About this Author
College chemistry, 1983
The 2002 Model
After 10 years of blogging. . .
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: firstname.lastname@example.org
April 4, 2014
I really got a kick out of this picture that Wavefunction put up on Twitter last night. It's from a 1981 article in Fortune, and you'll just have to see the quality of the computer graphics to really appreciate it.
That sort of thing has hurt computer-aided drug design a vast amount over the years. It's safe to say that in 1981, Merck scientists did not (as the article asserts) "design drugs and check out their properties without leaving their consoles". It's 2014 and we can't do it like that yet. Whoever wrote that article, though, picked those ideas up from the people at Merck, with their fuzzy black-and-white monitor shots of DNA from three angles. (An old Evans and Sutherland terminal?) And who knows, some of the Merck folks may have even believed that they were close to doing it.
But computational power, for the most part, only helps you out when you already know how to calculate something. Then it does it for you faster. And when people are impressed (as they should be) with all that processing power can do for us now, from smart phones on up, they should still realize that these things are examples of fast, smooth, well-optimized versions of things that we know how to calculate. You could write down everything that's going on inside a smart phone with pencil and paper, and show exactly what it's working out when it display this pixel here, that pixel there, this call to that subroutine, which calculates the value for that parameter over there as the screen responds to the presence of your finger, and so on. It would be wildly tedious, but you could do it, given time. Someone, after all, had to program all that stuff, and programming steps can be written down.
The programs that drove those old DNA pictures could be written down, too, of course, and in a lot less space. But while the values for which pixels to light up on the CRT display were calculated exactly, the calculations behind those were (and are) a different matter. A very precise-looking picture can be drawn and animated of an animal that does not exist, and there are a lot of ways to draw animals that do not exist. The horse on your screen might look exact in every detail, except with a paisley hide and purple hooves (my daughter would gladly pay to ride one). Or it might have a platypus bill instead of a muzzle. Or look just like a horse from outside, but actually be filled with helium, because your program doesn't know how to handle horse innards. You get the idea.
The same for DNA, or a protein target. In 1981, figuring out exactly what happened as a transcription factor approached a section of DNA was not possible. Not to the degree that a drug designer would need. The changing conformation of the protein as it approaches the electrostatic field of the charged phosphate residues, what to do with the water molecules between the two as they come closer, the first binding event (what is it?) between the transcription factor and the double helix, leading to a cascade of tradeoffs between entropy and enthalpy as the two biomolecules adjust to each other in an intricate tandem dance down to a lower energy state. . .that stuff is hard. It's still hard. We don't know how to model some of those things well enough, and the (as yet unavoidable) errors and uncertainties in each step accumulate the further you go along. We're much better at it than we used to be, and getting better all the time, but there's a good way to go yet.
But while all that's true, I'm almost certainly reading too much into that old picture. The folks at Merck probably just put one of their more impressive-looking things up on the screen for the Fortune reporter, and hey, everyone's heard of DNA. I really don't think that anyone at Merck was targeting protein-DNA interactions 33 years ago (and if they were, they splintered their lance against that one, big-time). But the reporter came away with the impression that the age of computer-designed drugs was at hand, and in the years since, plenty of other people have seen progressively snazzier graphics and thought the same thing. And it's hurt the cause of modeling for them to think that, because the higher the expectations get, the harder it is to come back to reality.
Update: I had this originally as coming from a Forbes article; it was actually in Fortune.
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March 17, 2014
Here's a new paper in J. Med. Chem. on software that tries to implement matched-molecular-pair type analysis. The goal is a recommendation - what R group should I put on next?
Now, any such approach is going to have to deal with this paper from Abbott in 2008. In that one, an analysis of 84,000 compounds across 30 targets strongly suggested that most R-group replacements had, on average, very little effect on potency. That's not to say that they don't or can't affect binding, far from it - just that over a large series, those effects are pretty much a normal distribution centered on zero. There are also analyses that claim the same thing for adding methyl groups - to be sure, there are many dramatic "magic methyl" enhancement examples, but are they balanced out, on the whole, by a similar number of dramatic drop-offs, along with a larger cohort of examples where not much happened at all?
To their credit, the authors of this new paper reference these others right up front. The answer to these earlier papers, most likely, is that when you average across all sorts of binding sites, you're going to see all sorts of effects. For this to work, you've got a far better chance of getting something useful if you're working inside the same target or assay. Here we get to the nuts and bolts:
The predictive method proposed, Matsy, relies on the hypothesis that a particular matched series tends to have a preferred activity order, for example, that not all six possible orders of [Br, Cl, F] are equally frequent. . .Although a rather straightforward idea, we have been unable to find any quantitative analysis of this question in the literature.
So they go on to provide one, with halogen substituents. There's not much to be found comparing pairs of halogen compounds head to head, but when you go to the longer series, you find that the order Br > Cl > F > H is by far the most common (and that appears to be just a good old grease effect). The next most common order just swaps the bromine and chlorine, but the third most common is the original order, in reverse. The other end of the distribution is interesting, too - for example, the least most common order is Br > H > F > Cl, which is believable, since it doesn't make much sense along any property axis.
They go on to do the same sorts of analyses for other matched series, and the question then becomes, if you have such a matched series in your own SAR, what does that order tell you about what to make next? The idea of "SAR transfer" has been explored, and older readers will remember the Topliss tree for picking aromatic substituents (do younger ones?)
The Matsy algorithm may be considered a formalism of aspects of how a medicinal chemist works in practice. Observing a particular trend, a chemist considers what to make next on the basis of chemical intuition, experience with related compounds or targets, and ease of synthesis. The structures suggested by Matsy preserve the core features of molecules while recommending small modifications, a process very much in line with the type of functional group replacement that is common in lead optimization projects. This is in contrast to recommendations from fingerprint-based similarity comparisons where the structural similarity is not always straightforward to rationalize and near-neighbors may look unnatural to a medicinal chemist.
And there's a key point: prediction and recommendation programs walk a fine line, between "There's no way I'm going out of my way to make that" and "I didn't need this program to tell me this". Sometimes there's hardly any space between those two territories at all. Where do this program's recommendations fall? As companies try this out in-house, some people will be finding out. . .
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February 28, 2014
Wavefunction has a post about this paper from J. Med. Chem. on a series of possible antitrypanosomals from the Broad Institute's compound collection. It's a good illustration of the power of internal hydrogen bonds - in this case, one series of isomers can make the bond, but that ties up their polar groups, making them less soluble but more cell-permeable. The isomer that doesn't form the internal H-bond is more polar and more soluble, but less able to get into cells. Edit - fixed this part.
So if your compound has too many polar functionalities, an internal hydrogen bond can be just the thing to bring on better activity, because it tones things down a bit. And there are always the conformational effects to keep in mind. Tying a molecule up like that is the same as any other ring-forming gambit in medicinal chemistry: death or glory. Rarely is a strong conformational restriction silent in the SAR - usually, you either hit the magic conformer, or you move it forever out of reach.
I particularly noticed Wavefunction's line near the close of his post: "If nothing else they provide a few more valuable data points on the way to prediction nirvana.". I know what he's talking about, and I think he's far from the only computational chemist with eschatological leanings. Eventually, you'd think, we'd understand enough about all the things we're trying to model for the models to, well, work. And yes, I know that there are models that work right now, but you don't know that they're going to work until you've messed with them a while, and there are other models that don't work but look equally plausible at first, etc., and very much etc. "Prediction nirvana" would be the state where you have an idea for a new structure, you enter it into your computational model, and it immediately tells you the right answer, every single time. In theory, I think this is a reachable state of affairs. In practice, it is not yet implemented.
And remember, people have spotted glows on that horizon before and proclaimed the imminent dawn. The late 1980s were such a time, but experiences like those tend to make people more reluctant to immanentize the eschaton, or at least not where anyone can hear. But we are learning more about enthalpic and entropic interactions, conformations, hydrogen bonds, nonpolar interactions, all those things that go into computational prediction of structure and binding interactions. And if we continue to learn more, as seems likely, won't there come a point when we've learned what we need to know? If not true computational nirvana, then surely (shrink those epsilons and deltas) as arbitrarily close an approach as we like?
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February 19, 2014
I'd like to throw a few more logs on the ligand efficiency fire. Chuck Reynolds of J&J (author of several papers on the subject, as aficionados know) left a comment to an earlier post that I think needs some wider exposure. I've added links to the references:
An article by Shultz was highlighted earlier in this blog and is mentioned again in this post on a recent review of Ligand Efficiency. Shultz’s criticism of LE, and indeed drug discovery “metrics” in general hinges on: (1) a discussion about the psychology of various metrics on scientists' thinking, (2) an assertion that the original definition of ligand efficiency, DeltaG/HA, is somehow flawed mathematically, and (3) counter examples where large ligands have been successfully brought to the clinic.
I will abstain from addressing the first point. With regard to the second, the argument that there is some mathematical rule that precludes dividing a logarithmic quantity by an integer is wrong. LE is simply a ratio of potency per atom. The fact that a log is involved in computing DeltaG, pKi, etc. is immaterial. He makes a more credible point that LE itself is on average non-linear with respect to large differences in HA count. But this is hardly a new observation, since exactly this trend has been discussed in detail by previous published studies (here, here, here, and here). It is, of course, true that if one goes to very low numbers of heavy atoms the classical definition of LE gets large, but as a practical matter medicinal chemists have little interest in extremely small fragments, and the mathematical catastrophe he warns us against only occurs when the number of heavy atoms goes to zero (with a zero in the denominator it makes no difference if there is a log in the numerator). Why would HA=0 ever be relevant to a med. chem. program? In any case a figure essentially equivalent to the prominently featured Figure 1a in the Shultz manuscript appears in all of the four papers listed above. You just need to know they exist.
With regard to the third argument, yes of course there are examples of drugs that defy one or more of the common guidelines (e.g MW). This seems to be a general problem of the community taking metrics and somehow turning them into “rules.” They are just helpful, hopefully, guideposts to be used as the situation and an organization’s appetite for risk dictate. One can only throw the concept of ligand efficiency out the window completely if you disagree with the general principle that it is better to design ligands where the atoms all, as much as possible, contribute to that molecule being a drug (e.g. potency, solubility, transport, tox, etc.). The fact that there are multiple LE schemes in the literature is just a natural consequence of ongoing efforts to refine, improve, and better apply a concept that most would agree is fundamental to successful drug discovery.
Well, as far as the math goes, dividing a log by an integer is not any sort of invalid operation. I believe that [log(x)]/y is the same as saying log(x to the one over y). That is, log(16) divided by 2 is the same as the log of 16 to the one-half power, or log(4). They both come out to about 0.602. Taking a BEI calculation as real chemistry example, a one-micromolar compound that weighs 250 would, by the usual definition, -log(Ki)/(MW/1000), have a BEI of 6/0.25, or 24. By the above rule, if you want to keep everything inside the log function, then say -log(0.0000001) divided by 0.25, that one-micromolar figure should be raised to the fourth power, then you take the log of the result (and flip the sign). One-millionth to the fourth power is one times ten to the minus twenty-fourth, so that gives you. . .24. No problem.
Shultz's objection that LE is not linear per heavy atom, though, is certainly valid, as Reynolds notes above as well. You have to realize this and bear it in mind while you're thinking about the topic. I think that one of the biggest problems with these metrics - and here's a point that both Reynolds and Shultz can agree on, I'll bet - is that they're tossed around too freely by people who would like to use them as a substitute for thought in the first place.
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February 14, 2014
Here's a nasty fight going on in molecular biology/bioinformatics. Lior Pachter of Berkeley describes some severe objections he has to published work from the lab of Manolis Kellis at MIT. (His two previous posts on these issues are here and here). I'm going to use a phrase that Pachter hears too often and say that I don't have the math to address those two earlier posts. But the latest one wraps things up in a form that everyone can understand. After describing what does look like a severe error in one of the Manolis group's conference presentations, which Pachter included in a review of the work, he says that:
. . .(they) spun the bad news they had received as “resulting from combinatorial connectivity patterns prevalent in larger network structures.” They then added that “…this combinatorial clustering effect brings into question the current definition of network motif” and proposed that “additional statistics…might well be suited to identify larger meaningful networks.” This is a lot like someone claiming to discover a bacteria whose DNA is arsenic-based and upon being told by others that the “discovery” is incorrect – in fact, that very bacteria seeks out phosphorous – responding that this is “really helpful” and that it “raises lots of new interesting open questions” about how arsenate gets into cells. Chutzpah. When you discover your work is flawed, the correct response is to retract it.
I don’t think people read papers very carefully. . .
He goes on to say:
I have to admit that after the Grochow-Kellis paper I was a bit skeptical of Kellis’ work. Not because of the paper itself (everyone makes mistakes), but because of the way he responded to my review. So a year and a half ago, when Manolis Kellis published a paper in an area I care about and am involved in, I may have had a negative prior. The paper was Luke Ward and Manolis Kellis “Evidence for Abundant and Purifying Selection in Humans for Recently Acquired Regulatory Functions”, Science 337 (2012) . Having been involved with the ENCODE pilot, where I contributed to the multiple alignment sub-project, I was curious what comparative genomics insights the full-scale $130 million dollar project revealed. The press releases accompanying the Ward-Kellis paper (e.g. The Nature of Man, The Economist) were suggesting that Ward and Kellis had figured out what makes a human a human; my curiosity was understandably piqued.
But a closer look at the paper, Pachter says, especially a dig into the supplementary material (always a recommended move) shows that the conclusions of the paper were based on what he terms "blatant statistically invalid cherry picking". See, I told you this was a fight. He also accuses Kellis of several other totally unacceptable actions in his published work, the sorts of things that cannot be brushed off as differences in interpretations or methods. He's talking fraud. And he has a larger point about how something like this might persist in the computational biology field (emphasis added):
Manolis Kellis’ behavior is part of a systemic problem in computational biology. The cross-fertilization of ideas between mathematics, statistics, computer science and biology is both an opportunity and a danger. It is not hard to peddle incoherent math to biologists, many of whom are literally math phobic. For example, a number of responses I’ve received to the Feizi et al. blog post have started with comments such as
“I don’t have the expertise to judge the math, …”
Similarly, it isn’t hard to fool mathematicians into believing biological fables. Many mathematicians throughout the country were recently convinced by Jonathan Rothberg to donate samples of their DNA so that they might find out “what makes them a genius”. Such mathematicians, and their colleagues in computer science and statistics, take at face value statements such as “we have figured out what makes a human human”. In the midst of such confusion, it is easy for an enterprising “computational person” to take advantage of the situation, and Kellis has.
You can peddle incoherent math to medicinal chemists, too, if you feel the urge. We don't use much of it day-to-day, although we've internalized more than we tend to realize. But if someone really wants to sell me on some bogus graph theory or topology, they'll almost certainly be able to manage it. I'd at least give them the benefit of the doubt, because I don't have the expertise to call them on it. Were I so minded, I could probably sell them some pretty shaky organic chemistry and pharmacokinetics.
But I am not so minded. Science is large, and we have to be able to trust each other. I could sit down and get myself up to speed on topology (say), if I had to, but the effort required would probably be better spent doing something else. (I'm not ruling out doing math recreationally, just for work). None of us can simultaneously be experts across all our specialities. So if this really is a case of publishing junk because, hey, who'll catch on, right, then it really needs to be dealt with.
If Pachter is off base, though, then he's in for a rough ride of his own. Looking over his posts, my money's on him and not Kellis, but we'll all have a chance to find out. After this very public calling out, there's no other outcome.
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January 14, 2014
Drug metabolism is a perennial topic for us small-molecule people. Watching your lovingly optimized molecules go through the shredding-machine of the liver is an instructive experience, not least when you consider how hard it would be for you to do some of the chemistry that it does. (For reference and getting up to speed on the details, the comments section here has had reader recommendations for the Drug Metabolism and Pharmacokinetics Quick Guide).
Here's a review of a new sites-of-metabolism predictor, FAME, a decision-tree type program that's been trained on data from 20,000 known compounds. It handles both Phase I and Phase II metabolism (a "Pharma 101" entry on that topic is here, for those who'd like to know more), and it looks like it's well worth considering if you're in need for something like this.
Here's my question for the med-chem and PK types: have you made use of predictive metabolism software? Did it save you time, or did you either go down the wrong alleys or not see anything you wouldn't have predicted yourself? I'm interested in real-world experiences, since I haven't had too many myself in this area.
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December 20, 2013
Diversity deck, diversity set, diversity collection: most chemical screening efforts try to have some bunch of compounds that are selected for being as unlike each other as possible. Fragment-based collections, being smaller by design, are particularly combed through for this property, in order to cover the most chemical space possible. But how, exactly, do you evaluate chemical diversity?
There are a lot of algorithmic approaches, and a new paper helpfully tries to sort them out for everyone. Here's the take-home:
We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior.
One of the best-performing methods was Bayes Activity Fingerprints, a technique proposed a few years ago by a group at Novartis. That (at least to my non-computational eyes) doesn't seem too surprising, since this new paper is trying to see how well diversity measure perform when compared to bioactivity space, and that earlier one was specifically adding in a measure to account for bioactivity space as well.
On the other hand, shape-based descriptors were problematic. One that turns up a lot is Principle Moments of Inertia (PMI), the scheme that separates compounds into rod-like, disk-like, and sphere-like shape families, but it and ROCS (based on overlaying molecular volumes) were definitely off in their own world when compared to the other descriptors. In fact, the authors found that there seemed to be no correlation at all between PMI diversity and diverse bioactivity, which should be worth thinking about. You'd apparently do better just picking things randomly than using PMI.
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December 10, 2013
Here are some slides from Anthony Nicholls of OpenEye, from his recent presentation here in Cambridge on his problems with molecular dynamics calcuations. Here's his cri du coeur (note: fixed a French typo from the original post there):
. . .as a technique MD has many attractive attributes that have nothing to do with its actual predictive capabilities (it makes great movies, it’s “Physics”, calculations take a long time, it takes skill to do right, “important” people develop it, etc). As I repeatedly mentioned in the talk, I would love MD to be a reliable tool - many of the things modelers try to do would become much easier. I just see little objective, scientific evidence for this as yet. In particular, it bothers me that MD is not held to the same standards of proof that many simpler, empirical approaches are - and this can’t be good for the field or MD.
I suspect he'd agree with the general principle that while most things that are worthwhile are hard, not everything that's hard is worthwhile. His slides are definitely fun to read, and worthwhile even if you don't give a hoot about molecular dynamics. The errors he's warning about apply to all fields of science. For example,he starts off with the definition of cognitive dissonance from Wikipedia, and proposes that a lot of the behavior you see in the molecular dynamics field fits the definitions of how people deal with this. He also maintains that the field seems to spend too much of its time justifying data retrospectively, and that this isn't a good sign.
I especially enjoyed his section on the "Tanimoto of Truth". That's comparing reality to experimental results. You have the cases where there should have been a result and the experiment showed it, and there shouldn't have been one, and the experiment reproduced that, too : great! But there are many more cases where only that first part applies, or gets published (heads I win, tails just didn't happen). And you have the inverse of that, where there was nothing, in reality, but your experiment told you that there was something. These false positives get stuck in the drawer, and no one hears about them at all. The next case, the false negatives, often end up in the "parameterize until publishable" category (as Nicholls puts it), or they get buried as well. The last category (should have been negative, experiment says they're negative) are considered so routine and boring that no one talks about them at all, although logically they're quite important.
