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 14, 2014
This will be a long one. I'm going to take another look at the Science paper that stirred up so much comment here on Friday. In that post, my first objection (but certainly not my only one) was the chemical structures shown in the paper's Figure 2. A number of them are basically impossible, and I just could not imagine how this got through any sort of refereeing process. There is, for example, a cyclohexadien-one structure, shown at left, and that one just doesn't exist as such - it's phenol, and those equilibrium arrows, though very imbalanced, are still not drawn to scale.
Well, that problem is solved by those structures being intended as fragments, substructures of other molecules. But I'm still positive that no organic chemist was involved in putting that figure together, or in reviewing it, because the reason that I was confused (and many other chemists were as well) is that no one who knows organic chemistry draws substructures like this. What you want to do is put dashed bonds in there, or R groups, as shown. That does two things: it shows that you're talking about a whole class of compounds, not just the structure shown, and it also shows where things are substituted. Now, on that cyclohexadienone, there's not much doubt where it's substituted, once you realize that someone actually intended it to be a fragment. It can't exist unless that carbon is tied up, either with two R groups (as shown), or with an exo-alkene, in which case you have a class of compounds called quinone methides. We'll return to those in a bit, but first, another word about substructures and R groups.
Figure 2 also has many structures in it where the fragment structure, as drawn, is a perfectly reasonable molecule (unlike the example above). Tetrahydrofuran and imidazole appear, and there's certainly nothing wrong with either of those. But if you're going to refer to those as common fragments, leading to common effects, you have to specify where they're substituted, because that can make a world of difference. If you still want to say that they can be substituted at different points, then you can draw a THF, for example, with a "floating" R group as shown at left. That's OK, and anyone who knows organic chemistry will understand what you mean by it. If you just draw THF, though, then an organic chemist will understand that to mean just plain old THF, and thus the misunderstanding.
If the problems with this paper ended at the level of structure drawing, which many people will no doubt see as just a minor aesthetic point, then I'd be apologizing right now. Update: although it is irritating. On Twitter, I just saw that someone spotted "dihydrophyranone" on this figure, which someone figured was close enough to "dihydropyranone", I guess, and anyway, it's just chemistry. But they don't. It struck me when I first saw this work that sloppiness in organic chemistry might be symptomatic of deeper trouble, and I think that's the case. The problems just keep on coming. Let's start with those THF and imidazole rings. They're in Figure 2 because they're supposed to be substructures that lead to some consistent pathway activity in the paper's huge (and impressive) yeast screening effort. But what we're talking about is a pharmacophore, to use a term from medicinal chemistry, and just "imidazole" by itself is too small a structure, from a library of 3200 compounds, to be a likely pharmacophore. Particularly when you're not even specifying where it's substituted and how. There are all kinds of imidazole out there, and they do all kinds of things.
So just how many imidazoles are in the library, and how many caused this particular signature? I think I've found them all. Shown at left are the four imidazoles (and there are only four) that exhibit the activity shown in Figure 2 (ergosterol depletion / effects on membrane). Note that all four of them are known antifungals - which makes sense, given that the compounds were chosen for the their ability to inhibit the growth of yeast, and topical antifungals will indeed do that for you. And that phenotype is exactly what you'd expect from miconazole, et al., because that's their known mechanism of action: they mess up the synthesis of ergosterol, which is an essential part of the fungal cell membrane. It would be quite worrisome if these compounds didn't show up under that heading. (Note that miconazole is on the list twice).
But note that there are nine other imidazoles that don't have that same response signature at all - and I didn't even count the benzimidazoles, and there are many, although from that structure in Figure 2, who's to say that they shouldn't be included? What I'm saying here is that imidazole by itself is not enough. A majority of the imidazoles in this screen actually don't get binned this way. You shouldn't look at a compound's structure, see that it has an imidazole, and then decide by looking at Figure 2 that it's therefore probably going to deplete ergosterol and lead to membrane effects. (Keep in mind that those membrane effects probably aren't going to show up in mammalian cells, anyway, since we don't use ergosterol that way).
