rings in them. There were several other properties that weren't mentioned at all, so I downloaded the compound set from the paper's supporting information and ran it through some in-house software that we use to break down such things.
I can't imagine, for example, evaluating a compound collection without taking a look at the molecular weights. Here's that graph - the X axis is the compound number, Y-axis is weight in Daltons:
The three different collections show up very well this way, too. The commercial compounds (almost every one under 500 MW) are on the left. Then you have that break of natural products in the middle, with some real whoppers. And after that, you have the various DOS libraries, which were apparently entered in batches, which makes things convenient.
Notice, for example that block of them standing up around 15,000 - that turns out to be the compounds from this 2004 Schreiber paper, which are a bunch of gigantic spirooxindole derivatives. In this paper, they found that this particular set was an outlier in the academic collection, with a lot more binding promiscuity than the rest of the set (and they went so far as to analyze the set with and without it included). The earlier paper, though, makes the case for these compounds as new probes of cellular pathways, but if they hit across so many proteins at the same time, you have to wonder how such assays can be interpreted. The experiments behind these two papers seem to have been run in the wrong order.
Note, also, that the commercial set includes a lot of small compounds, even many below 250 MW. This is down in the fragment screening range, for sure, and the whole point of looking at compounds of that molecular weight is that you'll always find something that binds to some degree. Downgrading the commercial set for promiscuous binding when you set the cutoffs that low isn't a fair complaint, especially when you consider that the DOS compounds have a much lower proportion of compounds in that range. Run a commercial/natural product/DOS comparison controlled for molecular weight, and we can talk.
I also can't imagine looking over a collection and not checking logP, but that's not in the paper, either. But here you are:
In this case, the natural products (around compound ID 7500) are much less obvious, but you can certainly see the different chemical classes standing out in the DOS set. Note, though, that those compounds explore high-logP regions that the other sets don't really touch.
How about polar surface area? Now the natural products really show their true character - looking over the structures, that's because there are an awful lot of polysaccharide-containing things in there, which will run your PSA up faster than anything:
And again, you can see the different libraries in the DOS set very clearly.
So there are a lot of other ways to distinguish these compounds, ways that (to be frank) are probably much more relevant to their biological activity. Just the molecular-weight one is a deal-breaker for me, I'm afraid. And that's before I start looking at the structures in the three collections at all. Now, that's another story.
I have to say, from my own biased viewpoint, I wouldn't pay money for any of the three collections. The natural product one, as mentioned, goes too high in molecular weight and is too polar for my tastes. I'd consider it for antibiotic drug discovery, but with gritted teeth. The commercial set can't make up its mind if it's a fragment collection or not. There are a bunch of compounds that are too small even for my tastes in fragments - 4-methylpyridine, for example. And there are a lot of ugly functional groups: imines of beta-napthylamine, which should not even get near the front door (unstable fluorescent compounds that break down to a known carcinogen? Return to sender). There are hydroxylamines, peroxides, thioureas, all kinds of things that I would just rather not spend my time on.
And what of the DOS collection? Well, to be fair, not all of it is DOS - there are a few compounds in there that I can't figure out, like isoquinoline, which you can buy from the catalog. But the great majority are indeed diversity-oriented, and (to my mind), diversity-oriented to a fault. The spirooxindole library is probably the worst - you should see the number of aryl rings decorating some of those things; it's like a fever dream - but they're not the only offenders in the "Let's just hang as many big things as we can off this sucker" category. Now, there are some interesting and reasonable DOS compounds in there, too, but there are also more endoperoxides and such. (And yes, I know that there are drug structures with endoperoxides in them, but damned few of them, and art is long while life is short). So no, I wouldn't have bought this set for screening, either; I'd have cherry-picked about 15 or 20% of it.
Summary of this long-winded post? I hate to say it, but I think this paper has its thumb on the scale. I'm just around the corner from the Broad Institute, though, so maybe a rock will come through my window this afternoon. . .
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+ TrackBacks (0) | Category: Academia (vs. Industry) | Drug Assays | Drug Development | Natural Products
October 26, 2010
Posted by Derek
Earlier this year, I wrote here about using calorimetry in drug discovery. Years ago, people would have given you the raised eyebrow if you'd suggested that, but it's gradually becoming more popular, especially among people doing fragment-based drug discovery. After all, the binding energy that we depend on for our drug candidates is a thermodynamic property, and you can detect the heat being given off when the molecules bind well. Calorimetry also lets you break that binding energy down into its enthalpic (delta-H) and entropic (T delta-S) components, which is hard to do by other means.
And there's where the arguing starts. As I mentioned back in March, one idea that's been floating around is that better drug molecules tend to have more of an enthalpic contribution to their binding. Very roughly speaking, enthalpic interactions are often what med-chemists call "positive" ones like forming a new hydrogen bond or pi-stack, whereas entropic interactions are often just due to pushing water molecules off the protein with some greasy part of your molecule. (Note: there are several tricky double-back-around exceptions to both of those mental models. Thermodynamics is a resourceful field!) But in that way, it makes sense that more robust compounds with better properties might well be more enthalpically-driven in their binding.
But we do not live in a world bounded by what makes intuitive sense. Some people think that the examples given in the literature for this effect are the only decent examples that anyone has. At the fragment conference I attended the other week, though, a speaker from Astex (a company that's certainly run a lot of fragment optimization projects) said that they're basically not seeing it. In their hands, some lead series are enthalpy-driven as they get better, some are entropy-driven, and some switch gears as the SAR evolves. Another speaker said that they, on the other hand, do tend to go with the enthalpy-driven compounds, but I'm not sure if that's just because they don't have as much data as the Astex people do.
So as far as I'm concerned, the whole concept that I talked about in March is still in the "interesting but unproven" category. We're all looking for new ways to pick better starting compounds or optimize leads, but I'm still not sure if this is going to do the trick. . .
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+ TrackBacks (0) | Category: Analytical Chemistry | Drug Assays | Life in the Drug Labs
October 21, 2010
Posted by Derek
There's a headline I've never written before, for sure. A new paper in PNAS describes an assay in nematodes to look for compounds that have an effect on nerve regeneration. That means that you have to damage neurons first, naturally, and doing that on something as small (and as active) as a nematode is not trivial.
The authors (a team from MIT) used microfluidic chips to direct single nematodes into a small chamber where they're held down briefly by a membrane. Then an operator picks out one of its neurons on an imagining screen, whereupon a laser beam cuts it. The nematode is then released into a culture well, where it's exposed to some small molecule to see what effect that has on the neuron's regrowth. It takes about 20 seconds to process a single C. elegans, in case you're wondering, and I can imagine that after a while you'd wish that they weren't streaming along quite so relentlessly.
The group tried about 100 bioactive molecules, targeting a range of known pathways, to see what might speed up or slow down nerve regeneration. As it happens, the highest hit rates were among the kinase inhibitors and compounds targeting cytoskeletal processes. (By contrast, nothing affecting vesicle trafficking or histone deacetylase activity showed any effect). The most significant hit was an old friend to kinase researchers, staurosporine. Interestingly, this effect was only seen on particular subtypes of neurons, suggesting that they weren't picking up some sort of broad-spectrum regeneration pathway.
The paper acknowledges that staurosporine has a number of different activities, but treats it largely as a PKC inhibitor. I'm not sure that that's a good idea, personally - I'd be suspicious of pinning any specific activity to that compound without an awful lot of follow-up, because it's a real Claymore mine when it comes to kinases. The MIT group did check to see if caspases (and apoptotic pathways in general) were involved, since those are well-known effects of staurosporine treatment, and they seem to have ruled those out. And they also followed up with some other PKC inhibitors, chelerythrine and Gö 6983, and these showed similar effects.
So they may be right about this being a PKC pathway, but that's a tough one to nail down. (And even if you do, there are plenty of PKC isoforms doing different things, but there aren't enough selective ligands known to unravel all those yet). Chelerythrine inhibits alanine aminotransferase, has had some doubts expressed about it before in PKC work, and also binds to DNA, which may be responsible for some of its activity in cells. Gö 6983 seems to be a better tool, but it's is in the same broad chemical class as staurosporine itself, so as a medicinal chemist I still find myself giving it the fishy eye.
This is very interesting work, nonetheless, and it's the sort of thing that no one's been able to do before. I'm a big fan of using the most complex systems you can to assay compounds, and living nematodes are a good spot to be in. I'd be quite interested in a broader screen of small molecules, but 20 seconds per nematode surgery is still too slow for the sort of thing a medicinal chemist like me would like to run - a diversity set of, say, ten or twenty thousand compounds, for starters. And there's always the problem we were talking about here the other day, about how easy it is to get compounds into nematodes at all. I wonder if there were some false negatives in this screen just because the critters had no exposure?
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+ TrackBacks (0) | Category: Biological News | Drug Assays | The Central Nervous System
October 13, 2010
Posted by Derek
For those who haven't seen it, I heard Adam Renslo of UCSF present this work yesterday. His group was looking for inhibitors of cruzain, a target for Chagas' disease, which is certainly a worthy cause (and a tough target). They found a series of oxadiazoles, which are, to be sure, rather ugly (but no uglier than a lot of chemical matter you see in the kinase field, among others). They had affinity, they had reasonable SAR, and the team drove the potencies down against the target. . .only to find, late in the game, that it was all an illusion.
These compounds are aggregators. (That link takes you to a post about a follow-up paper from the UCSF folks, covering this debacle and others). What's striking is that this artifact (compound aggregation under the assay conditions) mimicked a plausible SAR - it wasn't just some random thing that made the numbers hard to interpret. No, it looks like Renslo and his team ended up optimizing for aggregation. As he put it in his presentation, "You'll find what you're looking for".
His other quote at the end of the talk was "Small molecules are much stranger than we've been led to believe", and I can't argue with that one either. Before anyone makes a comment about how his group should have checked their assay more thoroughly, or how they shouldn't have been trying to push such an unpleasant-looking series of compounds anyway - in general, about how this wouldn't have happened to you - pause for a moment, and be honest. Renslo was in this paper, and I thank him for it.
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+ TrackBacks (0) | Category: Drug Assays
October 5, 2010
Posted by Derek
Here's an interesting example of a way that synthetic chemistry is creeping into the provinces of molecular biology. There have been a lot of interesting ideas over the years around the idea of polymers made to recognize other molecules. These appear in the literature as "molecularly imprinted polymers", among other names, and have found some uses, although it's still something of a black art. A group at Cal-Irvine has produced something that might move the field forward significantly, though.
In 2008, they reported that they'd made polymer particles that recognized the bee-sting protein melittin. Several combinations of monomers were looked at, and the best seemed to be a crosslinked copolymer with both acrylic acid and an N-alkylacrylamide (giving you both polar and hydrophobic possibilities). But despite some good binding behavior, there are limits to what these polymers can do. They seem to be selective for melittin, but they can't pull it out of straight water, which is a pretty stringent test. (If you can compete with the hydrogen-bonding network of bulk water that's holding the hydrophilic parts of your target, as opposed to relying on just the hydrophobic interactions with the other parts, you've got something impressive).
Another problem, which is shared by all polymer-recognition ideas, is that the materials you produce aren't very well defined. You're polymerizing a load of monomers in the presence of your target molecule, and they can (and will) link up in all sorts of ways. So there are plenty of different binding sites on the particles that get produced, with all sorts of affinities. How do you sort things out?
Now the Irvine group has extended their idea, and found some clever ways around these problems. The first is to use good old affinity chromatography to clean up the mixed pile of polymer nanoparticles that you get at first. Immobilizing melittin onto agarose beads and running the nanoparticles over them washes out the ones with lousy affinity - they don't hold up on the column. (Still, they had to do this under fairly high-salt conditions, since trying this in plain water didn't allow much of anything to stick at all). Washing the column at this point with plain water releases a load of particles that do a noticeably better job of recognizing melittin in buffer solutions.
The key part is coming up, though. The polymer particles they've made show a temperature-dependent change in structure. At RT, they're collapsed polymer bundles, but in the cold, they tend to open up and swell with solvent. As it happens, that process makes them lose their melittin-recognizing abilities. Incubating the bound nanoparticles in ice-cold water seems to only release the ones that were using their specific melittin-binding sites (as opposed to more nonspecific interactions with the agarose and the like). The particles eluted in the cold turned out to be the best of all: they show single-digit nanomolar affinity even in water! They're only a few per cent of the total, but they're the elite.
Now several questions arise: how general is this technique? That is, is melittin an outlier as a peptide, with structural features that make it easy to recognize? If it's general, then how small can a recognition target be? After all, enzymes and receptors can do well with ridiculously small molecules: can we approach that? It could be that it can't be done with such a simple polymer system - but if more complex ones can also be run through such temperature-transition purification cycles, then all sorts of things might be realized. More questions: What if you do the initial polymerization in weird solvents or mixtures? Can you make receptor-blocking "caps" out of these things if you use overexpressed membranes as the templates? If you can get the particles to the right size, what would happen to them in vivo? There are a lot of possibilities. . .
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+ TrackBacks (0) | Category: Analytical Chemistry | Chemical Biology | Chemical News | Drug Assays
August 25, 2010
Posted by Derek
OK, time (finally) for the latest chapter in the GSK-Sirtris saga. (This is going to get fairly geeky, so feel free to skip ahead if you're not into enzymology). You'll recall from previous installments that Amgen and Pfizer, among others, had disputed whether the reported sirtuin compounds worked the way that had originally been reported. GSK has now published a paper in the Journal of Biological Chemistry to address those questions. How well does this clear things up? Let's take things in order:
Claim 1: Resveratrol is not a direct activator of SIRT1 activity (Amgen). Building on two 2005 papers, the Amgen team said that resveratrol, the prototype SIRT1 ligand, only works in that manner when the fluorescent peptide (Fluor de Lys) was used in the assay. This is due, they found, exclusively to the fluorophore on the peptide - it's an artifact of the assay conditions. Without it, no activation was seen with protein assays in vitro, nor in cell assays. Native substrates (p53-derived peptide and PGC-1alpha) show nothing.
GSK's response: This is true. They too, found that activation of SIRT1 depends on the structure of the substrate. Without the fluorescent label, no activation is seen.
Claim 2: Not only is this true for resveratrol, it's true for SRT1720, SRT2183, and SRT 1460 (Pfizer). The Pfizer team did a similar breakdown of the assay conditions, and found (through several biophysical methods) that the fluorophore is indeed the crucial element in the activity seen in these assays. And again, since that's an artificial tag, the Fluor de Lys-based assays can have nothing to do with real in vivo activity. Native substrates (p53-derived peptide, full-length p53, and acetyl CoA synthase 1) show nothing.
GSK's response: As above, activation of SIRT1 depends on the structure of the substrate. Without the fluorescent label, no activation is seen. SRT1460 and SRT1720 do indeed bind to the fluorescent peptide, but not to the unlabeled versions. Looking over a broader range of structures, some of them interact with the fluorophore, and some don't. There's no correlation between this affinity and a compound's ability to activate SIRT1.
A screen of 5,000 compounds in this class turned up three that actually do work with nonfluorescent peptide substrates (compounds 22, 23, and 24 in the paper). None of these have been previously disclosed. They, however, that even these still don't work when the peptide substrate lacks both the fluorescent tag and a biotin tag.
What's more, when these three compounds are tested on a p53-derived 20-mer peptide substrate, they actually inhibit acetylation, instead of enhancing it. Looking closer at a range of peptide substrates, SRT1460 and other compounds can also inhibit or enhance acetylation, depending on what peptide is being used. An allosteric mechanism could explain these results. It seems more likely that there are at least two specific sites on SIRT1 that can bind these compounds - the active site and an allosteric one. Thus there are several species in equilibrium, depending on whether these sites have substrate or small molecule bound to them, and on how this binding stabilizes or destabilizes particular pathways. In the real cell, this may all be part of various protein-protein interactions.
Claim 3: SRT1720 does not lower glucose in a high-fat-fed mouse model (Pfizer). Even though exposure of the drug was as reported previously, they saw no evidence (at 30 mg/kilo) of glucose lowering or of any increased mitochondrial function. These animals showed increased food intake and weight gain. The 100 mpk dose was not well tolerated, and killed some animals.
GSK's response: not addressed in this paper. It's an enzymology study only.
Claim 4: Resveratrol, SRT 1460, SRT1720, and SRT2183 are not selective (Pfizer). A screen of over 100 targets showed all of these compounds hitting multiple targets, with resvertrol itself showing the closest thing to a clean profile. None of them, say the Pfizer team, are suitable pharmacological tools.
GSK's response: not addressed in this paper. None of the newly disclosed compounds have selectivity data of this sort attached to them, either. I'd be very curious to know how they look, and I'd be very leery of attaching much importance to their behavior in living systems until that's been done.
The take-home: On the enzymology level, this new paper seems to be solid work. But it's the sort of solid work that should have been done around the time that GSK bought Sirtris, and not something appearing in 2010 in response to major attacks in the literature. The first main claim of those attacking papers is, in fact, absolutely true: the original Fluor de Lys assay is worthless for characterizing these compounds. What we learn from this paper is that the assay is worthless for even more complicated reasons than originally thought, and that the whole series of SRT compounds behaves in ways that were not apparent from the published work, to put it lightly.
As to the selectivity and in vivo effects of these compounds, Pfizer's gauntlet is still thrown down right where they left it. The fact that these compounds are so much harder to understand than was originally thought, even in well-controlled enzyme assays, makes me wonder how easy it will be to figure out the rest of the story. . .
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+ TrackBacks (0) | Category: Aging and Lifespan | Drug Assays | Drug Development
July 12, 2010
Posted by Derek
Stuart Schreiber and Paul Clemons of the Broad Institute have a provocative paper out in JACS on natural products and their use in drug discovery. As many know, a good part of the current pharmacopeia is derived from natural product lead structures, and in many other cases a natural product was essential for identifying a target or pathway for a completely synthetic compound.
But are there as many of these cases as we think - or as there should be? This latest paper takes a large set of interaction data and tries to map natural product activities on to it. It's already know that there are genes all up and down the "interactome" spectrum, as you'd expect, with some that seem to be at the crossroads of dozens (or hundreds) of pathways, and others that are way out on the edges. And it's been found that disease targets tend to fall in the middle of this range, and not so much in the too-isolated or too-essential zones on either side.
That seems reasonable. But then comes the natural product activity overlay, and there the arguing can start. Natural products, the paper claims, tend to target the high-interaction essential targets at the expense of more specific disease targets. They're under-represented in the few-interaction group, and very much over-represented in the higher ones. Actually, that actually seems reasonable, too - most natural products are produced by organisms as essentially chemical warfare, and the harder they can hit, the better. Looking at subsets of the natural product list (only the most potent compounds, for example) did not make this effect vanish. Meanwhile, if you look at the list of approved drugs (minus the natural products on it), that group fits the middle-range interactivity group much more closely.
But what does that mean for natural products as drug leads? There would appear to be a mismatch here, with a higher likelihood of off-target effects and toxicity among a pure natural-product set. (The mismatch, to be more accurate, is between what we want exogenous chemicals to do versus what evolution has selected them to do). The paper ends up pointing out that additional sources of small molecules look to be needed outside of natural products themselves.
I'll agree with that. But I suspect that I don't agree with the implications. Schreiber has long been a proponent of "diversity-oriented synthesis" (DOS), and would seem to be making a case for it here without ever mentioning it by name. DOS is the idea of making large collections of very structurally diverse molecules, with an eye to covering as much chemical space as possible. My worries (expressed in that link above) are that the space it covers doesn't necessarily overlap very well with the space occupied by potential drugs, and that chemical space is too humungously roomy in any event to be attacked very well by brute force.
Schreiber made a pitch a few years ago for the technique, that time at the expense of small-molecule compound collections. He said that these were too simple to hit many useful targets, and now he's taking care of the natural product end of the spectrum by pointing out that they hit too many. DOS libraries, then, must be just in the right range? I wish he'd included data on some of them in this latest paper; it would be worthwhile to see where they fell in the interaction list.
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+ TrackBacks (0) | Category: Drug Assays | Drug Industry History | Toxicology
April 27, 2010
Posted by Derek
I've said several times that I think that mass spectrometry is taking over the analytical world, and there's more evidence of that in Angewandte Chemie. A group at Justus Liebig University in Giessen has built what has to be the finest imaging mass spec I've ever seen. It's a MALDI-type machine, which means that a small laser beam does the work of zapping ions off the surface of the sample. But this one has better spatial resolution than anything reported so far, and they've hooked it up to a very nice mass spec system on the back end. The combination looks to me like something that could totally change the way people do histology.
For the non-specialist readers in the audience, mass spec is a tremendous workhorse of analytical chemistry. Basically, you use any of a whole range of techniques (lasers, beams of ions, electric charges, etc.) to blast individual molecules (or their broken parts!) down through a chamber and determine how heavy each one is. Because molecular weights are so precise, this lets you identify a lot of molecules by both their whole weights - their "molecular ions" - and by their various fragments. Imagine some sort of crazy disassembler machine that rips things - household electronic gear, for example - up into pieces and weighs every chunk, occasionally letting a whole untouched unit through. You'd see the readouts and say "Ah-hah! Big one! That was a plasma TV, nothing else is up in that weight range. . .let's see, that mix of parts coming off it means that it must have been a Phillips model so-and-so; they always break up like that, and this one has the heavier speakers on it." But mass spec isn't so wasteful, fortunately: it doesn't take much sample, since there are such gigantic numbers of molecules in anything large enough to see or weigh.
Take a look at this image. That's a section of a mouse pituitary gland - on the right is a standard toluidine-blue stain, and on the left is the same tissue slice as imaged (before staining) by the mass spec. The green and blue colors are two different mass peaks (826.5723 and 848.5566, respectively), which correspond to different types of phospholipid from the cell membranes. (For more on such profiling, see here). The red corresponds to a mass peak for the hormone vasopressin. Note that the difference in phospholipid peaks completely shows the difference between the two lobes of the gland (and also shows an unnamed zone of tissue around the posterior lobe, which you can barely pick up in the stained preparation). The vasopressin is right where it's supposed to be, in the center of the posterior lobe.
One of the most interesting things about this technique is that you don't have to know any biomarkers up front. The mass spec blasts away at each pixel's worth of data in the tissue sample and collects whatever pile of varied molecular-weight fragments that it can collect. Then the operator is free to choose ions that show useful contrasts and patterns (I can imagine software algorithms that would do the job for you - pick two parts of an image and have the machine search for whatever differentiates them). For instance, it's not at all clear (yet) why those two different phospholipid ions do such a good job at differentiating out the pituitary lobes - what particular phospholipids they correspond to, why the different tissues have this different profile, and so on. But they do, clearly, and you can use that to your advantage.
As this technique catches on, I expect to see large databases of mass-based "contrast settings" develop as histologists find particularly useful readouts. (Another nice feature is that one can go back to previously collected data and re-process for whatever interesting things are discovered later on). And each of these suggests a line of research all its own, to understand why the contrast exists in the first place.
The second image shows ductal carcinoma in situ. On the left is an optical image, and about all you can say is that the darker tissue is the carcinoma. The right-hand image is colored by green (mass of 529.3998) and red (mass of 896.6006), which correspond to healthy and cancerous tissue, respectively (and again, we don't know why, yet). But look closely and you can see that some of the dark tissue in the optical image doesn't actually appear to be cancer - and some of dark spots in the lighter tissue are indeed small red cells of trouble. We may be able to use this technology to diagnose cancer subtypes more accurately than ever before - the next step will be to try this on a number of samples from different patients to see how much these markers vary. I also wonder if it's possible to go back to stored tissue samples and try to correlate mass-based markers with the known clinical outcomes and sensitivities to various therapies.
I'd also be interested in knowing if this technique is sensitive enough to find small-molecule drugs after dosing. Could we end up doing pharmacokinetic measurements on a histology-slide scale? Ex vivo, could we possibly see uptake of our compounds once they're applied to a layer of cells in tissue culture? Oh, mass spec imaging has always been a favorite of mine, and seeing this level of resolution just brings on dozens of potential ideas. I've always had a fondness for label-free detection techniques, and for methods that don't require you to know too much about the system before being able to collect useful data. We'll be hearing a lot more about this, for sure.
Update: I should note that drug imaging has certainly been accomplished through mass spec, although it's often been quite the pain in the rear. It's clearly a technology that's coming on, though.
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+ TrackBacks (0) | Category: Analytical Chemistry | Biological News | Cancer | Drug Assays
April 26, 2010
Posted by Derek
I don't think we saw this one coming: Charles River Labs has announced that they're buying WuXi PharmaTech. They're paying about a 28% premium over Friday's closing stock price - Charles River's CEO will stay on, and WuXi's founder (Li Ge) will serve as executive VP under him.
Charles River, which is strong in the animal-testing end of the business, has apparently decided that Wu Xi is one of their biggest competitors (I'd agree) and has decided to try to stake out a leading position in the whole contract-research space. It's interesting to me that the folks at Wu Xi bought into this reasoning as well, although (since they're a publicly traded company here in the US), a lucrative stock offer can be its own argument. One now wonders, though, about the company's statements on re-staffing some of their US labs when economic conditions improve. . .
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+ TrackBacks (0) | Category: Animal Testing | Business and Markets | Drug Assays | Drug Development
April 6, 2010
Posted by Derek
A reader sends along a note about this patent application from the University of Rochester. The inventor, David Goldfarb, seems to have used an assay (the subject of a previous application) to screen a library of commercially available compounds for potential life-extending properties in model organisms. Here's some detail on the screen from PubChem.
The abstract of the application makes it sound worse than it is: "A method for altering the lifespan of a eukaryotic organism. The method comprises the steps of providing a lifespan altering compound, and administering an effective amount of the compound to a eukaryotic organism, such that the lifespan of the organism is altered. . ." That sounds like one of those "Oh, get real" applications that the patent databases are cluttered with. But when you get to the claims, you find that a list of compounds is specifically given, with more- and most-preferred ones as you go down. And I don't have a problem with that, as far as it goes - the inventor has an assay, has run a bunch of compounds through it, and finds that some of them have utility that apparently no one else has recognized.
The compounds themselves, though. . .well, here are the specifically claimed ones on the list. I don't necessarily see aliphatic triketones extending my life, but perhaps I'm cynical.
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+ TrackBacks (0) | Category: Aging and Lifespan | Drug Assays | Patents and IP
March 30, 2010
Posted by Derek
A new paper in PLoS Biology looks at animal model studies reported for the treatment of stroke. The authors use statistical techniques to try to estimate how many have gone unreported. From a database with 525 sources, covering 16 different attempted therapies (which together come to 1,359 experiments and 19,956 animals), they find that only a very small fraction of the publications (about 2%) report no significant effects, which strongly suggests that there is a publication bias at work here. The authors estimate that there may well be around 200 experiments that showed no significant effect and were never reported, whose absence would account for around one-third of the efficacy reported across the field. In case you're wondering, the therapy least affected by publication bias was melatonin, and the one most affected seems to be administering estrogens.