All this can impart a heavy, heavy publication bias: you only hear about the stuff that worked, even if some of the examples you hear about really didn't. And unless you do a lot of runs yourself, you don't usually have a chance to see how robust the system really is, because the data you'd need aren't available. The organic synthesis equivalent is when you read one of those papers that do, in fact, work on the compounds in Table 1, but hardly any others. And you have to play close attention to Table 1 to realize that you know, there aren't any basic amines on that list (or esters, or amides, or what have you), are there?
The rest of the slides get into the details of molecular dynamic simulations, but he has some interesting comments on the paper I blogged about here, on modeling of allosteric muscarinic ligands. Nicholls says that "There are things to admire about this paper- chiefly that a prospective test seems to have been done, although not by the Shaw group." That caught my eye as well; it's quite unusual to see that, although it shouldn't be. But he goes on to say that ". . .if you are a little more skeptical it is easy to ask what has really been done here. In their (vast) supplementary material they admit that GLIDE docking results agree with mutagenesis as well (only, “not quite as well’, whatever that means- no quantification, of course). There’s no sense, with this data, of whether there are mutagenesis results NOT concordant with the simulations." And that gets back to his Tanimoto of Truth argument, which is a valid one.
He also points out that the predictions ended up being used to make one compound, which is not a very robust standard of proof. The reason, says Nicholls, is that molecular dynamics papers are held to a lower standard, and that's doing the field no good.
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December 9, 2013
Pick an empirical formula. Now, what's the most stable compound that fits it? Not an easy question, for sure, and it's the topic of this paper in Angewandte Chemie. Most chemists will immediately realize that the first problem is the sheer number of possibilities, and the second one is figuring out their energies. A nonscientist might think that this is the sort of thing that would have been worked out a long time ago, but that definitely isn't the case. Why think about these things?
What is this “Guinness” molecule isomer search good for? Some astrochemists think in such terms when they look for molecules in interstellar space. A rule with exceptions says that the most stable isomers have a higher abundance (Astrophys. J. 2009, 696, L133), although kinetic control undoubtedly has a say in this. Pyrolysis or biotechnology processes, for example, in anaerobic biomass-to-fuel conversions, may be classified on the energy scale of their products. The fate of organic aerosols upon excitation with highly energetic radiation appears to be strongly influenced by such sequences because of ion-catalyzed chain reactions (Phys. Chem. Chem. Phys. 2013, 15, 940). The magic of protein folding is tied to the most stable atomic arrangement, although one must keep in mind that this is a minimum-energy search with hardly any chemical-bond rearrangement. We should rather not think about what happens to our proteins in a global search for their minimum-energy structure, although the peptide bond is not so bad in globally minimizing interatomic energy. Regularity can help and ab initio crystal structure prediction for organic compounds is slowly coming into reach. Again, the integrity of the underlying molecule is usually preserved in such searches.
Things get even trickier when you don't restrict yourself to single compounds. It's pointed out that the low-energy form of the hexose empirical formula (C6H12O6) might well be a mixture of methane and carbon dioxide (which sounds like the inside of a comet to me). That brings up another reason this sort of thinking is useful: if you want to sequester carbon dioxide, what's the best way to do it? What molecular assemblies are most energetically favorable, and at what temperatures do they exist, and what level of complexity? At larger scales, we'll also need to think about such things in the making of supramolecular assemblies for nanotechnology.
The author, Martin Suhm of Göttingen, calls for a database of the lowest-energy species for each given formula as an invitation for people to break the records. I'd like to see someone give it a try. It would provide challenges for synthesis, spectroscopy and (especially) modeling and computational chemistry.
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November 18, 2013
I don't know how many readers out there use the cLogP function in ChemDraw, but you might want to take a look at the illustration here before you use it again. A reader alerted me to this glitch: drawing in explicit hydrogens sends it into an even stranger world of fantasy than most calculated logP values inhabit. There seems to be a limit, though, as that cyclopropane series illustrates. (Note that the "logP" function resists temptation).
PerkinElmer says that there are several logP calculating functions built in, but that cLogP is from BioByte. I don't think that they got their money's worth from them. Now, this isn't the first glitch like this I've seen - a lot of chemical drawing and searching programs choke on details here and there. But this is the sort of thing you'd have thought would have been fixed by now.
Update: it has indeed been fixed, according to the fast-acting Phillip Skinner at PerkinElmer. But apparently many of us out in user-land aren't using updated versions. For what it's worth, I have ChemBioDraw Ultra 13.02 v. 3020 - are others getting more reliable numbers with newer software?
Note: here's where the patch can be downloaded.
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October 31, 2013
I have to admit that I enjoyed seeing this question asked: how come we still use wind tunnels in aerodynamic engineering? Why don't we just model everything in software? The answers, from people who've actually done some of the work, are what you might expect: the models all involve degrees of approximation, gloss over some effects that are sometimes important, can help you but have to be given a reality check, etc. (Turbulent flow is no joke, theoretically or computationally, as any physicist can tell you).
The exact same sorts of answers, with a few nouns swapped out, could be given for the similar question of why we don't just design drugs using computer simulations. I'm asked that often by people outside the field, and have run into many people over the years who assume that it's just the way that drug discovery is done. But no, the continued existence of med-chem departments, such as they are, is testimony to the empirical nature of the business. It's sometimes maddening, but reality can be that way.
Note: every good modeler I've worked with has made it very clear that they know that they're working with approximations of reality - in fact, I think that's a prerequisite for someone to be a good modeler.
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October 29, 2013
Medicinal chemists talk a lot more about residence time and off rate than they used to. It's become clear that (at least in some cases) a key part of a drug's action is its kinetic behavior, specifically how quickly it leaves its binding site. You'd think that this would correlate well with its potency, but that's not necessarily so. Binding constants are a mix of on- and off-rates, and you can get to the same number by a variety of different means. Only if you're looking at very similar compounds with the same binding modes can you expect the correlation your intuition is telling you about, and even then you don't always get it.
There's a new paper in J. Med. Chem. from a team at Boehringer Ingelheim that takes a detailed look at this effect. The authors are working out the binding to the muscarinic receptor ligand tiotropium, which has been around a long time. (Boehringer's efforts in the muscarinic field have been around a long time, too, come to think of it). Tiotropium binds to the m2 subtype with a Ki of 0.2 nM, and to the m3 subtype with a Ki of 0.1 nM. But the compound has a much slower off rate on the m3 subtype, enough to make it physiologically distinct as an m3 ligand. Tiotropium is better known by its brand name Spiriva, and if its functional selectivity at the m3 receptors in the lungs wasn't pretty tight, it wouldn't be a drug. By carefully modifying its structure and introducing mutations into the receptor, this group hoped to figure out just why it's able to work the way it does.
The static details of tiotropium binding are well worked out - in fact, there's a recent X-ray structure, adding to the list of GPCRs that have been investigated by X-ray crystallography. There are plenty of interactions, as those binding constants would suggest:
The orthosteric binding sites of hM3R and hM2R are virtually identical. The positively charged headgroup of the antimuscarinic agent binds to (in the class of amine receptors highly conserved) Asp3.32 (D1483.32) and is surrounded by an aromatic cage consisting of Y1493.33, W5046.48, Y5076.51, Y5307.39, and Y5347.43. In addition to that, the aromatic substructures of the ligands dig into a hydrophobic region close to W2004.57 and the hydroxy groups, together with the ester groups, are bidentally interacting with N5086.52, forming close to optimal double hydrogen bonds. . .
The similarity of these binding sites was brought home to me personally when I was working on making selective antagonists of these myself. (If you want a real challenge, try differentiating m2 and m4). The authors point out, though, and crucially, that if you want to understand how different compounds bind to these receptors, the static pictures you get from X-ray structures are not enough. Homology modeling helps a good deal, but only if you take its results as indicators of dynamic processes, and not just swapping out residues in a framework.
Doing point-by-point single changes in both the tiotropium structure and the the receptor residues lets you use the kinetic data to your advantage. Such similar compounds should have similar modes of dissociation from the binding site. You can then compare off-rates to the binding constants, looking for the ones that deviate from the expected linear relationship. What they find is that the first event when tiotropium leaves the binding site is the opening of the aromatic cage mentioned above. Mutating any of these residues led to a big effect on the off-rate compared to the effect on the binding constant. Mutations further up along the tunnel leading to the binding site behaved in the same way: pretty much identical Ki values, but enhanced off-rates.
These observations, the paper says with commendable honesty, don't help the medicinal chemists all that much in designing compounds with better kinetics. You can imagine finding a compound that takes better advantage of this binding (maybe), but you can also imagine spending a lot of time trying to do that. The interaction with the asapragine at residue 508 is more useful from a drug design standpoint:
Our data provide evidence that the double hydrogen interaction of N5086.52 with tiotropium has a crucial influence on the off-rates beyond its influence on Ki. Mutation of N5086.52 to alanine accelerates the dissociation of tiotropium more than 1 order of magnitude than suggested by the Ki. Consequently, tiotropium derivatives devoid of the interacting hydroxy group show overproportionally short half-lives. Microsecond MD simulations show that this double hydrogen bonded interaction hinders tiotropium from moving into the exit channel by reducing the frequency of tyrosine-lid opening movements. Taken together, our data show that the interaction with N5086.52 is indeed an essential prerequisite for the development of slowly dissociating muscarinic receptor inverse agonists. This hypothesis is corroborated by the a posteriori observation that the only highly conserved substructure of all long-acting antimuscarinic agents currently in clinical development or already on the market is the hydroxy group.
But the extracellular loops also get into the act. The m2 subtype's nearby loop seems to be more flexible than the one in m3, and there's a lysine in the m3 that probably contributes some electrostatic repulsion to the charged tiotropium as it tries to back out of the protein. That's another effect that's hard to take advantage of, since the charged region of the molecule is a key for binding down in the active site, and messing with it would probably not pay dividends.
But there are some good take-aways from this paper. The authors note that the X-ray structure, while valuable, seems to have large confirmed the data generated by mutagenesis (as well it should). So if you're willing to do lots of point mutations, on both your ligand and your protein, you can (in theory) work some of these fine details out. Molecular dynamics simulations would seem to be of help here, too, also in theory. I'd be interested to hear if people can corroborate that with real-world experience.
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October 28, 2013
Readers will remember the spirited discussion about the usefulness of molecular dynamics in the comments thread to this post. Now Anthony Nicholls of OpenEye Software, who ignited that particular powder train, is going to defend his position in person this Wednesday. There will be a lunchtime seminar and discussion at the OpenEye offices (222 3rd Street, Suite 3210 in Cambridge). If you're in the area and going, you'll need to contact them to make sure that there's room. There will surely be a report from Ash at Curious Wavefunction if you can't make it, though.
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October 23, 2013
G-protein coupled receptors are one of those areas that I used to think I understood, until I understood them better. These things are very far from being on/off light switches mounted in drywall - they have a lot of different signaling mechanisms, and none of them are simple, either.
One of those that's been known for a long time, but remains quite murky, is allosteric modulation. There are many compounds known that clearly are not binding at the actual ligand site in some types of GPCR, but (equally clearly) can affect their signaling by binding to them somewhere else. So receptors have allosteric sites - but what do they do? And what ligands naturally bind to them (if any)? And by what mechanism does that binding modulate the downstream signaling, and are there effects that we can take advantage of as medicinal chemists? Open questions, all of them.
There's a new paper in Nature that tries to make sense of this, and trying by what might be the most difficult way possible: through computational modeling. Not all that long ago, this might well have been a fool's errand. But we're learning a lot about the details of GPCR structure from the recent X-ray work, and we're also able to handle a lot more computational load than we used to. That's particularly true if we are David Shaw and the D. E. Shaw company, part of the not-all-that-roomy Venn diagram intersection of quantitative Wall Street traders and computational chemists. Shaw has the resources to put together some serious hardware and software, and a team of people to make sure that the processing units get frequent exercise.
They're looking at the muscarinic M2 receptor, an old friend of mine for which I produced I-know-not-how-many antagonist candidates about twenty years ago. The allosteric region is up near the surface of the receptor, about 15A from the acetylcholine binding site, and it looks like all the compounds that bind up there do so via cation/pi interactions with aromatic residues in the protein. (That holds true for compounds as diverse as gallamine, alcuronium, and strychnine), and the one shown in the figure. This is very much in line with SAR and mutagenesis results over the years, but there are some key differences. Many people had thought that the aromatic groups of the ligands the receptors must have been interacting, but this doesn't seem to be the case. There also don't seem to be any interactions between the positively charged parts of the ligands and anionic residues on nearby loops of the protein (which is a rationale I remember from my days in the muscarinic field).
The simulations suggest that the two sites are very much in communication with each other. The width and conformation of the extracellular vestibule space can change according to what allosteric ligand occupies it, and this affects whether the effect on regular ligand binding is positive or negative, and to what degree. There can also, in some cases, be direct electrostatic interactions between the two ligands, for the larger allosteric compounds. I was very glad to see that the Shaw group's simulations suggested some experiments: one set with modified ligands, which would be predicted to affect the receptor in defined ways, and another set with point mutations in the receptor, which would be predicted to change the activities of the known ligands. These experiments were carried out by co-authors at Monash University in Australia, and (gratifyingly) seem to confirm the model. Too many computational papers (and to be fair, too many non-computational papers) don't get quite to the "We made some predictions and put our ideas to the test" stage, and I'm glad this one does.
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September 4, 2013
Over at Practical Fragments, Dan Erlanson has comments on the Michael Shultz paper that I wrote about here. He goes into details on some of the problems that turn up when you try to apply various compound metrics across a broad range of molecular weights and/or lipophilicities. In the most obvious example, the indices that are based on Heavy Atom Count (HAC) will jump around much more in the low-molecular-weight range, and none of the proposed refinements can quite fix this. And with the alternative LELP measure, you have to watch out when you're at very low LogP values.
Shultz's preferred LipE/LLE metric avoids that problem, and it's size-independent as well. That part can be either a bug or a feature, depending on your perspective. For the most part, I think that's useful, but in the early stages of fragment optimization, I think that a size-independent measurement is not what you want. The whole point in that stage is to pick the starting points with the most binding for their size, and a well-designed fragment library shouldn't have too many big problems with lipophilicity (those will come along later). So I take Shultz's point about the validity of LLE in general, but I think that I'll be using it and either LE or BEI (HAC-driven and molecular-weight driven) measure of binding efficiency when I'm working in the fragment end of things. How to weight those will be a judgment call, naturally, but judgment calls are, in theory, what we're being paid for, right?
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August 22, 2013
Here's a new paper from Michael Shultz of Novartis, who is trying to cut through the mass of metrics for new compounds. I cannot resist quoting his opening paragraph, but I do not have a spare two hours to add all the links:
Approximately 15 years ago Lipinski et al. published their seminal work linking molecular properties with oral absorption.1 Since this ‘Big Bang’ of physical property analysis, the universe of parameters, rules and optimization metrics has been expanding at an ever increasing rate (Figure 1).2 Relationships with molecular weight (MW), lipophilicity,3 and 4 ionization state,5 pKa, molecular volume and total polar surface area have been examined.6 Aromatic rings,7 and 8 oxygen atoms, nitrogen atoms, sp3 carbon atoms,9 chiral atoms,9 non-hydrogen atoms, aromatic versus non-hydrogen atoms,10 aromatic atoms minus sp3 carbon atoms,6 and 11 hydrogen bond donors, hydrogen bond acceptors and rotatable bonds12 have been counted and correlated.13 In addition to the rules of five came the rules of 4/40014 and 3/75.15 Medicinal chemists can choose from composite parameters (or efficiency indices) such as ligand efficiency (LE),16 group efficiency (GE), lipophilic efficiency/lipophilic ligand efficiency (LipE17/LLE),18 ligand lipophilicity index (LLEAT),19 ligand efficiency dependent lipophilicity (LELP), fit quality scaled ligand efficiency (LE_scale),20 percentage efficiency index (PEI),21 size independent ligand efficiency (SILE), binding efficiency index (BEI) or surface binding efficiency index (SEI)22 and composite parameters are even now being used in combination.23 Efficiency of binding kinetics has recently been introduced.24 A new trend of anthropomorphizing molecular optimization has occurred as molecular ‘addictions’ and ‘obesity’ have been identified.25 To help medicinal chemists there are guideposts,21 rules of thumb,14 and 26 a property forecast index,27 graphical representations of properties28 such as efficiency maps, atlases,29 ChemGPS,30 traffic lights,31 radar plots,32 Craig plots,33 flower plots,34 egg plots,35 time series plots,36 oral bioavailability graphs,37 face diagrams,28 spider diagrams,38 the golden triangle39 and the golden ratio.40
He must have enjoyed writing that one, if not tracking down all the references. This paper is valuable right from the start just for having gathered all this into one place! But as you read on, you find that he's not too happy with many of these metrics - and since there's no way that they can all be equally correct, or equally useful, he sets himself the task of figuring out which ones we can discard. The last reference in the quoted section below is to the famous "Can a biologist fix a radio?" paper:
While individual composite parameters have been developed to address specific relationships between properties and structural features (e.g. solubility and aromatic ring count) the benefit may be outweighed by the contradictions that arise from utilizing several indices at once or the complexity of adopting and abandoning various metrics depending on the stage of molecular optimization. The average medicinal chemist can be overwhelmed by the ‘analysis fatigue’ that this plethora of new and contradictory tools, rules and visualizations now provide, especially when combined with the increasing number of safety, off-target, physicochemical property and ADME data acquired during optimization efforts. Decision making is impeded when evaluating information that is wrong or excessive and thus should be limited to the absolute minimum and most relevant available.
As Lazebnik described, sometimes the more facts we learn, the less we understand.
And he discards quite a few. All the equations that involve taking the log of potency and dividing by the heavy atom count (HAC), etc., are playing rather loose with the math:
To be valid, LE must remain constant for each heavy atom that changes potency 10-fold. This is not the case as a 15 HAC compound with a pIC50 of 3 does not have the same LE as a 16 HAC compound with a pIC50 of 4 (ΔpIC50 = 1, ΔHAC = 1, ΔLE = 0.07). A 10-fold change in potency per heavy atom does not result in constant LE as defined by Hopkins, nor will it result in a constant SILE, FQ or LLEAT values. These metrics do not mathematically normalize size or potency because they violate the quotient rule of logarithms. To obey this rule and be a valid mathematical function HAC would subtracted from pIC50 and rendered independent of size and reference potency.
Note that he's not recommending that last operation as a guideline, either. Another conceptual problem with plain heavy atom counting is that it treats all atoms the same, but that's clearly an oversimplification. But dividing by some form of molecular weight is an oversimplification, too: a nitrogen differs from an oxygen by a lot more than that 1 mass unit. (This topic came up here a little while back). But oversimplified or not - heck, mathematically valid or not - the question is whether these things help out enough when used as metrics in the real world. And Shultz would argue that they don't. Keeping LE the same (or even raising it) is supposed to be the sign of a successful optimization, but in practice, LE usually degrades. His take on this is that "Since lower ligand efficiency is indicative of both higher and lower probabilities of success (two mutually exclusive states) LE can be invalidated by not correlating with successful optimization."