There are other imidazole-containing antifungals on the list that are not marked down for "ergosterol depletion / effects on membrane". Ketonconazole is SGTC_217 and 1066, and one of those runs gets this designation, while the other one gets signature 118. Both bifonazole and sertaconazole also inhibit the production of ergosterol - although, to be fair, bifonazole does it by a different mechanism. It gets annotated as Response Signature 19, one of the minor ones, while sertaconazole gets marked down for "plasma membrane distress". That's OK, though, because it's known to have a direct effect on fungal membranes separate from its ergosterol-depleting one, so it's believable that it ends up in a different category. But there are plenty of other antifungals on this list, some containing imidazoles and some containing triazoles, whose mechanism of action is also known to be ergosterol depletion. Fluconazole, for example, is SGTC_227, 1787 and 1788, and that's how it works. But its signature is listed as "Iron homeostasis" once and "azole and statin" twice. Itraconzole is SGTC_1076, and it's also annotated as Response Signature 19. Voriconazole is SGTC_1084, and it's down as "azole and statin". Climbazole is SGTC_2777, and it's marked as "iron homeostasis" as well. This scattering of known drugs between different categories is possibly and indicator of this screen's ability to differentiate them, or possibly an indicator of its inherent limitations.
Now we get to another big problem, the imidazolium at the bottom of Figure 2. It is, as I said on Friday, completely nuts to assign a protonated imidazole to a different category than a nonprotonated one. Note that several of the imidazole-containing compounds mentioned above are already protonated salts - they, in fact, fit the imidazolium structure drawn, rather than the imidazole one that they're assigned to. This mistake alone makes Figure 2 very problematic indeed. If the paper was, in fact, talking about protonated imidazoles (which, again, is what the authors have drawn) it would be enough to immediately call into question the whole thing, because a protonated imidazole is the same as a regular imidazole when you put it into a buffered system. In fact, if you go through the list, you find that what they're actually talking about are N-alkylimidazoliums, so the structure at the bottom of FIgure 2 is wrong, and misleading. There are two compounds on the list with this signature, in case you were wondering, but the annotation may well be accurate, because some long-chain alkylimidazolium compounds (such as ionic liquid components) are already known to cause mitochondrial depolarization.
But there are several other alkylimidazolium compounds in the set (which is a bit odd, since they're not exactly drug-like). And they're not assigned to the mitochondrial distress phenotype, as Figure 2 would have you think. SGTC_1247, 179, 193, 1991, 327, and 547 all have this moeity, and they scatter between several other categories. Once again, a majority of compounds with the Figure 2 substructure don't actually map to the phenotype shown (while plenty of other structural types do). What use, exactly, is Figure 2 supposed to be?
Let's turn to some other structures in it. The impossible/implausible ones, as mentioned above, turn out to be that way because they're supposed to have substituents on them. But look around - adamantane is on there. To put it as kindly as possible, adamantane itself is not much of a pharmacophore, having nothing going for it but an odd size and shape for grease. Tetrahydrofuran (THF) is on there, too, and similar objections apply. When attempts have been made to rank the sorts of functional groups that are likely to interact with protein binding sites, ethers always come out poorly. THF by itself is not some sort of key structural unit; highlighting it as one here is, for a medicinal chemist, distinctly weird.
What's also weird is when I search for THF-containing compounds that show this activity signature, I can't find much. The only things with a THF ring in them seem to be SGTC_2563 (the complex natural product tomatine) and SGTC_3239, and neither one of them is marked with the signature shown. There are some imbedded THF rings as in the other structural fragments shown (the succinimide-derived Diels-Alder ones), but no other THFs - and as mentioned, it's truly unlikely that the ether is the key thing about these compounds, anyway. If anyone finds another THF compound annotated for tubulin folding, I'll correct this post immediately, but for now, I can't seem to track one down, even though Table S4 says that there are 65 of them. Again, what exactly is Figure 2 supposed to be telling anyone?
Now we come to some even larger concerns. The supplementary material for the paper says that 95% of the compounds on the list are "drug-like" and were filtered by the commercial suppliers to eliminate reactive compounds. They do caution that different people have different cutoffs for this sort of thing, and boy, do they ever. There are many, many compounds in this collection that I would not have bothered putting into a cell assay, for fear of hitting too many things and generating uninterpretable data. Quinone methides are a good example - as mentioned before, they're in this set. Rhodanines and similar scaffolds are well represented, and are well known to hit all over the place. Some of these things are tested at hundreds of micromolar.