I hadn't seen this sort of study before, and the methods they used to arrive at these results are interesting. If you plot the precision of the studies (Y axis) versus the effect size (X axis), you should (in theory) get a triangular cloud of data. As the precision goes down, the spread of measurements across the X-axis increases, and as the precision goes up, the studies should start to converge on the real effect of the treatment, whatever that might be. (In this study, the authors looked only at reported changes in infarct size as a measure of stroke efficacy). But in many of the reported cases, the inverted-funnel shape isn't symmetrical - and every single time that happens, it turns out that the gaps are in the left-hand side of the triangle, the not-as-precise and negative-effect regions of the plots. This doesn't appear to be just due to less-precise studies tending to show positive effects for some reason - it strongly suggests that there are negative studies that just haven't been reported.
The authors point out that applying their statistical techniques to reported human clinical studies is more problematic, since smaller (and thus less precise) trials may well involve unrepresentative groups of patients. But animal studies are much less prone to this problem.
The loss of experiments that showed no effect shouldn't surprise anyone - after all, it's long been known that publishing such papers is just plain harder than publishing ones that show something happening. There's an obvious industry bias toward only showing positive data, but there's an academic one, too, which affects basic research results. As the authors put it:
These quantitative data raise substantial concerns that publication bias may have a wider impact in attempts to synthesise and summarise data from animal studies and more broadly. It seems highly unlikely that the animal stroke literature is uniquely susceptible to the factors that drive publication bias. First, there is likely to be more enthusiasm amongst scientists, journal editors, and the funders of research for positive than for neutral studies. Second, the vast majority of animal studies do not report sample size calculations and are substantially underpowered. Neutral studies therefore seldom have the statistical power confidently to exclude an effect that would be considered of biological significance, so they are less likely to be published than are similarly underpowered “positive” studies. However, in this context, the positive predictive value of apparently significant results is likely to be substantially lower than the 95% suggested by conventional statistical testing. A further consideration relating to the internal validity of studies is that of study quality. It is now clear that certain aspects of experimental design (particularly randomisation, allocation concealment, and the blinded assessment of outcome) can have a substantial impact on the reported outcome of experiments. While the importance of these issues has been recognised for some years, they are rarely reported in contemporary reports of animal experiments.
And there's an animal-testing component to these results, too, of course. But lest activists seize on the part of this paper that suggests that some animal testing results are being wasted, they should consider the consequences (emphasis below mine):
The ethical principles that guide animal studies hold that the number of animals used should be the minimum required to demonstrate the outcome of interest with sufficient precision. For some experiments, this number may be larger than those currently employed. For all experiments involving animals, nonpublication of data means those animals cannot contribute to accumulating knowledge and that research syntheses are likely to overstate biological effects, which may in turn lead to further unnecessary animal experiments testing poorly founded hypotheses.
This paper is absolutely right about the obligation to have animal studies mean something to the rest of the scientific community, and it's clear that this can't happen if the results are just sitting on someone's hard drive. But it's also quite possible that for even some of the reported studies to have meant anything, that they would have had to have used more animals in the first place. Nothing's for free.
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+ TrackBacks (0) | Category: Animal Testing | Cardiovascular Disease | Clinical Trials | Drug Assays | The Scientific Literature
March 29, 2010
Posted by Derek
For the medicinal chemists in the audience, I wanted to strongly recommend a new paper from a group at Roche. It's a tour through the various sorts of interactions between proteins and ligands, with copious examples, and it's a very sensible look at the subject. It covers a number of topics that have been discussed here (and throughout the literature in recent years), and looks to be an excellent one-stop reference.
In fact, read the right way, it's a testament to how tricky medicinal chemistry is. Some of the topics are hydrogen bonds (and why they can be excellent keys to binding or, alternatively, of no use whatsoever), water molecules bound to proteins (and why disturbing them can account for large amounts of binding energy, or, alternatively, kill your compound's chances of ever binding at all), halogen bonds (which really do exist, although not everyone realizes that), interactions with aryl rings (some of which can be just as beneficial coming in 90 degrees to where you might imagine), and so on.
And this is just to get compounds to bind to their targets, which is the absolute first step on the road to a drug. Then you can start worrying about how to have your compounds not bind to things you don't want (many of which you probably don't even realize even are out there). And about how to get it to decent blood levels, for a decent amount of time, and into the right compartments of the body. And at that point, it's nearly time to see if it does any good for the disease you're trying to target!
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March 26, 2010
Posted by Derek
The discussion of "privileged scaffolds" in drugs here the other day got me to thinking. A colleague of mine mentioned that there may well be structures that don't hit nearly as often as you'd think. The example that came to his mind was homopiperazine, and he might have a point; I've never had much luck with those myself. That's not much of a data set, though, so I wanted to throw the question out for discussion.
We'll have to be careful to account for Commercial Availability Bias (which at least for homopiperazines has decreased over the years) and Synthetic Tractability Bias. Some structures don't show up much because they just don't get made much. And we'll also have to be sure that we're talking about the same things: benzo-fused homopiperazines (and other fused seven-membered rings) hit like crazy, as opposed to the monocyclic ones, which seem to be lower down the scale, somehow.
It's not implausible that there should be underprivileged scaffolds. The variety of binding sites is large, but not infinite, and I'm sure that it follows a power-law distribution like so many other things. The usual tricks (donor-acceptor pairs spaced about so wide apart, pi-stacking sandwiches, salt bridges) surely account for much more than their random share of the total amount of binding stabilization out there in the biosphere. And some structures are going to match up with those motifs better than others.
So, any nominations? Have any of you had structural types that seem as if they should be good, but always underperform?
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March 24, 2010
Posted by Derek
Here's a new article on the concept of "privileged scaffolds", the longstanding idea that there seem to be more biologically active compounds built around some structures than others. This doesn't look like it tells me anything I didn't know, but it's a useful compendium of such structures if you're looking for one. Overall, though, I'm unsure of how far to push this idea.
On the one hand, it's certainly true that some structural motifs seem to match up with binding sites more than others (often, I'd say, because of some sort of donor-acceptor pair motif that tends to find a home inside protein binding sites). But in other cases, I think that the appearance of what looks like a hot scaffold is just an artifact of everyone ripping off something that worked - others might have served just as well, but people ran with what had been shown to work. And then there are other cases, where I think that the so-called privileged structure should be avoided for everyone's good: our old friend rhodanine makes an appearance in this latest paper, for example. Recall this this one has been referred to as "polluting the literature", with which judgment I agree.
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Posted by Derek
I've spoken about fragment-based drug design and ligand efficiency here a few times. There's a new paper in J. Med. Chem. that puts some numbers on that latter concept. (Full disclosure - I've worked with its author, although I had nothing to do with this particular paper).
For the non-chemists in the crowd who want to know what I'm talking about, fragment-based methods are an attempt to start with smaller, weaker-binding chemical structures than we usually work with. But if you look at how much affinity you're getting for the size of the molecules, you find that some of these seemingly weaker compounds are actually doing a great job for their size. Starting from these and building out, with an eye along the way toward keeping that efficiency up, could be a way of making better final compounds than you'd get by starting from something larger.
Looking over a number of examples where the starting compounds can be compared to the final drugs (not a trivial data set to assemble, by the way), this work finds that drugs, compared to their corresponding leads, tend to have similar to slightly higher binding efficiencies, although there's a lot of variability. They also tend to have similar logP values, which is a finding that doesn't square with some previous analyses (which showed things getting worse during development). But drugs are almost invariably larger than their starting points, so no matter what, one of the keys is not to make the compounds greasier as you add molecular weight. (My "no naphthyls" rule comes from this, actually).
There are a few examples of notably poor ligand-efficient starting structures that have nonetheless been developed into drugs. Interestingly, several of these are the HIV protease inhibitors, with Reyataz (atazanavir) coming in as the least ligand-efficient drug in the whole data set. A look at its structure will suffice. The wildest one on the list appears to be no-longer-marketed amprenavir, whose original lead was 53 micromolar and weighed over 600, nasty numbers indeed. I would not recommend emulating that one. In case you're wondering, the most ligand efficient drug in the set is Chantix (varenicline).
In the cases where ligand efficiency actually went down along the optimization route, inspection of the final structures shows that in many cases, the discovery team was trading efficiency for some other property (PK, solubility, etc.) To me, that's another good argument to make things as efficient as you can, because that gives you something to trade. A big, chunky, lashed-together structure doesn't give you much room to maneuver.
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March 23, 2010
Posted by Derek
You know, you'd think that we'd understand the way things bind to proteins well enough to be able to explain why biotin sticks so very, very tightly to avidins. That's one of the most impressive binding events in all of biology, short of pushing electrons and forming a solid chemical bond - biotin's stuck in there at femtomolar levels. It's so strong and so reliable that this interaction is the basis for untold numbers of laboratory and commercial assays - just hang a biotin off one thing, expose it to something else that has an avidin (most often streptavidin) coated on it, and it'll stick, or else something is Very Wrong. So we have that all figured out.
Wrong. Turns out that there's a substantial literature given to arguing about just why this binding is so tight. One group holds out for hydrophobic interactions (which seems rather weird to me, considering that biotin's rather polar by most standards). Another group has a hydrogen-bonding explanation, which (on the surface) seems more feasible to me. Now a new paper says that the computational methods applied so far can't handle electrostatic factors well, and that those are the real story.
I'm not going to take a strong position on any of these; I'll keep my head down while the computational catapults launch at each other. But it's definitely worth noting that we apparently can't explain the strongest binding site interaction that we know of. It's the sort of thing that we'd all like to be able to generate at will in our med-chem programs, but how can we do that when we don't even know what's causing it?
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March 15, 2010
Posted by Derek
It's easy to lose sight of what a drug is supposed to do. Many conditions come on so slowly that we have to use blood chemistry or other markers to see the progress of therapy in a realistic time. And over time, that blood marker can get confused with the disease itself.
To pick one famous example, try cholesterol. Everyone you stop on the street will know that "high cholesterol is bad for you". But the first thing you have to do is distinguish between LDL and HDL cholesterol - if the latter is a large enough fraction of the total, the aggregate number doesn't matter as much. And fundamentally, there's not a disease called "high cholesterol" - that's a symptom of some other cluster of metabolic processes that have gone subtly off. And the endpoint of any therapy in that field isn't really to lower the number in a blood test: it's to prevent heart attacks and to extend healthy lifetimes, mortality and morbidity. As we're seeing with Vytorin, it may be possible to drop the numbers in a blood test but not see the benefit that's supposed to be there.
Another example of this came up over the weekend. The fibrates are a class of drugs that change lipid levels, although the way they work is still rather obscure. They're supposed to be ligands for the PPAR-alpha nuclear receptor, but they're not very potent against it when you study that closely. At any rate, they do lower triglycerides and have some other effects, which should be beneficial in patients whose lipids are off and are at risk for cardiac problems.
But are they? Type II diabetics tend to be people who fit that last category well, and that's where a lot of fenofibrate is prescribed (as Abbott's Tricor in the US, and under a number of other names around the world). A five-year study in over five thousand diabetic patients, though, has just shown no difference versus placebo. Again, there's no doubt that the drug lowers triglycerides and changes the HDL/LDL/VLDL ratios. It's just that, for reasons unknown, doing so with fenofibrate doesn't seem to actually help diabetic patients avoid cardiac trouble.
Mortality and morbidity: lowering them is a very tough test for any drug, but if you can't, then what's the point of taking something in the first place? This is something to keep in mind as the push for biomarkers delivers more surrogate endpoints. Some of them will, inevitably, turn out not to mean as much as they're supposed to mean.
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March 12, 2010
Posted by Derek
Freeman Dyson has written about his belief that molecular biology is becoming a field where even basement tinkerers can accomplish things. Whether we're ready for it or not, biohacking is on its way. The number of tools available (and the amount of surplus equipment that can be bought) have him imagining a "garage biotech" future, with all the potential, for good and for harm, that that entails.
Well, have a look at this garage, which is said to be somewhere in Silicon Valley. I don't have any reason to believe the photos are faked; you could certainly put your hands on this kind of equipment very easily in the Bay area. The rocky state of the biotech industry just makes things that much more available. From what I can see, that's a reasonably well-equipped lab. If they're doing cell culture, there needs to be some sort of incubator around, and presumably a -80 degree freezer, but we don't see the whole garage, do we? I have some questions about how they do their air handling and climate control (although that part's a bit easier in a California garage than it would be in a Boston one). There's also the issue of labware and disposables. An operation like this does tend to run through a goodly amount of plates, bottles, pipet tips and so on, but I suppose those are piled up on the surplus market as well.
But what are these folks doing? The blog author who visited the site says that they're "screening for anti-cancer compounds". And yes, it looks as if they could be doing that, but the limiting reagent here would be the compounds. Cells reproduce themselves - especially tumor lines - but finding compounds to screen, that must be hard when you're working where the Honda used to be parked. And the next question is, why? As anyone who's worked in oncology research knows, activity in a cultured cell line really doesn't mean all that much. It's a necessary first step, but only that. (And how many different cell lines could these people be running?)
The next question is, what do they do with an active compound when they find one? The next logical move is activity in an animal model, usually a xenograft. That's another necessary-but-nowhere-near-sufficient step, but I'm pretty sure that these folks don't have an animal facility in the basement, certainly not one capable of handling immunocompromised rodents. So put me down as impressed, but puzzled. The cancer-screening story doesn't make sense to me, but is it then a cover for something else? What?
If this post finds its way to the people involved, and they feel like expanding on what they're trying to accomplish, I'll do a follow-up. Until then, it's a mystery, and probably not the only one of its kind out there. For now, I'll let Dyson ask the questions that need to be asked, from that NYRB article linked above:
If domestication of biotechnology is the wave of the future, five important questions need to be answered. First, can it be stopped? Second, ought it to be stopped? Third, if stopping it is either impossible or undesirable, what are the appropriate limits that our society must impose on it? Fourth, how should the limits be decided? Fifth, how should the limits be enforced, nationally and internationally? I do not attempt to answer these questions here. I leave it to our children and grandchildren to supply the answers.
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March 2, 2010
Posted by Derek
I was just talking about greasy compounds the other day, and reasons to avoid them. Right on cue, there's a review article in Expert Opinion in Drug Discovery on lipophilicity. It has some nice data in it, and I wanted to share a bit of it here. It's worth noting that you can make your compounds too polar, as well as too greasy. Check these out - the med-chem readers will find them interesting, and who knows, others might, too:
So, what are these graphs? They show how well compound cross the membranes of Caco-2 cells, a standard assay for permeability. These cells (derived from human colon tissue) have various active-transport pumps going (in both directions), and you can grow them in a monolayer, expose one side to a solution of drug substance, and see how much compound appears on the other side and how quickly. (Of course, good old passive diffusion is also operating, too - a lot of compounds cross membranes by just soaked on through them).
Now, I have problems with extrapolating Caco-2 data too vigorously to the real world - if you have five drug candidates from the same series and want to rank order them, I'd suggest getting real animal data rather than rely on the cell assay. The array of active transport systems (and their intrinsic activity) may well not match up closely enough to help you - as usual, cultured cell lines don't necessarily match reality. But as a broad measure of whether a large set of compounds has a reasonable chance of getting through cell membranes, the assay's not so bad.
First, we have a bunch of compounds with molecular weights between 350 and 400 (a very desirable space to occupy). The Y axis is the partitioning between the two sides of the cells, and X axis is LogD, a standard measure of compound greasiness. That thin blue line is the cutoff for 100 nanomoles/sec of compound transport, so the green compounds above it travel across the membrane well, and the red ones below it don't cross so readily. You'll note that as you go to the left (more and more polar, as measured by logD), the proportion of green compounds gets smaller and smaller. They're rather hang out in the water than dive through any cell membranes, thanks.
So if you want a 50% chance of hitting that 100 nm/sec transport level, then you don't want to go much more polar than a LogD of 2. But that's for compounds in the 350-400 weight range - how about the big heavyweights? Those are shown in the second graph, for compounds greater than 500. Note that the distribution has scrunched disturbingly. Now almost everything is lousy, and if you want that 50% chance of good penetration, you're going to have to get up to a logD of at least 4.5.
That's not too good, because you're always fighting a two-front war here. If you make your compounds that greasy (or more) to try to improve their membrane-crossing behavior, you're opening yourself up (as I said the other day) to more metabolic clearance and more nonspecific tox, as your sticky compounds glop onto all sorts of things in vivo. (They'll be fun to formulate, too). Meanwhile, if you dip down too far into that really-polar left-hand side, crossing your fingers for membrane crossing, you can slide into the land of renal clearance, as the kidneys vacuum out your water-soluble wonder drug and give your customers very expensive urine.
But in general, you have more room to maneuver in the lower molecular weight range. The humungous compounds tend to not get through membranes at reasonable LogD values. And if you try to fix that by moving to higher LogD, they tend to get chewed up or do unexpectedly nasty things in tox. Stay low and stay happy.
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March 1, 2010
Posted by Derek
I've been involved in a mailing list discussion that I wanted to open up to a wider audience in drug discovery, so here goes. We spend our time (well, a lot of it, when we're not filling out forms) trying to get compound to bind well to our targets. And that binding is, of course, all about energy: the lower the overall energy of the system when your compound binds, relative to the starting state, the tighter the binding.
That energy change can be broken down (all can all chemical free energy changes) into an enthalpic part and an entropic part (that latter one depends on temperature, but we'll assume that everything's being done at a constant T and ignore that part). Roughly speaking, the enthalpic component is where you see effects of hydrogen bonds, pi-pi stacking, and other such "productive" interactions, and the entropic part is where you're pushing water molecules and side chains around - hydrophobic interactions and such.
That's a gross oversimplification, but it's a place to start. It's important to remember that these things are all tangled together in most cases. If you come in with a drug molecule and displace a water molecule that was well-attached to your binding pocket, you've broken some hydrogen bonds - for which you'll pay in enthalpy. But you may well have formed some, too, to your molecule - so you'll get some enthalpy term back. And by taking a bound water and setting it free, you'll pick up some good entropy change, too. But not all waters are so tightly bound - there are a few cases where they're actually at a lower entropy state in a protein pocket then they are out in solution, so displacing one of those actually hurts you in entropy. Hmm.
And as I mentioned here, you have the motion of your drug molecule to consider. If it goes from freely rotating to stuck when it binds (as it may well), then you're paying entropy costs. (That's one reason why tying down your structure into a ring can help so dramatically, when it helps at all). And don't forget the motion of the protein overall - if it's been flopping around until it folds over and clenches down on your molecule, there's another entropy penalty for you, which you'd better be able to make up in enthalpy. And so on.
There's been a proposal, spread most vigorously by Ernesto Freire of Johns Hopkins, that drug researchers should use calorimetry to pick compounds that have the biggest fraction of their binding from enthalpic interactions. (That used to be a terrible pain to do, but recent instruments have made it much more feasible). His contention is that the "best in class" drugs in long-lived therapeutic categories tend to move in that direction, and that we can use this earlier in our decision-making process. People doing fragment-based drug discovery are also urged to start with enthalpically-biased fragments, so that the drug candidate that grows out from them will have a better chance of ending up in the same category.
One possible reason for all this is that drugs that get most of their binding from sheer greasiness, fleeing the water to dive into a protein's sheltering cave, might not be so picky about which cave they pick. There's a persistent belief, which I think is correct, that very hydrophobic compounds tend to have tox problems, because they're often just not selective enough about where they bind. And then they tend to get metabolized and chewed up more, too, which adds to the problem.
And all that's fine. . .except for one thing: is anyone actually doing this? That's the question that came up recently, and (so far), for what it's worth, no one's willing to speak up and say that they are. Perhaps all this is a new enough consideration that all the work is still under wraps. But it will be interesting to see if it holds up or not. We need all the help we can get in drug discovery, so if this is real, then it's welcome. But we also don't need to run more assays that only confuse things, either, so it would be worth knowing if drug-candidate calorimetry falls into that roomy category, too. Opinions?
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February 19, 2010
Posted by Derek
A double complaint this morning, and both from the same literature item - if I were charging anything for the blog, I'd say that it's delivering value for the money. At any rate, the first kvetch is something that I know that many chemists have noticed when reading more biology/medical-oriented journals. You'll see some paper that talks about a new compound that does X, Y, and Z. It'll be named with some sort of code, and they'll tell you all about its interesting effects. . .but they don't get around to actually telling you what the damned stuff is.
As I say, this is a chemist's complaint. Many biologists are fine stipulating that there's a compound that will do these interesting things, because they're mostly interested in hearing about the interesting things themselves. It could just be Compound X as far as they're concerned. But chemists want to see what kind of structure it is that causes all these publication-worthy results, and sometimes we go away disappointed.
Or we have to dig. Take this PNAS paper on a broad-spectrum antiviral compound, LJ001. It looks quite interesting, with effects on a number of different viral types, and through a unique mechanism that targets viral membranes. But what is it? You'll look in vain through the whole paper to find out - that compound is LJ001 to you, Jack. You have to go to the supplemental material to find out, and to page 10 at that.
And that brings up the second complaint. LJ001 turns out to be a rhodanine, and regular readers will note that earlier this month some time was spent here talking about just how ugly and undesirable those are. It's very, very hard to get anyone in the drug business to take a compound in that class seriously, because they have such a poor track record. Looking over the small SAR table provided, I note that if you switch that thioamide group (the part that the chemists hate the most) to a regular amide, turning the thing into an thiazolidinedione, you lose all the activity.
TZDs aren't everyone's favorite group, but at least they've made it into marketed drugs. Rhodanines are no one's favorite group, and it would be a good thing of the authors of these papers would realize that, or at least acknowledge it if they do. It's not an irrational prejudice.
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February 8, 2010
Posted by Derek
There's an article out from a group in Australia on the long-standing problem of "frequent hitter" compounds. Everyone who's had to work with high-throughput screening data has had to think about this issue, because it's clear that some compounds are nothing but trouble. They show up again and again as hits in all sorts of assays, and eventually someone gets frustrated enough to flag them or physically remove them from the screening deck (although that last option is often a lot harder than you'd think, and compound flags can proliferate to the point that they get ignored).
The larger problem is whether there are whole classes of compounds that should be avoided. It's not an easy one to deal with, because the question turns on how you're running your assays. Some things are going to interfere with fluorescent readouts, by absorbing or emitting light of their own, but that can depend on the wavelengths you're using. Others will mung up a particular coupled assay readout, but leave a different technology untouched.
And then there's the aggregation problem, which we've only really become aware of in the past few years. Some compounds just like to stick together into huge clumps, often taking the assay's protein target (or some other key component) with them. At first, everyone thought "Ah-hah! Now we can really scrub the screening plates of all the nasties!", but it turns out that aggregation itself is an assay-dependent phenomenon. Change the concentrations or added proteins, and whoomph: compounds that were horrible before suddenly behave reasonably, while a new set of well-behaved structures has suddenly gone over to the dark side.
This new paper is another attempt to find "Pan-Assay Interference" compounds or PAINs, as they name them. (This follows a weird-acronym tradition in screening that goes back at least to Vertex's program to get undesirable structures out of screening collections, REOS, for "Rapid Elimination of, uh, Swill"). It will definitely be of interest to people using the AlphaScreen technology, since it's the result of some 40 HTS campaigns using it, but the lessons are worth reading about in general.
What they found was that (as you'd figure) that while it's really hard to blackball compounds permanently with any degree of confidence, the effort needs to be made. Still, even using their best set of filters, 5% of marketed drugs get flagged as problematic screening hits - in fact, hardly any database gives you a warning rate below that, with the exception of a collection of CNS drugs, whose properties are naturally a bit more constrained. Interestingly, they also report the problematic-structure rate for the collections of nine commercial compound vendors, although (frustratingly) without giving their names. Several of them sit around that 5% figure, but a couple of them stand out with 11 or 12% of their compounds setting off alarms. This, the authors surmise, is linked to some of the facile combinatorial-type reactions used to prepare them, particularly ones that leave enones or exo-alkenes in the final structures.
So what kinds of compounds are the most worrisome? If you're going to winnow out anything, you should probably start with these: Rhodanines are bad, which doesn't surprise me. (Abbott and Bristol Myers-Squibb have also reported them as troublesome). Phenol Mannich compounds and phenolic hydrazones are poor bets. And all sort of keto-heterocycles with conjugated exo alkenes make the list. There are several other classes, but those are the worst of the bunch, and I have to say, I'd gladly cross any of them off a list of screening hits.
But not everyone does. As the authors show, there are nearly 800 literature references to rhodanine compounds showing biological effects. A conspicuous example is here, from the good folks at Harvard, which was shown to be rather nonspecifically ugly here. What does all this do for you? Not much:
"Rather than being privileged structures, we suggest that rhodanines are polluting the scientific literature. . .these results reflect the extent of wasted resources that these nuisance compounds are generally causing. We suggest that a significant proportion of screening-based publications and patents may contain assay interference hits and that extensive docking computations and graphics that are frequently produced may often be meaningless. In the case of rhodanines, the answer set represents some 60 patents and we have found patents to be conspicuously prevalent for other classes of PAINS. This collectively represents an enormous cost in protecting intellectual property, much of which may be of little value. . ."
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January 29, 2010
Posted by Derek
Xconomy has a look inside the Merck-Sirna acquisition, an interview with Merck's head of that area. As you'd guess, he emphasizes that one of the biggest challenges in the field is delivery, and he makes the pitch that this is how Merck is going to make this work out:
What you often read about, but many people don’t understand, is how hard it is to make a drug. Our approach to RNA Therapeutics is made with a recognition of the full package it takes to launch a successful commercial product. . .That’s versus another strategy you see from smaller companies, which is to get an interesting experimental result, and publicly disclose it in an attempt to increase the value of your investment or a VC’s investment, without a real [awareness] of what it will take to make a therapeutic eight years later. . .
We immediately, after the acquisition, invested not just heavily in the RNA piece that is here in San Francisco, but we built an entire delivery group in West Point, PA. The thing that continues to differentiate Merck is that we have people with decades of experience in pharma R&D, drug safety, metabolism, pharmacokinetics. . .
Outside of RNA as a therapy in itself, he also talks about Merck's use of the technology to better understand its small-molecule targets. It's not something that you'll ever see press releases about, but trustworthy data of that sort is very useful and important. As the Xconomy interviewer notes, Wall Street values this sort of thing as basically zero (partly because you can't see the results of it for quite a while, if they're ever made public at all), but the value inside the company can be significant.