I think that's too much of a leap - because successful drug programs have had their LE go down during the process, that doesn't mean that this was a necessary condition, or that they should have been aiming for that. Perhaps things would have been even better if they hadn't gone down (although I realize that arguing from things that didn't happen doesn't have much logical force). Try looking at it this way: a large number of successful drug programs have had someone high up in management trying to kill them along the way, as have (obviously) most of the unsuccessful ones. That would mean that upper management decisions to kill a program are also indicative of both higher and lower probabilities of success, and can thus be invalidated, too. Actually, he might be on to something there.
Shultz, though, finds that he's not able to invalidate LipE (or LLE), variously known as ligand-lipophilicity efficiency or lipophilic ligand efficiency. That's p(IC50) - logP, which at least follows the way that logarithms of quotients are supposed to work. And it also has been shown to improve during known drug optimization campaigns. The paper has a thought experiment, on some hypothetical compounds, as well as some data from a tankyrase inhibitor series that seem to show the LipE behave more rationally than other metrics (which sometimes start pointing in opposite directions).
I found the chart below to be quite interesting. It uses the cLogP data from Paul Leeson and Brian Springthorpe's original LLE paper (linked in the above paragraph) to show what change in potency you would expect when you change a hydrogen in your molecule to one of the groups shown if you're going to maintain a constant LipE value. So while hydrophobic groups tend to make things more potent, this puts a number on it. A t-butyl, for example, should make things about 50-fold more potent if it's going to pull its weight as a ball of grease. (Note that we're not talking about effects on PK and tox here, just sheer potency - if you play this game, though, you'd better be prepared to keep an eye on things downstream).
On the other end of the scale, a methoxy should, in theory, cut your potency roughly in half. If it doesn't, that's a good sign. A morpholine should be three or four times worse, and if it isn't, then it's found something at least marginally useful to do in your compound's binding site. What we're measuring here is the partitioning between your compound wanting to be in solution, and wanting to be in the binding site. More specifically, since logP is in the equation, we're looking at the difference in the partitioning of your compound between octanol and water, versus its partitioning between the target protein and water. I think we can all agree that we'd rather have compounds that bind because they like something about the active site, rather than just fleeing the solution phase.
So in light of this paper, I'm rethinking my ligand-efficiency metrics. I'm still grappling with how LipE performs down at the fragment end of the molecular weight scale, and would be glad to hear thoughts on that. But Shultz's paper, if it can get us to toss out a lot of the proposed metrics already in the literature, will have done us all a service.
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August 16, 2013
Structural biology needs no introduction for people doing drug discovery. This wasn't always so. Drugs were discovered back in the days when people used to argue about whether those "receptor" thingies were real objects (as opposed to useful conceptual shorthand), and before anyone had any idea of what an enzyme's active site might look like. And even today, there are targets, and whole classes of targets, for which we can't get enough structural information to help us out much.
But when you can get it, structure can be a wonderful thing. X-ray crystallography of proteins, and protein-ligand complexes has revealed so much useful information that it's hard to know where to start. It's not the magic wand - you can't look at an empty binding site and just design something right at your desk that'll be a potent ligand right off the bat. And you can't look at a series of ligand-bound structures and say which one is the most potent, not in most situations, anyway. But you still learn things from X-ray structures that you could never have known otherwise.
It's not the only game in town, either. NMR structures are very useful, although the X-ray ones can be easier to get, especially in these days of automated synchroton beamlines and powerful number-crunching. But what if your protein doesn't crystallize? And what if there are things happening in solution that you'd never pick up on from the crystallized form? You're not going to watch your protein rearrange into a new ligand-bound conformation with X-ray crystallography, that's for sure. No, even though NMR structures can be a pain to get, and have to be carefully interpreted, they'll also show you things you'd never had seen.
And there are more exotic methods. Earlier this summer, there was a startling report of a structure of the HIV surface proteins gp120 and gp41 obtained through cryogenic electron microscopy. This is a very important and very challenging field to work in. What you've got there is a membrane-bound protein-protein interaction, which is just the sort of thing that the other major structure-determination techniques can't handle well. At the same time, though, the number of important proteins involved in this sort of thing is almost beyond listing. Cryo-EM, since it observes the native proteins in their natural environment, without tags or stains, has a lot of potential, but it's been extremely hard to get the sort of resolution with it that's needed on such targets.
Joseph Sodroski's group at Harvard, longtime workers in this area, published their 6-angstrom-resolution structure of the protein complex in PNAS. But according to this new article in Science, the work has been an absolute lightning rod ever since it appeared. Many other structural biologists think that the paper is so flawed that it never should have seen print. No, I'm not exaggerating:
Several respected HIV/AIDS researchers are wowed by the work. But others—structural biologists in particular—assert that the paper is too good to be true and is more likely fantasy than fantastic. "That paper is complete rubbish," charges Richard Henderson, an electron microscopy pioneer at the MRC Laboratory of Molecular Biology in Cambridge, U.K. "It has no redeeming features whatsoever."
. . .Most of the structural biologists and HIV/AIDS researchers Science spoke with, including several reviewers, did not want to speak on the record because of their close relations with Sodroski or fear that they'd be seen as competitors griping—and some indeed are competitors. Two main criticisms emerged. Structural biologists are convinced that Sodroski's group, for technical reasons, could not have obtained a 6-Å resolution structure with the type of microscope they used. The second concern is even more disturbing: They solved the structure of a phantom molecule, not the trimer.
Cryo-EM is an art form. You have to freeze your samples in an aqueous system, but without making ice. The crystals of normal ice formation will do unsightly things to biological samples, on both the macro and micro levels, so you have to form "vitreous ice", a glassy amorphous form of frozen water, which is odd enough that until the 1980s many people considered it impossible. Once you've got your protein particles in this matrix, though, you can't just blast away at full power with your electron beam, because that will also tear things up. You have to take a huge number of runs at lower power, and analyze them through statistical techniques. The Sodolski HIV structure, for example, is the product of 670,000 single-particle images.
But its critics say that it's also the product of wishful thinking.:
The essential problem, they contend, is that Sodroski and Mao "aligned" their trimers to lower-resolution images published before, aiming to refine what was known. This is a popular cryo-EM technique but requires convincing evidence that the particles are there in the first place and rigorous tests to ensure that any improvements are real and not the result of simply finding a spurious agreement with random noise. "They should have done lots of controls that they didn't do," (Sriram) Subramaniam asserts. In an oft-cited experiment that aligns 1000 computer-generated images of white noise to a picture of Albert Einstein sticking out his tongue, the resulting image still clearly shows the famous physicist. "You get a beautiful picture of Albert Einstein out of nothing," Henderson says. "That's exactly what Sodroski and Mao have done. They've taken a previously published structure and put atoms in and gone down into a hole." Sodroski and Mao declined to address specific criticisms about their studies.
Well, they decline to answer them in response to a news item in Science. They've indicated a willingness to take on all comers in the peer-reviewed literature, but otherwise, in print, they're doing the we-stand-by-our-results-no-comment thing. Sodroski himself, with his level of experience in the field, seems ready to defend this paper vigorously, but there seem to be plenty of others willing to attack. We'll have to see how this plays out in the coming months - I'll update as things develop.
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August 13, 2013
I had a very interesting email the other day, and my reply to it started getting so long that I thought I'd just turn it into a blog post. Here's the question:
How long can we expect to keep finding new drugs?
By way of analogy, consider software development. In general, it's pretty hard to think of a computer-based task that you couldn't write a program to do, at least in principle. It may be expensive, or may be unreasonably slow, but physical possibility implies that a program exists to accomplish it.
Engineering is similar. If it's physically possible to do something, I can, in principle, build a machine to do it.
But it doesn't seem obvious that the same holds true for drug development. Something being physically possible (removing plaque from arteries, killing all cancerous cells, etc.) doesn't seem like it would guarantee that a drug will exist to accomplish it. No matter how much we'd like a drug for Alzheimer's, it's possible that there simply isn't one.
Is this accurate? Or is the language of chemistry expressive enough that if you can imagine a chemical solution to something, it (in principle) exists. (I don't really have a hard and fast definition of 'drug' here. Obviously all bets are off if your 'drug' is complicated enough to act like a living thing.)
And if it is accurate, what does that say about the long-term prospects for the drug industry? Is there any risk of "running out" of new drugs? Is drug discovery destined to be a stepping-stone until more advanced medical techniques are available?
That's an interesting philosophical point, and one that had never occurred to me in quite that way. I think that's because programming is much more of a branch of mathematics. If you've got a Universal Turing Machine and enough tape to run through it, then you can, in theory, run any program that ever could be run. And any process that can be broken down into handling ones and zeros can be the subject of a program, so the Church-Turing thesis would say that yes, you can calculate it.
But biochemistry is most definitely a different thing, and this is where a lot of people who come into it from the math/CS/engineering side run into trouble. There's a famous (infamous) essay called "Can A Biologist Fix A Radio" that illustrates the point well. The author actually has some good arguments, and some legitimate complaints about the way biochemistry/molecular biology has been approached. But I think that his thesis breaks down eventually, and I've been thinking on and off for years about just where that happens and how to explain what makes things go haywire. My best guess is algorithmic complexity. It's very hard to reduce the behavior of biochemical systems to mathematical formalism. The whole point of formal notation is to express things in the most compact and information-rich way possible, but trying to compress biochemistry in this manner doesn't give you much of an advantage, at least not in the ways we've tried to do it so far.
To get back to the question at hand, let's get philosophical. I'd say that at the most macro level, there are solutions to all the medical problems. After all, we have the example of people who don't have multiple sclerosis, who don't have malaria, who don't have diabetes or pancreatic cancer or what have you. We know that there are biochemical states where these things do not exist; the problem is then to get an individual patient's state back to that situation. Note that this argument does not apply to things like life extension, limb regeneration, and so on: we don't know if humans are capable of these things or not yet, even if there may be some good arguments to be made in their favor. But we know that there are human brains without Alzheimer's.
To move down a level from this, though, the next question is whether there are ways to put a patient's cells and organs back into a disease-free state. In some cases, I think that the answer has to be, for all practical purposes, "No". I tend to think that the later stages of Alzheimer's (for example) are in fact incurable. Neurons are dead and damaged, what was contained in them and in their arrangement is gone, and any repair system can only go so far. Too much information has been lost and too much entropy has been let in. I would like to be wrong about this, but I don't think I am.
But for less severe states and diseases, you can imagine various interventions - chemical, surgical, genetic - that could restore things. So the question here becomes whether there are drug-like solutions. The answer is tricky. If you look at a biochemical mechanism and can see that there's a particular pathway involving small molecules, then certainly, you can say that there could be a molecule to be found as a treatment, even if we haven't found it yet. But the first part of that last sentence has to be unpacked.
Take diabetes. Type I diabetes is proximately caused by lack of insulin, so the solution is to take insulin. And that works, although it's certainly not a cure, since you have to take insuin for the rest of your life, and it's impossible to take it in a way that perfectly mimics the way your body would adminster it, etc. A cure would be to have working beta-cells again that respond just the way they're supposed to, and that's less likely to be achieved through a drug therapy. (Although you could imagine some small molecule that affects a certain class of stem cell, causing it to start the program to differentiate into a fully-formed beta cell, and so on). You'd also want to know why the original population of cells died in the first place, and how to keep that from happening again, which might also take you to some immunological and cell-cycle pathways that could be modulated by drug molecules. But all of these avenues might just as easily take you into genetically modified cloned cell lines and surgical implantation, too, rather than anything involving small-molecule chemistry.
Here's another level of complexity, then: insulin is certainly a drug, but it's not a small molecule of the kind I'd be making. Is there a small molecular that can replace it? You'd do very well with that indeed, but the answer (I think) is "probably not". If you look at the receptor proteins that insulin binds to, the recognition surfaces that are used are probably larger than small molecules can mimic. No one's ever found a small molecule insulin mimetic, and I don't think anyone is likely to. (On the other hand, if you're trying to disrupt a protein-protein interaction, you have more hope, although that's still an extremely difficult target. We can disrupt things a lot more easily than we can make them work). Even if you found a small-molecule-insulin, you'd be faced with the problem of dosing it appropriately, which is no small challenge for a tightly and continuously regulated system like that one. (It's no small challenge for administering insulin itself, either).
And even for mechanisms that do involve small-molecule signaling, like the G-protein coupled receptors, there are still things to worry about. Take schizophrenia. You can definitely see problems with neural systems in the brain when you study that disease, and these neurons respond to, among other things, small-molecue neurotransmitters that the body makes and uses itself - dopamine, serotonin, acetylcholine and others. There are a certain number of receptors for each of those, and although we don't have all the combinations yet, I could imagine, on a philosophical level, that we could eventually have selective drugs that are agonists, antagonists, partial agonists, inverse agonists, what have you at all the subtypes. We have quite a few of them now, for some of the families. And I can even imagine that we could eventually have most or all of the combinations: a molecule that's a dopamine D2 agonist and a muscarinic M4 antagonist, all in one, and so on and so on. That's a lot more of a stretch, to be honest, but I'll stipulate that it's possible.
So you have them all. Now, which ones do you give to help a schizophrenic? We don't know. We have guesses and theories, but most of them are surely wrong. Every biochemical theory about schizophrenia is either wrong or incomplete. We don't know what goes wrong, or why, or how, or what might be done to bend things back in the right direction. It might be that we're in the same area as Alzheimer's: perhaps once a person's brain has developed in such a way that it slips into schizophrenia, that there is no way at all to rewire things, in the same way that we can't ungrow a tree in order to change the shape of its canopy. I've no idea, and we're going to know a lot more about the brain by the time we can answer that one.
So one problem with answering this question is that it's bounded not so much by chemistry as by biology. Lots and lots of biology, most of it unknown. But thinking in terms of sheer chemistry is interesting, too. Consider "The Library of Babel", the famous story by Jorge Luis Borges. It takes place in some sort of universe that is no more (and no less) than a vast library containing every possible book that can be be produced with a 25-character set of letters and punctuation marks. This is, as a bit of reflection will show, a very, very large number, one large enough to contain everything that can possibly be written down. And all the slight variations. And all the misprints. And all the scrambled coded versions of everything, and so on and so on. (W. v. O. Quine extended this idea to binary coding, which brings you back to computability).
Now think about the universe of drug-like molecules. It is also very large, although it is absolutely insignificant compared to the terrifying Library of Babel. (It's worth noting that the Library contains all of the molecules that can ever exist, coded in SMILES strings - that thought just occurred to me at this very moment, and gives me the shivers). The universe of proteins works that way, too - an alphabet of twenty-odd letters for amino acids gives you the exact same situation as the Library, and if you imagine some hideous notation for coding in all the folding variants and post-translational modifications, all the proteins are written down as well.
These, then, encompass everything chemical compound up to some arbitrary size, and the original question is, is this enough? Are there questions for which none of these words are the answer? That takes you into even colder and deeper philosophical waters. Wittgenstein (among many others) wondered the same thing about our own human languages, and seems to have decided that there are indeed things that cannot be expressed, and that this marks the boundary of philosophy itself. Famously, his Tractacus ends with the line "Wovon man nicht sprechen kann, darüber muss man schweigen": whereof we cannot speak, we must pass over in silence.
We're not at that point in the language of chemistry and pharmacology yet, and it's going to be a long, long time before we ever might be. Just the fact, though, that computability seems like such a more reasonable proposition in computer science than druggability does in biochemistry tells you a great deal about how different the two fields are.
Update: On the subject of computabiity, I'm not sure how I missed the chance to bring Gödel's Incompleteness Theorem into this, just to make it a complete stewpot of math and philosophy. But the comments to this post point out that even if you can write a program, you cannot be sure whether it will ever finish the calculation. This Halting Problem is one of the first things ever to be proved formally undecidable, and the issues it raises are very close to those explored by Gödel. But as I understand it, this is decidable for a machine with a finite amount of memory, running a deterministic program. The problem is, though, that it still might take longer than the expected lifetime of the universe to "halt", which leaves you, for, uh, practical purposes, in pretty much the same place as before. This is getting pretty far afield from questions of druggability, though. I think.
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August 8, 2013
Fragment-based screening comes up here fairly often (and if you're interested in the field, you should also have Practical Fragments on your reading list). One of the complaints both inside and outside the fragment world is that there are a lot of primary hits that fall into flat/aromatic chemical space (I know that those two don't overlap perfectly, but you know the sort of things I mean). The early fragment libraries were heavy in that sort of chemical matter, and the sort of collections you can buy still tend to be.
So people have talked about bringing in natural-product-like structures, and diversity-oriented-synthesis structures and other chemistries that make more three-dimensional systems. The commercial suppliers have been catching up with this trend, too, although some definitions of "three-dimensional" may not match yours. (Does a biphenyl derivative count, or is that what you're trying to get away from?)
The UK-based 3D Fragment Consortium has a paper out now in Drug Discovery Today that brings together a lot of references to work in this field. Even if you don't do fragment-based work, I think you'll find it interesting, because many of the same issues apply to larger molecules as well. How much return do you get for putting chiral centers into your molecules, on average? What about molecules with lots of saturated atoms that are still rather squashed and shapeless, versus ones full of aromatic carbons that carve out 3D space surprisingly well? Do different collections of these various molecular types really have differences in screening hit rates, and do these vary by the target class you're screening against? How much are properties (solubility, in particular) shifting these numbers around? And so on.
The consortium's site is worth checking out as well for more on their activities. One interesting bit of information is that the teams ended up crossing off over 90% of the commercially available fragments due to flat structures, which sounds about right. And that takes them where you'd expect it to:
We have concluded that bespoke synthesis, rather than expansion through acquisition of currently available commercial fragment-sized compounds is the most appropriate way to develop the library to attain the desired profile. . .The need to synthesise novel molecules that expand biologically relevant chemical space demonstrates the significant role that academic synthetic chemistry can have in facilitating target evaluation and generating the most appropriate start points for drug discovery programs. Several groups are devising new and innovative methodologies (i.e. methyl activation, cascade reactions and enzymatic functionalisation) and techniques (e.g. flow and photochemistry) that can be harnessed to facilitate expansion of drug discovery-relevant chemical space.
And as long as they stay away from the frequent hitters/PAINS, they should end up with a good collection. I look forward to future publications from the group to see how things work out!
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July 31, 2013
Evolutionary and genetic processes fascinate many organic chemists, and with good reason. They've provided us with the greatest set of chemical catalysts we know of: enzymes, which are a working example of molecular-level nanotechnology, right in front of us. A billion years of random tinkering have accomplished a great deal, but (being human) we look at the results and wonder if we couldn't do things a bit differently, with other aims in mind than "survive or die".
This has been a big field over the years, and it's getting bigger all the time. There are companies out there that will try to evolve enzymes for you (here's one of the most famous examples), and many academic labs have tried their hands at it as well. The two main routes are random mutations and structure-based directed changes - and at this point, I think it's safe to say that any successful directed-enzyme project has to take advantage of both. There can be just too many possible changes to let random mutations do all the work for you (20 to the Xth power gets out of hand pretty quickly, and that's just the natural amino acids), and we're usually not smart enough to step in and purposefully tweak things for the better every time.