I recognize that one aim of a study like this is to stress the cells by any means necessary and see what happens, but even with that in mind, I think fewer nasty compounds could have been used, and might have given cleaner data. The curves seen in the supplementary data are often, well, ugly. See the comments section from the Friday post on that, but I would be wary of interpreting many of them myself.
There's another problem with these compounds, which might very well have also led to the nastiness of the assay curves. As mentioned on Friday, how can anyone expect many of these compounds to actually be soluble at the levels shown? I've shown a selection of them here; I could go on. I just don't see any way that these compounds can be realistically assayed at these levels. Visual inspection of the wells would surely show cloudy gunk all over the place. Again, how are such assays to be interpreted?
And one final point, although it's a big one. Compound purity. Anyone who's ever ordered three thousand compounds from commercial and public collections will know, will be absolutely certain that they will not all be what they say on the label. There will be many colors and consistencies, and LC/MS checks will show many peaks for some of these. There's no way around it; that's how it is when you buy compounds. I can find no evidence in the paper or its supplementary files that any compound purity assays were undertaken at any point. This is not just bad procedure; this is something that would have caused me to reject the paper all by itself had I refereed it. This is yet another sign that no one who's used to dealing with medicinal chemistry worked on this project. No one with any experience would just bung in three thousand compounds like this and report the results as if they're all real. The hits in an assay like this, by the way, are likely to be enriched in crap, making this more of an issue than ever.
Damn it, I hate to be so hard on so many people who did so much work. But wasn't there a chemist anywhere in the room at any point?
+ TrackBacks (0) | Category: Biological News | Chemical Biology | Chemical News | The Scientific Literature
March 28, 2014
Huntington's is a terrible disease. It's the perfect example of how genomics can only take you so far. We've known since 1993 what the gene is that's mutated in the disease, and we know the protein that it codes for (Huntingtin). We even know what seems to be wrong with the protein - it has a repeating chain of glutamines on one end. If your tail of glutamines is less than about 35 repeats, then you're not going to get the disease. If you have 36 to 39 repeats, you are in trouble, and may very well come down with the less severe end of Huntington's. If there are 40 or more, doubt is tragically removed.
So we can tell, with great precision, if someone is going to come down with Huntington's, but we can't do a damn thing about it. That's because despite a great deal of work, we don't really understand the molecular mechanism at work. This mutated gene codes for this defective protein, but we don't know what it is about that protein that causes particular regions of the brain to deteriorate. No one knows what all of Huntingtin's functions are, and not for lack of trying, and multiple attempts to map out its interactions (and determine how they're altered by a too-long N-terminal glutamine tail) have not given a definite answer.
But maybe, as of this week, that's changed. Solomon Snyder's group at Johns Hopkins has a paper out in Nature that suggests an actual mechanism. They believe that mutant Huntingtin binds (inappropriately) a transcription factor called "specificity protein 1", which is known to be a major player in neurons. Among other things, it's responsible for initiating transcription of the gene for an enzyme called cystathionine γ-lyase. That, in turn, is responsible for the last step in cysteine biosynthesis, and put together, all this suggests a brain-specific depletion of cysteine. Update: this could have numerous downstream consequences - this is the pathway that produces hydrogen sulfide, which the Snyder group has shown is an important neurotransmitter (one of several they've discovered), and it's also involved in synthesizing glutathione. Cysteine itself is, of course, often a crucial amino acid in many protein structures as well.)
Snyder is proposing this as the actual mechanism of Huntington's, and they have shown, in human tissue culture and in mouse models of the disease, that supplementation with extra cysteine can stop or reverse the cellular signs of the disease. This is a very plausible theory (it seems to me), and the paper makes a very strong case for it. It should lead to immediate consequences in the clinic, and in the labs researching possible therapies for the disease. And one hopes that it will lead to immediate consequences for Huntington's patients themselves. If I knew someone with the Huntingtin mutation, I believe that I would tell them to waste no time taking cysteine supplements, in the hopes that some of it will reach the brain.
+ TrackBacks (0) | Category: Biological News | The Central Nervous System
March 27, 2014
Here's another target validation initiative, with GSK, the EMBL, and the Sanger Institute joining forces. It's the Centre for Therapeutic Target Validation (CCTV):
CTTV scientists will combine their expertise to explore and interpret large volumes of data from genomics, proteomics, chemistry and disease biology. The new approach will complement existing methods of target validation, including analysis of published research on known biological processes, preclinical animal modelling and studying disease epidemiology. . .