Of course, there can be things that happen inside drug companies that significantly destroy value, too, and it's not like the stock market can see (or understand) many of those, either, but that's a topic for another post entirely. . .and on a not perhaps unrelated note, one part of the interview above seems to suggest that "POS" is an internal Merck acronym for. . .wait for it. . ."probability of success". I, uh, kid you not.
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January 26, 2010
Posted by Derek
Yesterday's post touched on something that all experienced drug discovery people have been through: the compound that works - until a new batch is made. Then it doesn't work so well. What to do?
You have a fork in the road here: one route is labeled "Blame the Assay" and the other one is "Blame the Compound". Neither can be ruled out at first, but the second alternative is easier to check out, thanks to modern analytical chemistry. A clean (or at least identical) LC/MS, a good NMR, even (gasp!) elemental analysis - all these can reassure you that the compound itself hasn't changed.
But sometimes it has. In my experience, the biggest mistake is to not fully characterize the original batch, particularly if it's a purchased compound, or if it comes from the dusty recesses of the archive. You really, really want to do an analytical check on these things. Labels can be mistaken, purity can be overestimated, compounds can decompose. I've seen all of these derail things. I believe I've mentioned a putative phosphatase inhibitor I worked on once, presented to me as a fine lead right out of the screening files. We resynthesized a batch of it, which promptly made the assay collapse. Despite having been told that the original compound had checked out just fine, I sent some out for elemental analysis, and marked some of the lesser-used boxes on the form while I was at it. This showed that the archive compound was, in fact, about a 1:1 zinc complex, for reasons that were lost in the mists of time, and that this (as you can imagine) did have a bit of an effect on the primary enzyme assay.
And I've seen plenty of things that have fallen apart on storage, and several commercial compounds that were clean as could be, but whose identity had no relation to what was on their labels (or their invoices for payment, dang it all). Always check, and always do that first. But what if you have, and the second lot doesn't work, and it appears to match the first in every way?
Personally, I say run the assay again, with whatever controls you can think of. I think at that point the chances of something odd happening there are greater than the chemical alternative, which is the dreaded Infinitely Active Impurity. Several times over the years, people have tried to convince me that even though some compound may look 99% clean, that all the activity is actually down there in the trace contaminants, and that if we just find it, we'll have something that'll be so potent that it'll make our heads spin. A successful conclusion to one of these snipe hunts is theoretically possible. But I have never witnessed one.
I'm willing to credit the flip side argument, the Infinitely Nasty Impurity, a bit more. It's easier to imagine something that would vigorously mess up an assay, although even then you generally need more than a trace. An equimolar amount of zinc will do. But an incredibly active compound, one that does just what you want, but in quantities so small that you've missed seeing it? Unlikely. Look for it, sure, but don't expect to find anything - and have 'em re-run that assay while you're looking.
Update: I meant to mention this, but a comment brings it up as well. One thing that may not show up so easily is a difference in the physical form of the compound, depending on how it's produced. This will mainly show up if you're (for example) dosing a suspension of powdered drug substance in an animal. A solution assay should cancel these things out (in vitro or in vivo), but you need to make sure that everything's really in solution. . .
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January 25, 2010
Posted by Derek
Nature has a short item on the Pfizer paper that questions the reproducibility of some key sirtuin work (covered here and here). There are some good points to temper the pessimism. Leonard Guarente of MIT, a key pioneer in the field, says:
". . . that the latest findings are neither surprising nor worrisome. The compounds may work only with fluorophore-conjugated peptides in vitro, says Guarente, but the situation is different in cells and in animals. The Nature paper, among others, went beyond the test tube and indicated that SIRT1 was more active in cells and in animals after application of the Sirtris compounds. Furthermore, resveratrol administration made no difference to the lifespan of yeast that did not have Sir23, indicating that the compound's action depends on this gene.
According to a statement from GlaxoSmithKline, Ahn's conclusion "ignores any possibility of direct activation of SIRT1 that may occur in a cellular environment that is not reproduced in vitro".
True, but there's still that problem of the Pfizer group not being able to reproduce the in vivo effects, which to me was perhaps the most worrisome part of the paper. Now, it's worth remembering that animal studies are not the easiest things in the world to do right, since there are so many variables. Small differences in animal strains and the like can sometimes throw things off severely. Even the Pfizer group admits this readily, with Kay Ahn telling Nature that "every in vivo experiment is a little bit different" and that "Under our conditions we didn't see beneficial effects, but we don't want to make a big conclusion out of those results."
That's an honorable way to put things, I have to say. Rather less honorable, though, at least to me, is David Sinclair's response from the Sirtris team. See what you think:
A possible explanation for the discrepancy, says Sinclair, is that Ahn and her colleagues did not provide information on the characterization of the compounds, which they synthesized themselves. So there is no way of knowing how pure they were or whether they're the same as those made by Sirtris. "The fact that mice died indicates that there may be an issue with purity," .
That's. . .not so good. In fact, it comes close to being insulting. Although I say a lot of uncomplimentary things about Pfizer's management, the fact remains that they have a lot of very good scientists there. And I assume that they can reproduce Sirtris's published procedures to make the sirtuin ligands. If they can't, frankly, that's Sirtris's fault. Everyone (well, everyone competent) checks out compounds thoroughly before putting them into an animal study. Asking "Are you sure you made the right stuff?" at this point is really a bit much, and doesn't do anything improve my opinion of Sirtris. (Which opinion actually was pretty good - until recently).
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+ TrackBacks (0) | Category: Aging and Lifespan | Drug Assays
January 12, 2010
Posted by Derek
As followers of the drug industry know, GlaxoSmithKline famously paid $720 million to buy Sirtris Pharmaceuticals in 2008. Sirtris is the most high-profile shop working on sirtuins and resveratrol-like pharmacology, which subject has received a massive amount of press (some accurate, some scrambled). I've been following the story with interest, since the literature has me convinced that the aging process can indeed be modified in a number of model organisms, which makes me think that it could be in humans as well. And I also feel sure that advances in this area could lead to many profound medical, social, and economic effects. (GSK, though, is going after diabetes first with the Sirtris deal, I should add - among other reasons, the FDA has no regulatory framework whatsoever for an antigeronic, if I can coin a word.)
But whatever the state of the anti-aging field, doubts have crept in about the wisdom of the Sirtris purchase. Last fall, a group at Amgen published a study suggesting that some of the SIRT1/resveratrol connections might be due an an experimental artifact caused by a particular fluorescent peptide. Now a group at Pfizer has piled on in the Journal of Biological Chemistry. They're looking over resveratrol and a series of sirtuin activators described by the Sirtris group in Nature.
And unfortunately, they also find trouble due to fluorogenic peptides. The TAMRA fluorophore on their peptide substrates seems to pervert the assay. While the Sirtris compounds looked like activators initially, switching to the native peptide substrates showed them to be worthless. Further study (calorimetry) showed that the activator compounds bind to a complex of SIRT1 and the fluorescent peptide substrate, but not to SIRT1 itself (or in the presence of native substrate without the fluorogenic group). That's not good.
But worse is to come:
"Despite a lack of evidence for the Sirtris series of compounds as direct SIRT1 activators, we investigated whether the in vivo efficacy demonstrated by SRT1720 in several rodent models diabetes could be validated and attributed to indirect activation of SIRT1. We therefore attempted to reproduce the in vivo efficacy for SRT1720 in mouse models of type 2 diabetes previously shown. . ."
That word "attempted" should tell you what comes next. The reported high dose of the compound (100 mpk) resulted in weight effects and death. The reported low dose (30 mpk) showed no effects at all on any diabetic parameters, but instead seemed to lead to increased feeding and weight gain. To complete the debacle, the Pfizer group screened the Sirtris compounds through a broad panel of assays, and found that all of them hit a number of other targets (and appear significantly worse than resvertarol itself, which is no one's idea of a clean compound to start with).
Basically, these folks have thrown down the gauntlet: they claim that the reported Sirtris compounds do not do what they are claimed to do, neither in vitro nor in vivo, and are worthless as model compounds for anything in this area of study. So what is GSK going to have to say about this? And what, if this paper is at all accurate, did they buy with their $720 million?
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January 5, 2010
Posted by Derek
I missed this paper when it came out back in October: "Reactome Array: Forging a Link Between Metabolome and Genome". I'd like to imagine that it was the ome-heavy title itself that drove me away, but I have to admit that I would have looked it over had I noticed it.
And I probably should have, because the paper has been under steady fire since it came out. It describes a method to metabolically profile a variety of cells though the use of a novel nanoparticle assay. The authors claim to have immobilized 1675 different biomolecules (representing common metabolites and intermediates) in such a way that enzymes recognizing any of them will set off a fluorescent dye signal. It's an ingenious and tricky method - in fact, so tricky that doubts set in quickly about the feasibility of doing it on 1675 widely varying molecular species.
And the chemistry shown in the paper's main scheme looks wonky, too, which is what I wish I'd noticed. Take a look - does it make sense to describe a positively charged nitrogen as a "weakly amine region", whatever that is? Have you ever seen a quaternary aminal quite like that one before? Does that cleavage look as if it would work? What happens to the indane component, anyway? Says the Science magazine blog:
In private chats and online postings, chemists began expressing skepticism about the reactome array as soon as the article describing it was published, noting several significant errors in the initial figure depicting its creation. Some also questioned how a relatively unknown group could have synthesized so many complex compounds. The dismay grew when supplementary online material providing further information on the synthesized compounds wasn’t available as soon as promised. “We failed to put it in on time. The data is quite voluminous,” says co-corresponding author Peter Golyshin of Bangor University in Wales, a microbiologist whose team provided bacterial samples analyzed by Ferrer’s lab.
Science is also coming under fire. “It was stunning no reviewer caught [the errors],” says Kiessling. Ferrer says the paper’s peer reviewers did not raise major questions about the chemical synthesis methods described; the journal’s executive editor, Monica Bradford, acknowledged that none of the paper’s primary reviewers was a synthetic organic chemist. “We do not have evidence of fraud or fabrication. We do have concerns about the inconsistencies and have asked the authors' institutions to try to sort all of this out by examining the original data and lab notes,” she says.
The magazine published an "expression of concern" before the Christmas break, saying that in response to questions the authors had provided synthetic details that "differ substantially" from the ones in the original manuscript. An investigation is underway, and I'll be very interested to see what comes of it.
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December 10, 2009
Posted by Derek
We spend a lot of time in this business talking about molecular scaffolds - separate chemical cores that we elaborate into more advanced compounds. And there's no doubt that such things exist, but is part of the reason they exist just an outcome of the way chemical research is done? Some analysis in the past has suggested that chemical types get explored in a success-breeds-success fashion, so that the (over)representation of some scaffold might not mean that it has unique properties. It's just that it's done what's been asked of it, so people have stuck with it.
A new paper in J. Med. Chem. from a group in Bonn takes another look at this question. They're trying to see if the so-called "privileged substructures" really exist: chemotypes that have special selectivity for certain target classes. Digging through a public-domain database (BindingDB), they found about six thousand compounds with activity toward some 259 targets. Many of these compounds hit more than one target, as you'd expect, so there were about 18,000 interactions to work with.
Isolating structural scaffolds from the compound set and analyzing them for their selectivity showed some interesting trends. They divide the targets up into communities (kinases, serine proteases, and so on), and they definitely find community-selective scaffolds, which is certainly the experience of medicinal chemists. Inside these sets, various scaffolds also show tendencies for selectivity against individual members of the community. Digging through their supporting information, though, it appears that a good number of the most-selective scaffolds tend to come from the serine protease community (their number 3), with another big chunk coming from kinases (their number 1a). Strip those (and some adenosine receptor ligands and DPP inhibitors, numbers 11 and 8) out, and you've taken out all the really eye-catching selectivity numbers out of their supplementary table S5. So I'm not sure that they've identified as many hot structures as one might think.
Another problem I have, when I look at these structures, is that a great number of them look too large for any useful further development. That's just a function of the data this team had to start with, but this gets back to the question of "drug-like" versus "lead-like" structures. I have a feeling that too many of the compounds in the BindingDB set are in the former category, or even beyond, which skews things a bit. Looking at a publication on it from 2007, I get the impression that a majority of compounds in it have a molecular weight greater than 400, with a definite long tail toward the higher weights. What medicinal chemists would like, of course, is a set of smaller scaffolds that will give them a greater chance of landing in a selective chemical space that can be developed. Some of the structures in this paper qualify, but definitely not all of them. . .
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December 7, 2009
Posted by Derek
Almost all of the drugs on the market target one or more small-molecule binding sites on proteins. But there's a lot more to the world than small-molecule binding sites. Proteins spend a vast amount of time interacting with other proteins, in vital ways that we'd like to be able to affect. But those binding events tend to be across broader surfaces, rather than in well-defined binding pockets, and we medicinal chemists haven't had great success in targeting them.
There are some successful examples, with a trend towards more of them in the recent literature. Inhibitors of interactions of the oncolocy target Bcl are probably the best known, with Abbott's ABT-737 being the poster child of the whole group.
But even though things seem to be picking up in this area, there's still a very long way to go, considering the number of possible useful interactions we could be targeting. And for every successful molecule that gets published, there are surely an iceberg of failed attempts that never make the literature. What's holding us back?
A new article in Drug Discovery Today suggests, as others have, that our compound libraries aren't optimized for finding hits in such assays. Given that the molecular weights of the compounds that are known to work tend toward the high side, that may well be true - but, of course, since the amount of chemical diversity up in those weight ranges is ridiculously huge, we're not going to be able to fix the situation through brute-force expansion of our screening libraries. (We'll table, for now, the topic of the later success rate of such whopper molecules).
Some recent work has suggested that there might be overall molecular shapes that are found more often in protein-protein inhibitors, but I'm not sure if everyone buys into this theory or not. This latest paper does a similar analysis, using 66 structurally diverse protein-protein inhibitors (PPIs) from the literature compared to a larger set (557 compounds) of traditional drug molecules. The PPIs tend to be larger and greasier, as feared>. They tried some decision-tree analysis to see what discriminated the two data sets, and found a shape description and another one that correlated more with aromatic ring/multiple-bond count. Overall, the decision tree stuff didn't shake things down as well as it does with data sets for more traditional target classes, which doesn't come as a surprise, either.
So the big questions are still out there: can we go after protein-protein targets with reasonably-sized molecules, or are they going to have to be big and ugly? And in either case, are there structures that have a better chance of giving us a lead series? If that's true, is part of the problem that we don't tend to have such things around already? If I knew the answers to these questions, I'd be out there making the drugs, to be honest. . .
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November 30, 2009
Posted by Derek
Now here's an oddity: medicinal chemists are used to seeing the two enantiomers (mirror image compounds, for those outside the field) showing different activity. After all, proteins are chiral, and can recognize such things - in fact, it's a bit worrisome when the enantiomers don't show different profiles against a protein target.
There are a few cases known where the two enantiomers both show some kind of activity, but via different binding modes. But I've never seen a case like this, where this happens at the same time in the same binding pocket. The authors were studying inhibitors of a biosynthetic enzyme from Burkholderia, and seeing the usual sorts of things in their crystal structures - that is, only one enantiomer of a racemic mixture showing up in the enzyme. But suddenly of their analogs showed both enantiomers simultaneously, each binding to different parts of the active site.
Interestingly, when they obtained crystal structures of the two pure enantiomers, the R compound looks pretty much exactly as it does in the two-at-once structure, but the S compound flips around to another orientation, one that it couldn't have adopted in the presence of the R enantiomer. The S compound is tighter-binding in general, and calorimetry experiments showed a complicated profile as the concentration of the two compounds was changed. So this does appear to be a real effect, and not just some weirdo artifact of the crystallization conditions.
The authors point out that many other proteins have binding sites that are large enough to permit this sort of craziness (P450 enzymes are a likely candidate, and I'd add PPAR binding sites to the list, too). We still do an awful lot of in vitro testing using racemic mixtures, and this makes a person wonder how many times this behavior has been seen before and not understood. . .
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November 17, 2009
Posted by Derek
There's a new paper out in Nature that presents an intriguing way to look for off-target effects of drug candidates. The authors (a large multi-center team) looked at a large number of known drugs (or well-characterized clinical candidates) and their activity profiles. They then characterized the protein targets by the similarities of the molecules that were known to bind to them.
That gave a large number of possible combinations - nearly a million, actually, and in most cases, no correlations showed up. But in about 7,000 examples, a drug matched some other ligand set to an interesting degree. On closer inspection, some of these off-target effects turned out to be already known (but had not been picked up during their initial searching using the MDDR database). Many others turned out to be trivial variations on other known structures.
But what was left over was a set of 3,832 predictions of meaningful off-target binding events. The authors took 184 of these out to review them carefully and see how well they held up. 42 of these turned out to be already confirmed in the primary literature, although not reported in any of the databases they'd used to construct the system - that result alone is enough to make one think that they might be on the right track here.
Of the remaining 142 correlations, 30 were experimentally feasible to check directly. Of these, 23 came back with inhibition constants less than 15 micromolar - not incredibly potent, but something to think about, and a lot better hit rate than one would expect by chance. Some of the hits were quite striking - for example, an old alpha-blocker, indoramin, showed a strong association for dopamine receptors, and turned out to be an 18 nM ligand for D4, which is better than it does on the alpha receptors themselves. In general, they uncovered a lot of new GPCR activities for older CNS drugs, which doesn't surprise me, given the polypharmacy that's often seen in that area.
But they found four examples of compounds that jumped into completely new target categories. Rescriptor (delavirdine), a reverse transcriptase inhibitor used against HIV, showed a strong score against histamine subtypes, and turned out to bind H4 at about five micromolar. That may not sound like much, but the drug's blood levels make that a realistic level to think about, and its side effects include a skin rash that's just what you might expect from such off-target binding.
There are some limitations. To their credit, the authors mention in detail a number of false positives that their method generated - equally compelling predictions of activities that just aren't there. This doesn't surprise me much - compounds can look quite similar to existing classes and not share their activity. I'm actually a bit surprised that their methods works as well as it does, and look forward to seeing refined versions of it.
To my mind, this would be an effort well worth some collaborative support by all the large drug companies. A better off-target prediction tool would be worth a great deal to the whole industry, and we might be able to provide a lot more useful data to refine the models used. Anyone want to step up?
Update: be sure to check out the comments section for other examples in this field, and a lively debate about which methods might work best. . .
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November 13, 2009
Posted by Derek
When we screen zillions of compounds from our files against a new drug target, what can we expect? How many hits will we get, and what percentage of those are actually worth looking at in more detail?
These are long-running questions, but over the last twenty years some lessons have been learned. A new paper in J. Med. Chem. emphasizes one of the biggest ones: if at all possible, run your assays with some sort of detergent in them.
Why would you do a thing like that? Compound aggregation. The last few years have seen a rapidly growing appreciation of this problem. Many small molecules will, under some conditions, clump together in solution and make a new species that has little or nothing to do with their individual members. These new aggregates can bind to protein surfaces, mess up fluorescent readouts, cause the target protein to stick to their surfaces instead, and cause all kinds of trouble. Adding detergent to the assay system cuts this down a great deal, and any compound that's a hit without detergent but loses activity with it should be viewed with strong suspicion.
The authors of this paper (from the NIH's Chemical Genomics Center and Brian Shoichet's lab at UCSF) were screening against the cysteine protease cruzain, a target for Chagas disease. They ran their whole library of compounds through under both detergent-free and detergent conditions and compared the results. In an earlier screening effort of this sort against beta-lactamase, nearly 95% of the hits (many of them rather weak) turned out to be aggregator compounds. This campaign showed similar numbers.
There were 15 times as many apparent hits in the detergent-free assay, for one thing. Some of these were apparently activating the enzyme, which is always a bit of an odd thing to explain, since inhibiting enzyme activity is a lot more likely. These activators almost completely disappeared under the detergent conditions, though. And even looking just at the inhibitors, 90% of the hit set in the detergent-free assay went away when detergent was added. (I should note that control cruzain inhibitors performed fine under both sets of assays, so it's not like the detergent itself was messing with the enzyme to any significant degree).
They point out another benefit to the detergent assay - it seems to improve the data by keeping the enzyme from sticking to the walls of the plastic tubes. That's a real problem which can kick your data around all over the place - I've encountered it myself, and heard a few horror stories over the years. But it's not something that's well appreciated outside of the people who set up assays for a living (and not always even among some of them).
So, let's get rid of those nasty aggegators, right? Not so fast. It turns out that some of the compounds that showed this problem during the earlier beta-lactamase work didn't cause a problem here, and vice versa. Even using different assays designed to detect aggregation alone gave varying results among sets of compounds. It appears that aggregation is quite sensitive to the specific assay conditions you're using, so trying to assemble a blacklist of aggregators is probably not going to work. You have to check things every time.
One other interesting point from this paper (and the previous one): curators of large screening collections spend a lot of time weeding out reactive compounds. They don't want things that will come in and react nonspecifically with labile groups on the target proteins, and that seems like a reasonable thing to do. But in these screens, the compounds with "hot" functional groups didn't have a particularly high hit rate. You'd expect a cysteine protease to be especially sensitive to this sort of thing, with that reactive thiol right in the active site, but not so. This ties in with the work from Benjamin Cravatt's group at Scripps, suggesting that even fairly reactive groups have a lot of constraints on them - they have to line up just right to form a covalent bond, and that just doesn't happen that often.
So perhaps we've all been worrying too much about reactive compounds, and not enough about the innocent-looking ones that clump up while we're not looking. Detergent is your friend!
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November 5, 2009
Posted by Derek
Resveratrol's a mighty interesting compound. It seems to extend lifespan in yeast and various lower organisms, and has a wide range of effects in mice. Famously, GlaxoSmithKline has expensively bought out Sirtris, a company whose entire research program started with resveratrol and similar compound that modulate the SIRT1 pathway.
But does it really do that? The picture just got even more complicated. A group at Amgen has published a paper saying that when you look closely, resveratrol doesn't directly affect SIRT1 at all. Interestingly, this conclusion has been reached before (by a group at the University of Washington), and both teams conclude that the problem is the fluorescent peptide substrate commonly used in sirtuin assays. With the fluorescent group attached, everything looks fine - but when you go to the extra trouble of reading things out without the fluorescent tag, you find that resveratrol doesn't seem to make SIRT1 do anything to what are supposed to be its natural substrates.
"The claim of resvertraol being a SIRT1 activator is likely to be an experimental artifact of the SIRT1 assay that employs the Fluor de Lys-SIRT1 peptide as a substrate. However, the beneficial metabolic effects of resveratrol have been clearly demonstrated in diabetic animal models. Our data do not support the notion that these metabolic effects are mediated by direct SIRT1 activation. Rather, they could be mediated by other mechanisms. . ."
They suggest activation of AMPK (an important regulatory kinase that's tied in with SIRT1) as one such mechanism, but admit that they have no idea how resveratrol might activate it. Does that process still require SIRT1 at all? Who knows? One thing I think I do know is that this has something to do with this Amgen paper from 2008 on new high-throughput assays for sirtuin enzymes.
One wonders what assay formats Sirtris has been using to evaluate their new compounds, and one also wonders what they make of all this now at GSK. Does one not? We can be sure, though, that there are plenty of important things that we don't know yet about sirtuins and the compounds that affect them. It's going to be quite a ride as we find them out, too.
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+ TrackBacks (0) | Category: Aging and Lifespan | Biological News | Drug Assays
September 10, 2009
Posted by Derek
I was looking through my RSS feed of journal articles this morning, and came across this new one in J. Med. Chem.. Now, there's nothing particularly unusual about this work. The authors are exploring a particular subtype of serotonin receptor (5-HT6), using some chemotypes that have been looked at in serotinergic ligands before. They switch the indole to an indene, put in a sulfonamide, change the aminoethyl side chain to a guanidine, and. . .wait a minute.
Guanidine? I thought that the whole point of making a 5-HT6 ligand was to get it into the brain, and guanidines don't have the best reputation for allowing you to do that. (They're not the easiest thing in the world to even get decent oral absorption from, either, come to think of it). So I looked through the paper to see if there were any in vivo numbers, and as far as I can see, there aren't.
Now, that's not necessarily the fault of the paper's authors. They're from an academic med-chem lab in Barcelona, and animal dosing (and animal PK measurements) aren't necessarily easy to get unless you have a dedicated team that does such things. But, still. The industrial medicinal chemist in me looks at these structures, finds them unlikely to ever reach their intended site of action, can find no evidence in the paper's references that anyone else has ever gotten such a guanidine hydrazone into the brain, either, and starts to have if-a-tree-falls-in-the-forest thoughts.
Now, it's true that we learn some more about the receptor itself by finding new ligands for it, and such compounds can be used for in vitro experiments. But it's not like there aren't other 5-HT6 antagonists out there, in several different chemical classes, and that's just from the first page of a PubMed search. Many of these compounds do, in fact, penetrate the brain, because they were developed by industrial groups for whom in vitro experiments are most definitely not an end in themselves.
I don't mean to single out the Barcelona group here. Their work isn't bad, and it looks perfectly reasonable to me. It's just that my years in industry have made me always ask what a particular paper tells me that I didn't know, and what use might some day be made of the results. Readers here will know that I have a weakness for out-there ideas and technologies, so it's not like I have to see an immediate practical application for everything. But I would like to see the hope of one. And for this work, and for a lot of medicinal chemistry that comes out of academic labs, I just don't see it.
Update: it's been pointed out in the comments that there's a value in academic work that doesn't have to be addressed in industry, that is, training the students who do it. That's absolutely right. But at the same time, couldn't people be trained just as well by working on systems that are a bit less dead on arrival?
And no, I'm not trying to make that case that academic labs should make drugs. If they want to try, then come on down. If they don't, that's fine, too - there's a lot of important research to be done in the world that has no immediate practical application. But this sort of paper that I've written about today seems to miss both of these boats simultaneously: it isn't likely to produce a drug, and it doesn't seem to be addressing any other pressing needs that I can see, either.
And yes, I could say the same about my own PhD work. "The world doesn't need another synthesis of a macrolide antibiotic", I told people at the time. "But I do". Does it have to be like that?
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+ TrackBacks (0) | Category: Academia (vs. Industry) | Drug Assays | Drug Development | The Central Nervous System | The Scientific Literature
August 11, 2009
Posted by Derek
I was looking over a paper in PNAS, where a group at Stanford describes finding several small molecules that inhibit Hedgehog signaling. That's a very interesting (and ferociously complex) area, and the more tools that are available to study it, the better.
But let me throw something out to those who have read (or will read) the paper. (Here's the PDF, which is open access). The researchers seem to have done a screen against about 125,000 compounds, and come up with four single-digit micromolar hits. Characterizing these against a list of downstream assays showed that each of these acts in a somewhat different manner on the Hedgehog pathway.