Here's a new paper that illustrates why the field is so interesting, and so tricky. The team (a collaboration between the University of Washington and the ETH in Zürich) has been trying to design a better retro-aldolase enzyme, with earlier results reported here. That was already quite an advance (15,000x rate enhancement over background), but that's still nowhere near natural enzymes of this class. So they took that species as a starting point and did more random mutations around the active site, with rounds of screening in between, which is how we mere humans have to exert selection pressure. This gave a new variant with another lysine in the active site, which some aldolases have already. Further mutational rounds (error-prone PCR and DNA shuffling) and screening let to a further variant that was over 4000x faster than the original enzyme.
But when the team obtained X-ray structures of this enzyme in complex with an inhibitor, they got a surprise. The active site, which had already changed around quite a bit with the addition of that extra lysine, was now a completely different place. A new substrate-binding pocket had formed, and the new lysine was now the catalytic residue all by itself. The paper proposes that the mechanistic competition between the possible active-site residues was a key factor, and they theorize that many natural enzymes may have evolved through similar paths. But given this, there are other questions:
The dramatic changes observed during RA95 evolution naturally prompt the question of whether generation of a highly active retro-aldolase required a computational design step. Whereas productive evolutionary trajectories might have been initiated from random libraries, recent experiments with the same scaffold dem- onstrate that chemical instruction conferred by computation greatly increases the probability of identifying catalysts. Although the programmed mechanisms of other computationally designed enzymes have been generally reinforced and refined by directed evolution, the molecular acrobatics observed with RA95 attest to the functional leaps that unanticipated, innovative mutations—here, replacement of Thr83 by lysine—can initiate.
So they're not ready to turn off the software just yet. But you have to wonder - if there were some way to run the random-mutation process more quickly, and reduce the time and effort of the mutation/screening/selection loop, computational design might well end up playing a much smaller role. (See here for more thoughts on this). Enzymes are capable of things that we would never think of ourselves, and we should always give them the chance to surprise us when we can.
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July 29, 2013
George Whitesides and his lab have another paper out on the details of how ligands bind to proteins. They're still using the favorite model enzyme of all time (carbonic anhydrase), the fruit fly and nematode of the protein world. Last time around, using a series of ligands and their analogs with an extra phenyl in their structure. The benzo-ligands had increased affinity, and this seemed to be mostly an enthalpic effect. After a good deal of calorimetry, etc., they concluded that the balancing act between enthalpy and entropy they saw over the group was different for ligand binding than it was for logP partitioning, and that means that it doesn't really match up with the accepted definition of a "hydrophobic effect".
In this study, they're looking at fluorinated analogs of the same compounds to see what that might do to the binding process. That makes the whole thing interesting for a medicinal chemist, because we make an awful lot of fluorinated analogs. You can start some interesting discussions about whether these are more hydrophobic than their non-F analogs, though, and this paper lands right in the middle of that issue.
The first result was that the fluorinated analogs bound to the enzyme (in their X-ray structures) with almost identical geometry. That makes the rest of the discussion easier to draw conclusions from (and more relevant). It's worth remembering, though, that very small changes can still add up. There was a bit of a shift in the binding pocket, actually, which they attribute to an unfavorable interaction between the fluorines and the carbonyl of a threonine residue. But the carbonic anhydrase pocket is pretty accomodating - the overall affinity of the compounds did not really change. That led to this conclusion:
Values of DG8bind, combined with an overall conserved binding geometry of each set of benzo- and fluorobenzo-extended ligands suggest that binding depends on a fine balance of interactions between HCA, the ligand, and molecules of water filling the pocket and surrounding the ligand, and that a simple analysis of interactions between the protein and ligand (Figure1E) is insufficient to understand (or more importantly, predict) the free energy of binding.
But although the overall free energy didn't change, the enthalpic and entropic components did (but arrived at the same place, another example to add to the long list of systems that do this). The differences seem to be in the Coulombic interaction with the binding pocket (worse enthalpy term - is that what shifted the structure over a bit in the X-ray?) and changes in energy of solvation as the ligand binds (better entropy term). Matched pairs of compounds didn't really show a difference in how many waters they displaced from the binding site.
So the take-home is that the hydrophobic effect is not all about releasing waters from protein binding surfaces, as has been proposed by some. It's a mixture of stuff, and especially depends on the structure of the water in the binding pocket and around the ligands, and the changes in these as the compounds leave bulk solvent and find their way into the binding site.
That makes things tricky for many compounds. Hydrophobic effects seem to be a big part of the binding energy of a lot of drug molecules (despite various efforts to cut back on this), and these Whitesides studies would say that modeling and predicting these energetic changes are going to be hard. Computationally, we'd have an easier time figuring out direct interactions between the protein and the ligand, the way we do with enthalpic interactions like hydrogen bonds. Keeping track of all those water molecules is more painful - but necessary.
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July 17, 2013
According to SciBx, here's another crack at computational solutions for drug discovery: MedChemica, a venture started by several ex-AstraZeneca scientists. They're going to be working with data from both AZ and Roche, using what sounds like a "matched molecular pair" approach:
Although other algorithms try to relate structure to biological function, most of the analyses look at modifications across a wide array of diverse structures. MedChemica's approach is to look at modifications in a set of similar structures and see how minor differences affect the compounds' biological activity.
Al Dossetter, managing director of MedChemica, said the advantage of the company's platform is the WizePairZ algorithm that looks at pairs of fragments that are similar in structure but differ by a chemical group, such as a change from chlorine to fluorine or the addition of a methyl group.
This platform, he told SciBX, captures the chemical environment of the fragment change. For example, it incorporates the fact that the effect of changing chlorine to fluorine on a molecule will depend on the surrounding structure. The result is a rule that is context dependent.
The MedChemica approach applies to small molecules and uses only partial chemical structures, thus keeping compound identities out of the picture.
Because the platform does not reveal compound identities, AstraZeneca and Roche can share knowledge without disclosing proprietary information.
The belief is that neither company's database on its own gives quite enough statistical power for this approach to work, so they're trying it on the pooled data:
smaller databases only allow researchers to extract one to five matched pairs, which have a low fidelity of prediction. Ten matched pairs are sufficient to draw a prediction, but reliability increases significantly with 20 matched pairs.
The MedChemica database contains 1.2 million datapoints, each of which represents a single molecule fragment in a single assay. It includes 31 different assays, although more are likely to be added in the future, and not all molecules have been tested in all assays.
The article says that AZ and Roche are in discussions with other companies about joining the collaboration. Everyone who joins will get a copy of the pooled database, in addition to being able to share in whatever insights MedChemica comes up with. A limitation is mentioned as well: this is all in vitro data, and its translation to animals or to the clinic provides room to argue.
That's a real concern, I'd say, although I can certainly see why they're doing things the way that they are. It's probably hard enough coming up with in vitro assays across the two companies that are run under similar enough conditions to be usefully paired. In vivo protocols are more varied still, and are notoriously tricky to compare across projects even inside the same company. Just off the top of my head, you have the dosing method (i.v., p.o., etc.), the level of compound given, the vehicle and formulation (a vast source of variability all in itself), the species and strain of animal, the presence of any underlying disease model (versus control animals), what time of day they were dosed and whether they were fed or fasted, whether they were male or female, how old the animals were, and so on and so on. And these factors would be needed just to compare things like PK data, blood levels and so on. If you're talking about toxicology or other effects, there's yet another list of stuff to consider. So yes, the earlier assays will be enough to handle for now.
But will they be enough to provide useful information? Here's where the arguing starts. Limitations of working with only in vitro data aside, you could also say that any trends that are subtle enough to need multi-company-sized pools of data might be too subtle to affect drug discovery very much. The counterargument to that is that some of these rules might still be quite real, but lost in the wilds of chemical diversity space due to lack of effective comparisons. (And the counterargument to that is that if you don't have very many example, how are you so sure that it's a rule?) I'm not sure which side of that one I come down on - "skeptical but willing to listen to data" probably describes me here - but this is the key question that MedChemica will presumably answer, one way or another.
Even so, that in vitro focus is going to be a long-term concern. One of the founders is quoted in the article as saying that the goal is to learn how to predict which compounds shouldn't be made. Fine, but "shouldn't have been made" is a characteristic that's often assigned only after a compound has been dosed in vivo. In the nastier cases, the ones you want to avoid the most, it's only realized after a compound has been in hundreds or thousands of humans in the clinic. The kinds of rules that MedChemica will come up with won't have any bearing on efficacy failures (nor are they meant to), but efficacy failures - failures of biological understanding - are depressingly common. Perhaps they've got a better chance at cutting down the number of "unexplained tox" failures, but that's still a very tall order as well as a very worthy goal.
Falling short of that, I worry, will mean that the MedChemica approach might end up - even if it works - by only optimizing a bit the shortest and cheapest part of the whole drug discovery process, preclinical med-chem. I sympathize - most of my own big ideas, when I get them, bear only on that part of the business, too. But is it the part that needs to be fixed the most? The hope is that there's a connection, but it takes quite a while to prove if one exists.
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June 17, 2013
That's my take-away from this paper, which takes a deep look at a reconstituted beta-adrenergic receptor via fluorine NMR. There are at least four distinct states (two inactive ones, the active one, and an intermediate), and the relationships between them are different with every type of ligand that comes in. Even the ones that look similar turn out to have very different thermodynamics on their way to the active state. If you're into receptor signaling, you'll want to read this one closely - and if you're not, or not up for it, just take away the idea that the landscape is not a simple one. As you'd probably already guessed.
Note: this is a multi-institution list of authors, but it did catch my eye that David Shaw of Wall Street's D. E. Shaw does make an appearance. Good to see him keeping his hand in!
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May 22, 2013
Just how many different small-molecule binding sites are there? That's the subject of this new paper in PNAS, from Jeffrey Skolnick and Mu Gao at Georgia Tech, which several people have sent along to me in the last couple of days.
This question has a lot of bearing on questions of protein evolution. The paper's intro brings up two competing hypotheses of how protein function evolved. One, the "inherent functionality model", assumes that primitive binding pockets are a necessary consequence of protein folding, and that the effects of small molecules on these (probably quite nonspecific) motifs has been honed by evolutionary pressures since then. (The wellspring of this idea is this paper from 1976, by Jensen, and this paper will give you an overview of the field). The other way it might have worked, the "acquired functionality model", would be the case if proteins tend, in their "unevolved" states, to be more spherical, in which case binding events must have been much more rare, but also much more significant. In that system, the very existence of binding pockets themselves is what's under the most evolutionary pressure.
The Skolnick paper references this work from the Hecht group at Princeton, which already provides evidence for the first model. In that paper, a set of near-random 4-helical-bundle proteins was produced in E. coli - the only patterning was a rough polar/nonpolar alternation in amino acid residues. Nonetheless, many members of this unplanned family showed real levels of binding to things like heme, and many even showed above-background levels of several types of enzymatic activity.
In this new work, Skolnick and Gao produce a computational set of artificial proteins (called the ART library in the text), made up of nothing but poly-leucine. These were modeled to the secondary structure of known proteins in the PDB, to produce natural-ish proteins (from a broad structural point of view) that have no functional side chain residues themselves. Nonetheless, they found that the small-molecule-sized pockets of the ART set actually match up quite well with those found in real proteins. But here's where my technical competence begins to run out, because I'm not sure that I understand what "match up quite well" really means here. (If you can read through this earlier paper of theirs at speed, you're doing better than I can). The current work says that "Given two input pockets, a template and a target, (our algorithm) evaluates their PS-score, which measures the similarity in their backbone geometries, side-chain orientations, and the chemical similarities between the aligned pocket-lining residues." And that's fine, but what I don't know is how well it does that. I can see poly-Leu giving you pretty standard backbone geometries and side-chain orientations (although isn't leucine a little more likely than average to form alpha-helices?), but when we start talking chemical similarities between the pocket-lining residues, well, how can that be?
But I'm even willing to go along with the main point of the paper, which is that there are not-so-many types of small-molecule binding pockets, even if I'm not so sure about their estimate of how many there are. For the record, they're guessing not many more than about 500. And while that seems low to me, it all depends on what we mean by "similar". I'm a medicinal chemist, someone who's used to seeing "magic methyl effects" where very small changes in ligand structure can make big differences in binding to a protein. And that makes me think that I could probably take a set of binding pockets that Skolnick's people would call so similar as to be basically identical, and still find small molecules that would differentiate them. In fact, that's a big part of my job.
But in general, I see the point they're making, but it's one that I've already internalized. There are a finite number of proteins in the human body. Fifty thousand? A couple of hundred thousand? Probably not a million. Not all of these have small-molecule binding sites, for sure, so there's a smaller set to deal with right there. Even if those binding sites were completely different from one another, we'd be looking at a set of binding pockets in the thousands/tens of thousands range, most likely. But they're not completely different, as any medicinal chemist knows: try to make a selective muscarinic agonist, or a really targeted serine hydrolase inhibitor, and you'll learn that lesson quickly. And anyone who's run their drug lead through a big selectivity panel has seen the sorts of off-target activities that come up: you hit someof the other members of your target's family to greater or lesser degree. You hit the flippin' sigma receptor, not that anyone knows what that means. You hit the hERG channel, and good luck to you then. Your compound is a substrate for one of the CYP enzymes, or it binds tightly to serum albumin. Who has even seen a compound that binds only to its putative target? And this is only with the counterscreens we have, which is a small subset of the things that are really out there in cells.
And that takes me to my main objection to this paper. As I say, I'm willing to stipulate, gladly, that there are only so many types of binding pockets in this world (although I think that it's more than 500). But this sort of thing is what I have a problem with:
". . .we conclude that ligand-binding promiscuity is likely an inherent feature resulting from the geometric and physical–chemical properties of proteins. This promiscuity implies that the notion of one molecule–one protein target that underlies many aspects of drug discovery is likely incorrect, a conclusion consistent with recent studies. Moreover, within a cell, a given endogenous ligand likely interacts at low levels with multiple proteins that may have different global structures.
"Many aspects of drug discovery" assume that we're only hitting one target? Come on down and try that line out in a drug company, and be prepared for rude comments. Believe me, we all know that our compounds hit other things, and we all know that we don't even know the tenth of it. This is a straw man; I don't know of anyone doing drug discovery that has ever believed anything else. Besides, there are whole fields (CNS) where polypharmacy is assumed, and even encouraged. But even when we're targeting single proteins, believe me, no one is naive enough to think that we're hitting those alone.
Other aspects of this paper, though, are fine by me. As the authors point out, this sort of thing has implications for drawing evolutionary family trees of proteins - we should not assume too much when we see similar binding pockets, since these may well have a better chance of being coincidence than we think. And there are also implications for origin-of-life studies: this work (and the other work in the field, cited above) imply that a random collection of proteins could still display a variety of functions. Whether these are good enough to start assembling a primitive living system is another question, but it may be that proteinaceous life has an easier time bootstrapping itself than we might imagine.
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February 28, 2013
I saw this story this morning, about IBM looking for more markets for its Watson information-sifting system (the one that performed so publicly on "Jeopardy". And this caught my eye for sure:
John Baldoni, senior vice president for technology and science at GlaxoSmithKline, got in touch with I.B.M. shortly after watching Watson’s “Jeopardy” triumph. He was struck that Watson frequently had the right answer, he said, “but what really impressed me was that it so quickly sifted out so many wrong answers.”
That is a huge challenge in drug discovery, which amounts to making a high-stakes bet, over years of testing, on the success of a chemical compound. The failure rate is high. Improving the odds, Mr. Baldoni said, could have a huge payoff economically and medically.
Glaxo and I.B.M. researchers put Watson through a test run. They fed it all the literature on malaria, known anti-malarial drugs and other chemical compounds. Watson correctly identified known anti-malarial drugs, and suggested 15 other compounds as potential drugs to combat malaria. The two companies are now discussing other projects.
“It doesn’t just answer questions, it encourages you to think more widely,” said Catherine E. Peishoff, vice president for computational and structural chemistry at Glaxo. “It essentially says, ‘Look over here, think about this.’ That’s one of the exciting things about this technology.”
Now, without seeing some structures and naming some names, it's completely impossible to say how valuable the Watson suggestions were. But I would very much like to know on what basis these other compounds were suggested: structural similarity? Mechanisms in common? Mechanisms that are in the same pathway, but hadn't been specifically looked at for malaria? Something else entirely? Unfortunately, we're probably not going to be able to find out, unless GSK is forthcoming with more details.
Eventually, there's coing to be another, somewhat more disturbing answer to that "what basis?" question. As this Slate article says, we could well get to the point where such systems make discoveries or correlations that are correct, but beyond our ability to figure out. Watson is most certainly not there yet. I don't think anything is, or is really all that close. But that doesn't mean it won't happen.
For a look at what this might be like, see Ted Chiang's story "Catching Crumbs From the Table", which appeared first in Nature, and then in his collection Stories of Your Life and Others, which I highly recommend, as "The Evolution of Human Science".
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February 8, 2013
There's a fascinating paper out on the concept of "drug-likeness" that I think every medicinal chemist should have a look at. It would be hard to count the number of publications on this topic over the last ten years or so, but what if we've been kidding ourselves about some of the main points?
The big concept in this area is, of course, Lipinski criteria, or Rule of Five. Here's what the authors, Peter Kenny and Carlos Montanari of the University of São Paulo, have to say:
No discussion of drug-likeness would be complete without reference to the influential Rule of 5 (Ro5) which is essentially a statement of property distributions for compounds taken into Phase II clinical trials. The focus of Ro5 is oral absorption and the rule neither quantifies the risks of failure associated with non-compliance nor provides guidance as to how sub-optimal characteristics of compliant compounds might be improved. It also raises a number of questions. What is the physicochemical basis of Ro50s asymmetry with respect to hydrogen bond donors and acceptors? Why is calculated octanol/water partition coefficient (ClogP) used to specify Ro50s low polarity limit when the high polarity cut off is defined in terms of numbers of hydrogen bond donors and acceptors? It is possible that these characteristics reflect the relative inability of the octanol/water partitioning system to ‘see’ donors (Fig. 1) and the likelihood that acceptors (especially as defined for Ro5) are more common than donors in pharmaceutically-relevant compounds. The importance of Ro5 is that it raised awareness across the pharmaceutical industry about the relevance of physico- chemical properties. The wide acceptance of Ro5 provided other researchers with an incentive to publish analyses of their own data and those who have followed the drug discovery literature over the last decade or so will have become aware of a publication genre that can be described as ‘retrospective data analysis of large proprietary data sets’ or, more succinctly, as ‘Ro5 envy’.
There, fellow med-chemists, doesn't this already sound like something you want to read? Thought so. Here, have some more:
Despite widespread belief that control of fundamental physicochemical properties is important in pharmaceutical design, the correlations between these and ADMET properties may not actually be as strong as is often assumed. The mere existence of a trend is of no interest in drug discovery and strengths of trends must be known if decisions are to be accurately described as data-driven. Although data analysts frequently tout the statistical significance of the trends that their analysis has revealed, weak trends can be statistically significant without being remotely interesting. We might be confident that the coin that lands heads up for 51 % of a billion throws is biased but this knowledge provides little comfort for the person charged with predicting the result of the next throw. Weak trends can be beaten and when powered by enough data, even the feeblest of trends acquires statistical significance.