This new collaboration draws on the diverse, specialised skills from scientific institutes and the pharmaceutical industry. Scientists from the Wellcome Trust Sanger Institute will contribute their unique understanding of the role of genetics in health and disease and EMBL-EBI, a global leader in the analysis and dissemination of biological data, will provide bioinformatics-led insights on the data and use its capabilities to integrate huge streams of different varieties of experimental data. GSK will contribute expertise in disease biology, translational medicine and drug discovery.
That's about as much detail as one could expect for now. It's hard to tell what sorts of targets they'll be working on, and by "what sorts" I mean what disease areas, what stage of knowledge, what provenance, and everything else. But the press release goes on to say that the information gathered by this effort will be open to the rest of the scientific community, which I applaud, and that should give us a chance to look under the hood a bit.
It's hard for me to say anything bad about such an effort, other than wishing it done on a larger scale. I was about to say "other than wishing it ten times larger", but I think I'd rather have nine other independent efforts set up than making this one huge, for several reasons. Quis validet ipsos validares, if that's a Latin verb and I haven't mangled it: Who will validate the validators? There's enough trickiness and uncertainty in this stuff for plenty more people to join in.
+ TrackBacks (0) | Category: Biological News | Drug Assays
March 24, 2014
Some of you may remember the "Google Flu" effort, where the company was going to try to track outbreaks of influenza in the US by mining Google queries. There was never much clarification about what terms, exactly, they were going to flag as being indicative of someone coming down with the flu, but the hype (or hope) at the time was pretty strong:
Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. . .
So how'd that work out? Not so well. Despite a 2011 paper that seemed to suggest things were going well, the 2013 epidemic wrong-footed the Google Flu Trends (GFT) algorithms pretty thoroughly.
This article in Science finds that the real-world predictive power has been pretty unimpressive. And the reasons behind this failure are not hard to understand, nor were they hard to predict. Anyone who's ever worked with clinical trial data will see this one coming:
The initial version of GFT was a particularly problematic marriage of big and small data. Essentially, the methodology was to find the best matches among 50 million search terms to fit 1152 data points. The odds of finding search terms that match the propensity of the flu but are structurally unrelated, and so do not predict the future, were quite high. GFT developers, in fact, report weeding out seasonal search terms unrelated to the flu but strongly correlated to the CDC data, such as those regarding high school basketball. This should have been a warning that the big data were overfitting the small number of cases—a standard concern in data analysis. This ad hoc method of throwing out peculiar search terms failed when GFT completely missed the nonseasonal 2009 influenza A–H1N1 pandemic.
The Science authors have a larger point to make as well:
“Big data hubris” is the often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis. Elsewhere, we have asserted that there are enormous scientific possibilities in big data. However, quantity of data does not mean that one can ignore foundational issues of measurement and construct validity and reliability and dependencies among data. The core challenge is that most big data that have received popular attention are not the output of instruments designed to produce valid and reliable data amenable for scientific analysis.
The quality of the data matters very, very, much, and quantity is no substitute. You can make a very large and complex structure out of toothpicks and scraps of wood, because those units are well-defined and solid. You cannot do the same with a pile of cotton balls and dryer lint, not even if you have an entire warehouse full of the stuff. If the individual data points are squishy, adding more of them will not fix your analysis problem; it will make it worse.
Since 2011, GFT has missed (almost invariably on the high side) for 108 out of 111 weeks. As the authors show, even low-tech extrapolation from three-week-lagging CDC data would have done a better job. But then, the CDC data are a lot closer to being real numbers. Something to think about next time someone's trying to sell you on a BIg Data project. Only trust the big data when the little data are trustworthy in turn.
Update: a glass-half-full response in the comments.
+ TrackBacks (0) | Category: Biological News | Clinical Trials | Infectious Diseases
March 20, 2014
This time last year I mentioned a particularly disturbing-looking compound, sold commercially as a so-called "selective inhibitor" of two deubiquitinase enzymes. Now, I have a fairly open mind about chemical structures, but that thing is horrible, and if it's really selective for just those two proteins, then I'm off to truck-driving school just like Mom always wanted.