And that's fine - the original screen would have picked up a variety of mechanisms, and there certainly are a variety out there to be picked up. I can believe that a list of compounds would differentiate on closer inspection. What I keep looking for, though, is (first) a mention that these compounds were run through some sort of general screening panel for other enzyme and/or receptor activities. They did look for three different kinase activities that had been shown to interfere (and didn't see them), but I'd feel much better about using some new structures as probes if I'd run them through a big panel of secondary assays first.
Second, I've been looking for some indication that there might have been some structure-activity relationships observed. I assume that each of these compounds might well have been part of a series - so how did the related structures fare? Having a one-off compound doesn't negate the data, naturally, although it certainly does make it harder to build anything from the hit you've found. But SAR is another factor that I'd immediately look for after a screen, and it seems strange to me that I can't find any mention of it.
Have I missed these things, or are they just not there? If they aren't, is that a big deal, or not? Thoughts?
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July 17, 2009
Posted by Derek
I seem to have been putting a lot of graphics up this week, so here's another one. This is borrowed from a recent Science paper on the future of natural-products based drug discovery. It's interesting both from that viewpoint, and because of the general approval numbers:
And there you have it. Outside of anomalies like 2005, we can say, I think, that the 1980s were a comparative Golden Age of Drug Approvals, that the 1990s held their own but did not reach the earlier heights, and that since 2000 the trend has been dire. If you want some numbers to confirm your intuitions, you can just refer back to this.
As far as natural products go, from what I can see, the percentage of drugs derived from them has remained roughly constant: about half. Looking at the current clinical trial environment, though, the authors see this as likely to decline, and wonder if this is justified or not. They blame two broad factors, one of them being the prevailing drug discovery culture:
The double-digit yearly sales growth that drug companies typically enjoyed until about 10 years ago has led to unrealistically high expectations by their shareholders and great pressure to produce "blockbuster drugs" with more than $1 billion in annual sales (3). In the blockbuster model, a few drugs make the bulk of the profit. For example, eight products accounted for 58% of Pfizer’s annual worldwide sales of $44 billion in 2007.
As an aside, I understand the problems with swinging for the fences all the time, but I don't see the Pfizer situation above as anything anomalous. That's a power-law distribution, and sales figures are exactly where you'd expect to see such a thing. A large drug company with its revenues evenly divided out among a group of compounds would be the exception, wouldn't it?
The other factor that they say has been holding things back is the difficulty of screening and working with many natural products, especially now that we've found many of the obvious candidates. A lot of hits from cultures and extracts are due to compounds that you already know about. The authors suggest that new screening approaches could get around this problem, as well as extending the hunt to organisms that don't respond well to traditional culture techniques.
None of these sound like they're going to fix things in the near term, but I don't think that the industry as a whole has any near-term fixes. But since the same techniques used to isolate and work with tricky natural product structures will be able to help out in other areas, too, I wish the people working on them luck.
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July 15, 2009
Posted by Derek
I've been meaning to get around to a very interesting paper from the Shoichet group that came out a month or so ago in Nature Chemical Biology. Today's the day! It examines the content of screening libraries and compares them to what natural products generally look like, and they turn up some surprising things along the way. The main question they're trying to answer is: given the huge numbers of possible compounds, and the relatively tiny fraction of those we can screen, why does high-throughput screening even work at all?
The first data set they consider is the Generated Database (GDB), a calculated set of all the reasonable structures with 11 or fewer nonhydrogen atoms, which grew out of this work. Neglecting stereochemistry, that gives you between 26 and 27 million compounds. Once you're past the assumptions of the enumeration (which certainly seem defensible - no multiheteroatom single-bond chains, no gem-diols, no acid chlorides, etc.), then there are no human bias involved: that's the list.
The second list is everything from the Dictionary of Natural Products and all the metabolites and natural products from the Kyoto Encyclopedia of Genes and Genomes. That gives you 140,000+ compounds. And the final list is the ZINC database of over 9 million commercially available compounds, which (as they point out) is a pretty good proxy for a lot of screening collections as well.
One rather disturbing statistic comes out early when you start looking at overlaps between these data sets. For example, how many of the possible GDB structures are commercially available? The answer: 25,810 of them - in other words, you can only buy fewer than 0.01% of the possible compounds with 11 heavy atoms or below, making the "purchasable GDB" a paltry list indeed.
Now, what happens when you compare that list of natural products to these other data sets? Well, for one thing, the purchasable part of the GDB turns out to be much more similar to the natural product list than the full set. Everything in the GDB has at least 20% Tanimoto similarity to at least one compound in the natural products set, not that 20% means much of anything in that scoring system. But only 1% of the GDB has a 40% Tanimoto similarity, and less than 0.005% has an 80% Tanimoto similarity. That's a pretty steep dropoff!
But the "purchasable GDB" holds up much better. 10% of that list has 100% Tanimoto similarity (that is, 10% of the purchasable compounds are natural products themselves). The authors also compare individual commercial screening collections. If you're interested, ChemBridge and Asinex are the least natural-product-rich (about 5% of their collections), whereas IBS and Otava are the most (about 10%).
So one answer to "why does HTS ever work for anything" is that compound collections seem to be biased toward natural-product type structures, which we can reasonably assume have generally evolved to have some sort of biological activity. It would be most interesting to see the results of such an analysis run from inside several drug companies against their own compound collections. My guess is that the natural product similarities would be even higher than the "purchasable GDB" set's, because drug company collections have been deliberately stocked with structural series that have shown activity in one project or another.
That's certainly looking at things from a different perspective, because you can also hear a lot of talk about how our compound files are too ugly - too flat, too hydrophobic, not natural-product-like enough. These viewpoints aren't contradictory, though - if Shoichet is right, then improving those similarities would indeed lead to higher hit rates. Compared to everything else, we're already at the top of the similarity list, but in absolute terms there's still a lot of room for improvement.
So how would one go about changing this, assuming that one buys into this set of assumptions? The authors have searched through the various databases for ring structures, taking those as a good proxy for structural scaffolds. As it turns out 83% of the ring scaffolds among the natural products are unrepresented among the commercially available molecules - a result that I assume that Asinex, ChemBridge, Life Chemicals, Otava, Bionet and their ilk are noting with great interest. In fact, the authors go even further in pointing out opportunities, with a table of rings from this group that closely resemble known drug-like ring systems.
But wait a minute. . .when you look at those scaffolds, a number of them turn out to be rather, well, homely. I'd be worried about elimination to form a Michael acceptor in compound 19, for example. I'm not crazy about the N,S acetal in 21 or the overall stability of the acetals in 15, 17 and 31. The propiolactone in 23 is surely reactive, as is the quinone in 25, and I'd be very surprised if that's not what they owe their biological activities to. And so on.
All that said, there are still some structures in there that I'd be willing to check out, and there must be more of them in that 83%. No doubt a number of the rings that do sneak into the commercial list are not very well elaborated, either. I think that there is a real commercial opportunity here. A company could do quite well for itself by promoting its compound collection as being more natural-product similar than the competition, with tractable molecules, and a huge number of them unrepresented in any other catalog.
Now all you'd have to do is make these things. . .which would require hiring synthetic organic chemists, and plenty of them. These things aren't easy to make, or to work with. And as it so happens, there are quite a few good ones available these days. Anyone want to take this business model to heart?
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+ TrackBacks (0) | Category: Drug Assays | Drug Industry History | In Silico
June 29, 2009
Posted by Derek
Not long ago, I wrote about a Pfizer program for smaller companies to come screen their targets against Pfizer's compound bank. Now Eli Lilly has flipped that around. In an initiative to bring other people's compounds out of the stockrooms and off the shelves, they'll screen them for free.
These aren't single-target assays. The company has four phenotypic screens going (for Alzheimer's, diabetes, cancer, and osteoporosis) and will look for improvement by any mechanism that comes to hand. No chemical structure information is shown to Lilly (I assume that they just know the molecular weight so they can run a dilution series). If something looks interesting, the company and the owners of the chemical matter have 120 days to come to terms for any further development deal - if not, then all rights revert to the submitter, and they can publish the data from the screens.
Lilly's working out a universal material transfer agreement, in collaboration with a number of universities, so that the paperwork stays the same every time. That's a good move. The lawyering can be a real holdup - in my experience, every party in these agreements usually comes in with slightly different wording in their magic legal spells, requiring several rounds of reconciliation before everyone's ready to sign.
I think that this is a worthwhile idea, and that they'll get a lot of takers. There are plenty of compounds sitting around in academic labs gathering dust, so why not send 'em in? The worst that can happen is nothing, and the best is that the compound actually turns out to be worth something. But will anything come out of it? The closest program to this is surely the National Cancer Institute's long-standing (since 1990) NCI-60 screening program, which also runs at no cost to the submitters. Even so, a recent reference mentions that there are between 40,000 and 50,000 compound in the NCI database, which actually seems rather small, considering. (To be fair, the program is not being funded at the levels that it was during the early 1990s). The only marketed compound that I'm aware of that can be said to have come out of the NCI-60 screen is Velcade (bortezomib), known then as PS-341, which was sent in for screening by Proscript Pharmaceuticals in the mid-1990s. Many other interesting structures have turned up along the way, though, which for various reasons haven't made it all the way through.
It'll be quite interesting to see what sort of hit rate Lilly's phenotypic assays call up - I hope they tell us. I have a lot of sympathy for the mechanism-agnostic approach myself, and I'd like to see how closely my bias are aligned to reality.
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+ TrackBacks (0) | Category: Drug Assays | Drug Development
June 19, 2009
Posted by Derek
After yesterday's post on pathway patents, I figured that I should talk about high-throughput screening in academia. I realize that there are some serious endeavors going on, some of them staffed by ex-industry people. So I don't mean to come across as thinking that academic screening is useless, because it certainly isn't.
What is probably is useless for is enabling a hugely broad patent application like the one Ariad licensed. But the problem with screening for such cases isn't that the effort would come from academic researchers, because industry couldn't do it, either: Merck, Pfizer, GSK and Novartis working together probably couldn't have sufficiently enabled that Ariad patent; it's a monster.
It's true that the compound collections available to all but the very largest academic efforts don't compare in size to what's out there in the drug companies. My point yesterday was that since we can screen those big collections and still come up empty against unusual new targets (again and again), that smaller compound sets are probably at even more of a disadvantage. Chemical space is very, very large. The total number of tractable compounds ever made (so far) is still not a sufficiently large screening collection for some targets. That's been an unpleasant lesson to learn, but I think that it's the truth.
That said, I'm going to start sounding like the pointy-haired boss from Dilbert and say "Screen smarter, not harder". I think that fragment-based approaches are one example of this. Much smaller collections can yield real starting points if you look at the hits in terms of ligand efficiency and let them lead you into new chemical spaces. I think that this is a better use of time, in many cases, than the diversity-oriented synthesis approach, which (as I understand it) tries to fill in those new spaces first and screen second. I don't mind some of the DOS work, because some of it's interesting chemistry, and hey, new molecules are new molecules. But we could all make new molecules for the rest of our lives and still not color in much of the map. Screening collections should be made interesting and diverse, but you have to do a cost/benefit analysis of your approach to that.
I'm more than willing to be proven wrong about this, but I keep thinking that brute force is not going to be the answer to getting hits against the kinds of targets that we're having to think about these days - enzyme classes that haven't yielded anything yet, protein-protein interactions, protein-nucleic acid interactions, and other squirrely stuff. If the modelers can help with these things, then great (although as I understand it, they generally can have a rough time with the DNA and RNA targets). If the solution is to work up from fragments, cranking out the X-ray and NMR structural data as the molecules get larger, then that's fine, too. And if it means that chemists just need to turn around and generate fast targeted libraries around the few real hits that emerge, a more selective use of brute force, then I have no problem with that, either. We're going to need all the help we can get.
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+ TrackBacks (0) | Category: Academia (vs. Industry) | Drug Assays | Drug Development
May 19, 2009
Posted by Derek
I've heard that Pfizer is doing something unusual with its proprietary compound collection: they're offering to let other people screen it.
Now, that's quite a step. Most companies guard their compounds pretty closely, considering them to be key assets. But I'm told that Pfizer has been meeting with several other (mostly smaller) companies, offering their (entire?) compound library as a screening resource. As I understand it, you need to come to them with a reasonably formatted HTS assay, and there's a fee in the high hundreds of thousands to run the screen.
That doesn't seem like much of a moneymaker, to be honest. The whole thing appears to me to be a way for Pfizer to strike deals with a number of other companies, since the compounds that come out of the screen will (likely as not) be covered by Pfizer's own patents. I haven't heard of how the IP issues are to be worked out in these deals, but that's the first thing that occurs to me. Anyone have more details?
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April 29, 2009
Posted by Derek
What a mess! Science has a retraction of a 2005 paper, which is always a nasty enough business, but in this case, the authors can’t agree on whether it should be retracted or not. And no one seems to be able to agree on whether the original results were real, and (even if they weren’t) whether the technique the paper describes works anyway. Well.
The original paper (free full text), from two Korean research groups, described a drug target discovery technique with the acronym MAGIC (MAGnetism-based Interaction Capture). It’s a fairly straightforward idea in principle: coat a magnetic nanoparticle with a molecule whose target(s) you’re trying to identify. Now take cell lines whose proteins have had various fluorescent tags put on them, and get the nanoparticles into them. If you then apply a strong magnetic field to the cells, the magnetic particles will be pulled around, and they’ll drag along whichever proteins have associated with your bait molecule. Watch the process under a microscope, and see which fluorescent spots move in which cells.
Papers were published (in both Science and Nature Chemical Biology), patent applications were filed (well, not in that order!), startup money was raised for a company to be called CGK. . .and then troubles began. Word was that the technique wasn’t reproducible. One of the authors (Yong-Weon Yi) asked that his name be removed from the publications, which was rather problematic of him, considering that he was also an inventor on the patent application. Early last year, investigations by the Korean Advanced Institute of Science and Technology came to the disturbing conclusion that the papers “do not contain any scientific truth”, and the journals flagged them.
The Nature Chemical Biology paper was retracted last July, but the Science paper has been a real rugby scrum, as the journal details here. The editorial staff seems to have been unable to reach one of the authors (Neoncheol Jung), and they still don’t know where he is. That’s disconcerting, since he’s still listed as the founding CEO of CGK. A complex legal struggle has erupted between the company and the KAIST about who has commercial rights to the technology, which surely isn’t being helped along by the fact that everyone is disagreeing about whether it works at all, or ever has. Science says that they’ve received parts of the KAIST report, which states that the authors couldn’t produce any notebooks or original data to support any of the experiments in the paper. This is Most Ungood, of course, and on top of that, two of the authors also appear to have stated that the key experiments (where they moved the fluorescent proteins around) were not carried out as the paper says. Meanwhile, everyone involved is now suing everyone else back in Korea for fraud, for defamation, and who knows. The target date for all this to be resolved is somewhere around the crack of doom.
Emerging from the fiery crater, CGK came up with another (very closely related) technique, which they published late last year in JACS. (If nothing else, everyone involved is certainly getting their work into an impressive list of journals. If only the papers wouldn’t keep sliding right back out. . .) That one has stood up so far, but it’s only April. I presume that the editorial staff at JACS asked for all kinds of data in support, but (as this whole affair shows) you can’t necessarily assume that everyone’s doing the job they’re supposed to do.
The new paper, most interestingly, does not reference the previous work at all, which I suppose makes sense on one level. But if you just came across it de novo, you wouldn't realize that people (at the same company!) had already been (supposedly) working on magnetic particle assays in living cells. Looking over this one and comparing it to the original Science paper, one of the biggest differences seems to be how the magnetic particles are made to expose themselves to the cytoplasm. The earlier work mentioned coating the particles with a fusogenic protein (TAT-HA2) that was claimed to help with this process; that step is nowhere to be found in the JACS work. Otherwise, the process looks pretty much identical to me.
Let’s come up for air, then, and ask how well useful these ideas could be, stipulating (deep breath) that they work. Clearly, there’s some utility here. But I have to wonder how useful this protocol will be for general target fishing expeditions. Fluorescent labeling of proteins is indeed one of the wonders of the world (and was the subject of a recent a well-deserved Nobel prize). But not all proteins can be labeled without disturbing their function – and if you don’t know what the protein’s up to in the first place, you’re never sure if you’ve done something to perturb it when you add the glowing parts. There are also a lot of proteins, of course, to put it mildly, and if you don’t have any idea of where to start looking for targets, you still have a major amount of work to do. The cleanest use I can think of for these experiments is verifying (or ruling out) hypotheses for individual proteins.
But that's if it works. And at this point, who knows? I'll be very interested to follow this story, and to see if anyone else picks up this technique and gets it to work. Who's brave enough?
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+ TrackBacks (0) | Category: Biological News | Drug Assays | The Dark Side | The Scientific Literature
March 13, 2009
Posted by Derek
A few readers have told me that I’m being too hard on antibacterial drug discovery, at least on target-based efforts in the field. The other day I asked if anyone could name a single antibacterial drug on the market that had been developed from a target, rather than by screening or modification of existing drugs and natural products, and the consensus was that there’s nothing to point to yet.
The objections are that antibacterials are an old field, and that for many years these natural products (and variations thereof) were pretty much all that anyone needed. Even when target-based drug discovery got going in earnest (gathering momentum from the 1970s through the 1980s), the antibacterial field was in general thought to be pretty well taken care of, so correspondingly less effort was put into it. Even now, there’s still a lot of potential in modifying older compounds to evade resistance, which is not something that a lot of other drug discovery areas have the option of doing.
And I have to say, these points have something to them. It’s true that antibacterials are something of a world apart; this was the first field of modern pharmaceutical discovery, and the struggle against living, adapting organisms makes it different than most other therapeutic areas even today. The lack of target-driven successes is surely due in part to historical factors. (The relative success of the later-blooming antiviral therapeutic targets is evidence in favor of this, too).
That said, I think that it’s not generally realized how few target-based drugs there are in the field (approximately none), so I did want to highlight that. And it does seem to be the case that working up from targets in the area is a hard row to hoe. There’s a rather disturbing review from GlaxoSmithKline that makes that case:
"From the 70 HTS campaigns run between 1995–2001 (67 target based, 3 whole cell), only 5 leads were delivered, so that, on average, it took 14 HTS runs to discover one lead. Based on GSK screening metrics, the success rate from antibacterial HTS was four- to five-fold lower than for targets from other therapeutic areas at this time. To be sure, this was a disappointing and financially unsustainable outcome, especially in view of the length of time devoted to this experiment and considering that costs per HTS campaign were around US$1 million. Furthermore, multiple high-quality leads are needed given the attrition involved in the lead optimization and clinical development processes required to create a novel antibiotic.
GSK was not the only company that had difficulty finding antibacterial leads from HTS. A review of the literature between 1996 and 2004 shows that >125 antibacterial screens on 60 different antibacterial targets were run by 34 different companies25. That none of these screens resulted in credible development candidates is clear from the lack of novel mechanism molecules in the industrial antibacterial pipeline. We are only aware of two compounds targeting a novel antibacterial enzyme (PDF) that have actually progressed as far as Phase I clinical trials, and technically speaking PDF was identified as an antibacterial target well before the genome era."
So although the history is a mitigating factor, the field does seem to have its. . .special character. The GSK authors discuss some of the possible reasons for this, but those can be the topic of another post or two; they're worth it.
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+ TrackBacks (0) | Category: Drug Assays | Drug Industry History | Infectious Diseases
March 6, 2009
Posted by Derek
There are a huge number of techniques in the protein world that relay on tying down some binding partner onto some kind of solid support. When you’re talking about immobilizing proteins, that’s one thing – they’re large beasts, and presumably there’s some tether that can be bonded to them to string off to a solid bead or chip. It’s certainly not always easy, but generally can be done, often after some experimentation with the length of the linker, its composition, and the chemistry used to attach it.
But there are also plenty of ideas out there that call for doing the same sort of thing to small molecules. The first thing that comes to mind is affinity chromatography – take some small molecule that you know binds to a given protein or class of proteins well, attach it to some solid resin or the like, and then pour a bunch of mixed proteins over it. In theory, the binding partner will stick to its ligand as it finds it, everything else will wash off, and now you’ve got pure protein (or a pure group of related proteins) isolated and ready to be analyzed. Well, maybe after you find a way to get them off the solid support as well.
That illustrates one experimental consideration with these ideas. You want the association between the binding partners to be strong enough to be useful, but (in many cases) not so incredibly strong that it can never be broken up again. There are a lot of biomolecule purification methods that rely on just these sorts of interactions, but those often use some well-worked-out binding pair that you introduce into the proteins artificially. Doing it on native proteins, with small molecules that you just dreamed up, is quite another thing.
But that would be very useful indeed, if you could get it work reliably. There are techniques available like surface plasmon resonance, which can tell with great sensitivity if something is sticking close to a solid surface. At least one whole company (Graffinity) has been trying to make a living by (among other things) attaching screening libraries of small molecules to SPR chips, and flowing proteins of interest over them to look for structural lead ideas.
And Stuart Schreiber and his collaborators at the Broad Institute have been working on the immobilized-small-molecule idea as well, trying different methods of attaching compound libraries to various solid supports. They’re looking for molecules that disrupt some very tough (but very interesting) biological processes, and have reported some successes in protein-protein interactions, a notoriously tempting (and notoriously hard) area for small-molecule drug discovery.
The big problem that people tend to have with all these ideas – and I’m one of those people, in the end – is that it’s hard to see how you can rope small molecules to a solid support without changing their character. After all, we don’t have anything smaller than atoms to make the ropes out of. It’s one thing to do this to a protein – that’ll look like a tangle of yarn with a small length of it stretching out to the side. But on the small molecule scale, it’s a bit like putting a hamster on a collar and leash designed for a Doberman. Mr. Hamster is not going to be able to enjoy his former freedom of movement, and a blindfolded person might, on picking him up, have difficulty recognizing his essential hamsterhood.
There's also the problem of how you attach that leash and collar, even if you decide that you can put up with it once it's on. Making an array of peptides on a solid support is all well and good - peptides have convenient handles at both ends, and there are a lot of well-worked-out reactions to attach things to them. But small molecules come in all sorts of shapes, sizes, and combinations of functional groups (at least, they'd better if you're hoping to see some screening hits with them). Trying to attach such a heterogeneous lot of stuff through a defined chemical ligation is challenging, and I think that the challenge is too often met by making the compound set less diverse. And after seeing how much my molecules can be affected by adding just one methyl group in the right (or wrong) place, I’m not so sure that I understand the best way to attach them to beads.
So I’m going to keep reading the tethered-small-molecule-library literature, and keep an eye on its progress. But I worry that I’m just reading about the successes, and not hearing as much about the dead ends. (That’s how the rest of the literature tends to work, anyway). For those who want to catch up with this area, here's a Royal Society review from Angela Koehler and co-workers at the Broad that'll get you up to speed. It's a high-risk, high-reward research area, for sure, so I'll always have some sympathy for it.
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+ TrackBacks (0) | Category: Analytical Chemistry | Drug Assays | General Scientific News
February 24, 2009
Posted by Derek
Medicinal chemists spend a lot of their time exploring and trying to make sense of structure-activity relationships (SARs). We vary our molecules in all kinds of ways, have the biologists run them through the assays, and then sit down to make sense of the results.
And then, like as not, we get up again after a few minutes, shaking our heads. Has anyone out there ever worked on a project where the entire SAR made sense? I’ve always considered it a triumph if even a reasonable majority of the compounds fit into an interpretable pattern. SAR development is a perfect example of things not quite working out the way that they do in textbooks.
The most common surprise when you get your results back, if that phrase “common surprise” makes any sense, is to find that you’ve pushed some trend a bit too far. Methyl was pretty good, ethyl was better, but anything larger drops dead. I don’t count that sort of thing – those are boundary conditions, for the most part, and one of the things you do in a med-chem program is establish the limits under which you can work. But there are still a number of cases where what you thought was a wall turns out to have a secret passage or two hidden in it. You can’t put any para-substituents on that ring, sure. . .unless you have a basic amine over on the other end of the molecule, and then you suddenly can.
I’d say that a lot of these get missed, because after a project’s been running a while, various SAR dogmas get propagated. There are features of the structure space that “everybody knows”, and that few people want to spend their time violating. But it’s worth devoting a small (but real) amount of effort to going back and checking some of these after the lead molecule has evolved a bit, since you can get surprised.
Some projects I’ve worked on have so many conditional clauses of this sort built into their SAR that you wonder whether there are any boundaries at all. This works, unless you have this, but if you have that over there it can be OK, although there is that other compound which didn’t. . .making sense of this stuff can just be impossible. The opposite situation, the fabled Perfectly Additive SAR, is something I’ve never encountered in person, although I’ve heard tales after the fact. That’s the closest we come to the textbooks, where you can mix and match groups and substituents any way you like, predicting as you go from the previous trends just how they’ll come out. I have to think that any time you can do this, that it has to be taking place in a fairly narrow structure space – surely we can always break any trend like this with a little imagination.
Another well-known bit of craziness is the Only Thing That Works There. You’ll have whole series of compounds that have to have a a methyl group at some position, or they’re all dead. Nothing smaller, nothing larger, nothing with a different electronic flavor: it’s methyl or death. (Or fluoro, or a thiazole, or what have you – I’ve probably seen this with methyl more than with other groups, but it can happen all over the place). A sharp SAR is certainly nothing to fear; it’s probably telling you that you really are making good close contacts with the protein target somewhere. But it can be unnerving, and sometimes there’s not a lot of room left on the ledge when you have more than one constraint like this.
Why does all this go on? Multiple binding modes, you have to think. Proteins are flexible beasts, and they've got lots of ways to react to ligands. And it's important never to forget that we can't predict their responses, at least not yet and not very well. And of course, in all this discussion, we've just been considering one target protein. When you think about the other things your molecule might be hitting in cells or in a whole animal, and that the SAR relationships for those off-target things are just as fluid and complicated as for your target, well. . .you can see why medicinal chemistry is not going away anytime soon. Or shouldn't, anyway.
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+ TrackBacks (0) | Category: Drug Assays | In Silico | Life in the Drug Labs
January 21, 2009
Posted by Derek
I was blithely throwing around the term “chemical space” in yesterday’s post. So, what am I talking about, and how much room is in there, anyway?
Let's narrow it down to organic compounds, to start with, or at least compounds that are mostly organic. A working definition, as far as people interested in biology and medicine go, might then be “the domain of chemical compounds compatible with living systems”. That excludes the red-hot reactive stuff and the unstable exploders, but leaves most everything else. Let’s also ignore macromolecules of various kinds and cut back to “drug-like” sizes – say, molecular weight 500 or less. That way we don’t have infinite numbers of polymers going off in all directions; that should help. And that leaves us with. . .?