So, where are the authors going with all this entertaining invective? (Not that there's anything wrong with that; I'm the last person to complain). They're worried that the transformations that primary drug property data have undergone in the literature have tended to exaggerate the correlations between these properties and the endpoints that we care about. The end result is pernicious:
Correlation inflation becomes an issue when the results of data analysis are used to make real decisions. To restrict values of properties such as lipophilicity more stringently than is justified by trends in the data is to deny one’s own drug-hunting teams room to maneuver while yielding the initiative to hungrier, more agile competitors.
They illustrate this by reference to synthetic data sets, showing how one can get rather different impressions depending on how the numbers are handled along the way. Representing sets of empirical points by using their average values, for example, can cause the final correlations to appear more robust than they really are. That, the authors say, is just what happened in this study from 2006 ("Can we rationally design promiscuous drugs?) and in this one from 2007 ("The influence of drug-like concepts on decision-making in medicinal chemistry"). The complaint is that showing a correlation between cLogP and median compound promiscuity does not imply that there is one between cLogP and compound promiscuity per se. And the authors note that the two papers manage to come to opposite conclusions about the effect of molecular weight, which does make one wonder. The "Escape from flatland" paper from 2009 and the "ADMET rules of thumb" paper from 2008 (mentioned here) also come in for criticism on this point - binning averaged data from a large continuous set and then treated those as real objects for statistic analysis. Ones conclusions depend strongly on how many bins one uses. Here's a specific take on that last paper:
The end point of the G2008 analysis is ‘‘a set of simple interpretable ADMET rules of thumb’’ and it is instructive to examine these more closely. Two classifications (ClogP<4 and MW<400 Da; ClogP>4 or MW>400 Da) were created and these were combined with the four ionization state classifications to define eight classes of compound. Each combination of ADMET property and compound class was labeled according to whether the mean value of the ADMET property was lower than, higher than or not significantly different from the average for all compounds. Although the rules of thumb are indeed simple, it is not clear how useful they are in drug discovery. Firstly, the rules only say whether or not differences are significant and not how large they are. Secondly, the rules are irrelevant if the compounds of interest are all in the same class. Thirdly, the rules predict abrupt changes in ADMET properties going from one class to another. For example, the rules predict significantly different aqueous solubility for two neutral compounds with MW of 399 and 401 Da, provided that their ClogP values do not exceed 4. It is instructive to consider how the rules might have differed had values of logP and MW of 5 and 500 Da (or 3 and 300 Da) had been used to define them instead of 4 and 400 Da.
These problems also occur in graphical representations of all these data, as you'd imagine, and the authors show several of these that they object to. A particular example is this paper from 2010 ("Getting physical in drug discovery"). Three data sets, whose correlations in their primary data do not vary significantly, generate very different looking bar charts. And that leads to this comment:
Both the MR2009 and HY2010 studies note the simplicity of the relationships that the analysis has revealed. Given that drug discovery would appear to be anything but simple, the simplicity of a drug-likeness model could actually be taken as evidence for its irrelevance to drug discovery. The number of aromatic rings in a molecule can be reduced by eliminating rings or by eliminating aromaticity and the two cases appear to be treated as equivalent in both the MR2009 and HY2010 studies. Using the mnemonic suggested in MR2009 one might expect to make a compound more developable by replacing a benzene ring with cyclohexadiene or benzoquinone.
The authors wind up by emphasizing that they're not saying that things like lipophilicity, aromaticity, molecular weight and so on are unimportant - far from it. What they're saying, though, is that we need to be aware of how strong these correlations really are so that we don't fool ourselves into thinking that we're addressing our problems, when we really aren't. We might want to stop looking for huge, universally applicable sets of rules and take what we can get in smaller, local data sets within a given series of compounds. The paper ends with a set of recommendations for authors and editors - among them, always making primary data sets part of the supplementary material, not relying on purely graphical representations to make statistical points, and a number of more stringent criteria for evaluating data that have been partitioned into bins. They say that they hope that their paper "stimulates debate", and I think it should do just that. It's certainly given me a lot of things to think about!
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January 30, 2013
Here are some angry views that I don't necessarily endorse, but I can't say that they're completely wrong, either. A programmer bids an angry farewell to the bioinformatics world:
Bioinformatics is an attempt to make molecular biology relevant to reality. All the molecular biologists, devoid of skills beyond those of a laboratory technician, cried out for the mathematicians and programmers to magically extract science from their mountain of shitty results.
And so the programmers descended and built giant databases where huge numbers of shitty results could be searched quickly. They wrote algorithms to organize shitty results into trees and make pretty graphs of them, and the molecular biologists carefully avoided telling the programmers the actual quality of the results. When it became obvious to everyone involved that a class of results was worthless, such as microarray data, there was a rush of handwaving about “not really quantitative, but we can draw qualitative conclusions” followed by a hasty switch to a new technique that had not yet been proved worthless.
And the databases grew, and everyone annotated their data by searching the databases, then submitted in turn. No one seems to have pointed out that this makes your database a reflection of your database, not a reflection of reality. Pull out an annotation in GenBank today and it’s not very long odds that it’s completely wrong.
That's unfair to molecular biologists, but is it unfair to the state of bioinformatic databases? Comments welcome. . .
Update: more comments on this at Ycombinator.
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January 28, 2013
We medicinal chemists talk a good game when it comes to the the hydrophobic effect. It's the way that non-water-soluble molecules (or parts of molecules) like to associate with each other, right? Sure thing. And it works because of. . .well, van der Waals forces. Or displacement of water molecules from protein surfaces. Or entropic effects. Or all of those, plus some other stuff that, um, complicated to explain. Something like that.
Here's a paper in Angewandte Chemie that really bears down on the topic. The authors study the binding of simple ligands to thermolysin, a well-worked-out system for which very high-resolution X-ray structures are available. And what they find is, well, that things really are complicated to explain:
In summary, there are no universally valid reasons why the hydrophobic effect should be predominantly “entropic” or “enthalpic”; small structural changes in the binding features of water molecules on the molecular level determine whether hydrophobic binding is enthalpically or entropically driven.
Admittedly, this study reaches the limits of experimental accuracy accomplishable in contemporary protein–ligand structural work. . .Surprising pairwise systematic changes in the thermodynamic data are experienced for complexes of related ligands, and they are convincingly well reflected by the structural properties. The present study unravels small but important details. Computational methods simulate molecular properties at the atomic level, and are usually determined by the summation of many small details. However, details such as those observed here are usually not regarded by these computational methods as relevant, simply because we are not fully aware of their importance for protein–ligand binding, structure–activity relationships, and rational drug design in general. . .
I think that there are a lot of things in this area of which we're not fully aware. There are many others that we treat as unified phenomena, because we've given them names that make us imagine that they are. The hydrophobic effect is definitely one of these - George Whitesides is right when he says that there are many of them. But when all of these effects, on closer inspection, break down into tiny, shifting, tricky arrays of conflicting components, can you blame us for simplifying?
+ TrackBacks (0) | Category: "Me Too" Drugs | Chemical News | In Silico
January 17, 2013
Here's a recent paper in J. Med. Chem. on halogen bonding in medicinal chemistry. I find the topic interesting, because it's an effect that certainly appears to be real, but is rarely (if ever) exploited in any kind of systematic way.
Halogens, especially the lighter fluorine and chlorine, are widely used substituents in medicinal chemistry. Until recently, they were merely perceived as hydrophobic moieties and Lewis bases in accordance with their electronegativities. Much in contrast to this perception, compounds containing chlorine, bromine, or iodine can also form directed close contacts of the type R–X···Y–R′, where the halogen X acts as a Lewis acid and Y can be any electron donor moiety. . .
What seems to be happening is that the electron density around the halogen atom is not as smooth as most of us picture it. You'd imagine a solid cloud of electrons around the bromine atom of a bromoaromatic, but in reality, there seems to be a region of slight positivecharge (the "sigma hole") out on the far end. (As a side effect, this give you more of a circular stripe of negative charge as well). Both these effects have been observed experimentally.
Now, you're not going to see this with fluorine; that one is more like most of us picture it (and to be honest, fluorine's weird enough already). But as you get heavier, things become more pronounced. That gives me (and probably a lot of you) an uneasy feeling, because traditionally we've been leery of putting the heavier halogens into our molecules. "Too much weight and too much hydrophobicity for too little payback" has been the usual thinking, and often that's true. But it seems that these substituents can actually earn out their advance in some cases, and we should be ready to exploit those, because we need all the help we can get.
Interestingly, you can increase the effect by adding more fluorines to the haloaromatic, which emphasizes the sigma hole. So you have that option, or you can take a deep breath, close your eyes, and consider. . .iodos:
Interestingly, the introduction of two fluorines into a chlorobenzene scaffold makes the halogen bond strength comparable to that of unsubstituted bromobenzene, and 1,3-difluoro-5-bromobenzene and unsubstituted iodobenzene also have a comparable halogen bond strength. While bromo and chloro groups are widely employed substituents in current medicinal chemistry, iodo groups are often perceived as problematic. Substituting an iodoarene core by a substituted bromoarene scaffold might therefore be a feasible strategy to retain affinity by tuning the Br···LB (Lewis base) halogen bond to similar levels as the original I···LB halogen bond.
As someone who values ligand efficiency, the idea of putting in an iodine gives me the shivers. A fluoro-bromo combo doesn't seem much more attractive, although almost anything looks good compared to a single atom that adds 127 mass units at a single whack. But I might have to learn to love one someday.
The paper includes a number of examples of groups that seem to be capable of interacting with halogens, and some specific success stories from recent literature. It's probably worth thinking about these things similarly to the way we think about hydrogen bonds - valuable, but hard to obtain on purpose. They're both directional, and trying to pick up either one can cause more harm than good if you miss. But keep an eye out for something in your binding site that might like a bit of positive charge poking at it. Because I can bet that you never thought to address it with a bromine atom!
Update: in the spirit of scientific inquiry, I've just sent in an iodo intermediate from my current work for testing in the primary assay. It's not something I would have considered doing otherwise, but if anyone gives me any grief, I'll tell them that it's 2013 already and I'm following the latest trends in medicinal chemistry.
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January 14, 2013
Virtual screening is what many people outside the field are thinking of when they talk about the use of computational models in drug discovery. There are many other places where modeling can pitch in, but one of the dreams has always been to take a given protein target and a long list of chemical structures, hit the button, and come back to a sorted list of which ones are going to bind well. That list could be as long as "every compound in our screening deck", or "every available compound in the commercial catalogs", or "everything that our chemists can think of and draw on a whiteboard, whether it's ever been made or not". So these virtual collections can get rather large, but that's what computer power is for, right?
Despite what some people might think, we're not exactly there yet. But we're not exactly not there, either, if you know what I mean. Like much of drug discovery, it's in that awkward age. Virtual screening is certainly real, and it can be useful, but it can also waste your time if you're not careful. And that's where this paper comes in - it's a fine overview of the issue that you need to think about if you're interested in trying this technique.
For one thing, you need to decide if you're going to be taking a drug target whose structure you know pretty well and modeling a bunch of small compounds into it, or if you're taking a bunch of small molecules whose activities you know pretty well and trying to find more compounds like them. These two approaches call for some different methods, and have different potential problems. The second one, especially in the older literature, often goes under the name of QSAR, for quantitative structure-activity relationship. But as the authors point out, "virtual screening" as a name has some advantages, because many people have been burned by things labeled "QSAR" over the years. They're also being used for different purposes, which is probably a good thing:
A fundamental assumption inherent in QSAR and pharmacophore-based VS is the “similar property principle”, that is, the general observation that molecules with similar structure are likely to have similar properties. While this assumption holds true in many cases, there are many counter-examples in the field of QSAR which lead to erroneous predictions and can shake the confidence of the experimental community in the prospective utility of QSAR modeling. Interestingly, this has not yet (or not to the same extent) been the case with VS. The difference is that QSAR is typically employed to evaluate a limited number of synthetic candidates, where errors are more noticeable and costly. However, when these techniques are applied on a massive scale to screen large chemical libraries, errors are much more easily tolerated as the objective is to increase the number and diversity of hits over what would have been otherwise a random selection.
The authors extensively cover the previous literature on computational screening - successful examples, warnings of trouble, theoretical predictions both optimistic and pessimistic. It would take you quite a while to assemble this list on your own, so that by itself recommends this paper to anyone interested in the area. But they go on to codify the various pitfalls to look out for.
"Such as expecting it to work", the cynics in the audience will remark. I say that sort of thing under my breath for time to time myself - or audibly, as the case may be. But this is the sort of paper that I can really endorse, because it's a completely realistic view of what you can expect with current technology. And that comes down to "Less than you want", but still "More than you might think". You're not going to able to feed the software the complete pile of all the chemical supplier catalogs and come back to find the nanomolar leads printing out. But you can get pointers toward parts of chemical space that you wouldn't have thought about (or wouldn't have been able to physically screen).
One tricky part is that when a virtual screening effort is successful (for whatever value you assign to "success"), it can be hard to tell why, and likewise for failures. There are so many places where things can disconnect - proteins are mobile, and small molecules even more so, and accounting for these conformational ensembles is not trivial. Binding interactions are not always well understood, or well modeled. Water molecules are pesky, but can be vitally important. You might have picked inappropriate controls (positive or negative), or be weighting the various computed factors in the wrong way. Either of those will send your calculation further and further off the rails.
And so on. The paper goes into detail on these possibilities and more; I highly recommend it for anyone getting into virtual screening (or for anyone already doing it, to keep the troubleshooting guide in one handy place).
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January 10, 2013
There's a paper out in Nature with the provocative title of "Automated Design of Ligands to Polypharmcological Profiles". Admittedly, to someone outside my own field of medicinal chemistry, that probably sounds about as dry as the Atacama desert, but it got my attention.
It's a large multi-center contribution, but the principal authors are Andrew Hopkins at Dundee and Bryan Roth at UNC-Chapel Hill. Using James Black's principle that the best place to find a new drug is to start with an old drug, what they're doing here is taking known ligands and running through a machine-learning process to see if they can introduce new activities into them. Now, those of us who spend time trying to take out other activities might wonder what good this is, but there are a some good reasons: for one thing, many CNS agents are polypharmacological to start with. And there certainly are situations where you want dual-acting compounds, CNS or not, which can be a major challenge. And read on - you can run things to get selectivity, too.
So how well does their technique work? The example they give starts with the cholinesterase inhibitor donepezil (sold as Aricept), which has a perfectly reasonable med-chem look to its structure. The groups' prediction, using their current models, was the it had a reasonable chance of having D4 dopaminergic activity, but probably not D2 (which numbers were borne out by experiment, and might have something to do with whatever activity Aricept has for Alzheimer's). I'll let them describe the process:
We tested our method by evolving the structure of donepezil with the dual objectives of improving D2 activity and achieving blood–brain barrier penetration. In our approach the desired multi-objective profile is defined a priori and then expressed as a point in multi-dimensional space termed ‘the ideal achievement point’. In this first example the objectives were simply defined as two target properties and therefore the space has two dimensions. Each dimension is defined by a Bayesian score for the predicted activity and a combined score that describes the absorption, distribution, metabolism and excretion (ADME) properties suitable for blood–brain barrier penetration (D2 score = 100, ADME score = 50). We then generated alternative chemical structures by a set of structural transformations using donepezil as the starting structure. The population was subsequently enumerated by applying a set of transformations to the parent compound(s) of each generation. In contrast to rules-based or synthetic-reaction-based approaches for generating chemical structures, we used a knowledge-based approach by mining the medicinal chemistry literature. By deriving structural transformations from medicinal chemistry, we attempted to mimic the creative design process.
Hmm. They rank these compounds in multi-dimensional space, according to distance from the ideal end point, filter them for chemical novelty, Lipinski criteria, etc., and then use the best structures as starting points for another round. This continues until you reach close enough to the desired point, or until you dead-end on improvement. In this case, they ended up with fairly active D2 compounds, by going to a lactam in the five-membered ring, lengthening the chain a bit, and going to an arylpiperazine on the end. They also predicted, though, that these compounds would hit a number of other targets, which they indeed did on testing.
How about something a bit more. . .targeted? They tried taking these new compounds through another design loop, this time trying to get rid of all the alpha-adrenergic activity they'd picked up, while maintaining the 5-HT1A and dopamine receptor activity they now had. They tried it both ways - running the algorithms with filtration of the alpha-active compounds at each stage, and without. Interestingly, both optimizations came up with very similar compounds, differing only out on the arylpiperazine end. The alpha-active series wanted ortho-methoxyphenyl on the piperazine, while the alpha-inactive series wanted 2-pyridyl. These preferences were confirmed by experiment as well. Some of you who've worked on adrenergics might be saying "Well, yeah, that's what the receptors are already known to prefer, so what's the news here?" But keep in mind, what the receptors are known to prefer is what's been programmed into this process, so of course, that's what it's going to recapitulate. The idea is for the program to keep track of all the known activities - the huge potential SAR spreadsheet - so you don't have to try to do it yourself, with you own grey matter.
The last example asks whether, starting from donezepil, potent and selective D4 compounds could be evolved. I'm going to reproduce the figure from the paper here, to give an idea of the synthetic transformations involved:
So, donezepil (compound 1) is 614 nM against D4, and after a few rounds of optimization, you get structure 13, which is 9 nM. Not bad! Then if you take 13 as a starting point, and select for structural novelty along the way, you get 18 (five micromolar against D4), 20, 21, and (S)-27 (which is 90 nM at D4). All of these compounds picked up a great deal more selectivity for D4 compared to the earlier donezepil-derived scaffolds as well.
Well, then, are we all out of what jobs we have left? Not just yet. You'll note that the group picked GPCRs as a field to work in, partly because there's a tremendous amount known about their SAR preferences and cross-functional selectivities. And even so, of the 800 predictions made in the course of this work, the authors claim about a 75% success rate - pretty impressive, but not the All-Seeing Eye, quite yet. I'd be quite interested in seeing these algorithms tried out on kinase inhibitors, another area with a wealth of such data. But if you're dwelling among the untrodden ways, like Wordsworth's Lucy, then you're pretty much on your own, I'd say, unless you 're looking to add in some activity in one of the more well-worked-out classes.
But knowledge piles up, doesn't it? This approach is the sort of thing that will not be going away, and should be getting more powerful and useful as time goes on. I have no trouble picturing an eventual future where such algorithms do a lot of the grunt work of drug discovery, but I don't foresee that happened for a while yet. Unless, of course, you do GPCR ligand drug discovery. In that case, I'd be contacting the authors of this paper as soon as possible, because this looks like something you need to be aware of.
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December 11, 2012
I notied this piece on Slate (originally published in New Scientist) about Kaggle, a company that's working on data-prediction algorithms. Actually, it might be more accurate to say that they're asking other people to work on data-prediction algorithems, since they structure their tasks as a series of open challenges, inviting all comers to submit their best shots via whatever computational technique they think appropriate.