Here's an enlightening look through the literature at this whole class of compound, which has appeared again and again. The trail seems to go back to this 2001 paper in Biochemistry. By 2003, you see similar motifs showing up as putative anticancer agents in cell assays, and in 2006 the scaffold above makes its appearance in all its terrible glory.
The problem is, as Jonathan Baell points out in that HTSpains.com post, that this series has apparently never really had a proper look at its SAR, or at its selectivity. It wanders through a series of publications full of on-again off-again cellular readouts, with a few tenuous conclusions drawn about its structure - and those are discarded or forgotten by the time the next paper comes around. As Baell puts it:
The dispiriting thing is that with or without critical analysis, this compound is almost certainly likely to end up with vendors as a “useful tool”, as they all do. Further, there will be dozens if not hundreds of papers out there where entirely analogous critical analyses of paper trails are possible.
The bottom line: people still don’t realize how easy it is to get a biological readout. The more subversive a compound, the more likely this is. True tools and most interesting compounds usually require a lot more medicinal chemistry and are often left behind or remain undiscovered.
Amen to that. There is way too much of this sort of thing in the med-chem literature already. I'm a big proponent of phenotypic screening, but setting up a good one is harder than setting up a good HTS, and working up the data from one is much harder than working up the data from an in vitro assay. The crazier or more reactive your "hit" seems to be, the more suspicious you should be.
The usual reply to that objection is "Tool compound!" But the standards for a tool compound, one used to investigate new biology and cellular pathways, are higher than usual. How are you going to unravel a biochemical puzzle if you're hitting nine different things, eight of which you're totally unaware of? Or skewing your assay readouts by some other effect entirely? This sort of thing happens all the time.
I can't help but think about such things when I read about a project like this one, where IBM's Watson software is going to be used to look at sequences from glioblastoma patients. That's going to be tough, but I think it's worth a look, and the Watson program seems to be just the correlation-searcher for the job. But the first thing they did was feed in piles of biochemical pathway data from the literature, and the problem is, a not insignificant proportion of that data is wrong. Statements like these are worrisome:
Over time, Watson will develop its own sense of what sources it looks at are consistently reliable. . .if the team decides to, it can start adding the full text of articles and branch out to other information sources. Between the known pathways and the scientific literature, however, IBM seems to think that Watson has a good grip on what typically goes on inside cells.
Maybe Watson can tell the rest of us, then. Because I don't know of anyone actually doing cell biology who feels that way, not if they're being honest with themselves. I wish the New York Genome Center and IBM luck in this, and I still think it's a worthwhile thing to at least try. But my guess is that it's going to be a humbling experience. Even if all the literature were correct in every detail, I think it would be one. And the literature is not correct in every detail. It has compounds like that one at the top of the entry in it, and people seem to think that they can draw conclusions from them.
+ TrackBacks (0) | Category: Biological News | Cancer | Chemical Biology | Drug Assays | The Scientific Literature
March 12, 2014
OK, now that recent stem cell report is really in trouble. One of the main authors, Teruhiko Wakayama, is saying that the papers should be withdrawn. Here's NHK:
Wakayama told NHK he is no longer sure the STAP cells were actually created. He was in charge of important experiments to check the pluripotency of the cells.
He said a change in a specific gene is key proof that the cells are created. He said team members were told before they released the papers that the gene had changed.
Last week, RIKEN disclosed detailed procedures for making STAP cells after outside experts failed to replicate the results outlined in the Nature article.
Wakayama pointed out that in the newly released procedures, RIKEN says this change didn't take place.
He said he reviewed test data submitted to the team's internal meetings and found multiple serious problems, such as questionable images.
These are the sorts of things that really should be ironed out before you make a gigantic scientific splash, you'd think. But I can understand how these things happen, too - a big important result, a groundbreaking discovery, and you think that someone else is probably bound to find the same thing within a month. Within a week. So you'd better publish as fast as you can, unless you feel like being a footnote when the history gets written and the prizes get handed out. There are a few details that need to be filled in? That's OK - just i-dotting and t-crossing, that stuff will be OK. The important thing is the get the discovery out to the world.
But that stuff comes back to bite you, big-time. Andrew Wiles was able to fix his proof of Fermat's Last Theorem post-announcement, but (a) that problem was non-obvious (he didn't know it was there at first), and (b) biology ain't math. Cellular systems are flaky, fluky, and dependent on a lot of variables, some of which you might not even be aware of. An amazing result in an area as tricky as stem cell generation needs a lot of shaking down, and it seems that this one has gotten it. Well, it's getting it now.