A ridiculously large set of compounds, still. You can see how things get out of control pretty quickly if you just consider a building-block problem. Imagine breaking compounds down into simple units - an aryl ring, an ether, a tertiary amine, and so on. What sorts of numbers do you get when you start mixing and matching them? Well, there are an awful lot of possible building blocks. You could quickly fill out a hundred different examples of each of those three subunits, so there's one hundred to the third, or a million possible compounds without even exerting yourself very much.
This sort of thought experiment has been done several times. One estimate done by this fragment approach and considering only stable structures came in between 10 to the twentieth and ten to the twenty-fourth compounds that could potentially be prepared using known synthetic methods. (See here for another "how many compounds are possible?" paper, from a different angle - the group that did that work has followed it up recently, which will be the subject of another post sometime). Needless to say, that is considerably larger than the total number of organic compounds ever described in reality. There's not enough carbon, oxygen, and nitrogen on earth to prepare a vial of each of these, and where would you put the vials? The terrifying thing is that this is actually one of the lower estimates, and thus perhaps a very reasonable and conservative one. You can find ten-to-the-sixtieth estimates out there, which is a figure that cannot be dealt with by human efforts.
These sorts of numbers are why some people doubt the utility of just cranking out neat structures. But looked at from the other direction, the number of compounds we have available isn't nearly so impressive, so making new ones, especially long lists of new ones, makes a difference in what we actually have in hand. But is it a difference akin to buying a thousand lottery tickets rather than buying one?
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January 20, 2009
Posted by Derek
Ever hear of Diversity-Oriented Synthesis? It’s an odd bird. DOS tries to maximize the number of structures and scaffolds produced from a given synthetic scheme – to find the most efficient ways to populate the largest amount of chemical space. In a way, it’s the contrapositive of natural product synthesis, which focuses all its effort into producing one specific molecule at a time. I should add that DOS isn’t about producing mixtures; its goal is discrete compounds, but plenty of them, and all over the map. (Here's more background from David Spring at Cambridge).
The point of this is to increase the diversity of compounds libraries for biological screening. And that’s traditionally been the concern of the drug companies, but (as far as I can tell) there’s very little DOS going on inside the industry. All the publications in the field, at any rate, seem to come from academia. Companies certainly do care about the diversity of their screening libraries, but they don’t seem to be addressing the issue through the “maximum diversity in the fewest steps” philosophy.
There’s a recent paper in Ang. Chem. that will give you a good flavor of what’s going on in this area. A group led by Adam Nelson at Leeds has published an interesting approach that relies on olefin metathesis. An ingenious use of protecting groups and sequential metathesis reactions builds up a wide variety of structural backbones pretty quickly. (Another key feature is the use of fluorous tagging for purification, which will be the topic of another future post around here). Metathesis was certainly a good choice, since that gives you a chance to form a lot of carbon-carbon bonds in a lot of ways, all using basically the same reaction conditions. In just a few steps (around five or six) they ended up with about 80 quite different scaffolds.
Stuart Schreiber, an early advocate of DOS, wrote up a “News and Views” piece for Nature about this paper, and he makes the case this way:
” The resulting products differ from the compounds found in most small-molecule screening collections. Typically purchased from commercial vendors, the compounds in such collections frequently lack chirality and are structurally simple. This means that they can bind to only a small number of biological targets. The compounds in commercial libraries also tend to be structurally similar — their 'diversity' is limited to variations in appendages attached to a small number of common skeletons. This undesirable combination of properties means that, although enormous numbers of compounds (often more than a million) are frequently tested in screenings, at great expense, in the case of undruggable targets relatively few biologically active 'hits' are found. In principle, a smaller library of compounds that contains a more diverse range of molecular shapes, such as those made by Morton et al., would provide both more hits for less money, and hits for the more challenging biological targets.”
I see where Schreiber is coming from, but there are some details being overlooked here. One big point is that smaller compounds actually tend to hit more targets, just not with as much absolute potency: that's the whole idea behind fragment-based drug design. Larger, more complex molecules tend to be more selective, but when they happen to fit, they can fit very well indeed. You need a huge pile of them to have a chance of finding one of those, though. (I think that a happy medium would be a DOS approach to not-very-large compounds, but that doesn't give you that much room to maneuver).
Another point is that the key thing about the collections you can buy is that they often depend on just a few bond-forming reactions. You get an awful lot of amides, ureas, and sulfonamides, since by gosh, those sure can be cranked out. To me, that’s the first thing that makes the Leeds compounds stand out: none of these classic library-making transformations was exploited. Unfortunately, the other things that make the Leeds compounds stand out aren’t necessarily good. For one thing, there are no basic nitrogens in any of the structures. The paper lists a big class of azacycles, but in every case, the nitrogens are capped with nosyl groups, which completely wipe out their character. And while it’s true that you can get biological activity without nitrogen, you’ll get a lot more with it. A useful extension of the chemistry would be to use some sort of (update: more easily) removable group on the nitrogens, so that each scaffold could be unmasked at the end – that would give you the basic nitrogens back, and you could then make a few amides and the like off of them for good measure.
The compound set is also heavy on alkenes, which isn't surprising, given the metathesis chemistry. There's nothing wrong with those per se, but it would be worth taking all the scaffolds through a hydrogenation reaction to saturate the bonds, giving you another compound set. Alternatively, if you want to be a real buckaroo, take them through a Simmon-Smith reaction and turn them into cyclopropanes - that could be messy, but cyclopropanes are very much under-represented in compound libraries, compared to how many of them could potentially exist. A bigger problem is that one of the linking groups the Leeds team uses is a silyl ketal. That’s not the most chemically attractive group in the world, nor the most stable, and as a medicinal chemist I would have avoided it.
That brings up another point about well, the point of these libraries. Schreiber makes the pitch that if we're going to do chemical biology on the tougher interaction targets (protein-protein, protein-nucleic acid, and so on), then we're going to need all the chemical diversity we can get. That's hard to dispute! But a lot depends on whether these compounds are meant to be in vitro tools, or real leads for drug discovery. You can put up with silyl ketals (or worse) if the former, but not for the latter. (Many medicinal chemists would say that if you have some functional group that you're just going to have to remove, then don't put it in there in the first place).
And that's the gap between academia and industry on this approach, right there. The in vitro tools, used to discover pathways and interactions, are more the province of the university labs, and the drug leads are more the concern of industry. As it stands now, the drug company folks look at many of the DOS libraries and say "Hmm. . .sort of, but not quite". That's probably going to change, and if I had to guess, I'd say that one way into industrial practice might be through chemical vendors. There are a number of companies who make their livings by offering unique building block compounds to the drug industry - as DOS matures, these people may sense a commercial opportunity and move in.
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November 14, 2008
Posted by Derek
So, you’re making an enzyme inhibitor drug, some compound that’s going to go into the protein’s active site and gum up the works. You usually want these things to be potent, so you can be sure that you’ve knocked down the enzyme, so you can give people a tiny, convenient pill, and so you don’t have to make heaps of the compound to sell. How potent is potent? And how potent can you get?
Well, we’d like nanomolar. For the non-chemists in the crowd, that’s a concentration measure based on the molecular weight of the compound. If the molecular weight of the drug is 400, which is more typical than perhaps it should be, then 400 grams of the stuff is one mole. And 400 grams dissolved in a liter of solvent to make a liter of solution would then give you a one molar (1 M) solution. (The original version of this post didn't make that important distinction, which I'll chalk up to my not being completely awake on the train ride first thing in the morning. The final volume you get on taking large amounts of things up in a given amount of solvent can vary quite a bit, but concentration is based, naturally, on what you end up with. And it’s a pretty flippin’ unusual drug substance than can be dissolved in water to that concentration, let me tell you right up front). So, four grams in a liter would be 0.01 M, or 10 millimolar, and foru hundred milligrams per liter would be a 1 millimolar solution. A one micromolar solution would be 400 micrograms (0.0004 grams) per liter, and a one nanomolar solution would be 400 nanograms (400 billionths of a gram) per liter. And that’s the concentration that we’d like to get to show good enzyme inhibition. Pretty potent, eh?
But you can do better – if you want to, which is a real question. Taking it all the way, your drug can go in and attach itself to the active site of its target by a real chemical bond. Some of those bond-forming reactions are reversible, and some of them aren’t. Even the reversible ones are a lot tighter than your usual run of inhibitor.
You can often recognize them by their time-dependent inhibition. With a normal drug, it doesn’t take all that long for things to equilibrate. If you leave the compound on for ten, twenty, thirty minutes, it usually doesn’t make a huge difference in the binding constant, because it’s already done what it can do and reached the balance it’s going to reach. But a covalent inhibitor, that’ll appear to get more and more potent the longer it stays in there, since more and more of the binding sites are being wiped out. (One test for reversibility after seeing that behavior is to let the protein equilibrate with fresh blank buffer solution for a while, to see if its activity ever comes back). You can get into hair-splitting arguments if your compound binds so tightly that it might as well be covalent; at some point they're functionally equivalent.
There are several drugs that do this kind of thing, but they’re an interesting lot. You have the penicillins and their kin – that’s what that weirdo four-membered lactam ring is doing, spring-loaded for trouble once it gets into the enzyme. The exact same trick is used in Alli (orlistat), the pancreatic lipase inhibitor. And there are some oncology drugs that covalently attach to their targets (and, in some cases, to everything else they hit, too). But you’ll notice that there’s a bias toward compounds that hit bacterial enzymes (instead of circulating human ones), don’t get out of the gut, or are toxic and used as a last resort.
Those classes don’t cover all the covalent drugs, but there’s enough of that sort of thing to make people nervous. If your compound has some sort of red-hot functional group on it, like some of those nasty older cancer compounds, you’re surely going to mess up a lot of other proteins that you would rather have left alone. And what happens to the target protein after you’ve stapled your drug to it, anyway? One fear has been that it might present enough of a different appearance to set off an immune response, and you don’t want that, either.
But covalent inhibition is actually a part of normal biochemistry. If you had a compound with a not-so-lively group, one that only reacted with the protein when it got right into the right spot – well, that might be selective, and worth a look. The Cravatt lab at Scripps has been looking into what kinds of functional groups react with various proteins, and as we get a better handle on this sort of thing, covalency could make a comeback. Some people maintain that it never left!
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November 11, 2008
Posted by Derek
I wrote a while back about the problem of compounds sticking to labware. That sort of thing happens more often than you’d think, and it can really hose up your assay data in ways that will send you running around in circles. Now there’s a report in Science of something that’s arguably even worse. (Here's a good report on it from Bloomberg, one of the few to appear in the popular press).
The authors were getting odd results in an assay with monoamine oxidase B enzyme, and tracked it down to two compounds leaching out of the disposable plasticware (pipette tips, assay plates, Eppendorf vials, and so on). Oleamide is used as a “slip agent” to keep the plastic units from sticking to each other, but it’s also a MAO-B inhibitor. Another problem was an ammonium salt called DiHEMDA, which is put in as a general biocide – and it appears to be another MAO-B inhibitor.
Neither of them are incredibly potent, but if you’re doing careful kinetic experiments or the like, it’s certainly enough to throw things off. The authors found that just rinsing water through various plastic vessels was enough to turn the solution into an enzyme inhibitor. Adding organic solvents (10% DMSO, methanol) made the problem much worse; presumably these extract more contaminants.
And it’s not just this one enzyme. They also saw effects on a radioligand binding assay to the GABA-A receptor, and they point out that the biocides used are known to show substantial protein and DNA binding. These things could be throwing assay data around all over the place – and as we work in smaller and smaller volumes, with more complex protocols, the chances of running into trouble increase.
What to do about all this? Well, at a minimum, people should be sure to run blank controls for all their assays. That’s good practice, but sometimes it gets skipped over. This effect has probably been noted many times before as some sort of background noise in such controls, and many times you should be able to just subtract it out. But there are still many experiments where you can’t get away from the problem so easily, and it’s going to make your error bars wider no matter what you do about it. There are glass inserts for 96-well plates, and there are different plastics from different manufacturers. But working your way through all that is no fun at all.
As an aside, this sort of thing might still make it into the newspapers, since there have been a lot of concerns about bisphenol A and other plastic contaminants. In this case, I think the problem is far greater for lab assays than it is for human exposures. I’m not so worried about things like oleamide, since these are found in the body anyway, and can easily be metabolized. The biocides might be a different case, but I assume that we’re loaded with all kinds of substances, almost all of them endogenous, that are better inhibitors of enzymes like MAO-B. And at any rate, we’re exposed to all kinds of wild stuff at low levels, just from the natural components of our diet. Our livers are there to deal with just that sort of thing, but that said, it’s always worth checking to make sure that they’re up to the job.
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October 16, 2008
Posted by Derek
A key step in all drug discovery programs are the cellular and animal models. The cells are the first time that the compounds are exposed to a living system (with cellular membranes that keep things out). The animals, of course, are a very stringent test indeed, with the full inventory of absorption, metabolism, and excretion machinery, along with the possibility of side effects in systems that you might not have even considered.
So it’s a tricky business to make sure that these tests are being done in the most meaningful way possible. You can knock your project out of promising areas for development if your model systems are too tough – and it’s even easier to water them down in the interest of getting numbers that make everyone feel better. “As stringent as they need to be” is the rule, but it’s a hard one to handle in practice.
Take, for example, the antibacterial field. The first cell assays there are unusually meaningful, since they’re being done on the real live targets of the drugs. (That doesn’t do much to get you past the high barrier of animal testing, though, since you have to see if your compounds that kill bacteria in a dish will still do it in that much more demanding environment). But there are all sorts of strains of bacteria out there, and it’s up to you to choose the ones that will tell you the most about what your compounds can do.
One way that bacteria evade being killed off by our wonder drug candidates is by pumping the compounds right back out once they get in. There are quite a few of the efflux pumps, and wild-type bacteria (particularly the resistant strains) are well stocked with them. You can culture all sorts of mutants, though, with these various transport mechanisms ablated or wiped out completely. If your compound doesn’t work on the normal lines, but cuts a swath through some of these, you have good evidence that your problem is efflux pumping, not some intrinsic problem with your target mechanism.
The problem is, we often don’t have a very good idea of what to do about efflux pumping. These proteins recognize a huge variety of different structures, and there aren’t really many useful ways to predict what they’ll take up versus what they’ll leave alone. In many cases, you just have to throw all sorts of variations at them and hope for the best. (The same goes for the other situations where active transport can be a big factor, such as with cancer cells and the blood-brain barrier).
So, how do you set up your assays? You can run the crippled bacteria first, which will give you an idea of the intrinsic potencies of your compounds, minus the pumping difficulty. That may be the way to go but you’d better follow that up with some things closer to wild-type, or you’re going to end up kidding yourself. Having a compound that infallibly kills only those bacteria that can’t spit it out is probably not going to do you (or anyone else) much good, considering what the situation is like out in the real world.
The same principle holds for other assays, all the way up to rats. If you run a relative pushover model in oncology, you can put up a very impressive plot of how powerful your compounds are. But what does that do for you in the end? Or for cancer patients, whose malignant cells are much more wily and aggressive? The best course, I’d say, is to run the watered-down models if they can tell you something that will help you move things along. But get to the wild-types, the real thing, as soon as possible. Those latter models may tell you things that you don’t want to hear – but that doesn’t mean that you don’t need to hear them.
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September 4, 2008
Posted by Derek
X-ray crystallography is wonderful stuff – I think you’ll get chemists to generally agree on that. There’s no other technique that can provide such certainty about the structure of a compound – and for medicinal chemists, it has the invaluable ability to show you a snapshot of your drug candidate bound to its protein target. Of course, not all proteins can be crystallized, and not all of them can be crystallized with drug ligands in them. But an X-ray structure is usually considered the last word, when you can get one – and thanks to automation, computing power, and to brighter X-ray sources, we get more of them than ever.
But there are a surprising number of ways that X-ray data can mislead you. For an excellent treatment of these, complete with plenty of references to the recent literature, see an excellent paper coming out in Drug Discovery Today from researchers at Astra-Zeneca (Andy Davis and Stephen St.-Gallay) and Uppsala University (Gerard Kleywegt). These folks all know their computational and structural biology, and they’re willing to tell you how much they don’t know, either.
For starters, a small (but significant) number of protein structures derived from X-ray data are just plain wrong. Medicinal chemists should always look first at the resolution of an X-ray structure, since the tighter the data, the better the chance there is of things being as they seem. The authors make the important point that there’s some subjective judgment involved on the part of a crystallographer interpreting raw electron-density maps, and the poorer the resolution, the more judgment calls there are to be made:
Nevertheless, most chemists who undertake structure-based design treat a protein crystal structure reverently as if it was determined at very high resolution, regardless of the resolution at which the structure was actually determined (admittedly, crystallographers themselves are not immune to this practice either). Also, the fact that the crystallographer is bound to have made certain assumptions, to have had certain biases and perhaps even to have made mistakes is usually ignored. Assumptions, biases, ambiguities and mistakes may manifest themselves (even in high-resolution structures) at the level of individual atoms, of residues (e.g. sidechain conformations) and beyond.
Then there’s the problem of interpreting how your drug candidate interacts with the protein. The ability to get an X-ray structure doesn’t always correlate well with the binding potency of a given compound, so it’s not like you can necessarily count on a lot of clear signals about why the compound is binding. Hydrogen bonds may be perfectly obvious, or they can be rather hard to interpret. Binding through (or through displacement of) water molecules is extremely important, too, and that can be hard to get a handle on as well.
And not least, there’s the assumption that your structure is going to do you good once you’ve got it nailed down:
It is usually tacitly assumed that the conditions under which the complex was crystallised are relevant, that the observed protein conformation is relevant for interaction with the ligand (i.e. no flexibility in the active-site residues) and that the structure actually contributes insights that will lead to the design of better compounds. While these assumptions seem perfectly reasonable at first sight, they are not all necessarily true. . .
That’s a key point, because that’s the sort of error that can really lead you into trouble. After all, everything looks good, and you can start to think that you really understand the system, that is until none of your wonderful X-ray-based analogs work out they way you thought they would. The authors make the point that when your X-ray data and your structure-activity data seem to diverge, it’s often a sign that you don’t understand some key points about the thermodynamics of binding. (An X-ray is a static picture, and says nothing about what energetic tradeoffs were made along the way). Instead of an irritating disconnect or distraction, it should be looked at as a chance to find out what’s really going on. . .
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+ TrackBacks (0) | Category: Analytical Chemistry | Drug Assays | In Silico
July 16, 2008
Posted by Derek
At various points in my drug discovery career, I’ve worked on G-protein-coupled receptor (GPCR) targets. Most everyone in the drug industry has at some point – a significant fraction of the known drugs work through them, even though we have a heck of a time knowing what their structures are like.
For those outside the field, GPCRs are a ubiquitous mode of signaling between the interior of a cell and what’s going on outside it, which accounts for the hundreds of different types of the things. They’re all large proteins that sit in the cell membrane, looped around so that some of their surfaces are on the outside and some poke through to the inside. The outside folds have a defined binding site for some particular ligand - a small molecule or protein – and the inside surfaces interact with a variety of other signaling proteins, first among them being the G-proteins of the name. When a receptor’s ligand binds from the outside, that sets off some sort of big shape change. The protein’s coils slide and shift around in response, which changes its exposed surfaces and binding patterns on the inside face. Suddenly different proteins are bound and released there, which sets off the various chemical signaling cascades inside the cell.
The reason we like GPCRs is that many of them have binding sites for small molecules, like the neurotransmitters. Dopamine, serotonin, acetylcholine – these are molecules that medicinal chemists can really get their hands around. The receptors that bind whole other proteins as external ligands are definitely a tougher bunch to work with, but we’ve still found many small molecules that will interact with some of them.
Naturally, there are at least two modes of signaling a GPCR can engage in: on and off. A ligand that comes in and sets off the intracellular signaling is called an agonist, and one that binds but doesn’t set off those signals is called an antagonist. Antagonist molecules will also gum up the works and block agonists from doing their things. We have an easier time making those, naturally, since there are dozens of ways to mess up a process compared to the ways there are of running it correctly!
Now, when I was first working in the GPCR field almost twenty years ago, it was reasonably straightforward. You had your agonists and you had your antagonists – well, OK, there were those irritating partial agonists, true. Those things set off the desired cellular signal, but never at the levels that a full agonist would, for some reason. And there were a lot of odd behaviors that no one quite knew how to explain, but we tried to not let those bother us.
These days, it’s become clear that GPCRs are not so simple. There appear to be some, for example, whose default setting is “on”, with no agonist needed. People are still arguing about how many receptors do this in the wild, but there seems little doubt that it does go on. These constituitively active receptors can be turned off, though, by the binding of some ligands, which are known as inverse agonists, and there are others, good old antagonists, that can block the action of the inverse agonists. Figuring out which receptors do this sort of thing - and which drugs - is a full time job for a lot of people.
It’s also been appreciated in recent years that GPCRs don’t just float around by themselves on the cell surface. Many of them interact with other nearby receptors, binding side-by-side with them, and their activities can vary depending on the environment they’re in. The search is on for compounds that will recognize receptor dimers over the good ol’ monomeric forms, and the search is also on for figuring out what those will do once we have them. To add to the fun, these various dimers can be with other receptors of their own kind (homodimers) or with totally different ones, some from different families entirely (heterodimers). This area of research is definitely heating up.
And recently, I came across a paper which looked at how a standard GPCR can respond differently to an agonist depending on where it's located in the membrane. We're starting to understand how heterogeneous the lipids in that membrane are, and that receptors can move from one domain to another depending on what's binding to them (either on their outside or inside faces). The techniques to study this kind of thing are not trivial, to put it mildly, and we're only just getting started on figuring out what's going on out there in the real world in real time. Doubtless many bizarre surprises await.
So, once again, the "nothing is simple" rule prevails. This kind of thing is why I can't completely succumb to the gloom that sometimes spreads over the industry. There's just so much that we don't know, and so much to work on, and so many people that need what we're trying to discover, that I can't believe that the whole enterprise is in as much trouble as (sometimes) it seems. . .
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July 11, 2008
Posted by Derek
Here's an interesting idea: Merck, Lilly, and Pfizer are bankrolling a startup company to look for new technologies for drug development. Enlight Biosciences will focus on the biggest bottlenecks and risk points in the process, including new imaging techniques for preclinical and clinical evaluation of drug candidates, predictive toxicology and pharmacokinetics, clinical biomarkers, new models of disease, delivery methods for protein- and nucleic acid-based therapies, and so on.
It's safe to say that if any real advances are made in any of these, the venture will have to be classed as a success. These are hard problems, and it's not like there's been no financial incentive to solve any of them. (On the contrary - billions of dollars are out there waiting for anyone who can truly do a better job at these things). I wish these people a lot of luck, and I'm glad to see them doing what they're doing, but I do wish that there were more details available on how they plan to go about things. The opening press release leaves a lot of things unspoken, no doubt by design. (For instance, where are the labs going to be? What's the hoped-for balance of industry types to academics? How many people do they plan to have working on these things, and how will the companies involved plan to share the resulting technologies?)
Enlight is a creation of Puretech Ventures, a Boston VC firm that's been targeting early-stage ideas in these areas. Getting buy-in from the three companies above will definitely help, but their commitment isn't too clear at present. For now, it looks like they're getting to take a fresh look at some areas of great interest, without necessarily having to spend a lot of their own money. The press release says that Enlight will "direct up to $39 million" toward the areas listed on their web site, but those problems will eat thirty-nine million dollars without even reaching for the salt. Further funding is no doubt in the works, with the Merck/Pfizer/Lilly names as a guarantee of seriousness, and if any of these projects pan out, the money will arrive with alacrity.
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June 3, 2008
Posted by Derek
We recently encountered a problem that’s (unfortunately) a rather common one. An enzyme assay turned up an interesting hit compound, with some characteristics that we were hoping to see for leads against our target. A re-test showed that yes, the activity appeared to be real, which was interesting, since this hit was a welcome surprise from a class of compounds that we weren’t expecting much from.
It was a comparatively old compound in the files, and all we could find out was that it had been purchased rather than made in house. Looking around, it seemed that there were very few literature references to things of this type, and only one commercial source: the Sigma-Aldrich Library of Rare chemicals, known as SALOR. That, though, was a potential warning flag.
Those compounds come from an effort started by Aldrich’s Alfred Bader many years ago, who started trolling around various academic labs looking for unusual compounds that no one wanted to keep around any more. Over time the company has accumulated a horde of oddities that are often found nowhere else, but there are several catches. For one, these things are usually available only in small quantities, tens of milligrams for the most part. That’s plenty for the screening files, but you’re not going to make a bunch of analogs starting from what comes out of a SALOR vial. Another catch is that the compounds are sold, very explicitly, as is: the university sources tell Aldrich what’s on the label, so that’s what they sell you and caveat emptor all the way, dude.
So often as not, you get what we got, a nice-looking white powder which, on closer analysis, turned out to only have a vague relationship to the structure on its label. We knew that we were in trouble as soon as the first NMR came out: way too much stuff in one region, nowhere near enough in some others. Mass spec confirmed that this thing weighed more than twice as much as what it was supposed to. We’ve since pretty much nailed down what the stuff really is, and our interest in it has decreased as each of the veils has been removed from the real structure.
We’re correcting the data in our own screening files, of course. And yes, we’re going to tell the folks at Aldrich to change their label, too, assuming they have any of this stuff left. At least the next person will know what they’re getting. For once. But there are more of these things waiting out there – in every large compound collection, in every catalog, in every collection of data are mistakes. Watch for them.
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+ TrackBacks (0) | Category: Drug Assays | Life in the Drug Labs
May 20, 2008
Posted by Derek
For those who were wondering, my copper reactions the other day worked out just fine. They started out a beautiful blue (copper iodide and an amino acid in straight DMSO – if that’s not blue it’s maybe going to be green, and if it’s not either one you’ve done something wrong). Of course, the color doesn’t stay. The copper ends up as part of a purple-brown sludge that has to be filtered out of the mix, which is the main downside of those Ullman reactions, no matter how people try to scrub them up for polite company.
And DMSO is the other downside, because you have to wash that stuff out with a lot of water. That’s one of the lab solvents that everyone has heard of, even if they slept through high school chemistry. But it’s not one that we use for reactions very much, because it’s something of a pain. It dissolves most everything, which is a good quality, but along with that one comes the ability to contaminate most everything. If your product is pretty greasy and nonpolar, you can partition the reaction between water and some more organic solvent (ether’s what I used this time), and wash it around a lot. But if your product is really polar, you could be in for a long afternoon.