PA: How exactly do these competitions work?
JH: They rely on techniques like data mining and machine learning to predict future trends from current data. Companies, governments, and researchers present data sets and problems, and offer prize money for the best solutions. Anyone can enter: We have nearly 64,000 registered users. We've discovered that creative-data scientists can solve problems in every field better than experts in those fields can.
PA: These competitions deal with very specialized subjects. Do experts enter?
JH: Oh yes. Every time a new competition comes out, the experts say: "We've built a whole industry around this. We know the answers." And after a couple of weeks, they get blown out of the water.
I have a real approach-avoidance conflict with this sort of thing. I tend to root for outsiders and underdogs, but naturally enough, when they're coming to blow up what I feel is my own field of expertise, that's a different story, right? And that's just what this looks like: the Merck Molecular Activity Challenge, which took place earlier this fall. Merck seems to have offered up a list of compounds of known activity in a given assay, and asked people to see if they could recapitulate the data through simulation.
Looking at the data that were made available, I see that there's a training set and a test set. They're furnished as a long run of molecular descriptors, but the descriptors themselves are opaque, no doubt deliberately (Merck was not interested in causing themselves any future IP problems with this exercise). The winning team was a group of machine-learning specialists from the University of Toronto and the University of Washington. If you'd like to know a bit more about how they did it, here you go. No doubt some of you will be able to make more of their description than I did.
But I would be very interested in hearing some more details on the other end of things. How did the folks at Merck feel about the results, with the doors closed and the speaker phone turned off? Was it better or worse than what they could have come up with themselves? Are they interested enough in the winning techniques that they've approached the high-ranking groups with offers to work on virtual screening techniques? Because that's what this is all about: running a (comparatively small) test set of real molecules past a target, and then switching to simulations and screening as much of small molecule chemical space as you can computationally stand. Virtual screening is always promising, always cost-attractive, and sometimes quite useful. But you never quite know when that utility is going to manifest itself, and when it's going to be another goose hunt. It's a longstanding goal of computational drug design, for good reason.
So, how good was this one? That also depends on the data set that was used, of course. All of these algorithm-hunting methods can face a crucial dependence on the training sets used, and their relations to the real data. Never was "Garbage In, Garbage Out" more appropriate. If you feed in numbers that are intrinsically too well-behaved, you can emerge with a set of rules that look rock-solid, but will take ou completely off into the weeds when faced with a more real-world situation. And if you go to the other extreme, starting with wooly multi-binding-mode SAR with a lot of outliers and singletons in it, you can end up fitting equations to noise and fantasies. That does no one any good, either.
Back last year, I talked about the types of journal article titles that make me keep on scrolling past them, and invited more. One of the comments suggested "New and Original strategies for Predictive Chemistry: Why use knowledge when fifty cross-correlated molecular descriptors and a consensus of over-fit models will tell you the same thing?". What I'd like to know is, was this the right title for this work, or not?
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November 26, 2012
As mentioned the other day, this will be a post for people to ask questions directly to Philip Skinner (SDBioBrit) of Perkin-Elmer/Cambridgesoft. He's doing technical support for ChemDraw, ChemDraw4Excel, E-Notebook, Inventory, Registration, Spotfire, Chem3D, etc., and will be monitoring the comments and posting there. Hope it helps some people out!
Note - he's out on the West Coast of the US, so allow the poor guy time to get up and get some coffee in him!
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August 22, 2012
Hang around a bunch of medicinal chemists (no, really, it's more fun than you'd think) and you're bound to hear discussion of cLogP. For the chemists in the crowd, I should warn you that I'm about to say nasty things about it.
For the nonchemists in the crowd, logP is a measure of how greasy (or how polar) a compound is. It's based on a partition experiment: shake up a measured amount of a compound with defined volumes of water and n-octanol, a rather greasy solvent which I've never seen referred to in any other experimental technique. Then measure how much of the compound ends up in each layer, and take the log of the octanol/water ratio. So if a thousand times as much compound goes into the octanol as goes into the water (which for drug substances is quite common, in fact, pretty good), then the logP is 3. The reason we care about this is that really greasy compounds (and one can go up to 4, 5, 6, and possibly beyond), have problems. They tend to dissolve poorly in the gut, have problems crossing membranes in living systems, get metabolized extensively in the liver, and stick to a lot of proteins that you'd rather they didn't stick to. Fewer high-logP compounds are capable of making it as drugs.
So far, so good. But there are complications. For one thing, that description above ignores the pH of the water solution, and for charged compounds that's a big factor. logD is the term for the distribution of all species (ionized or not), and logD at pH 7.4 (physiological) is a valuable measurement if you've got the possibility of a charged species (and plenty of drug molecules do, thanks to basic amines, carboxylic acids, etc.) But there are bigger problems.
You'll notice that the experiment outlined in the second paragraph could fairly be described as tedious. In fact, I have never seen it performed. Not once, and I'll bet that the majority of medicinal chemists never have, either. And it's not like it's just being done out of my sight; there's no roomful of automated octanol/water extraction machines clanking away in the basement. I should note that there are other higher-throughput experimental techniques (such as HPLC retention times) that also correlate with logP and have been used to generate real numbers, but even those don't account for the great majority of the numbers that we talk about all the time. So how do we manage to do that?
It has to do with a sleight of hand I've performed while writing the above sections, which some of you have probably already noticed. Most of the time, when we talk about logP values in early drug discovery, we're talking about cLogp. That "c" stands for calculated. There are several programs that estimate logP based on known values for different rings and functional groups, and with different algorithms for combining and interpolating them. In my experience, almost all logP numbers that get thrown around are from these tools; no octanol is involved.
And sometimes that worries me a bit. Not all of these programs will tell you how solid those estimates are. And even if they will, not all chemists will bother to check. If your structure is quite close to something that's been measured, then fine, the estimate is bound to be pretty good. But what if you feed in a heterocycle that's not in the lookup table? The program will spit out a number, that's what. But it may not be a very good number, even if it goes out to two decimal places. I can't even remember when I might have last seen a cLogP value with a range on it, or any other suggestion that it might be a bit fuzzy.
There are more subtle problems, too - I've seen some oddities with substitutions on saturated heterocyclic rings (morpholine, etc.) that didn't quite seem to make sense. Many chemists get these numbers, look at them quizzically, and say "Hmm, I didn't know that those things sorted out like that. Live and learn!" In other words, they take the calculated values as reality. I've even had people defend these numbers by explaining to me patiently that these are, after all, calculated logP values, and the calculated log P values rank-order like so, and what exactly is my problem? And while it's hard to argue with that, we are not putting our compounds into the simulated stomachs of rationalized rodents. Real-world decisions can be made based on numbers that do not come from the real world.
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June 12, 2012
One of the major worries during a clinical trial is toxicity, naturally. There are thousands of reasons a compound might cause problem, and you can be sure that we don't have a good handle on most of them. We screen for what we know about (such as hERG channels for cardiovascular trouble), and we watch closely for signs of everything else. But when slow-building low-incidence toxicity takes your compound out late in the clinic, it's always very painful indeed.
Anything that helps to clarify that part of the business is big news, and potentially worth a lot. But advanced in clinical toxicology come on very slowly, because the only thing worse than not knowing what you'll find is thinking that you know and being wrong. A new paper in Nature highlights just this problem. The authors have a structural-similarity algorithm to try to test new compounds against known toxicities in previously tested compounds, which (as you can imagine) is an approach that's been tried in many different forms over the years. So how does this one fare?
To test their computational approach, Lounkine et al. used it to estimate the binding affinities of a comprehensive set of 656 approved drugs for 73 biological targets. They identified 1,644 possible drug–target interactions, of which 403 were already recorded in ChEMBL, a publicly available database of biologically active compounds. However, because the authors had used this database as a training set for their model, these predictions were not really indicative of the model's effectiveness, and so were not considered further.
A further 348 of the remaining 1,241 predictions were found in other databases (which the authors hadn't used as training sets), leaving 893 predictions, 694 of which were then tested experimentally. The authors found that 151 of these predicted drug–target interactions were genuine. So, of the 1,241 predictions not in ChEMBL, 499 were true; 543 were false; and 199 remain to be tested. Many of the newly discovered drug–target interactions would not have been predicted using conventional computational methods that calculate the strength of drug–target binding interactions based on the structures of the ligand and of the target's binding site.
Now, some of their predictions have turned out to be surprising and accurate. Their technique identified chlorotrianisene, for example, as a COX-1 inhibitor, and tests show that it seems to be, which wasn't known at all. The classic antihistamine diphenhydramine turns out to be active at the serotonin transporter. It's also interesting to see what known drugs light up the side effect assays the worst. Looking at their figures, it would seem that the topical antiseptic chlorhexidine (a membrane disruptor) is active all over the place. Another guanidine-containing compound, tegaserod, is also high up the list. Other promiscuous compounds are the old antipsychotic fluspirilene and the semisynthetic antibiotic rifaximin. (That last one illustrates one of the problems with this approach, which the authors take care to point out: toxicity depends on exposure. The dose makes the poison, and all that. Rifaximin is very poorly absorbed, and it would take very unusual dosing, like with a power drill, to get it to hit targets in a place like the central nervous system, even if this technique flags them).
The biggest problem with this whole approach is also highlighted by the authors, to their credit. You can see from those figures above that about half of the potentially toxic interactions it finds aren't real, and you can be sure that there are plenty of false negatives, too. So this is nowhere near ready to replace real-world testing; nothing is. But where it could be useful is in pointing out things to test with real-world assays, activities that you probably hadn't considered at all.
But the downside of that is that you could end up chasing meaningless stuff that does nothing but put the fear into you and delays your compound's development, too. That split, "stupid delay versus crucial red flag", is at the heart of clinical toxicology, and is the reason it's so hard to make solid progress in this area. So much is riding on these decisions: you could walk away from a compound, never developing one that would go on to clear billions of dollars and help untold numbers of patients. Or you could green-light something that would go on to chew up hundreds of millions of dollars of development costs (and even more in opportunity costs, considering what you could have been working on instead), or even worse, one that makes it onto the market and has to be withdrawn in a blizzard of lawsuits. It brings on a cautious attitude.
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April 4, 2012
Now here's something that might be about to remake the economy, or (on the other robotic hand) it might not be ready to just yet. And it might be able to help us out in drug R&D, or it might turn out to be mostly beside the point. What the heck am I talking about, you ask? The so-called "Artificial Intelligence Economy". As Adam Ozimek says, things are looking a little more futuristic lately.
He's talking about things like driverless cars and quadrotors, and Tyler Cowen adds the examples of things like Apple's Siri and IBM's Watson, as part of a wider point about American exports:
First, artificial intelligence and computing power are the future, or even the present, for much of manufacturing. It’s not just the robots; look at the hundreds of computers and software-driven devices embedded in a new car. Factory floors these days are nearly empty of people because software-driven machines are doing most of the work. The factory has been reinvented as a quiet place. There is now a joke that “a modern textile mill employs only a man and a dog—the man to feed the dog, and the dog to keep the man away from the machines.”
The next steps in the artificial intelligence revolution, as manifested most publicly through systems like Deep Blue, Watson and Siri, will revolutionize production in one sector after another. Computing power solves more problems each year, including manufacturing problems.
Two MIT professors have written a book called Race Against the Machine about all this, and it appears to be sort of a response to Cowen's earlier book The Great Stagnation. (Here's an article of theirs in The Atlantic making their case).
One of the export-economy factors that it (and Cowen) bring up is that automation makes a country's wages (and labor costs in general) less of a factor in exports, once you get past the capital expenditure. And as the size of that expenditure comes down, it becomes easier to make that leap. One thing that means, of course, is that less-skilled workers find it harder to fit in. Here's another Atlantic article, from the print magazine, which looked at an auto-parts manufacturer with a factory in South Carolina (the whole thing is well worth reading):
Before the rise of computer-run machines, factories needed people at every step of production, from the most routine to the most complex. The Gildemeister (machine), for example, automatically performs a series of operations that previously would have required several machines—each with its own operator. It’s relatively easy to train a newcomer to run a simple, single-step machine. Newcomers with no training could start out working the simplest and then gradually learn others. Eventually, with that on-the-job training, some workers could become higher-paid supervisors, overseeing the entire operation. This kind of knowledge could be acquired only on the job; few people went to school to learn how to work in a factory.
Today, the Gildemeisters and their ilk eliminate the need for many of those machines and, therefore, the workers who ran them. Skilled workers now are required only to do what computers can’t do (at least not yet): use their human judgment.
But as that article shows, more than half the workers in that particular factory are, in fact, rather unskilled, and they make a lot more than their Chinese counterparts do. What keeps them employed? That calculation on what it would take to replace them with a machine. The article focuses on one of those workers in particular, named Maddie:
It feels cruel to point out all the Level-2 concepts Maddie doesn’t know, although Maddie is quite open about these shortcomings. She doesn’t know the computer-programming language that runs the machines she operates; in fact, she was surprised to learn they are run by a specialized computer language. She doesn’t know trigonometry or calculus, and she’s never studied the properties of cutting tools or metals. She doesn’t know how to maintain a tolerance of 0.25 microns, or what tolerance means in this context, or what a micron is.
Tony explains that Maddie has a job for two reasons. First, when it comes to making fuel injectors, the company saves money and minimizes product damage by having both the precision and non-precision work done in the same place. Even if Mexican or Chinese workers could do Maddie’s job more cheaply, shipping fragile, half-finished parts to another country for processing would make no sense. Second, Maddie is cheaper than a machine. It would be easy to buy a robotic arm that could take injector bodies and caps from a tray and place them precisely in a laser welder. Yet Standard would have to invest about $100,000 on the arm and a conveyance machine to bring parts to the welder and send them on to the next station. As is common in factories, Standard invests only in machinery that will earn back its cost within two years. For Tony, it’s simple: Maddie makes less in two years than the machine would cost, so her job is safe—for now. If the robotic machines become a little cheaper, or if demand for fuel injectors goes up and Standard starts running three shifts, then investing in those robots might make sense.
At this point, some similarities to the drug discovery business will be occurring to readers of this blog, along with some differences. The automation angle isn't as important, or not yet. While pharma most definitely has a manufacturing component (and how), the research end of the business doesn't resemble it very much, despite numerous attempts by earnest consultants and managers to make it so. From an auto-parts standpoint, there's little or no standardization at all in drug R&D. Every new drug is like a completely new part that no one's ever built before; we're not turning out fuel injectors or alternators. Everyone knows how a car works. Making a fundamental change in that plan is a monumental challenge, so the auto-parts business is mostly about making small variations on known components to the standards of a given customer. But in pharma - discovery pharma, not the generic companies - we're wrenching new stuff right out of thin air, or trying to.
So you'd think that we wouldn't be feeling the low-wage competitive pressure so much, but as the last ten years have shown, we certainly are. Outsourcing has come up many a time around here, and the very fact that it exists shows that not all of drug research is quite as bespoke as we might think. (Remember, the first wave of outsourcing, which is still very much a part of the business, was the move to send the routine methyl-ethyl-butyl-futile analoging out somewhere cheaper). And this takes us, eventually, to the Pfizer-style split between drug designers (high-wage folks over here) and the drug synthesizers (low-wage folks over there). Unfortunately, I think that you have to go the full reducio ad absurdum route to get that far, but Pfizer's going to find out for us if that's an accurate reading.
What these economists are also talking about is, I'd say, the next step beyond Moore's Law: once we have all this processing power, how do we use it? The first wave of computation-driven change happened because of the easy answers to that question: we had a lot of number-crunching that was being done by hand, or very slowly by some route, and we now had machines that could do what we wanted to do more quickly. This newer wave, if wave it is, will be driven more by software taking advantage of the hardware power that we've been able to produce.
The first wave didn't revolutionize drug discovery in the way that some people were hoping for. Sheer brute force computational ability is of limited use in drug discovery, unfortunately, but that's not always going to be the case, especially as we slowly learn how to apply it. If we really are starting to get better at computational pattern recognition and decision-making algorithms, where could that have an impact?
It's important to avoid what I've termed the "Andy Grove fallacy" in thinking about all this. I think that it is a result of applying first-computational-wave thinking too indiscriminately to drug discovery, which means treating it too much like a well-worked-out human-designed engineering process. Which it certainly isn't. But this second-wave stuff might be more useful.
I can think of a few areas: in early drug discovery, we could use help teasing patterns out of large piles of structure-activity relationship data. I know that there are (and have been) several attempts at doing this, but it's going to be interesting to see if we can do it better. I would love to be able to dump a big pile of structures and assay data points into a program and have it say the equivalent of "Hey, it looks like an electron-withdrawing group in the piperidine series might be really good, because of its conformational similarity to the initial lead series, but no one's ever gotten back around to making one of those because everyone got side-tracked by the potency of the chiral amides".
Software that chews through stacks of PK and metabolic stability data would be worth having, too, because there sure is a lot of it. There are correlations in there that we really need to know about, that could have direct relevance to clinical trials, but I worry that we're still missing some of them. And clinical trial data itself is the most obvious place for software that can dig through huge piles of numbers, because those are the biggest we've got. From my perspective, though, it's almost too late for insights at that point; you've already been spending the big money just to get the numbers themselves. But insights into human toxicology from all that clinical data, that stuff could be gold. I worry that it's been like the concentration of gold in seawater, though: really there, but not practical to extract. Could we change that?
All this makes me actually a bit hopeful about experiments like this one that I described here recently. Our ignorance about medicine and human biochemistry is truly spectacular, and we need all the help we can get in understanding it. There have to be a lot of important things out there that we just don't understand, or haven't even realized the existence of. That lack of knowledge is what gives me hope, actually. If we'd already learned what there is to know about discovering drugs, and were already doing the best job that could be done, well, we'd be in a hell of a fix, wouldn't we? But we don't know much, we're not doing it as well as we could, and that provides us with a possible way out of the fix we're in.
So I want to see as much progress as possible in the current pattern-recognition and data-correlation driven artificial intelligence field. We discovery scientists are not going to automate ourselves out of business so quickly as factory workers, because our work is still so hypothesis-driven and hard to define. (For a dissenting view, with relevance to this whole discussion, see here). It's the expense of applying the scientific method to human health that's squeezing us all, instead, and if there's some help available in that department, then let's have it as soon as possible.
+ TrackBacks (0) | Category: Drug Assays | Drug Development | Drug Industry History | In Silico | Pharmacokinetics | Toxicology
February 21, 2012
Here's a huge review that goes over most everything you may have wanted to know about what's called "rational drug design". The authors are especially addressing selectivity, but that's a broad enough topic to cover all the important features. (If you can't access the paper, here's a key graphic from it).
"Rational", it should be understood, generally tends to mean "computationally modeled" in the world of drug discovery. And that's certainly how this review is pitched. I'm of two minds - at least - about the whole area (a personal bias that has made for some lively discussions over the years). Some of those discussions have taken place between my own ears as well, because I'm still not sure that all my opinions about computational drug design are self-consistent.