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February 27, 2014
Ah, the good old central nervous system, and its good old receptors. Especially the good old ion channels - there's an area with enough tricky details built into it to keep us all busy for another few decades. Here's a good illustration, in a new paper from Nature Chemical Biology. The authors, from Berkeley, are looking at the ionotropic glutamate receptors, an important (and brainbendingly complex) group. These are the NMDA, AMPA, and kainate receptors, if you name them by their prototype ligands, and they're assembled as tetramers from mix-and-match subunit proteins, providing a variety of species even before you start talking about splice variants and the like. This paper used a couple of the simpler kainate systems as a proving ground.
They're working with azobenzene-linked compounds that can be photoisomerized, and using that property as a switch. Engineering a Cys residue close to the binding pocket lets them swivel the compound in and out (as shown), and this gives them a chance to see how many of the four individual subunits need to be occupied, and what the states of the receptor are along the way. (The ligand does nothing when it's not tethered to the protein). The diagram shows the possible occupancy states, and the colored-in version shows what they found for receptor activation.
You apparently need two ligands just to get anything to happen (and this is consistent with previous work on these systems). Three ligands buys you more signaling, and the four peaks things out. Patch-clamp studies had already shown that these things are apparently capable of stepwise signaling, and this work nails that down ingeniously. Presumably this whole tetramer setup has been under selection to take advantage of that property, and you'd have to assume that the NMDA and AMPA receptors (extremely common ones, by the way) are behaving similarly. The diagram shows the whole matrix of what seems to be going on.
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February 21, 2014
Update: the nomenclature of these enzymes is messy - see the comments.
Here's another activity-based proteomics result that I've been meaning to link to - in this one, the Cravatt group strengthens the case for carboxylesterase 3 as a potential target for metabolic disease. From what I can see, that enzyme was first identified back in about 2004, one of who-knows-how-many others that have similar mechanisms and can hydrolyze who-knows-how-many esters and ester-like substrates. Picking your way through all those things from first principles would be a nightmare - thus the activity-based approach, where you look for interesting phenotypes and work backwards.
In this case, they were measuring adipocyte behavior, specifically differentiation and lipid accumulation. A preliminary screen suggested that there were a lot of serine hydrolase enzymes active in these cells, and a screen with around 150 structurally diverse carbamates gave several showing phenotypic changes. The next step in the process is to figure out what particular enzymes are responsible, which can be done by fluorescence labeling (since the carbamates are making covalent bonds in the enzyme active sites. They found my old friend hormone-sensitive lipase, as well they should, but there was another enzyme that wasn't so easy to identify.
One particular carbamate, the unlovely but useful WWL113, was reasonably selective for the enzyme of interest, which turned out to be the abovementioned carboxyesterase 3 (Ces3). The urea analog (which should be inactive) did indeed show no cellular readouts, and the carbamate itself was checked for other activities (such as whether it was a PPAR ligand). These established a strong connection between the inhibitor, the enzyme, and the phenotypic effects.
With that in hand, they went on to find a nicer-looking compound with even better selectivity, WWL229. (I have to say, going back to my radio-geek days in the 1970s and early 1980s, that I can't see the letters "WWL" without hearing Dixieland jazz, but that's probably not the effect the authors are looking for). Using an alkyne derivative of this compound as a probe, it appeared to label only the esterase of interest across the entire adipocyte proteome. Interestingly, though, it appears that WWL13 was more active in vivo (perhaps due to pharmacokinetic reasons?)
And those in vivo studies in mice showed that Ces3 inhibition had a number of beneficial effects on tissue and blood markers of metabolic syndrome - glucose tolerance, lipid profiles, etc. Histologically, the most striking effect was the clearance of adipose deposits from the liver (a beneficial effect indeed, and one that a number of drug companies are interested in). This recapitulates genetic modification studies in rodents targeting this enzyme, and shows that pharmacological inhibition could do the job. And while I'm willing to bet that the authors would rather have discovered a completely new enzyme target, this is solid work all by itself.