That mighty solvation is something you need to look out for if you spill the stuff on yourself, of course. DMSO is famous for skin penetration (no, I have no idea if it does anything for arthritis). And while many of my compounds are not very physiologically active, I’d rather not dose myself with them to check those numbers. At the extreme end of the scale, a solution of cyanide in DMSO is potentially very dangerous stuff indeed. I’ve done cyanide reactions like that, many times, but always while paying attention to the task at hand.
Where DMSO really gets used is in the compound repository. That dissolves-everything property is handy when you have a few hundred thousand compounds to handle. The standard method for some years has been to keep compounds in the freezer in some defined concentration in DMSO – the solvent freezes easily, down around where water does (Not so! Actually, I've seen in freeze in a chilly lab a couple of times, now that I'm reminded of that in the comments to this post. Pure DMSO solidifies around 17 to 19 C, which is about 64 F C - a bit lower with those screening compounds dissolved in it, though).
But there are problems. For one thing, DMSO isn’t inert. That’s another reason it doesn’t get as much use as a lab solvent; there are many reaction conditions during which it wouldn’t be able to resist joining the party. You can oxidize things by leaving them in DMSO open to air, which isn’t what you want to do to the compound screening collection, so the folks there do as much handling under nitrogen as they can. Compounds sitting carelessly in DMSO tend to turn yellow, which is on the way to red, which is on the way to brown, and there are no pure brown wonder drugs.
Another difficulty is that love for water. Open DMSO containers will pull water in right out of the air, and a few careless freeze/thaw cycles with a screening plate will not only blow your carefully worked out concentrations, it may well also start crashing your compounds out of solution. The less polar ones will start decided that pure DMSO is one thing, but 50/50 DMSO/water is quite another. So not only do you want to work under nitrogen, if you can, but dry nitrogen, and you want to make sure that those plates are sealed up well while they’re in the freezer. (As an alternative, you can go ahead and put water in from the start, taking the consequences). All of these concerns begin to wear down the advantages of DMSO as a universal solvent, but not quite enough to keep people from using it.
And what about the compounds that don’t dissolve in the stuff? Well, it’s a pretty safe bet that a small molecule that can’t go into DMSO is going to have a mighty hard time becoming a drug, and it’s a very unattractive lead to start from, too. That’s the sort of molecule that would tend to just go right through the digestive tract without even noticing that there are things trying to get it into solution. And as for something given i.v., well, if you can’t get it to go into straight DMSO, what are the chances you’re going to get it into some kind of saline injection solution? Or the chances that it won’t crash out in the vein for an instant embolism? No, the zone of non-DMSO-soluble small organics is not a good place to hunt. We’ll leave proteins out of it, but if anyone knows of a small molecule drug that can’t go into DMSO, I’d like to hear about it. Taxol, maybe?
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+ TrackBacks (0) | Category: Drug Assays | Life in the Drug Labs
April 3, 2008
Posted by Derek
I was having a discussion the other day about which therapeutic areas have the best predictive assays. That is, what diseases can you be reasonably sure of treating before your drug candidate gets into (costly) human trials? As we went on, things settled out roughly like this:
Cardiovascular (circulatory): not so bad. We’ve got a reasonably good handle on the mechanisms of high blood pressure, and the assays for it are pretty predictive, compared to a lot of other fields. (Of course, that’s also now one of the most well-served therapeutic areas in all of medicine). There are some harder problems, like primary pulmonary hypertension, but you could still go into humans with a bit more confidence than usual if you had something that looked good in animals.
Cardiovascular (lipids): deceptive. There aren’t any animals that handle lipids quite the way that humans do, but we’ve learned a lot about how to interpolate animal results. That plus the various transgenic models gives you a reasonable read. The problem is, we don’t really understand human lipidology and its relation to disease as well as we should (or as well as a lot of people think we do), so there are larger long-term problems hanging over everything. But yeah, you can get a new drug with a new mechanism to market. Like Vytorin.
CNS: appalling. That goes for the whole lot – anxiety, depression, Alzheimer’s, schizophrenia, you name it. The animal models are largely voodoo, and the mechanisms for the underlying diseases are usually opaque. The peripheral nervous system isn’t much better, as anyone who’s worked in pain medication will tell you ruefully. And all this is particularly disturbing, because the clinical trials here are so awful that you’d really appreciate some good preclinical pharmacology: patient variability is extreme, the placebo effect can eat you alive, and both the diseases and their treatments tend to progress very, very slowly. Oh, it’s just a nonstop festival of fun over in this slot. Correspondingly, the opportunities are huge.
Anti-infectives: good, by comparison. It’s not like you can’t have clinical failures in this area, but for the most part, if you can stop viruses or kill bugs in a dish, you can do it in an animal, or in a person. The questions are always whether you can do it to the right extent, and just how long it’ll be before you start seeing resistance. With antibacterials that can be, say, "before the end of your clinical trials". There aren’t as many targets here as everyone would like, and none of them is going to be a gigantic blockbuster, but if you find one you can attack it with more confidence than usual.
Diabetes: pretty good, up to a point. There are a number of well-studied animal models here, and if your drug’s mechanism fits their quirks and limitations, then you should be in fairly good shape. Not by coincidence, this is also a pretty well-served area, by current standards. If you’re trying something off the beaten path, though, a route that STZ or db/db rats won’t pick up well, then things get harder. Look out, though, because this disease area starts to intersect with lipids, which (it bears saying again) We Don't Understand Too Well.
Obesity: deceptive in the extreme. There are an endless number of ways to get rats to lose weight. Hardly any of them, though, turn out to be relevant to humans or relevant to something humans would consider paying for. (Relentless vertigo would work to throw the animals off their feed, for example, but would probably be a loser in the marketplace. Although come to think of it, there is Alli, so you never know). And the problem here is always that there are so many overlapping backup redundant pathways for feeding behavior, so the chances for any one compound doing something dramatic are, well, slim. The expectations that a lot of people have for a weight-loss therapy are so high (thanks partly to years of heavily advertised herbal scams and bizarre devices), but the reality is so constrained.
Oncology: horrible, just horrible. No one trusts the main animal models in this area (rat xenografts of tumor lines) as anything more than rough, crude filters on the way to clinical trials. And no one should. Always remember: Iressa, the erstwhile AstraZeneca wonder drug from a few years back, continues to kick over all kinds of xenograft models. It looks great! It doesn’t work in humans! And it's not alone, either. So people take all kinds of stuff into the clinic against cancer, because what else can you do? That leads to a terrifying overall failure rate, and has also led to, if you can believe it, a real shortage of cancer patients for trials in many indications.
OK, those are some that I know about from personal experience. I’d be glad to hear from folks in other areas, like allergy/inflammation, about how their stuff rates. And there are a lot of smaller indications I haven’t mentioned, many of them under the broad heading of immunology (lupus, MS, etc.) whose disease models range from “difficult to run and/or interpret” on the high side all the way down to “furry little random number generators”.
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March 27, 2008
Posted by Derek
There’s an excellent paper in the most recent issue of Chemistry and Biology that illustrates some of what fragment-based drug discovery is all about. The authors (the van Aalten group at Dundee) are looking at a known inhibitor of the enzyme chitinase, a natural product called argifin. It’s an odd-looking thing – five amino acids bonded together into a ring, with one of them (an arginine) further functionalized with a urea into a sort of side-chain tail. It’s about a 27 nM inhibitor of the enzyme.
(For the non-chemists, that number is a binding affinity, a measure of what concentration of the compound is needed to shut down the enzyme. The lower, the better, other things being equal. Most drugs are down in the nanomolar range – below that are the ulta-potent picomolar and femtomolar ranges, where few compounds venture. And above that, once you get up to 1000 nanomolar, is micromolar, and then 1000 micromolar is one millimolar. By traditional med-chem standards, single-digit nanomolar = good, double-digit nanomolar = not bad, triple-digit nanomolar or low micromolar = starting point to make something better, high micromolar = ignore, and millimolar = can do better with stuff off the bottom of your shoe.
What the authors did was break this argifin beast up, piece by piece, measuring what that did to the chitinase affinity. And each time they were able to get an X-ray structure of the truncated versions, which turned out to be a key part of the story. Taking one amino acid out of the ring (and thus breaking it open) lowered the binding by about 200-fold – but you wouldn’t have guessed that from the X-ray structure. It looks to be fitting into the enzyme in almost exactly the same way as the parent.
And that brings up a good point about X-ray crystal structures. You can’t really tell how well something binds by looking at one. For one thing, it can be hard to see how favorable the various visible interactions might actually be. And for another, you don’t get any information at all about what the compound had to pay, energetically, to get there.
In the broken argifin case, a lot of the affinity loss can probably be put down to entropy: the molecule now has a lot more freedom of movement, which has to be overcome in order to bind in the right spot. The cyclic natural product, on the other hand, was already pretty much there. This fits in with the classic med-chem trick of tying back side chains and cyclizing structures. Often you’ll kill activity completely by doing that (because you narrowed down on the wrong shape for the final molecule), but when you hit, you hit big.
The structure was chopped down further. Losing another amino acid only hurt the activity a bit more, and losing still another one gave a dipeptide that was still only about three times less potent than the first cut-down compound. Slicing that down to a monopeptide, basically just a well-decorated arginine, sent the activity down another sixfold or so – but by now we’re up to about 80 micromolar, which most medicinal chemists would regard as the amount of activity you could get by testing the lint in your pocket.
But they went further, making just the little dimethylguanylurea that’s hanging off the far end. That thing is around 500 micromolar, a level of potency that would normally get you laughed at. But wait. . .they have the X-ray structures all along the way, and what becomes clear is that this guanylurea piece is binding to the same site on the protein, in the same manner, all the way down. So if you’re wondering if you can get an X-ray structure of some 500 micromolar dust bunny, the answer is that you sure can, if it has a defined binding site.
And the value of these various derivatives almost completely inverts if you look at them from a binding efficiency standpoint. (One common way to measure that is to take the minus log of the binding constant and divide by the molecular weight in kilodaltons). That’s a “bang for the buck” index, a test of how much affinity you’re getting for the weight of your molecule. As it turns out, argifin – 27 nanomolar though it be – isn’t that efficient a binder, because it weighs a hefty 676. The binding efficiency index comes out to just under 12, which is nothing to get revved up about. The truncated analogs, for the most part, aren’t much better, ranging from 9 to 15.
But that guanylurea piece is another story. It doesn’t bind very tightly, but it bats way above its scrawny size, with a BEI of nearly 28. That’s much more impressive. If the whole argifin molecule bound that efficiently, it would be down in the ten-to-the-minus nineteenth range, and I don’t even know the name of that order of magnitude. If you wanted to make a more reasonably sized molecule, and you should, a compound of MW 400 would be about ten femtomolar with a binding efficiency like that. There’s plenty of room to do better than argifin.
So the thing to do, clearly, is to start from the guanylurea and build out, checking the binding efficiency along the way to make sure that you’re getting the most out of your additions. And that is exactly the point of fragment-based drug discovery. You can do it this way, cutting down a larger molecule to find what parts of it are worth the most, or you can screen to find small fragments which, though not very potent in the absolute sense, bind very efficiently. Either way, you take that small, efficient piece as your anchor and work from there. And either way, some sort of structural read on your compounds (X-ray or NMR) is very useful. That’ll give you confidence that your important binding piece really is acting the same way as you go forward, and give you some clues about where to build out in the next round of analogs.
This particular story may be about as good an illustration as one could possibly find - here's hoping that there are more that can work out this way. Congratulations to van Aalten and his co-workers at Dundee and Bath for one of the best papers I've read in quite a while.
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February 14, 2008
Posted by Derek
I’ve been reading an interesting paper from JACS with the catchy title of “Optimization of Activity-Based Probes for Proteomic Profiling of Histone Deacetylase Complexes”. This is work from Benjamin Cravatt's lab at Scripps, and it says something about me, I suppose, that I found that title of such interest that I immediately printed off a copy to study more closely. Now I’ll see if I can interest anyone who wasn’t already intruiged! First off, some discussion of protein tagging, so if you’re into that stuff already, you may want to skip ahead.
So, let’s say you have a molecule that has some interesting biological effect, but you’re not sure how it works. You have suspicions that it’s binding to some protein and altering its effects (always a good guess), but which protein? Protein folks love fluorescent assays, so if you could hang some fluorescent molecule off one end of yours, perhaps you could start the hunt: expose your cells to the tagged molecule, break them open, look for the proteins that glow. There are complications, though. You’d have to staple the fluorescent part on in a way that didn’t totally mess up that biological activity you care about, which isn’t always easy (or even possible). The fact that most of the good fluorescent tags are rather large and ugly doesn’t help. But there’s more trouble: even if you manage to do that, what’s to keep your molecule from drifting right back off of the protein while you’re cleaning things up for a look at the system? Odds are it will, unless it has a really amazing binding constant, and that’s not the way to bet.
One way around that problem is sticking yet another appendage on to the molecule, a so-called photoaffinity label. These groups turn into highly reactive species on exposure to particular wavelengths of light, ready to form a bond with the first thing they see. If your molecule is carrying one when it’s bound to your mystery protein, shining light on the system will likely cause a permanent bond to form between the two. Then you can do all your purifications and separations, and look at your leisure for which proteins fluoresce.
This is “activity-based protein profiling”, and it’s a hot field. There are a lot of different photoaffinity labels, and a lot of ways to attach them, and likewise with the fluorescent groups. The big problem, as mentioned above, is that it’s very hard to get both of those on your molecule of interest and still keep its biological activity – that’s an awful lot of tinsel to carry around. One slick solution is to use a small placeholder for the big fluorescent part. This, ideally, would be some little group that will hide out innocently during the whole protein-binding and photoaffinity-labeling steps, then react with a suitably decorated fluorescent partner once everything’s in place. This assembles your glowing tag after the fact.
A favorite way to do that step is through an azide-acetylene cycloaddition reaction, the favorite of Barry Sharpless’s “click” reactions. Acetylenes are small and relatively unreactive, and at the end of the process, after you’ve lysed the cells and released all their proteins, you can flood your system with azide-substituted fluorescent reagent. The two groups react irreversibly under mild catalytic conditions to make a triazole ring linker, which is a nearly ideal solution that’s getting a lot of use these days (more on this another day).
So, now to this paper. What this group did was label a known compound (from Ron Breslow's group at Columbia) that targets histone deacetylase (HDAC) enzymes, SAHA, now on the market as Vorinostat. There are a lot of different subtypes of HDAC, and they do a lot of important but obscure things that haven’t been worked out yet. It’s a good field to discover protein function in.
When they modified SAHA in just the way described above, with an acetylene and a photoaffinity group, it maintained its activity on the known enzymes, so things looked good. They then exposed it to cell lysate, the whole protein soup, and found that while it did label HDAC enzymes, it seemed to label a lot of other things in the background. That kind of nonspecific activity can kill an assay, but they tried the label out on living cells anyway, just to see what would happen.
Very much to their surprise, that experiment led to much cleaner and more specific labeling of HDACs. The living system was much nicer than the surrogate, which (believe me) is not how things generally go. Some HDACs were labeled much more than others, though, and my first thought on reading that was “Well, yeah, sure, your molecule is a more potent binder to some of them”.
But that wasn’t the case, either. When they profiled their probe molecule’s activity versus a panel of HDAC enzymes, they did indeed find different levels of binding – but those didn’t match up with which ones were labeled more in the cells. (One explanation might be that the photoaffinity label found some of the proteins easier to react with than others, perhaps due to what was nearby in each case when the reactive species formed).
Their next step was to make a series of modified SAHA scaffolds and rig them up with the whole probe apparatus. Exposing these to cell lysate showed that many of them performed fine, labeling HDAC subtypes as they should, and with different selectivities than the original. But when they put these into cells, none of them worked as well as the plain SAHA probe – again, rather to their surprise. (A lot of work went into making and profiling those variations, so I suspect that this wasn’t exactly the result the team had hoped for - my sympathies to Cravatt and especially to his co-author Cleo Salisbury). The paper sums the situation up dryly: "These results demonstrate that in vitro labeling is not necessarily predictive of in situ labeling for activity-based protein profiling probes".
And that matches up perfectly with my own prejudices, so it must be right. I've come to think, over the years, that the way to go is to run your ideas against the most complex system you think that they can stand up to - in fact, maybe one step beyond that, because you may have underestimated them. A strict reductionist might have stopped after the cell lysate experiments in this case - clearly, this probe was too nonspecific, no need to waste time on the real system, eh? But the real system, the living cell, is real in complex ways that we don't understand well at all, and that makes this inference invalid.
The same goes for medicinal chemistry and drug development. If you say "in vitro", I say "whole cells". If you've got it working in cells, I'll call for mice. Then I'll see your mice and raise you some dogs. Get your compounds as close to reality as you can before you pass judgment on them.
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January 29, 2008
Posted by Derek
I've had some questions about animal models and testing, so I thought I'd go over the general picture. As far as I can tell, my experience has been pretty representative.
There are plenty of animal models used in my line of work, but some of them you see more than others. Mice and rats are, of course, the front line. I’ve always been glad to have a reliable mouse model, personally, because that means the smallest amount of compound is used to get an in vivo readout. Rats burn up more hard-won material. That's not just because they're uglier, since we don’t dose based on per cent ugly, but rather because they're much larger and heavier. The worst were some elderly rodents I came across years ago that were being groomed for a possible Alzheimer’s assay – you don’t see many old rats in the normal course of things, but I can tell you that they do not age gracefully. They were big, they were mean, and they were, well, as ratty as an animal can get. (They were useless for Alzheimer's, too, which must have been their final revenge).
You can’t get away from the rats, though, because they’re the usual species for toxicity testing. So if your pharmacokinetics are bad in the rat, you’re looking at trouble later on – the whole point of tox screens is to run the compound at much higher than usual blood levels, which in the worst cases you may not be able to reach. Every toxicologist I’ve known has groaned, though, when asked if there isn’t some other species that can be used – just this time! – for tox evaluation. They’d much rather not do that, since they have such a baseline of data for the rat, and I can’t blame them. Toxicology is an inexact enough science already.
It’s been a while since I’ve personally seen the rodents at all, though, not that I miss them. The trend over the years has been for animal facilities to become more and more separated from the other parts of a research site – separate electronic access, etc. That’s partly for security, because of people like this, and partly because the fewer disturbances among the critters, the better the data. One bozo flipping on the wrong set of lights at the wrong time can ruin a huge amount of effort. The people authorized to work in the animal labs have enough on their hands keeping order – I recall a run of assay data that had an asterisk put next to it when it was realized that a male mouse had somehow been introduced into an all-female area. This proved disruptive, as you’d imagine, although he seemed to weather it OK.
Beyond the mouse and rat, things branch out. That’s often where the mechanistic models stop, though – there aren’t as many disease models in the larger animals, although I know that some cardiovascular disease studies are (or have been) run in pigs, the smallest pigs that could be found. And I was once in on an osteoporosis compound that went into macaque monkeys for efficacy. More commonly, the larger animals are used for pharmacokinetics: blood levels, distribution, half-life, etc. The next step for most compounds after the rat is blood levels in dogs – that’s if there’s a next step at all, because the huge majority of compounds don’t get anywhere near a dog.
That’s a big step in terms of the seriousness of the model, because we don’t use dogs lightly. If you’re getting dog PK, you have a compound that you’re seriously considering could be a drug. Similarly, when a compound is finally picked to go on toward human trials, it first goes through a more thorough rat tox screen (several weeks), then goes into two-week dog tox, which is probably the most severe test most drug candidates face. The old (and cold-hearted) saying is that “drugs kill dogs and dogs kill drugs”. I’ve only rarely seen the former happen (twice, I think, in 19 years), but I’ve seen the second half of that saying come true over and over. Dogs are quite sensitive – their cardiovascular systems, especially – and if you have trouble there, you’re very likely done. There’s always monkey data – but monkey blood levels are precious, and a monkey tox screen is extremely rare these days. I’ve never seen one, at any rate. And if you have trouble in the dog, how do you justify going into monkeys at all? No, if you get through dog tox, you're probably going into man, and if you don't, you almost certainly aren't.
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January 22, 2008
Posted by Derek
There’s been a big trend the last few years in the industry to try to build our molecules up from much smaller pieces than usual. “Fragment-based” drug discovery is the subject of many conferences and review articles these days, and I’d guess that most decent-sized companies have some sort of fragment effort going on. (Recent reviews on the topic, for those who want them).
Many different approaches come under that heading, though. Generally, the theme is to screen a collection of small molecules, half the size or less of what you’d consider a reasonable molecular weight for a final compound, and look for something that binds. At those sizes, you’re not going to find the high affinities that you usually look for, though. We usually want our clinical candidates to be down in the single-digit nanomolar range for binding constants, and our screening hits to be as far under one micromolar as we can get. In the fragment world, though, from what I can see, people regard micromolar compounds as pretty hot stuff, and are just glad not to be up in the millimolar range. (For people outside the field, it’s worth noting that a nanomolar compound binds about a million times better than a millimolar one).
Not all the traditional methods of screening molecules will pick up weak binders like that. (Some assays are actually designed not to read out at those levels, but to only tell you about the really hot compounds). For the others, you’d think you could just run things like you usually do, just by loading up on the test compounds, but that’s problematic. For one thing, you’ll start to chew up a lot of compound supplies at that rate. Another problem is that not everything stays in solution for the assay when you try to run things at that concentration. And if you try to compensate by using more DMSO or whatever to dissolve your compounds, you can kill your protein targets with the stuff when it goes in. Proteins are happy in water (well, not pure distilled water, but water with lots of buffer and salts and junk like the inside of a cell has). They can take some DMSO, but it’ll eventually make even the sturdiest of them unhappy at some point. (More literature on fragment screening).
And once you’ve got your weak-binding low-molecular weight stuff, what then? First, you have to overcome the feeling, natural among experienced chemists, that you’re working on stuff you should be throwing away. Traditional medicinal chemistry – analog this part, add to that part, keep plugging away – may not be the appropriate thing to do for these leads. There are just too many possibilities – you could easily spend years wandering around. So many companies depend on structural information about the protein target and the fragments themselves to tell them where these little guys are binding and where the best places to build from might be. That can come from NMR studies or X-ray crystal determinations, most commonly.
Another hope, for some time now, has been that if you could discover two fragments that bound to different sites, but not that far from each other, that you could then stitch them together to make a far better compound. (See here for more on this idea). That’s been very hard to realize in practice, though. Finding suitable pairs of compounds is not easy, for starters. And getting them linked, as far as I can see, can be a real nightmare. A lot of the linking groups you can try will alter the binding of the fragments themselves – so instead of going from two weak compounds to one strong one, you go from two weak ones to something that’s worse than ever. Rather than linking two things up, a lot of fragment work seems to involve building out from a single piece.
But that brings up another problem, exemplified by this paper. These folks took a known beta-lactamase inhibitor, a fine nanomolar compound, and broke it up into plausible-looking fragments, to see if it could have been discovered that way. But what they found, each time they checked the individual pieces, was that each of them bound in a completely different way than it did when it was part of the finished molecule. The binding mode was emergent, not additive, and it seems clear that most (all?) of the current fragment approaches would have been unable to arrive at the final structure. The authors admit that this may be a special case, but there’s no reason to assume that it’s all that special.
So fragment approaches, although they seem to be working out in some cases, are probably always going to miss things. But hey, we miss plenty of things with the traditional methods, too. Overall, I’m for trying out all kinds of odd things, because we need all the help we can get. Good luck to the fragment folks.
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December 11, 2007
Posted by Derek
Man, do we ever have a lot of assays in this business. Almost every drug development project has a long list of them, arranged in what we call a screening cascade. You check to make sure that your new molecule hits your protein target, then you try it on one or more living cell lines. There are assays to check its potency against related targets (some of which you may want, most of which you don’t), and assays to measure the properties of the compound itself, like how well it dissolves. Then it’s on to blood levels in animals, and finally to a disease model in some species or another.
Not all these assays are of equal importance, naturally. And not all of them do what they’re supposed to do for you. Some processes are so poorly understood that we’re willing to try all sorts of stuff to get a read on them. I would put the Caco-2 assay firmly in that category.
Caco ("cake-o")-2 cells are a human colon cancer cell line. When you grow them in a monolayer, they still remember to form an “inside” and an “outside” – the two sides of the layer act differently, and they pump compounds across from one side to the other. This sort of active transport is very widespread in living systems, and it’s very important in drug absorption and distribution, and from a practical standpoint we don’t know much about it at all. Membranes like the gut wall or the lining of the brain’s blood vessels do this sort of thing all the time, and pump out things they don’t like. Cancer cells and bacteria do it to compounds they judge to be noxious, which covers a lot of the things we try to use to kill them. Knowing how to avoid this kind of thing would be worth billions of dollars, and would give us a lot more effective drugs.
The Caco-2 cell assay is an attempt to model some of this process in a dish, so you don’t have to find out about it in a mouse (or a human). You put a test amount of your compound on one side of the layer of cells, and see how much of it gets through to the other side – then you try it in reverse, to see how much of that flow was active transport and how much was just passive leak-through diffusion. The ratio between those two amounts is supposed to give you a read on how much of a substrate your compound is for these efflux pumps, particularly a widespread one called P-glycoprotein.
I have seen examples in the literature where this assay appears to have given useful data. Unfortunately, as far as I can remember, I cannot recall ever having participated in such a project. Every time I’ve worked with Caco-2 data, it’s been a spread of numbers that didn’t correlate well with gut absorption, didn’t correlate well with brain levels, and didn’t help to prioritize anything. That may be unfair – after all, I’ve had people tell me that ‘s worked out for them – but I think that even in those cases people had to run quite a few compounds through before they believed that the assay was really telling them something. The published data on these things can turn out to be a small, shiny heap on the summit of a vast pile of compost - the unimpressive or uninterpretable attempts that never show up in any journal, anywhere.
You can think of several reasons for these difficulties, and there are surely more that none of us have thought of yet. These are colon cells, not cells from the small intestine (where the great majority of absorption takes place) or from the blood-brain barrier. They're from a carcinoma line, not a normal population (which is why they're still happily living in dishes). But that means that they’re far removed from their origins, to boot. (It’s well known that many cell lines lose some of their characteristics and abilities as you culture them. They’re not getting the stimuli they were in their native environment, and they shed functions and pathways as they’re no longer being called for). There’s also the problem that they’re human cells, but they’re often used to correlate with data from rodent models. Our major features overlap pretty well (most mouse poisons are human poisons, for example), but the fine details can be difficult to line up.