On the one hand, drug potency is a physical act which is mediated by physical laws. Computing the change in free energy during such a process should be feasible. But it turns out to be rather difficult - proteins flex and bonds rotate, water molecules assist and interfere, electrostatic charges help and hinder, hydrogen bonds are vital (and hard to model), and a dozen other sorts of interactions between clouds of electrons weigh in as well. Never forget, too, that free energy changes have an entropy component, and that's not trivial to model, either. I keep wondering if the error bars of the various assumptions and approximations don't end up swamping out the small changes that we're interested in predicting.
But, on that other hand, there are certainly cases where modeling has helped out a great deal. A cynic would say that we've been sure to hear about those, while the cases where it had no impact at all (or did actual harm) don't make the journals very often. It can't be denied, though, that modeling really has been (at times) the tool for the job. It would be interesting to know if the frequency of that happening has been increasing over time, as our tools get better.
Because on the third hand, it's been a poor bet to go against the relentless computational tide over the last few decades. You'd have to think that sheer computing power will end up making molecular modeling ever more capable and useful, as we learn more about what we're doing. Mind you, there were people back in the mid-1980s who thought we'd already reached that point. I'm not saying that they were the best-informed people at that time, but they certainly did exist. I wonder sometimes what it would have been like, to show people in 1985 what the state of rational drug design would be like in 2012. Would they be excited, or vaguely disappointed?
And then there's that word "rational". I think that its adoption might have been the best advertising that the field's ever achieved, because it makes everything else seem irrational (or at least arational) by default. I mean, do you just wanna make compounds, or do you want to think about what you're doing? I also wonder what might have changed if that phrase had never been adopted - perhaps expectations wouldn't have gotten out of hand in the computational field's early days, but it might not have received the attention (and money) that it did, either. . .
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January 26, 2012
There's a new paper out in Nature Chemistry called "Quantifying the Chemical Beauty of Drugs". The authors are proposing a new "desirability score" for chemical structures in drug discovery, one that's an amalgam of physical and structural scores. To their credit, they didn't decide up front which of these things should be the miost important. Rather, they took eight properties over 770 well-known oral drugs, and set about figuring how much to weight each of them. (This was done, for the info-geeks among the crowd, by calculating the Shannon entropy for each possibility to maximize the information contained in the final model). Interestingly, this approach tended to give zero weight to the number of hydrogen-bond acceptors and to the polar surface area, which suggests that those two measurements are already subsumed in the other factors.
And that's all fine, but what does the result give us? Or, more accurately, what does it give us that we haven't had before? After all, there have been a number of such compound-rating schemes proposed before (and the authors, again to their credit, compare their new proposal with the others head-to-head). But I don't see any great advantage. The Lipinski "Rule of 5" is a pretty simple metric - too simple for many tastes - and what this gives you is a Rule of 5 with both categories smeared out towards each other to give some continuous overlap. (See the figure below, which is taken from the paper). That's certainly more in line with the real world, but in that real world, will people be willing to make decisions based on this method, or not?
The authors go for a bigger splash with the title of the paper, which refers to an experiment they tried. They had chemists across AstraZeneca's organization assess some 17,000 compounds (200 or so for each) with a "Yes/No" answer to "Would you undertake chemistry on this compound if it were a hit?" Only about 30% of the list got a "Yes" vote, and the reasons for rejecting the others were mostly "Too complex", followed closely by "Too simple". (That last one really makes me wonder - doesn't AZ have a big fragment-based drug design effort?) Note also that this sort of experiment has been done before.
Applying their model, the mean score for the "Yes" compounds was 0.67 (s.d.0.16), and the mean score for the "No" compounds was 0.49 (s.d. 0.23, which they say was statistically significant, although that must have been a close call. Overall, I wouldn't say that this test has an especially strong correlation with medicinal chemists' ideas of structural attractiveness, but then, I'm not so sure of the usefulness of those ideas to start with. I think that the two ends of the scale are hard to argue with, but there's a great mass of compounds in the middle that people decide that they like or don't like, without being able to back up those statements with much data. (I'm as guilty as anyone here).
The last part of the paper tries to extend the model from hit compounds to the targets that they bind to - a druggability assessment. The authors looked through the ChEMBL database, and ranked the various target by the scores of the ligands that are associated with them. They found that their mean ligand score for all the targets in there is 0.478. For the targets of approved drugs, it's 0.492, and for the orally active ones it's 0.539 - so there seems to be a trend, although if those differences reached statistical significance, it isn't stated in the paper.
So overall, I find nothing really wrong with this paper, but nothing spectacularly right with it, either. I'd be interested in hearing other calls on it as it gets out into the community. . .
+ TrackBacks (0) | Category: Drug Development | Drug Industry History | In Silico | Life in the Drug Labs
January 9, 2012
For a look into a possible drug-discovery future (from the computational optimist viewpoint), you might want to check out a brief bit of science fiction, "Alpha Shock", in the Journal of Computer-Aided Molecular Design. Some excerpts to give you the general idea:
". . .Of course, the compounds were of little value if they couldn’t be formulated. Sanjay was pressed for time, and nanobot development still took several weeks, so he had to go “old school.” Sanjay accessed World Crystallography Repository’s (WCR) formulation suite and entered the 2D structures of his compounds. The system linked to the Amazon Hyper-Cloud and initiated a series of quantum chemical calculations to develop a custom force field for the solid phase simulations. Unfortunately the preliminary results were disappointing, even after more than 100 million combinations of excipients, particle sizes, focusing tails, and polymorphs had been analyzed in detail. He would run a more complete search overnight, but the chances were that the 10-min simulation was telling him what he needed to know: don’t expect these exact compounds to be quite right. . .
. . .“In fact,” Dmitri continued, “I think the best tactic is to turn down the interaction of this transcription factor”—a protein popped out of one node on the map—“with that protein”—another protein materialized—“and this stretch of DNA.” A 3D model of the complex assembled in front of him, slowly rotating, with the most likely binding sites and points of intervention highlighted. “Of course, you only want to disrupt this interaction in the hippocampus, and only when D7 receptor functioning is high.” The relevant pathway maps showed the effects of the blockage on downstream signaling. “Oh, and naturally you also want to turn down oxphos in the mitochondria. So we need either a single molecule that can do both things, or a two-drug combo.”
The overall impression is a bit like Charles Stross, in its deliberate you-haven't-extrapolated-wildly-enough approach. But Stross doesn't put in as many computational chemistry inside jokes, which is probably better for his sales. My first impulse is the same one I have to, say, Ray Kurzweil, that all this stuff may (in fact probably is) on its way, but not by the dates stated. That position allows me to take flak from both sides, which must be some sort of feature that I value.
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November 7, 2011
An e-mail correspondent and I were discussing this question, and I thought it would be an interesting one for everyone. He's a computational guy, and he's been wondering where the best use of computation/modeling effort in drug research might be. The obvious place to apply it is in lead generation and SAR development - but is that the best place? Is it the rate-limiting step enough of the time?
Problem is, the things that are often limiting steps are not as amenable to modeling. These are things like toxicology, target selection, and the like, and I'm not sure what they're susceptible to, except that simulation is probably not the answer. Or not yet, anyway. So what's the sweet spot, the place that maximizes importance and feasibility?
Update: an early vote for clinical trial design, which is a strong contender. Can't say that that doesn't get right to the hard part. . .
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September 20, 2011
I wrote last year about Foldit, a collaborative effort to work on protein structure problems that's been structured as an open-access game. Now the team is back with another report on how the project is going, and it's interesting stuff. The headlines have generally taken the "Computer Gamers Solve Incredible Protein Problem That Baffled Scientists!" line, but that's not exactly the full story.
The Foldit collaboration participated in the latest iteration of a regular protein-structure prediction challenge, CASP9. And their results varied - in the category of proteins with known structural homologs, for example, they didn't perform all that well. The players, it turned out, sort of over-worked the structures, and made a lot of unnecessary changes to the peripheral parts of the proteins. Another category took on proteins that have no identified structural homologs, a much harder problem. But that had its problems, too, which illustrate both the difficulties of the Foldit approach and protein modeling in general:
For prediction problems for which there were no identifiable homologous protein structures—the CASP9 Free Modeling category—Foldit players were given the five Rosetta Server CASP9 submissions (which were publicly available to other prediction groups) as starting points, along with the Alignment Tool. . .In this Free Modeling category, some of the shortcomings of the Foldit predictions became clear. The main problem was a lack of diversity in the conformational space explored by Foldit players because the starting models were already minimized with the same Rosetta energy function used by Foldit. This made it very difficult for Foldit players to get out of these local minima, and the only way for the players to improve their Foldit scores was to make very small changes ('tunneling' to the nearest local minimum) to the starting structures. However, this tunneling did lead to one of the most spectacular successes in the CASP9 experiment.
. . .the Rosetta Server, which carried out a large-scale search for the lowest-energy structure using computing power from Rosetta@home volunteers, produced a remarkably accurate model . . . However, the server ranked this model fourth out of the five submissions. The Foldit Void Crushers team correctly selected this near-native model and further improved it by accurately moving the terminal helix, producing the best model for this target of any group and one of the best overall predictions at CASP9 . . . Thus, in a situation where one model out of several is in a near-native conformation, Foldit players can recognize it and improve it to become the best model. Unfortunately for the other Free Modeling targets, there were no similarly outstanding Rosetta Server starting models, so Foldit players simply tunneled to the nearest incorrect local minima.
In the Refinement challenge, where participants take a minimized structure and try to improve its accuracy, the Foldit players had similar problems with starting from structures that had already been minimized by the same tools that they were using. Every change tended to make things look worse. The team improved their performance by reposting one of the structures as a new challenge, this time keeping the parts that were known with confidence to be near-native, while more or less randomizing the other parts to give a greater diversity to the starting points.
And those really are some of the key problems in this work. There are an awful lot of energy minima out there, and which ones you can get to depend crucially on where you start looking. In order to get to a completely different manifold of protein structures, even ones with much better energies, you may well have to go through a zone where you look like you're ruining everything. (And most of the time, you probably are ruining everything - there's no way to know if there's a safe haven on the other side or not).
But this paper also reports the results that are getting the headlines, a structure for the Mason-Pfizer monkey retroviral protease. This is an interesting protein, because although it crystallizes readily (in several different forms), and although the structures of other retroviral proteases are known, no one has been able to solve this one from the available X-ray data. The Foldit players, however, came up with several proposals that fit the data well enough for the structure to finally fall out of the diffraction data. It does have some odd features in its protein loops, different enough from the other proteases for no one to have hit on it before.
And that really is an accomplishment, and the way it was solved (with different players building on the results of others, competing to get the best optimization scores) really is the way the Foldit is supposed to work. Their less impressive performance on the CASP9 problems, though, shows that the same protein prediction difficulties apply to Foldit players as apply to the rest of the modeling field. This isn't a magic technique, and Foldit gamers are not going to rampage through the structural biology world solving all the extant problems any time soon. But it's nothing to sneeze at, either.
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August 26, 2011
For those of you who are (or have always wanting to try being) molecular modelers, Cresset Design is holding a contest you might enjoy. They're putting up a molecule and giving out temporary licenses to their modeling software, and inviting people to come up with the closest bioisosteric match. The winner gets a free IPad2.
Of course, you're not going to be able to win by suggesting a para-fluoro group or by making a tetrazole-for-carboxylate switch. In their words:
We will use the Field alignment score for your molecule to the reference molecule as the primary judgment in designing the winner. However, molecules with high 2D similarity or high calculated logP with receive a penalty and are unlikely to win. Also entries with reasonable chemistry and good synthetic feasibility will be favoured. Feedback showing the score for your molecule and describing which properties of the molecule are being penalised will be provided on request. The winner will be the molecule that, in the opinion of the judges, represents the best design chosen from the top scoring results.
Fair enough, I'd say. I look forward to a follow-up from them at the end of the contest; I'd like to see what sort of stuff comes in.
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March 29, 2011
Man, am I getting all kinds of comments (here and by e-mail) about my views on modeling, QSAR, and the like. I thought it might be helpful for me to clarify my position on these things.
First off, structure. It's a valuable thing to have. My comments on the recent Nature Reviews Drug Discovery article were not meant to suggest otherwise, just to point out that the set of examples the authors picked to make this point was (in my view) flawed. It's actually surprisingly hard to come up with good comparison sets that isolate the effect of having structural information on the success of drug discovery projects. There are too many variables, and too many of them aren't independent. But just because a question (does having structural information help, overall?) is hard to answer doesn't mean that the answer is "no".
As an aside, since I've talked here about my admiration for fragment-based approaches, my own opinion should have been pretty clear already. Doing fragment-based drug discovery without good structural information looks to be very hard indeed.
Now, that said, there's structure and there's structure. Like every other tool in our kit, this one can be used well or used poorly. I think that fragment projects (to pick one example) get a lot of bang-for-the-buck out of structural data, and at the opposite end of the scale are those projects that only get good X-ray data after they've sent their compound to the clinic. No, wait, let me take that back. In those cases, the structure did no good, but it also did no harm. At the true opposite end of the scale are the projects where having structural data actually slowed things down. That's not frequent, but it does happen. Sometimes you have solid data, but for one reason or another the X-ray isn't corresponding to what's happening in real life. And sometimes this kicks in when medicinal chemists try to make too much out of less compelling structural data, just because it's all they have.
Now for in silico techniques. I have a similar attitude towards modeling of all kinds, but at one further remove than physical structure data. That is, I think it can be used well or used poorly, but I think that (for various reasons) the chances of using it poorly are somewhat increased. One reason is that modeling can be very hard to do well, naturally. And at the same time, tools with which to model conformations, docking, and so on are pretty widely available, which leads to a fair amount of work from people who really don't know what they're doing. Another reason is that the validity of any given model is of limited scope, as is the case with any mental construct that we have about what our molecules are doing, whether we used a software package or waved our hands around in the air. The software-package version of some binding model is more likely to have a wider range of usefulness than the hand-waving one, but they'll both break down at some point as you explore a range of compounds.
The key then is to figure out as quickly as possible if the project you're working on would be enhanced by modeling, or if such modeling would be merely ornamental, or even harmful. And that's not always easy to do. Any reasonable model is going to need a few iterations to get up to speed, generally requiring some specific compounds to be made by the chemists, and if you're running a project, you have to decide how much effort is worth spending to do that. You don't want to end up endlessly trying to refine the model, but at the same time, that model could turn out to be very useful after a few more turns of the crank. Which way to go? The same decisions apply, naturally, to the folks standing in front of the hoods, even without any modeling. How many more compounds are worth making in a given series? Would that effort be better used somewhere else? These calls are why we're paid the approximation of the big bucks.
So, while I don't think that modeling is an invariable boon to a project, neither do I think it's a waste of time. Sometimes it's one, and sometimes it's the other, and most of the time it's a mix of each - just like ideas at the bench. When modeling works, it can be a real help in sending the chemists down a productive path. On the other hand, you can certainly run a whole project with no modeling at all, just good old-fashioned analoging from the labs. It's the job of modelers to make the first possibility more likely and more attractive, and the job of the chemists and project managers to be open to that (and to be ready to emphasize or de-emphasize things as they develop).
This point of view seems reasonable to me (which is why I hold it!) But it also exposes me to complaints from people at both ends of the spectrum. I'm a lot more skeptical of in silico approaches than are many true believers, but I don't want to make the mistake of dismissing them outright.
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March 28, 2011
A friend on the computational/structural side of the business sent along this article from Nature Reviews Drug Discovery. The authors are looking through the Thomson database at drug targets that are the subject of active research in the industry, and comparing the ones that have structural information available to the ones that don't: enzyme targets (with high-resolution structures) and and GPCRs without it. They're trying to to see if structural data is worth enough to show up in the success rates (and thus the valuations) of the resulting projects.
Overall, the Thomson database has over a thousand projects in it from these two groups, a bit over 600 from the structure-enabled enzymes and just under 500 GPCR projects. What they found was that 70% of the projects in the GPCR category were listed as "suspended" or "discontinued", but only 44% of the enzyme projects were so listed. In order to correct for probability of success across different targets, the authors picked ten targets from each group that have led, overall, to similar numbers of launched drugs. Looking at the progress of the two groups, the structure-enabled projects are again lower in the "stopped" categories, with corresponding increases in discovery and the various clinical phases.
You have to go to the supplementary info for the targets themselves, but here they are: for the enzymes, it's DPP-IV, BCR-ABL, HER2 kinase, renin, Factor Xa, HDAC, HIV integrase, JAK2, Hep C protease, and cathepsin K. For the receptor projects, the list is endothelin A receptor, P2Y12, CXCR4, angiogensin II receptor, sphingosine-1-phosphate receptor, NK1, muscarinic M1, vasopressin V2, melatonin receptor, and adenosine A2A.
Looking over these, though, I think that the situation is more complicated than the authors have presented. For example, DPP-IV has good structural information now, but that's not how people got into the area. The cyanopyrrolidine class of inhibitors, which really jump-started the field, were made by analogy to a reported class of prolyl endopeptidase inhibitors (BOMCL 1996, p. 1163). Three years later, the most well-characterized Novartis compound in the series was being studied by classic enzymology techniques, because it still wasn't possible to say just how it was binding. But even more to the point, this is a well-trodden area now. Any DPP-IV project that's going on now is piggybacking not only on structural information, but on an awful lot of known SAR and toxicology.
And look at renin. That's been a target forever, structure or not. And it's safe to say that it wasn't lack of structural information that was holding the area back, nor was it the presence of it that got a compound finally through the clinic. You can say the same things about Factor Xa. The target was validated by naturally occurring peptides, and developed in various series by classical SAR. The X-ray structure of one of the first solid drug candidates in the area (rivaroxaban) bound to its target, came after the compound had been identified and the SAR had been optimized. Factor Xa efforts going on now also are standing on the shoulders of an awful lot of work.
In the case of histone deacetylase, the first launched drug in that category (SAHA, vorinostat) has already been identified before any sort of X-ray structure was available. Overall, that target is an interesting addition to the list, since there are actually a whole series of them, some of which have structural information and some of which don't. The big difficulty in that area is that we don't really know what the various roles of the different isoforms are, and thus how the profiles of different compounds might translate to the clinic, so I wouldn't say that structural data is helping with the rate-determining steps in the field.
On the receptor side, I also wouldn't say that it's lack of structural information that's necessarily holding things back in all of those cases, either. Take muscarinic M1 - muscarinic ligands have been known for a zillion years. That encompasses fairly selective antagonists, and hardly-selective-at-all agonists, so I'm not sure which class the authors intended. If they're talking about antagonists, then there are plenty already known. And if they're talking about agonists, I doubt that even detailed structural information would help, given the size of the native ligand (acetylcholine).
And the vasopressin V2 case is similar to some of the enzyme ones, in that there's already an approved drug in this category (tolvaptan), with several others in the same structural class chasing it. Then you have the adenosine A2A field, where long lists of agonists and antagonists have been found over the years, structure or not. The problem there has been finding a clinical use for them; all sorts of indications have been chased over the years, a problem that structural information would have not helped with in the least.
Now, it's true that there are projects in these categories where structure has helped out quite a bit, and it's also true that detailed GPCR structures would be welcome (and are slowly coming along, for that matter). I'm not denying either of those. But what does strike me is that there are so many confounding variables in this particular comparison, especially among the specific targets that are the subject of the article's featured graphic, that I just don't think that its conclusions follow.