+ TrackBacks (0) | Category: Biological News | Chemical Biology | Diabetes and Obesity
February 18, 2014
Oh, @#$!. That was my first comment when I saw this story. That extraordinary recent work on creating stem cells by subjected normal cells to acid stress is being investigated:
The RIKEN centre in Kobe announced on Friday that it is looking into alleged irregularities in the work of biologist Haruko Obokata, who works at the institution. She shot to fame last month as the lead author on two papers published in Nature that demonstrated a simple way to reprogram mature mice cells into an embryonic state by simply applying stress, such as exposure to acid or physical pressure on cell membranes. The RIKEN investigation follows allegations on blog sites about the use of duplicated images in Obokata’s papers, and numerous failed attempts to replicate her results.
PubPeer gets the credit for bringing some of the problems into the light. There are some real problems with figures in the two papers, as well as earlier ones from the same authors. These might be explicable as cimple mistakes, which is what the authors seem to be claiming, if it weren't for the fact that no one seems to be able to get the stem-cell results to reproduce. There are mitigating factors there, too - different cell lines, perhaps the lack of a truly detailed protocol from the original paper. But a paper should have enough details in it to be reproduced, shouldn't it?
Someone on Twitter was trying to tell me the other day that the whole reproducibility issue was being blown out of proportion. I don't think so. The one thing we seem to be able to reproduce is trouble.
Update: a list of the weirdest things (so far) about this whole business.
+ TrackBacks (0) | Category: Biological News | The Scientific Literature
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.
+ TrackBacks (0) | Category: Biological News | In Silico | The Dark Side | The Scientific Literature
February 10, 2014
Here's a very interesting feature from Cell - an interactive timeline on the journal's 40th anniversary, highlighting some of the key papers it's published over the years. This installment takes us up into the early 1980s. When you see the 1979 paper that brings the news that tyrosine groups on proteins actually get phosphorylated post-translation, the 1982 discovery of Ras as involved in human cancer cells, or another 1982 paper showing that telomeres have these weird repeating units on them, you realize how young the sciences molecular and cell biology really are.
+ TrackBacks (0) | Category: Biological News | The Scientific Literature
February 7, 2014
Here's something for metabolic disease people to think about: there's a report adding to what we know about the hormone irisin, secreted from muscle tissue, that causes some depots of white adipose tissue to become more like energy-burning brown fat. In the late 1990s, there were efforts all across the drug industry to find beta-3 adrenoceptor agonists to stimulate brown fat for weight loss and dyslipidemia. None of them ever made it through, and thus the arguments about whether they would actually perform as thought were never really settled. One of the points of contention was how much responsive brown adipose tissue adults had available, but I don't recall anything suspecting that it could be induced. In recent years, though, it's become clear that a number of factors can bring on what's been called "beige fat".
Irisin seems to be released in response to exercise, and is just upstream of the important transcriptional regulator PGC-1a. In fact, release of irisin might be the key to a lot of the beneficial effects of exercise, which would be very much worth knowing. In this study, a stabilized version of it, given iv to rodents, had very strong effects on body weight and glucose tolerance, just the sort of thing a lot of people could use.
One of the very interesting features of this area, from a drug discovery standpoint, is that no one has identified the irisin receptor just yet. Look for headlines on that one pretty soon, though - you can bet that a lot of people are chasing it as we speak.
Update: are human missing out on this, compared to mice and other species?
+ TrackBacks (0) | Category: Biological News | Diabetes and Obesity
February 5, 2014
You may remember a study that suggested that antioxidant supplement actually negated the effects of exercise in muscle tissue. (The reactive oxygen species generated are apparently being used by the cells as a signaling mechanism, one that you don't necessarily want to turn off). That was followed by another paper that showed that cells that should be undergoing apoptosis (programmed cell death) could be kept alive by antioxidant treatment. Some might read that and not realize what a bad idea that is - having cells that ignore apoptosis signals is believed to be a common feature in carcinogenesis, and it's not something that you want to promote lightly.
Here are two recent publications that back up these conclusions. The BBC reports on this paper from the Journal of Physiology. It looks like a well-run trial demonstrating that antioxidant therapy (Vitamin C and Vitamin E) does indeed keep muscles from showing adaptation to endurance training. The vitamin-supplemented group reached the same performance levels as the placebo group over the 11-week program, but on a cellular level, they did not show the (beneficial) changes in mitochondria, etc. The authors conclude:
Consequently, vitamin C and E supplementation hampered cellular adaptions in the exercised muscles, and although this was not translated to the performance tests applied in this study, we advocate caution when considering antioxidant supplementation combined with endurance exercise.