But people still run the Caco-2 assay. I think that now it’s mostly done in the hope, mostly forlorn, that this time it’ll turn out to model something crucial to this particular drug series. A representative list of compounds that have already been through the pharmacokinetic studies is tried, and the results are graphed against the blood levels. And, for the most part, the plots look like soup thrown against a wall – again. The quest to explain these things continues. . .
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October 11, 2007
Posted by Derek
Now we open the sedate, learned pages of Nature Methods, a fine journal that specializes in new techniques in molecular and chemical biology. In the August issue, the correspondence section features. . .well, a testy response to a paper that appeared last year in Nature Methods.
“Experimental challenge to a ‘rigorous’ BRET analysis of GPCR oligimerization” is the title. If you don’t know the acronyms, never mind – journals like this have acronyms like leopards have spots. The people doing the complaining, Ali Salahpour and Bernard Masri of Duke, are taking issue with a paper from Oxford by John James, Simon Davis, and co-workers. The original paper described a bioluminescence energy transfer (BRET) method to see if G-protein coupled receptors (GPCRs) were associating with each other on cell surfaces. (GPCRs are hugely important signaling systems and drug targets – think serotonin, dopamine, opiates, adrenaline – and it’s become clear in recent years that they can possibly hook up in various unsuspected combinations on the surfaces of cells in vivo).
Salahpour and Masri take strong exception to the Oxford paper’s self-characterization:
“Although the development of new approaches for BRET analysis is commendable, part of the authors’ methodological approach falls short of being ‘rigorous’. . .Some of the pitfalls of their type-1 and type-2 experiments have already been discussed elsewhere (footnote to another complaint about the same work, which also appeared earlier this year in the same journal - DBL). Here we focus on the type-2 experiments and report experimental data to refute some of the results and conclusions presented by James et al.”
That’s about an 8 out of 10 on the scale of nasty scientific language, translating as “You mean well but are lamentably incompetent.” The only way to ratchet things up further is to accuse someone of bad faith or fraud. I won’t go into the technical details of Salahpour and Masri’s complaints; they have to do with the mechanism of BRET, the effect on it of how much GPCR protein is expressed in the cells being studied, and the way James et al. interpreted their results versus standards. The language of these complaints, though, is openly exasperated, full of wording like “unfortunately”, “It seems unlikely”, “we can assume, at best” “(does) not permit rigorous conclusions to be drawn”, “might be erroneous”, “inappropriate and a misinterpretation”, “This could explain why”, “careful examination also (raises) some concerns”, and so on. After the bandilleros and picadors have done their work in the preceding paragraphs, the communication finishes up with another flash of the sword:
In summary, we agree with James and colleagues that type-2 experiments are useful and informative. . .Unfortunately, the experimental design proposed in James et al. to perform type-2 experiments seems incorrect and cannot be interpreted. . .”
James and Davis don’t take this with a smile, naturally. The journal gave them a space to reply to the criticisms, as is standard practice, and as they did for the earlier criticism. (At least the editors know that people are reading the papers they accept. . .) They take on many of the Salahpour/Masri points, claiming that their refutations were done under completely inappropriate conditions, among other things. And they finish up with a flourish, too:
"As we have emphasized, we were not the first to attempt quantitative analysis of BRET data. Previously, however, resonance energy transfer theory was misinterpreted (for example, ref. 4) or applied incorrectly (for example, ref. 5). (Note - reference 4 is to a paper by the first people to question their paper earlier this year, and reference 5 is to the work of Salahpour himself, a nice touch - DBL). The only truly novel aspect of our experiments is that we verified our particular implementation of the theory by analyzing a set of very well-characterized. . .control proteins. (Note - "as opposed to you people" - DBL). . . .In this context, the technical concerns of Salahpour and Masri do not seem relevant."
It's probably safe to say that the air has not yet been cleared. I'm not enough of a BRET hand to say who's right here, but it looks like we're all going to have some more chances to make up our minds (and to appreciate the invective along the way).
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+ TrackBacks (0) | Category: Biological News | Drug Assays | The Scientific Literature
August 20, 2007
Posted by Derek
As I've mentioned before, advances in molecular biology have continued to make all sorts of brute-force approachs possible - things that would have been laughed at (or, more likely, not even proposed at all) a few years ago.
Another recent example of this is a paper earlier this year in Nature from the group of Michael White at UT-Southwestern. The authors selected a lung cancer cell line that's know to be very sensitive to Taxol (paclitaxel), and looked for possible targets that might increase the drug's effectiveness. (It's a good compound to pick for a study like this, since it's simultaneously quite effective and quite toxic).
So, how do you go fishing for such combinations? These days, you set up 21,127 experimental wells, each one contained some cells and some silencing RNA molecules targeting, one at a time, 21,127 different human genes. And you look to see if knocking down expression of any of those genes increased the potency of a normally ineffective dose of the drug. (There were four different siRNAs per gene, actually, and each one was run in triplicate with and without Taxol, leading to a Whole Lotta 96-well plates. I'm glad I'm not paying for all the pipet tips, I can tell you that for sure.)
As you'd imagine, working up the data from this kind of thing takes as long, or longer, than setting one up. After comparing everything to the control wells and to each other several different ways, they ended up with 87 candidate genes whose knockdown seems to make the drug more effective. Gratifyingly, many of these make one kind of sense or another - there are several genes, for example, that are known to be involved in spindle formation, which is the target of paclitaxel itself.
Even more interestingly, not all the hits were obvioius. Another group of genes code for parts of the proteasome. That part of the cell is targeted by Millennium's Velcade (bortezomib), and it's recently been reported that the combination of Velcade and paclitaxel is more effective than expected. And there's another combination that seemingly hasn't been tried at all: the experiment suggests that inhibitors of vacuolar ATP-ase should synergize with Taxol, and (as it happens) a compound called salicylihalamide A has been looked at for just that target. They tried this experimental combination out on the cells, and it seems to work well - so, in humans?
As a commentary in the New England Journal of Medicine on this work dryly put it, "This hypothesis should be tested." And so it should. I've always had doubts about how far one can extrapolate cell data in cancer studies, but this kind of thing will tell us for sure. If something hits from this work, more such studies will come pouring out - they're getting easier to do all the time, you know. . .
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July 10, 2007
Posted by Derek
There's a problem in the drug industry that people have recognized for some years, but we're not that much closer to dealing with it than we were then. We keep coming up with these technologies and techniques which seem as if they might be able to help us with some of our nastiest problems - I'm talking about genomics in all its guises, and metabolic profiling, and naturally the various high-throughput screening platforms, and others. But whether these are helping or not (and opinions sure do vary), one thing that they all have in common is that they generate enormous heaps of data.
We're not the only field to wish that the speed of collating and understanding all these results would start to catch up with the speed with which they're being generated. But some days I feel as if the two curves don't even have the same exponent in their equations. High-throughput screening data are fairly manageable, as these things go, and it's a good thing. When you can rip through a million compounds screening a new target, generating multiple-point binding curves along the way, you have a good-sized brick of numbers. But you're looking for just the ones with tight binding and reasonable curves, which is a relatively simple operation, and by the time you're done there may only be a couple of dozen compounds worth looking at. (More often than you'd think, there may be none at all).
But genomics/metabolomics/buzzwordomics platforms are tougher. In these cases, we don't actually know what we're looking for much of the time. I mean, we don't understand what the huge majority of the genes on a gene-chip assay really do, not in any useful detail, anyway. So the results of a given assay aren't the horserace leader board of a binding assay; they're more like a huge, complicated fingerprint or an abstract painting. We can say that yes, this compound seems to be different from that one, which is certainly different from this one over here but maybe similar to these on the left - but sometimes that's about all we can say.
Of course, the story isn't supposed to stop there, and everyone's hoping it won't. The idea is that we'll learn to interpret these things as we see more and more compounds and their ultimate effects. Correlations, trends, and useful conclusions are out there (surely?) and if we persevere we'll uncover them. The problem is, finding these things looks like requiring the generation of still more endless terabytes of data. It takes nerve to go on, but we seem to have no other choice.
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April 26, 2007
Posted by Derek
When I wrote about lousy animal models of disease a few days ago, there was a general principle at the back of my mind. (There generally is - my wife, over the years, has become accustomed to the sudden dolly-back panorama shots that appear unannounced in my conversation). It was: that a bad model system is much, much worse than no model system at all.
I've been convinced of that for a long time. When you have no model for what you're doing, you're forced to realize that you have no clear idea of what's going on. That's uncomfortable, to be sure, but you at least realize the situation. But when you have a poor model, the temptation to believe in it, at least partially, is hard to resist. Even if it's giving you the right answers at a rate worse than chance, you can still take (irrational) comfort in knowing that at least you're not flying blind - even as you do worse than the people who are.
There are many reasons to hold on to an underperforming model. Sometimes pride is the problem. I've seen groups that stuck with assays just because they'd invented them, even though the method was slowly wasting everyone's time. Never underestimate cluelessness, either. People will use worthless techniques for quite a while if they're not in the habit of checking to see if they're any good. But the biggest reason that useless procedures hang around, I'm convinced, is fear.
Fear, that is, of being left out in the middle of the field with no models, no insights, and no path forward at all. It's a bad feeling, rather scary, and rather difficult to explain to upper management if you're a project leader. Better, then, to hold on to the assays and models you have, to defend them even if you're not sure you trust them. With any luck, the project will end (although probably not happily) before the facts have to be faced. As Belloc advised children in other situations: "Always keep ahold of Nurse / For fear of finding something worse."
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March 12, 2007
Posted by Derek
I wanted to link tonight to the "Milkshake Manifesto" over at OrgPrep Daily. It's a set of rules for med-chem, and looking them over, I agree with them pretty much across the board. There's a general theme in them of getting as close to the real system as you can, which is a theme I've sounded many times.
That applies to things like "Rule of Five" approximations and docking scores - useful, perhaps if you're sorting through a huge pile of compounds that you have to prioritize, not so useful if you've already got animal data.
He also takes a shot at Caco-2 cells and other such approximations to figure out membrane and tissue penetration. I've never yet seen an in vitro assay for permeability that I would trust - it's just too complicated, and it may never yield to a reductionist approach.
I'm a big fan of reductionism, don't get me wrong, but it's not the tool for every job. Living systems are especially tricky to pare down, and you can simplify yourself right out of any useful data if you're not very careful. The closer to the real world, the better off you are. It isn't easy, and it isn't cheap, but nothing good ever came easy or cheap, did it?
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+ TrackBacks (0) | Category: Drug Assays | Drug Development | In Silico
February 5, 2007
Posted by Derek
Here's an interesting press release on a potential new class of anticancer drugs. It has a nice hook ("Lab mistake leads to cancer finding!"), and the work itself isn't bad at all. It's an neat biochemical result, which might eventually lead to something. You have to know a bit about drug discovery and development to spot the problem, though - and not that many people do, which provides the ecological niche for this whole blog, frankly.
The discovery (from the University of Rochester) has to do with PPAR-gamma compounds, an area of research I've spent some time in. I didn't spend enough time there to understand it, mind you - no one has spent enough time to do that yet, no matter how long they've been at it. I wrote about some of the complexities here in 2004, and things have not become any more intelligible since then. The PPARs are nuclear receptors, affecting gene transcription when small molecules bind to them. There are, however, zillions of different binding modes in these things and they affect a list of genes that stretches right out the door. Some get upregulated, some down, and these vary according to patterns that we're only beginning to understand.
The Rochester group found that a particular class of compounds, the PPAR-gamma antagonists, had an unexpected toxic effect on some tumor cell lines. Their tubulin system was disrupted - that's a structural protein which is very important during cell division, and is the target for other known oncology drugs (like Taxol). The PPAR ligands seem to be messing with tubulin through a different route than anyone's seen before, though, and that definitely makes it worth following up on.
But the tone of the press release is too optimistic. (I should turn that line into some sort of macro, since I could use it twenty times a day). It mentions "high-dose" PPAR antagonist therapy as a possible cancer treatment, but take a look at the concentrations used: 10 to 100 micromolar. Even for cells in a dish, that's really hammering things down. And there's hardly any chance that you could attain these levels in a real-world situation, dosing a whole animal (or human). As blood levels go, those are huge.
But how about using more potent compounds? Of the three that are mentioned in the paper, BADGE is pretty dead, but the other two are actually quite potent. Tellingly, nothing happened at all with any of them up to 1 micromolar. These things will mess with other PPAR-gamma driven processes at much lower concentrations, so you have to wonder what's really going on here. And keep in mind that other PPAR compounds whose mode of action is roughly the opposite of these have been suggested as potential anticancer agents, too - this sort of thing happens all the time with nuclear receptors, and reflects their head-grabbing complexity.
This is still worth figuring out; don't get me wrong. There might be a new mechanism here that could lead to something, eventually, although it looks to be a tough problem. But that's the part of this work that's interesting - the level of activity seen here isn't. If I had a dollar for every compound that affects tumor cells at 50 micromolar, I wouldn't need to be sending my CV out these days.
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January 10, 2007
Posted by Derek
After yesterday's post, several people brought up the issue of inverted screening cascades. What happens when your compound works better in the mice than it did in the cells? Worse, what if it would have worked in the mice, but you never put it in there because it was so weak in the cell assays?
These kinds of questions are worth worrying about, because we almost never get a chance to answer them. For obvious reasons, the vast majority of compounds that make it into animal models are ones that looked good in the earlier steps. You'd have to think that the hit rate in vivo would be much lower for the dud compounds, but you'd have to be pretty arrogant to think that it would be zero, too.
As I recall (and I was just down the hall when it happened), the discovery of Schering-Plough's cholesterol absorption inhibitor came out of a compound that made it into an animal model and worked well, even though it turned out later to be a loser at the project's original target. (I believe that the in vitro assay was down that week for some reason, but one of my former colleagues will probably set me straight if I'm wrong about that). This sort of thing is food for thought, all right, extreme example though it might be. Even if your compounds don't suddenly hit a new target, there's still room for plenty of surprises in pharmacokinetics and the like.
But it would be unethical just to shove everything into animals, tempting though it is sometimes. And it would cost an insane amount, too - let's not forget that. But I do advocate getting as close to the real disease as quickly as possible. You can really waste time and effort by over-optimizing in vitro, all the time convincing yourself that you're doing the right thing.
Then there's the ultimate question in this line: how many compounds are there that don't work well in the animal models, but would be good in humans? I've wondered about this for many years, and I'm going to go on wondering, because data points are mighty scarce. Human biomarkers might eventually lead to some companies crossing their fingers and going into man with a compound that they expect to outdo the animal models. But it's going to take a lot of nerve. (And here's another complication - those upside surprises that might show up in the animals? How many of those are going to translate to humans, do you think? Not all of them, clearly. . .)
I have no doubt that there are many potentially useful drugs that are abandoned early. False negatives are probably on the shelves all around us. I don't see that as a strong argument against animal use (what, after all, is the alternative?), but it sure isn't a big argument for it, either. It's just, for now, the way things are.
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+ TrackBacks (0) | Category: Clinical Trials | Drug Assays | Drug Development
Posted by Derek
I've noticed a few stories making the rounds recently about possible new cancer therapies. Johns Hopkins has press-released the work of a group there on, and several news outlets have picked up on a British study on the effect of vanilloid agonists (such as the hot-pepper compound capsaicin) on cancer cells.
And all this is fine, until the word "cure" starts being tossed around. It always is. The number of times you see it, though, is inversely proportional to how reliable your favorite news source is. I wish the Nottingham and JHU people all the best in their research, and I hope that their projects lead to something good. But they have a long way to go, which you might not realize from the "Johns Hopkins Patents Cancer Cure" and "Hot Peppers Can Cure Cancer" headlines.
You see, these studies are all on cell cultures. I've worked on several cancer research programs, and I'm sure that other readers who've done the same can back me up here: unless you've seen cancer drug discovery work at close range, you may have no idea of just how many compounds work against cancer cells in a dish. It isn't that hard. I have absolutely no idea of how many thousands of compounds I could dig up from our files that will just totally wipe out a lot of the common cancer cell lines - in culture, that is.
We don't even bother looking at a compound unless it goes through cultured cell lines like a flaming sword. Problem is, a good number of those compounds will go through normal cells in the same fashion, which isn't exactly what the oncology market is looking for. And of the ones that are left, the ones that aren't hideous toxins - well, a lot of those hit the skids when they go into a live mouse model. Drug candidates that rip through the cell assays but fizzle in the mouse are very easy to come by. Anyone who does oncology drug discovery can furnish you with piles of them, and you're welcome to the darn things.
Now comes the really ugly part. We've ditched the nonselective cell killers, and we've shaken out the compounds that can't cut it in a live animal. How many of these actually work in human beings? Nowhere near as many as we'd like, that's for sure. AstraZeneca's drug Iressa is always useful to keep in mind. That one was going to be a huge hit, back when it was in development. But in real patients, well. . .for the vast majority of them, it just doesn't do much at all. There are a few responders (some of whom we can screen for), but otherwise, you'd have to call the compound a massive failure in the real world. Oh, but you should see it kick through the cell assays, and watch what it'll do for the mice.
Our assays just aren't that predicitive. It's a big problem, and everyone in the field knows it, but so far (despite crazy expenditures of time, money, and brainpower), no one's been able to improve things much. Anyone who does cancer work knows not to celebrate until the human trials data come back, and you'd better be careful even then. So the next time you read about some amazing thing happening to cells in a dish, well - wish the researchers luck. And go back to what you were doing before. There's time.
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October 22, 2006
Posted by Derek
"You like those scatterplots, don't you?", someone said to me the other day. And I can't deny it. On most projects that my lab has been assigned to, at some point I end up messing around with all the project data, plotting one thing against another and looking for correlations.
Often what I find is negative. Plotting liver microsome stability (a measure, in theory, of one of the major pathways for drug metabolism) against compound blood levels in animal dosing has rarely, in my perhaps unrepresentative experience, shown much of a correlation. In vivo blood levels are just too complicated, and influenced by too many other things. But I'm often surprised by how many people assume that there's a correlation - because, to a first approximation, it sort of makes sense that there might be - without actually having run the numbers.
That's a theme that keeps recurring: a fair amount of what people think they know about their project isn't true. I think it's because we keep reaching for simple explanations and rules of thumb, in hopes that we can get some sort of grip on the data. We give these too much weight, though, especially if we don't examine them every so often to see if they're still holding up (or if they ever did in the first place).
Another factor is good ol' fear. It's unnerving to face up to the fact that you don't know why your compounds are behaving the way that they are, and that you don't know what to do about it. It's no fun to plot your primary assay data against your secondary data and see a dropped-paintcan scatter instead of a correlation, because that kind of thing can set your whole project back months (or kill it altogether). One of the biggest problems in an information-driven field is that not everyone wants to know.
One time when I was giving the numbers a complete run-through, I noticed one of the plots actually seemed to have a fairly good shape to it. Y-axis was potency (plotted as -log), and there it was, actually increasing - broadly, messily, but undeniably - with the X-axis, which was. . .corporate compound number, the one assigned to each new compound as it was sent in for the assay. Oh, well. It showed that we were making progress, anyway. And at least nobody suggested that we attempt to give the compounds numbers from years in the future, in order to make them instant surefire winners. I've heard sillier suggestions.
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August 30, 2006
Posted by Derek
If you want to make your friends in the cell culture lab jump, just walk up behind them and shout "mycoplasma!" (What's that? You say you have no friends in the cell culture lab? Hmm. . .)
Mycoplasma is a scary word because they're scary little organisms. They're bacteria, just barely, running much smaller than usual and without any sort of cell wall. They also have the tiniest genomes you're likely to ever see - being parasitic allows them to get away with a pretty limited instruction set. They can cause diseases in humans and other animals (excellent review here), but they just love to hang out with your cultured cell lines, too.
From their (admittedly rather limited) perspective, what's not to like? Constant temperature, lots of food, and plenty of well-taken-care-of cells to mooch off of. Problem is, once they get in there, they alter the behavior of the cells they've infected, and you can't trust the results of your assays with them any more. Every cell culture lab tests for these things, and every one of them still has the occasional outbreak. It's the price of doing business. If the cells aren't precious, they're tossed - if they are, there are some antibiotics that will generally kill the little creatures off, but you still have to watch things closely for a while. (If you don't want to test them yourself, you can send samples to these guys, and they'll do it for you).
There have been periodic mycoplasma spasms in many research areas, as various groups have found that their results are suspect due to contamination. Since the little beasts pass right through filters that will strain out normal bacteria, and can't even be reliably seen under normal microscopy conditions in many cell cultures, a little paranoia is justified. Have you checked your cells recently?
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June 21, 2006
Posted by Derek
Here's a question for my readers in the research community: what assay have you dealt that turned out to be the biggest waste of time and effort? I can think of several strong nominees, but I'll lead off with one from quite a while ago.
This one happened in an antiviral group, and I believe that they were targeting a viral protease. Several chemists started cranking away on the lead compound, turning out analogs for the primary assay. But there was no decent SAR trend to latch on to. Things would look (briefly) sensible, then fall apart again, and there was only a scatter when you tried to correlate things with the secondary assay.
After some three or four months, the reason for all this became clear (it doesn't always, I have to note). Turns out, as it was told to me, that a biologist on the project had everything tested against the wrong enzyme. Who knows what it was, but it sure wasn't the protease of interest. What's more, he had apparently realized early on that it wasn't the right stuff, and was frantically working in the background trying to get the right stuff running. It never worked out. He ended up generated week after week of meaningless data, hoping that the project would go away. Instead, as it turned out, he went away (and not by choice).
So that's my entry. No doubt horrors will quickly emerge to beat it.
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June 18, 2006
Posted by Derek
Well, my last post on biological systems and their ingredients really touched a nerve (see here for an example). I guess I should, um, clarify my position before the leaky bottles of beta-mecaptoethanol start arriving by FedEx.
I already knew the reasons for several of the components I spoke about - EDTA, for example. And I realize that there's a reason for everything that's in there. But what throws me as a chemist is that some of these recipes seem to be handed on "just because they work" Does a particular enzyme prep need EDTA in it or not? Many times, no one checks, because it probably won't harm things and it's better to be on the safe side, so in it goes. It may be hard for a biologist to understand how odd that feels to a synthetic organic chemist, but I can tell you for sure that it does.
One of the commentors to the last post brought up an important point: biologists optimize for the function of a system. And that often means having a lot of buffers, chelators, cofactors, adjuvants, reducing agents, and chaperones floating around in there with your protein of interest, to keep it thinking that it's still in some kind of cellular environment, thus putting it in the mood to do what it's supposed to be doing. There's no point in trying to see how minimal you can make the system if it's working the way you want it to already.
But we chemists are minimalists. We optimize for the function of a system, too, but in our case, purity is usually a good first variable to tune up. The cleaner everything is in our reactions, the better it generally works. That means pure, distilled solvents, with no water in them. It means an inert gas atmosphere, so there's no reactive oxygen around. And it means that your starting materials and reagents should be as clean as you can practically get them, because when there's two percent of this or five percent of that in the flask, things often start to go wrong in unpredictable ways. When a reaction wipes out on us, the first thing we check is whether everything was clean enough.
So you can imagine how biology looks to an organic chemist, whose ideal reaction is a clear solution in a clear glass flask, with one pure solvent and two pure reactants cleanly converting to only one product. Biological systems, to us, look like trying to do science by adding squirts of barbecue sauce to bowls of beef stew. Of course, as the biologists know, the stuff in those bowls was derived from stew (and worse), and was born to the stuff. It won't work unless things achieve a certain level of stewiness, and the surest way to kill it would be to turn an organic chemist loose on it to clean it all up.
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June 15, 2006
Posted by Derek
You know, I mean no offense to all my pharmacologist friends and readers, but. . .do y'all really know why all those things are in your buffers and solutions? I've been wrestling with this the last few days, trying to straighten out my "vial thirty-three" problem, and it's been interesting.
There's some reducing agent in there, naturally. Can't have those thiols turning into disulfides and balling up the protein, I understand - but does something bad happen if it's not in there? Generally, no one finds out, because, hey, why mess with it? And there's some EDTA, and some salt, and their function is? Well, as far as I can tell, they're also in there because they've sort of always been. Same goes for the squirt of detergent (Brij-35 or some such), and the tiny bit of bovine serum albumin, of all things. It's just part of the old-fashioned recipe from Grandma's Protein Kitchen.
Now, organic chemistry has a little of this, true, but it hasn't reached quite the Ancient Runestone levels of enzymology. We like to use tetrahydrofuran (THF) for a lot of organometallic reactions, for example, but at least we know that that's because THF is a good co-ordinator to metal cations. At least we don't have six other trace constituents in there that we always use whether we need 'em or not. Another example is how we tend to stick to good ol' ethyl acetate and hexane to run TLC plates, rather than look into other solvent combinations that might do a better job - probably because there are just too many of them to investigate, and EtOAc/hexane works well enough.
And that, I think, is the problem that the biologists face. Biochemical systems are tricky. They have way too many variables, which means that their degrees of freedom have to be reduced just to get anything to work. So all sorts of recipes and rules of thumb are handed down. Not all of them are optimal, but they're mostly decent and will allow you to get on with the project without wasting too much time. Especially in the early part of a project, an immediate 70% effectiveness is worth a lot more than a 98% that would take you a month of work to tweak up to.
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August 18, 2005
Posted by Derek
One of the odd things about science is that you spend a good amount of time trying to prove that you don't know what you're talking about. At least, if you're doing it right, you should.
Take the first part of a drug discovery project, for instance. Most of them have a "primary assay", which is usually done against a purified protein in vitro, under fairly artificial conditions. Compounds that meet some standard of activity against that target then move on to the secondary assay, which is supposed to be aimed at the same process, but now it's done in living cells. That's a much tougher test. (It's a big leap from pure proteins to cells, about the same size as the leap from cells to whole animals.)
The hope is that the two assays will correlate with each other, but it's never a perfect fit. Generally, what you see is some of the active compounds dropping out for no apparent reason in the cell assay. If your target is in the cytoplasm, then there's always the possibility that these compounds don't penetrate into the cell as well as the others. Or they make it in, but are pumped right back out before they can get anything accomplished. Or perhaps they find some other (even tighter) binding site once they're inside, on some protein unrelated to the readout of your assay. There are always plenty of ways to explain these misfires.
And that's fine, as far as it goes. But if you don't double back and check these things out occasionally, you run the risk of fooling yourself. If your two assays don't correlate very well, it might be that cell penetration is lousing things up, sure - and it might also be that your assays aren't measuring the same thing. Or it could be that your target from the first assay isn't as important as you thought it was. These are the sorts of thing you really ought to be sure about.
So you need to keep yourself honest. Take some of your not-so-good compounds, the ones you'd normally discard after the first cut, and take them on to the cell assay regardless. They'd better not work! Test some of the compounds on a closely related cell line that doesn't have your target in it, if you've got some - is your target really the reason for the activity you're seeing?