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August 9, 2010
David Baker's lab at the University of Washington has been working on several approaches to protein structure problems. I mentioned Rosetta@home here, and now the team has published an interesting paper on another one of their efforts, FoldIt.
That one, instead of being a large-scale passive computation effort, is more of an active process - in fact, it's active enough that it's designed as a game:
We hypothesized that human spatial reasoning could improve both the sampling of conformational space and the determination of when to pursue suboptimal conformations if the stochastic elements of the search were replaced with human decision making while retaining the deterministic Rosetta algorithms as user tools. We developed a multiplayer online game, Foldit, with the goal of producing accurate protein structure models through gameplay. Improperly folded protein conformations are posted online as puzzles for a fixed amount of time, during which players interactively reshape them in the direction they believe will lead to the highest score (the negative of the Rosetta energy). The player’s current status is shown, along with a leader board of other players, and groups of players working together, competing in the same puzzle.
So how's it working out? Pretty well, actually. It turns out that human players are willing to do more extensive rearrangements to the protein chains in the quest for lower energies than computational algorithms are. They're also better at evaluating which positions to start from. Both of these remind me of the differences between human chess play and machine play, as I understand them, and probably for quite similar reasons. Baker's team is trying to adapt the automated software to use some of the human-style approaches, when feasible.
There are several dozen participants who clearly seem to have done better in finding low-energy structures than the rest of the crowd. Interestingly, they're mostly not employed in the field, with "Business/Financial/Legal" making up the largest self-declared group in a wide range of fairly even-distributed categories. Compared to the "everyone who's played" set, the biggest difference is that there are far fewer students in the high-end group, proportionally. That group of better problem solvers also tends to be slightly more female (although both groups are still mostly men), definitely older (that loss of students again), and less well-stocked with college graduates and PhDs. Make of that what you will.
Their conclusion is worth thinking about, too:
The solution of challenging structure prediction problems by Foldit players demonstrates the considerable potential of a hybrid human–computer optimization framework in the form of a massively multiplayer game. The approach should be readily extendable to related problems, such as protein design and other scientific domains where human three-dimensional structural problem solving can be used. Our results indicate that scientific advancement is possible if even a small fraction of the energy that goes into playing computer games can be channelled into scientific discovery.
That's crossed my mind, too. In my more pessimistic moments, I've imagined the human race gradually entertaining itself to death, or at least to stasis, as our options for doing so become more and more compelling. (Reading Infinite Jest a few years ago probably exacerbated such thinking). Perhaps this is one way out of that problem. I'm not sure that it's possible to make a game compelling enough when it's hooked up to some sort of useful gear train, but it's worth a try.
+ TrackBacks (0) | Category: Biological News | In Silico | Who Discovers and Why
June 22, 2010
The folks at Cresset sent me a note about a free download of some software that they've developed for molecular fields (an approach you can read more about here). Fieldview is a free tool for trying this out yourself, and can be had here. Worth a look for the computationally curious, especially at the price. . .
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June 18, 2010
A reader points me to this discussion, which is trying to figure out what the most useful discovery made via bioinfomatics is so far. There's a $100 prize for the winning suggestion, just to keep the discussion moving (and no, I don't anticipate offering cash bounties around here any time soon!) The early going seems to have ended up in the "Hold it, that's not bioinformatics, is it?" ditch, but that's not a useless discussion, either.
So if you have some suggestions, hop over there and add them to the fray, or vote for the ones that you like so far. I'm racking my brain a bit myself.
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May 17, 2010
I'll have the opportunity to sit in on a few talks during a conference on free energy calculations in drug design. Since I'm not a computational guy myself, I'll be picking my sessions carefully, but I am interested in hearing what the state of the art is.
If we could just walk right up and calculate the free energies of binding events reliably, that would mean that the era of high throughput screening would begin to come to its end - well, in the physical world, anyway. Depending on how lengthy the computations needed to be, we could (in theory) just sit back and let the hardware hum while it ran through all the compounds we could think up - then we'd come back in on Monday and see who the winners were. Despite what some of you outside the field of medicinal chemistry might have read, we are not exactly to this point yet. That phrase "in theory" covers an awful lot of ground. But progress is apparently being made (here's a recent paper (PDF) with background).
So here's a question for the readership: what would you most want such calculations to be able to do for you? What would convince you that they're actually believable? And how close to you think that we actually are to that? Your comments will go directly to the ears of a roomful of high-powered modelers, so feel free to unload.
That thought of a roomful of computational chemists, though, reminds me inexorably of a story about Robert Oppenheimer that Freeman Dyson retells here. At a theoretical physics conference in Vancouver, the attendees were on a boat ride among the islands when the weather turned impenetrably foggy. Someone asked what the consequences for physics would be if the boat sank, and Oppenheimer instantly said "It wouldn't do any permanent good". There, that should ensure me a warm welcome at the meeting!
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May 10, 2010
Here's a new paper from the folks at the Burnham Institute and UCSD on a new target for vaccinia virus. They're going after a virulence factor (N1L) through computational screening, which is a challenge, since this is a protein-protein interaction.
They pulled out a number of structures, which have some modest activity in cell infection assays. In addition, they showed through calorimetry that the compounds do appear to be affecting the target protein, specifically its equilibrium between monomeric and oligomeric forms. But the structures of their best hits. . .well, here's the table. You can ignore compounds 6 and 8; they show up as cytotoxic. But the whole list is pretty ghastly, at least to my eyes.
These sorts of highly aromatic polyphenol structures have two long traditions in medicinal chemistry: showing activity in assays, for the first part, and not being realizable as actual drugs, for the second. There's no doubt that they can do a lot of things; it's just that getting them to do them in a real-world situation is not trivial. Part of the problem is specificity (and associated toxicity) and part of it is pharmacokinetics. As you'd imagine, these compounds can have rather funky clearance behavior, what with all those phenols.
So I'd regard these as proof-of-concept compounds that validate N1L as a target. I think that we'll need to wait for someone to format up an assay for high-throughput (non-virtual) screening to see if something more tractable comes up. Either that, or rework the virtual screens on the basis that we've seen enough polyphenols come up on this target already. . .
Note: readers of the paper will note that our old friend resveratrol turns up as an active compound as well. It's very much in the polyphenol tradition; make of that what you will.
+ TrackBacks (0) | Category: In Silico | Infectious Diseases | Pharmacokinetics
My take on the recent news that Bill Gates has invested ten million dollars in the computational drug design company Schrödinger is here at Nature News. (They've recently made all their stories open-access, by the way, so you don't need a subscription to get the full stories).
In short, I think that patient billionaire money is just the sort of thing the field needs, because anyone with a short timeline and a need for a good return is going to have a rough time of it. . .
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May 4, 2010
I was talking with a colleague yesterday, and I suddenly had an insight into an opportunity in scientific publishing. We were discussing the various computational/modeling papers that you see out in the literature. Some of them are quite interesting, many are worth looking at if it's your particular field - but many others are, well, not so great. I should mention up front that the same objections apply - and how - to the non-computational literature, of course. But there are a number of second-tier (and lower) journals to soak up those sorts of papers in the other disciplines.
What surprises me is that there's no Computational Chemistry Letters or some such. Communications in Computational Chemistry? CADD Comm? This would be the dumping ground for the piles of unconvincing computer-driven stuff that gets sent around by people who have paid a bit too much attention to the sales brochures that came with their software packages.
The barriers for entry to such things have been getting lower and lower, while the real state of the art has been getting more and more complicated. That's created a gap into which too much stuff falls. Who will speak for the bottom-dwelling "We modeled it, therefore it's real" constituency? The advent of systems biology has created more opportunities than ever for these folks. Isn't it time that there was an expensive, low-impact, completely disregardable journal for them, too?
+ TrackBacks (0) | Category: In Silico | The Scientific Literature
March 29, 2010
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!
+ TrackBacks (0) | Category: Drug Assays | In Silico | Life in the Drug Labs
March 23, 2010
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?
+ TrackBacks (0) | Category: Drug Assays | In Silico
March 22, 2010
I mentioned Benford's Law in passing in this post (while speculating on how long people report their reactions to have run when publishing their results). That's the rather odd result that many data sets don't show a random distribution of leading digits - rather, 1 is the first digit around 30% of the time, 2 leads off about 18% of the time, and so on down.
For data that come from some underlying power-law distribution, this actually makes some sense. In that case, the data points spend more time being collected in the "lag phase" when they're more likely to start with a 1, and proportionally less and less time out in the higher-number-leading areas. The law only holds up when looking at distributions that cover several orders of magnitude - but all the same, it also seems to apply to data sets where there's no obvious exponential growth driving the numbers.
Lack of adherence to Benford's Law can be acceptable as corroborative evidence of financial fraud. Now a group from Astellas reports that several data sets used in drug discovery (such as databases of water solubility values) obey the expected distribution. What's more, they're suggesting that modelers and QSAR people check their training data sets to make sure that those follow Benford's Law as well, as a way to make sure that the data have been randomly selected.
Is anyone willing to try this out on a bunch of raw clinical data to see what happens? Could this be a way to check the integrity of reported data from multiple trial centers? You'd have to pick your study set carefully - a lot of the things we look for don't cover a broad range - but it's worth thinking about. . .
+ TrackBacks (0) | Category: Clinical Trials | In Silico | The Dark Side
December 10, 2009
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. . .
+ TrackBacks (0) | Category: Drug Assays | Drug Development | In Silico
December 7, 2009
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. . .
+ TrackBacks (0) | Category: Drug Assays | Drug Industry History | In Silico
November 17, 2009
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. . .
+ TrackBacks (0) | Category: Drug Assays | In Silico | Toxicology
I've been remiss in not mentioning this, but I just found out recently that Warren DeLano (the man behind the excellent open-source PyMOL program) passed away suddenly earlier this month. He was 37 - another unfortunate loss of a scientist who had done a lot of fine work and was clearly on the way to doing much more.
I notice that as I write this I have a PyMOL window open on my desktop; I use the program regularly to look at protein structures. Si monumentum requiris, circumspice.
+ TrackBacks (0) | Category: Current Events | In Silico
July 16, 2009
I had a printout of the structure of maitotoxin on my desk the other day, mostly as a joke to alarm anyone who came into my office. "Yep, here's the best hit from the latest screen. . .I hear that you're on the list to run the chemistry end. . .what's that you say?"
This is, needless to say, one of the largest and scariest marine natural product structures ever determined (and that determination has been no stroll past the dessert table, either).
But that' hasn't stopped people from messing around with it. And there's much speculation that other people are strongly considering messing around with it, too - you synthetic chemists can guess the sorts of people that this might be, and their names, and what it might be like to sit through the seminars that result, and so on.
I fear that a total synthesis of maitotoxin would be largely a waste of time, but I'm willing to hear arguments against that position. Just looking at it, though, inspires thought. This eldrich beastie has 98 chiral centers. So let's do some math. If you're interested in the SAR of such molecules, you have your choice of (two to the 98th) possible isomers, which comes out to a bit over (3 times ten to the 29th) compounds. This is. . .a pretty large number. If you're looking for 10mg of each isomer to add to your screening collection (no sense in going back and making them again), then you're looking at a good bit over half the mass of the entire Earth. And that's just in sheer compounds; we're not counting the weight of vials, which will, I'd say, safely move you up toward the planetary weight of a low-end gas giant. We will ignore shelving considerations in the interest of time.
Recall that yesterday's post gave a number of about 27 million compounds below 11 heavy atoms. You could toss 27 million compounds into a collection of ten to the 29th and never see them again, of course. But that brings up two points: one, that the small-compound estimate ignores stereochemistry, and we've been getting those insane maitotoxin numbers by considering nothing but. The thing is, with only 11 non-hydrogen atoms, there aren't quite as many chances for things to get out of control. The GDB compound set goes up only to 110 million or so if you consider stereoisomers, which actually isn't nearly as much as I'd thought.
But the second point is that this shows you why the Berne group stopped at 11 heavy atoms, because the problem becomes intractable really fast as you go higher. It's worth remembering that the GDB people actually threw out over 98% of their scaffolds because they represented potential ring structures that are too strained to be very stable. And they only considered C, N, O and F as heavy atoms (even adding sulfur was considered too much to deal with, computationally). Then they tossed out another 98 or 99% of the structures that emerged from that enumeration as reactive and/or unstable. Relax your standards a bit, allow another atom or two, bump up the molecular weight, do any of those and you're going to exceed anyone's computational capacity. Update: the Berne group has just taken a crack at it, and managed a reasonable set up to 13 heavy atoms, with various simplifying assumptions to ease the burden. If you want to mess around with it, it's here, free of charge).
No, there are a lot of compounds out there. And if you look at the really big ones - and maitotoxin is nothing if not a really big one - there are whole universes contained just in each of them. (Bonus points for guessing the source of the name of the post, by the way).
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July 15, 2009
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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July 7, 2009
While we're on the topic of hydrogen bonds and computations, there's a paper coming out in JACS that attempts to answer an old question. Why, exactly, does every living thing on earth use so much ribose? It's the absolute, unchanging carbohydrate backbone to all the RNA on Earth, and like the other things in this category (why L amino acids instead of D?), it's attracted a lot of speculation. If you subscribe to the RNA-first hypothesis of the origins of life, then the question becomes even more pressing.
A few years ago, it was found that ribose, all by itself, diffuses through membranes faster than the other pentose sugars. This results holds up for several kinds of lipid bilayers, suggesting that it's not some property of the membrane itself that's at work. So what about the ability of the sugar molecules to escape from water and into the lipid layers?
Well, they don't differ much in logP, that's for sure, as the original authors point out. This latest paper finds, though, by using molecular dynamic simulations that there is something odd about ribose. In nonpolar environments, its hydroxy groups form a chain of hydrogen-bond-like interactions, particularly notable when it's in the beta-pyranose form. These aren't a factor in aqueous solution, and the other pentoses don't seem to pick up as much stabilization under hydrophobic conditions, either.
So ribose is happier inside the lipid layer than the other sugars, and thus pays less of a price for leaving the aqueous environment, and (both in simulation and experimentally) diffuses across membranes ten times as quickly as its closely related carboyhydate kin. (Try saying that five times fast!) This, as both the original Salk paper and this latest one note, leads to an interesting speculation on why ribose was preferred in the origins of life: it got there firstest with the mostest. (That's a popular misquote of Nathan Bedford Forrest's doctrine of warfare, and if he's ever come up before in a discussion of ribose solvation, I'd like to hear about it).
+ TrackBacks (0) | Category: Biological News | In Silico | Life As We (Don't) Know It
Hydrogen bonds are important. There, that should be an sweepingly obvious enough statement to get things started. But they really are - hydrogen bonding accounts for the weird properties of water, for one thing, and it's those weird properties that are keeping us alive. And leaving out the water (a mighty big step), internal hydrogen bonding is still absolutely essential to the structure of large biological molecules - proteins, complex carbohydrates, DNA and RNA, and so on.
But we don't understand hydrogen bonds all that well, dang it all. It's not like we're totally ignorant of them, for sure, but there are a lot of important things that we don't have a good handle on. One of these may just have been illustrated by this paper in Nature Structural and Molecular Biology by a group from Scripps. They've been working on understanding the fact that all hydrogen bonds are not created equal. By carefully going through a lot of protein mutants, they have evidence for the idea that H-bonds that form in polar environments are weaker than ones that form in nonpolar ones.
That makes sense, on the face of it. One way to think of it is that a hydrogen bond in a locally hydrophobic area is the only game in town, and counts for more. But this work claims that such bonds can be worth as much as 1.2 kcal/mole more than the wimpier ones, which is rather a lot. Those kinds of energy differences could add up very quickly when you're trying to understand why a protein folds up the way it does, or why one small molecule binds more tightly than another one.
Do we take such things into account when we're trying to compute these energies? Generally speaking, no, we do not - well, not yet. If these folks are right, though, we'd better start.
Update: note that the paper itself doesn't suggest that this is a new idea - they reference work going back to 1963 (!) on the topic. What they're trying to do is put more real numbers into the mix. And that's what my last paragraph above is trying to state (and perhaps overstate): it's difficult to account for these thing computationally, since they vary so widely, and since we don't have that good a computational handle on hydrogen bonds in general. The more real world data that can be fed back into the models, the better.
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July 2, 2009
Moore's Law: number of semiconductors on a chip doubling every 18 months or so, etc. Everyone's heard of it. But can we agree that anyone who uses it as a metaphor or perscription for drug research doesn't know what they're talking about?
I first came across the comparison back during the genomics frenzy. One company that had bought into the craze in a big way press-released (after a rather interval) that they'd advanced their first compound to the clinic based on this wonderful genomics information. I remember rolling my eyes and thinking "Oh, yeah", but on a hunch I went to the Yahoo! stock message boards (often a teeming heap of crazy, then as now). And there I found people just levitating with delight at this news. "This is Moore's Law as applied to drug discovery!" shouted one enthusiast. "Do you people realize what this means?" What it meant, apparently, was not only that this announcement had come rather quickly. It also meant that this genomics stuff was going to discover twice as many drugs as this real soon. And real soon after that, twice as many more, and so on until the guy posting the comment was as rich as Warren Buffet, because he was a visionary who'd been smart enough to load himself into the catapult and help cut the rope. (For those who don't know how that story ended, the answer is Not Well: the stock that occasioned all this hyperventilation ended up dropping by a factor of nearly a hundred over the next couple of years. The press-released clinical candidate was never, ever, heard of again).
I bring this up because a reader in the industry forwarded me this column from Bio-IT World, entitled, yes, "Only Moore's Law Can Save Big Pharma". I've read it three times now, and I still have only the vaguest idea of what it's talking about. Let's see if any of you can do better.
The author starts off by talking about the pressures that the drug industry is under, and I have no problem with him there. That is, until he gets to the scientific pressures, which he sketches out thusly:
Scientifically, the classic drug discovery paradigm has reached the end of its long road. Penicillin, stumbled on by accident, was a bona fide magic bullet. The industry has since been organized to conduct programs of discovery, not design. The most that can be said for modern pharmaceutical research, with its hundreds of thousands of candidate molecules being shoveled through high-throughput screening, is that it is an organized accident. This approach is perhaps best characterized by the Chief Scientific Officer of a prominent biotech company who recently said, "Drug discovery is all about passion and faith. It has nothing to do with analytics."
The problem with faith-based drug discovery is that the low hanging fruit has already been plucked, driving would be discoverers further afield. Searching for the next miracle drug in some witch doctor's jungle brew is not science. It's desperation.
The only way to escape this downward spiral is new science. Fortunately, the fuzzy outlines of a revolution are just emerging. For lack of a better word, call it Digital Chemistry.
And when the man says "fuzzy outline", well, you'd better take him at his word. What, I know you're all asking, is this Digital Chemistry stuff? Here, wade into this:
Tomorrow's drug companies will build rationally engineered multi-component molecular machines, not small molecule drugs isolated from tree bark or bread mold. These molecular machines will be assembled from discrete interchangeable modules designed using hierarchical simulation tools that resemble the tool chains used to build complex integrated circuits from simple nanoscale components. Guess-and-check wet chemistry can't scale. Hit or miss discovery