Most of the time, you'll find that things are just fine. The inactive compounds really are inactive all the way through. But I've seen the exceptions occur, and more than once. You don't want to wait any longer than necessary to find out that your project is a dud. And worse yet, you really don't want someone else to find out for you. It leads to some of those awkward scenes we'd all rather avoid.
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August 1, 2005
Posted by Derek
As a drug discovery project moves along, we synthesize lots of new compounds, test them, and pick the best ones to make in large quantities. Simple, eh? Try your hand, then, at some of these questions, all of which have come up in the course of my career so far:
1. If you're running an experiment in vivo, and your control compound (from a competitor) is a highly active, hard-to-beat standard - how do you interpret your results when you know that this compound has made it to market and is no great shakes in human patients?
2. What do you do when you have to make a large batch of some compound for advanced pre-clinical work, and there's only one person in the whole department who can really get the crucial reaction to work? Do you tell people that you have a good large-scale route, or not?
3. How about a bit earlier in the game - how do you deal with it when you have a high-yielding, clean route to a key intermediate that lots of your people are using, but it uses a reaction that you know, for a fact, that the scale-up group won't touch later on?
4. How do you handle things when your primary biological assay keeps acting up - by factors of five to ten? Do you normalize the numbers to a standard each time and hope for the best, or do you start to doubt the usefulness of the whole assay?
5. For bonus points, how do you tell which numbers you've been getting are closer to the truth - the ones that say your compounds are really active, or the ones that say that they stink?
6. How do you interpret things when the in vivo assay tells you that your compounds have wonderfully long durations of action, but the blood levels tell you that they completely disappeared from circulation long before?
7. What does it mean when your best compound is intolerant of even slight structural changes? Do you just run with it (after all, you only need one compound, right?) Or do you hammer away trying to find something that can be safely modified in order to have a back-up?
Are there right answers? Well, presumably. I know what answers I'd give to some of these, but I make no guarantees that they're the right ones. . .
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+ TrackBacks (0) | Category: Drug Assays | Drug Development | Life in the Drug Labs
June 19, 2005
Posted by Derek
I had a question a while back about how often researchers are fooling themselves when they think they've found a new signaling pathway or a new disease target. That one's pretty easy to answer, to a rough approximation: the less work you do, the better the chance that you're fooling yourself.
But it can take years before you know if you were right, so there's really not enough data to give a more quantitative answer. Take a notorious example, beta-amyloid in Alzheimer's disease. That's been noted as a sign of the disease every since Alois Alzheimer described it nearly one hundred years ago. Huge mountains of data have piled up since then about the disease and what might be causing it, but we're still not one hundred per cent sure if amyloid plaques in your brain give you Alzheimer's or if Alzheimer's gives you amyloid plaques in your brain. Most of the money is on the former, but it's not quite a sealed case yet.
The same uncertainty hovers around everywhere. Let's say you study a particular form of cancer, and you find that there's a particular kinase that's always found in greater than normal amounts in the tumor cells as compared to normal ones. Is that a new target for therapy? The answer is a firm, resounding, "maybe!"
Perhaps it's the real deal, but there are other enzymes that will step right in to do your kinase's job if you inhibit it - in that case, you'd better be prepared to take those on, too, or get ready to pack it in. Perhaps it's part of the real problem, but it's just a sideshow. If it's not the key or limiting step in any given pathway, inhibiting it won't do anyone much good. Or maybe it's there to phosphorylate the realculprit, in which case you should put some resources on tracking that thing down, too - it could be a better handle on the disease. But on the other hand, maybe your kinse is only acting downstream of that real culprit, phosphorylating something else entirely, which is an extreme example of the sideshow possibility mentioned above. Or it may be that this kinase is upregulated because it's part of a mechanism that's trying (unsuccessfully) to get the cancer cell to shut down. You probably wouldn't want to inhibit that!
Unraveling all this is not a job for the impatient, or for the light of wallet either, for that matter. So many of these pathways have turned out to be more complicated than anyone had ever imagined, that it's gotten to the point that people are questioning the whole reductionist-molecular-biology approach to drug targets. Eight or ten years ago, I would have considered that a radical or even crazy position. These days, I kind of want to sign up. . .
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May 27, 2005
Posted by Derek
I've started my Memorial Day weekend early (thus the mid-morning posting time.)
One of the comments to the previous post mentions the "let's make these compounds because we can" attitude, and points out that this was the fallacy underlying the combichem boom (and bust.) True enough - I should have clarified my point by saying that the compounds I was recommending were much more targeted. They're related to a structural series that we know we're interested in, but we haven't made tested anything from this particular group yet.
And, truth be told, I don't mind the blue-sky let's-make-some-compounds approach, as long as it's done in moderation. Throwing some interesting structures into the screening files is never a waste of time, although there are often more pressing things to do.
I don't approve of sending in things that are poor candidates for starting off an optimization project, though. If something with a molecular weight of 1300 hits in your assay, there's often not much you can do about it. That's at least twice a reasonable molecular weight, and large compounds like that often can't be cut down to size. Their binding modes are complex, interesting, and almost impossible to deal with in any practical manner, unfortunately. Getting a handle on things like this is a longstanding problem in drug discovery, so unless you feel like solving it, you shouldn't add to it.
Similarly, anyone who sends in reactive compounds like acid chlorides deserves a whack over the head. Those things, assuming they don't fall apart in storage, will tear up most assays they're run in, and it's not like they're ever going to be drugs. Same goes for things like organotelluriums and other out-there elements. I have a fairly liberal attitude (silicon-carbon bonds are OK with me), but there's a limit. If you think someone's going to be happy when your nickel complex hits in their enzyme assay, you are not in touch with consensus reality.
The problem with the combichem boom wasn't always the underlying compounds, although some of them were stinkers (and most of them sure could have been cleaner.) I think the real trouble was how oversold the whole thing became. If you weren't buying or cranking out huge libraries, you were missing the gold rush. Vast untapped veins of drug leads were out there in those hills! Without the hype, things wouldn't have looked so bad. But hey, without the hype, most of those libraries wouldn't have been made. . .
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March 2, 2005
Posted by Derek
I spoke yesterday about going through lists of chemical structures, looking for ones that we might want to keep in our screening libraries and, simultaneously, for the ones that we never want to see again. There's a paper from last year in the Journal of Medicinal Chemistry (47, 4891) that's an embarrassing reminder of just how hard it is to do that consistently.
It's from an effort led by Michael Lajiness at what was then Pharmacia/Upjohn (and is now Pfizer, which might account for the lead author now being at Eli Lilly, if you can follow all that.) They had about 22,000 compounds to sort through to see if they wanted to purchase them for the screening files, so they broke them out into 11 lists of 2000 compounds each. Thirteen medicinal chemists volunteered (or were volunteered, unless I miss my guess) to go over these lists. Eight members of the team reviewed two separate lists, and one wild man reviewed three.
The authors of the paper took a look at the list of rejected compounds from each reviewer, correctly (in my view) believing that this list is more significant than the list of what was accepted. After all, an ugly structure that makes it through may well never hit in an assay, and if it does it'll go through many more layers of scrutiny. A structure that's rejected, though, disappears from the company's screening universe forever. False negatives could have bigger consequences than false positives.
So, when more than one chemist went over the same list of 2000 compounds, how similar were their reject lists? Not very! On the average, two medicinal chemists would agree to reject the same compounds only about 23% of the time. (I knew that the overlap wasn't going to be perfect, but that's a lot worse than I was expecting.)
To continue the punishment, the lists had each been, without the knowledge of the reviewers, seeded with the same set of 250 compounds, all of which had been rejected by a previous review. The chemist-to-chemist rejection overlap in this smaller set of potential losers was still only 28%. Not as much of an improvement as you'd hope for. . .
And now the whipped topping and chocolate sprinkles: recall that many of the reviewers did more than one list. That means that they got to see that same group of 250 compounds more than once, in the context of different lists. How did the same people do when they saw the exact same compounds? They only rejected them about 50% of the time, it pains me to report.
It looks as if potential drug leads follow the same rule as Tolstoy's comment in Anna Karenina: Good compounds are all alike, while bad compounds are each bad in their own way. It seems that the Pharmacia reviewers didn't reject many good structures, but they let varying (and inconsistent) numbers of bad ones through (with no particular correlation to their industrial experience, I should add.) The possible reasons advance for this variation include personal bias, inattention (and I wouldn't minimize that factor, not in a list of 2000 compounds), and a general human inability to sort through large complex data sets.
And right at the end, the authors allude to a bigger problem: If this is how consistently our med-chem intuition works, how well does it serve us during drug development? In a research project, there are plenty of decisions to be made about what compounds to make, what structural series to emphasize and which ones to set aside. Just how bad at this are we, really? I'm afraid to find out.
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March 1, 2005
Posted by Derek
How do we accumulate our piles of test compounds over here in the drug industry? Well, mostly, we make them ourselves. But we also buy collections of compounds. Some of them are from other companies that have gone under, and some of them are from outfits that do nothing but produce libraries of (putatively) interesting compounds. (You can buy some that they've already shared with other companies for a discount, or you can have a new one made up for you for a higher price.) That was a much hotter business ten years ago than it is now, but it's still going.
And we buy compounds from university labs. That's a little-known way (outside of chemistry, at any rate) that professors and their research groups earn some extra spending money. (Naturally enough, this practice also leads to some elbow-throwing between the research groups and the universities involved when each of them want a piece of the profits.) We generally pay a set price per compound, but you wouldn't want to buy every single thing that academia offers. Some of the stuff is quite interesting and useful, but many of the structures will become drug leads only when swine take to the skies.
I've helped evaluate lists of potential purchases before, and they're a mixed bag indeed. Once I looked over a collection from Leo Paquette's group at Ohio State. Now, he and his group did a lot of nice chemistry over the years, and there were a lot of useful compounds on the list. But there were also plenty of intermediates from his famous synthesis of dodecahedrane. Those represented a tremendous amount of effort from his students and post-docs, and were part of the history of organic synthesis.
And I didn't want us to buy them. For one thing, they didn't look much like drugs to me. "But what if they hit in our assays?" said one of my colleagues, trying to make the case that we should buy some. "That's what I'm worried about," I said. What indeed? If one of those structures turned out to be a wildly potent ligand for some protein target, what exactly were we going to be able to do about it? Follow the twenty-nine step synthesis to make more of it? No, in this case, I thought we were better off with nothing than with something we could never use. We passed.
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January 18, 2005
Posted by Derek
I've mentioned before that one of our big problems in the drug industry seems to be finding compounds that work in man. I know, that sounds pretty obvious, but the statement improves when you consider the reasons why compounds fail. Recent studies have suggested that these days, fewer compounds are failing through some of the traditional pathways, like unexpectedly poor blood levels or severe toxicity.
In the current era, we seem to be getting more compounds that make it to man with reasonable pharmacokinetics (absorption from the gut, distribution and blood levels, etc.) and reasonably clean toxicity profiles. Not all of them, by any means - there are still surprises - but the stuff that makes it into the clinic these days is of a higher standard than it was twenty years ago. But that leaves the biggest single reason for clinical failure now as lack of efficacy against the disease.
That failure is the sum of several others. We're attacking some diseases that are harder to understand (Alzheimer's, for example), and we're doing so with some kind of mechanistic reason behind most of the compounds. Which is fine, as long as your understanding of the disease is good enough to be pretty sure that the mechanism is as important as you think it is. But the floor is deep with the sweepings of mechanistically compelling ideas that didn't work out at all in the clinic - dopamine D3 ligands for schizophrenia, leptin (and galanin, and neutropeptide Y) for obesity, renin inhibitors for hypertension. I'm tempted to add "highly targeted angiogenesis inhibitors for cancer" to the list. The old-fashioned way of finding a compound that works, and no matter how, probably led to fewer efficacy breakdowns (for all that method's other problems.)
Another basic problem is that our methods of evaluating efficacy, short of just giving the compound to a sick person and watching them for a while, aren't very reliable. If I had to pick the therapeutic area that's most in need of a revamp, I'd have to say cancer. The animal models there are numerous, rich in data, and will tell you things that you want to hear. It's just that they don't seem to do a very good job telling you about what's going to work in man. I will point out that Iressa, for one, works just fine in many of the putatively relevant models.
The journal Nature Reviews: Drug Discovery (which is probably the best single journal to read for someone trying to understand pharma research) published a provocative article a couple of years ago on this subject. The author (the now late) David Horrobin, compared some parts of modern drug discovery to Hesse's Glass Bead Game: complex, interesting, internally consistent and of no relevance to the world outside. They got a lot of mail. Now the journal has promised a series of papers over the next few months on animal models and their relevance to human disease, and I'm looking forward to them. We need to hit the reset button on some of our favorites.
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May 24, 2004
Posted by Derek
Before getting started, I'd like to recommend the discussion going on in the Comments section of the "All the Myriad Ways" post below. If you find the topic of gene patents at all interesting, it's worth keeping up with. Me, I'm just watching for now, feeling like Teresa Nielsen Hayden as the discussion takes off on its own. (I show up in her comments section every so often myself, although I largely stay out of the political discussions because I think they'd throw things at me.)
On to the main topic tonight. There's an article in the latest Nature Reviews: Drug Discovery called "Prospects for Productivity" from Bruce Booth and Rodney Zimmel at McKinsey Consulting. They're talking about the now-familiar drug drought that seems to have affected everyone the last few years. It's real enough, although they make the point (which I've brought up myself, in a column for Contract Pharmamagazine) that people have been complaining about a drug shortage for decades.
Booth and Zimmel do a good job of running down the usual suspects. In their order, they have:
1. Lack of payoff from genomics. This one, they say, has "clearly driven part of the productivity decline." I can second that, because I (and friends of mine around the industry) have seen it at work right in front of them. There was a panic that made everyone start working on genome-derived targets, long before we knew enough to accomplish anything. In most cases, we still don't.
2. Poor chemical libraries. This is an earlier problem, but one whose effects are still working their way through the portfolio. The combinatorial chemistry craze (the craze before genomics, if you're keeping score at home) caused a lot of people to make a lot of compounds that had no chance of ever becoming drugs. Why? Because they could make them! And someone else might make them first! We're smarter now, theoretically. B & Z don't go into the details, but this one hit some companies harder than others, depending on how early and how hard they fell for combichem evangelism. Some careers never really recovered.
3. Tougher regulation. B & Z discount this, for the most part, as whining from the drug companies (not their exact wording!) They're probably right, although they correctly note that seemingly minor changes at the FDA have ended up costing huge amounts of money and time on our end. But this still isn't the major thing hurting us, not that it isn't still fun to complain about.
4. Tougher internal scrutiny. This is a real one, too, although it's hard to quantify. We've gotten more cautious over the years, as we've tried to keep from taking drugs deep into clinical trials before finding out that they have some ruinous problem. The early-stage filters and hurdles we've put in probably work a little too well, though. Unnervingly, there are any number of drugs on the market now that never would have made it through the current regimes. The verdict all depends on how many loser projects we're avoiding at the same time, a number that's pretty much unknowable. Ah, what an industry.
5. Unfulfilled technological hopes. This overlaps with some of their other categories (such as all that genomics money we're never going to see again). But Booth and Zimmel draw special attention to the problem of the industry spending huge amounts on better and better in vitro technology (as in the previous point), only to find that it still doesn't translate well to animal models, much less clinical practice. Presumably, we're eventually going to figure out what we're doing, but we're probably going to hose away still more cash while we're doing it, too.
6. Too big to innovate? Readers will recognize this as a particular favorite of mine, what with my happy attitude toward huge mergers. Proponents of such would do well do digest this quote: "Whether size itself is good or bad for R&D remains to be seen, (Heresy! Says the board at Pfizer! - DBL) but the simple fact is that a greater proportion of innovation is occurring outside the industry leaders." Their estimates show a meaningful decline just over the past seven years or so, which is rather alarming for the big guys.
Not a bad roundup. The article has a lot of other useful stuff in it, too; I highly recommend it. They have a few ideas for getting out of our current fix, which I'll try to get to in a future post. None of them strike me as particularly resonant rallying calls ("Improve investment discipline"), but that doesn't mean that they're wrong, either.
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April 22, 2004
Posted by Derek
I mentioned the other day that not everything in that Stuart Schreiber interview sounded sane to me, (although more of it does than I'd expected). The interviewer, Joanna Owens, asks him to expand on a statement he made about ten years ago: famously (in some circles, at any rate) Schreiber said that he wanted to - and thought that eventually he could - produce a small-molecule partner for every human gene.
A worthy goal, to be sure, but a honking big one, too. To his credit, though, Schreiber isn't making light of it:
". . .that challenge understates what we really want to do, which is to use small molecules to modulate the individual function(s) of multifunctional proteins, activating or inactivating individual functions as necessary. This is one of the differences between small molecules, for example, and the knockout of knowckdown technologies, where you inactivate everything to do with the protein of interest."
Note how things have appropriately expanded. There are a lot more proteins than there are genes (a lot more, given the surprisingly lowball figure for the total size of the human genome), and the number of protein activities is several times larger than that. He's absolutely right that this figure is the real bottom line. But here comes that Muhammed Ali side of his personality:
"Small molecules allow you to gain control rapidly, and can be delivered simply but, most importantly, we've shown that we can discover molecules that only modulate one of several functions of a single protein. . .(the scientific community has) identified 5000 out of the required 500,000 small molecules, which is similar to where the Human Genome Project was in year two of its 12-year journey. That might be a useful calibration - optimistically, we're ten years away."
Midway through that paragraph is where I start pulling back on a set of imaginary reins. Whoa up, there, Schreibster! Let's take the assumptions in order:
Small molecules allow you to gain control rapidly. . . Compared to transcription-level technology, this is largely correct. But the effects of small-molecule treatment often take a while to make themselves known, for a variety of reasons that we don't fully understand. The problem's particularly acute in larger systems - look at how long it takes for many CNS drugs to have any meaningful clinical effect. And these complex systems have other weird aspects, which make the phrase "gain control" seem a bit too confident. U-shaped dose-response relationships are common. Look at what you find in toxicology, where you see threshold effects and even hormesis, with large and small doses of the same substance showing opposite effects.
. . .and can be delivered simply. . . Well, when they can be delivered at all, I guess. But there more of them that come bouncing back at us than we'd like. In every drug research program I've been involved with, there are plenty of reasonable-looking compounds that hit the molecular target hard, but then don't perform in the cellular assay. You can come up with a lot of hand-waving rationales: perhaps the main series of compounds is riding in on some sort of active transport and these outliers can't, or they're getting actively pumped back out of the cell, or they hit some other sinkhole binding site that the others escape, and so on. Figuring out what's going on is an entire research project in itself, and rarely undertaken. Every time someone tells me that drug delivery is simple, I can feel my hair begin to frizz.
. . .we've shown that we can discover molecules that only modulate one of several functions of a single protein. . . True enough, and a very interesting accomplishment. But the generality of it is, to put the matter gently, unproven. It would not surprise me at all if there turn out to be many proteins whose functions can't be independently inhibited. The act of binding a small molecule to alter one of the functions would cause the other ones to change. And a bigger problem will be distinguishing these effects from the consequences of actually taking out that first function cleanly: how will you know when you've altered the system?
. . .which is similar to where the Human Genome Project was in year two. . . True, but that and forty dollars will get you an Aldrich Chemical can opener. The comparison isn't just optimistic - it's crazy. The problems that the genome sequencers faced were engineering problems - difficult, tricky, infuriating ones, but with solutions that were absolutely within the realm of possibility. Faster machines were made, with more computing power, and new techniques were applied to make use of them.
But as I've been saying, I'm not sure that the Maximum Inhibitor Library that Schreiber's talking about is even possible at all. Don't get me wrong - I hope that it is. We'll learn so much biochemistry that our heads will hurt. But its feasibility is very much open to question, to many questions, and we won't even begin to know the answers until we've put in a lot more work.
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March 17, 2004
Posted by Derek
I sent off a manuscript to a chemical journal not long ago. There's an initial flurry of e-mail activity when you do that - we've received your manuscript, we've sent your manuscript out to reviewers - and then a more or less prolonged period of silence. The next thing you hear is whether the paper's been accepted or not, along with the referee comments.
Mine were the usual mix of helpful suggestions and things that make you roll your eyes. One of the latter was a comment that immediately pegged the reviewer as someone from academia. They noticed that the data from our primary assay, against a human enzyme, didn't always match up well with the secondary assay, which was against a rodent cell line, and wanted some more explanation for why some groups of compounds weren't active.
To which I could only reply "You and me both!" That's a constant problem in medicinal chemistry. A majority of projects are set up in that format, with a cell-free assay as the first filter, then cells expressing the target as the next hurdle. And it's just about inevitable that there will be whole groups of compounds that work fine in the first assay, but just wipe out in the second one.
Why should that be? As far as we know, there are two general ways that compounds can get into cells: passive transport and active transport. The passive route is just diffusion across the cell membrane: "Wonder drug? You're soaking in it!" It's affected by broad trends in molecular size, polarity, and so on. The second route is when your compound hitches a ride on some transport protein.
There are hundreds of these things involved in opening up channels into and out of the cell. Some of the famous ones move ions (calcium, potassium and the like), which makes sense. Those are small and electrically charged, so they're not going to just wander across the membrane on their own, and the cellular machinery depends on keeping such membrane potentials tightly controlled. Then there are transporters for large proteins, which are too huge to diffuse by themselves, and for essential classes of small molecules like fatty acids.
No one's sure how many of these things exist. Just in the last few years, there's been a whole new class discovered, the aquaporins, which (as the name implies) move water itself across the cell membrane. You wouldn't think that you'd need an active transport system for that (at least a lot of people didn't think so) but the things turn out to be ubiquitous. If there's a transporter for water, there can be one for anything.
The efflux pumps I spoke of the other day in antibiotic resistance are active transport proteins, too, naturally. Those complicate things by taking compounds that diffuse perfectly nicely into cells and making them look like they're bouncing off a layer of armor plate instead. You'll also get that effect when your standard project compounds ride in on some transport system, then you make some small structural change which causes your drug to lose its train ticket.
It's a lot of work to figure out what's going on, and often you can't get a handle on it, anyway. Many of these transport systems don't have specific inhibitors, so it's not like you can switch them off one by one to see which one is the problem. If you have a good way to monitor your compound on a cellular level (like a fluorescent probe), you can actually see the things going in and being pumped back out sometimes, or you can see if the transport system can be saturated as you load up on drug. But there's no way you can do this for hundreds of drug candidates on every project.
So, it's just one of those things. I'm on a project right now that has the same thing going on. We make tiny changes to our molecules, and the cell activity suddenly gets a hundred times better, or a thousand times worse. But are these trends going to translate to the cells inside a real animal? And if they do, will they be relevant to the active transport systems in humans? Bite your tongue.
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| Category: Drug Assays | The Scientific Literature
February 25, 2004
Posted by Derek
Credit where it's due! Yesterday I mentioned the original chemist who started the ezetimibe story, but I should note that the drug itself was synthesized by another former colleague of mine, Stuart Rosenblum. He and a host of others developed a huge series of analogs, which built in more acitivity and greater in vivo stability. Just the way drug development is supposed to work, actually.
This drug is also used as an example in a very interesting front-page Wall Street Journal article yesterday. It's a public version of a debate that's been going on inside the industry for a few years now: has the huge increase in compound screening (and compound synthesis) done any good? The article does a pretty good job discussing the issue, although it does mix the two technologies together a bit. It's a very interesting topic, which I'll return to here soon.
And while you're at it, the same issue of the newspaper has (in the Money and Investing section) a nice piece on how drug companies tend to bury news of clinical failures. Different companies handle this differently, of course, but with some of them you really have to watch closely. The article makes the same point I did a while ago - investing in this industry is more of a gamble than most people think. Don't just buy one company's stock if you're looking at biotech and pharma - there's no way you can really know what's going on. Spread your risk.
These articles confirm the Journal's status as the best newspaper when it comes to covering the drug business. The New York Times tries, and sometimes has good work in it, but isn't in the same class. As for magazines, I'd say that Forbes does very well, as does their online site with its copious coverage from Matthew Herper.
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February 24, 2004
Posted by Derek
There's a paper in the latest issue of Science from a team at Schering-Plough that may have tracked down how the company's cholesterol absorption inhibitor (Zetia, ezetimibe) works. That news really takes me back.
It's been years now, so it won't do any harm to mention that I used to work there. I had a ringside seat for the early years of that project, because it all happened right around the corner from my old lab. Ezetimibe was discovered fortuitously when one of my colleagues synthesized and sent in the original structures of the class for a project targeting a cholesterol handling enzyme known as ACAT. I believe that the in vitro assay was down that week, so the compounds went into the open slots for mouse testing, where they worked better than anything they'd seen. But when the protein assay came back on line, it was discovered that the compounds had no affinity for ACAT at all. Food for thought, that was.
The chemist involved was named Duane Burnett, and a search for "Burnett DA" in Pubmed will send you to most of the chemistry literature on the subject (along with this review). He had indeed hit on some features of a cholesterol binding site (which was his aim, based on blackboard-level structure modeling - no computers involved.) The compounds seemed to hit an unknown target in the small intestine that helped transport dietary cholesterol. The search for the protein involved began in about 1993, and seems to have concluded successfully in 2002-2003, years later than anyone thought it would take.
In the mid-1990s, all the classic methods for tracking down an unknown binding site were tried. The lead structure was biotinylated, modified with radiolabels, photoaffinity tags, and fluorescent groups (along with various combinations of these.) None of these methods identified the target.
They finally tracked down the protein by brute force genomics, using a cDNA library prepared from rat intestinal lining, coupled with sequence searching for the features you'd expect in a transmembrane protein with a steroid binding site. The evidence seems clear that the protein they've found is a key for ezetimibe's actions, but - most oddly - it still doesn't seem to bind to the protein. That would certainly explain the failure of all those modified compounds to pull out the target, but it does make you wonder what's going on. (Is there another real target? But if so, why wasn't that identified through the modified compounds? And so on.)
It took a lot of nerve to go on with that project, and I have to salute the people who kept it going. As with many other successful projects, there were several points along the way where it seemed like the whole effort was going to fail. As it turns out, ezetimibe is one of the main (few?) bright spots in Schering-Plough's portfolio. Merck, their eventual partner for the drug, values it pretty highly, too. I'm glad I got the chance to see it happen.
Credit where it's due! I should note that ezetimibe itself was synthesized by another former colleague of mine, Stuart Rosenblum. He and a host of others developed a huge series of analogs, which built in more acitivity and greater in vivo stability. Just the way drug development is supposed to work, actually.
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