Then there's this report in The Scientist, covering this paper in Science Translational Medicine. The title says it all: "Antioxidants Accelerate Lung Cancer Progression in Mice". In this case, it looks like reactive oxygen species should normally be activating p53, but taking antioxidants disrupts this signaling and allows early-stage tumor cells (before their p53 mutates) to grow much more quickly.
So in short, James Watson appears to be right when he says that reactive oxygen species are your friends. This is all rather frustrating when you consider the nonstop advertising for antioxidant supplements and foods, especially for any role in preventing cancer. It looks more and more as if high levels of extra antioxidants can actually give people cancer, or at the very least, help along any cancerous cells that might arise on their own. Evidence for this has been piling up for years now from multiple sources, but if you wander through a grocery or drug store, you'd never have the faintest idea that there could be anything wrong with scarfing up all the antioxidants you possibly can.
The supplement industry pounces on far less compelling data to sell its products. But here are clear indications that a large part of their business is actually harmful, and nothing is heard except the distant sound of crickets. Or maybe those are cash registers. Even the wildly credulous Dr. Oz reversed course and did a program last year on the possibility that antioxidant supplements might be doing more harm than good, although he still seems to be pitching "good" ones versus "bad". Every other pronouncement from that show is immediately bannered all over the health food aisles - what happened to this one?
This shouldn't be taken as a recommendation to go out of the way to avoid taking in antioxidants from food. But going out of your way to add lots of extra Vitamin C, Vitamin E, N-acetylcysteine, etc., to your diet? More and more, that really looks like a bad idea.
Update: from the comments, here's a look at human mortality data, strongly suggesting no benefit whatsoever from antioxidant supplementation (and quite possibly harm from beta-carotene, Vitamin A, and Vitamin E),
+ TrackBacks (0) | Category: Biological News | Cancer
February 3, 2014
The advent of such techniques as CRISPR has people thinking again about gene therapy, and no wonder. This has always been a dream of molecular medicine - you could wipe all sorts of rare diseases off the board by going in and fixing their known genetic defects. Actually doing that, though, has been extremely difficult (and dangerous, since patients have died in the attempt).
But here's a report of embryonic gene modification in cynomologous monkeys, and if it works in cynos, it's very likely indeed to work in humans. In vitro fertilization plus CRISPR/Cas9 - neither of these, for better or worse, are all that hard to do, and my guess is that we're very close to seeing someone try this - probably not in the US at first, but there are plenty of other jurisdictions. There's a somewhat disturbing angle, though: I don't see much cause (or humanly acceptable cause) for generating gene-knockout human beings, which is what this technique would most easily provide. And for fixing genetic defects, well, you'd have to know that the single-cell embryo actually has the defect, and unless both parents are homozygous, you're not going to be sure (can't sequence the only cell you have, can you?) So the next easiest thing is to add copies of some gene you find desirable, and that will take us quickly into uneasy territory.
A less disturbing route might be to see if the technique can be used to gene-edit the egg and sperm cells before fertilization. Then you've got the possibility of editing germ cell lines in vivo, which really would wipe these diseases out of humanity (except for random mutations), but that will be another one of those hold-your-breath steps, I'd think. It's only a short step from fixing what's wrong to enhancing what's already there - it all depends on where you slide the scale to define "wrong". More fast-twitch muscle fibers, maybe? Restore the ability to make your own vitamin C? Switch the kid's lipoproteins to ApoA1 Milano?
For a real look into the future, combine this with last week's startling report of the generation of stem cells by applying stress to normal tissue samples. This work seems quite solid, and there are apparently anecdotal reports (see the end of this transcript) of some of it being reproduced already. If so, we would appear to be vaulting into a new world of tissue engineering, or at least a new world of being able to find out what's really hard about tissue engineering. ("Just think - horrible, head-scratching experimental tangles that were previously beyond our reach can finally be. . .")
Now have a look at this news about a startup called Editas. They're not saying what techniques they're going to use (my guess is some proprietary variant of CRISPR). But whatever they have, they're going for the brass ring:
(Editas has) ambitious plans to create an entirely new class of drugs based on what it calls “gene editing.” The idea is similar, yet different, from gene therapy: Editas’ goal is to essentially target disorders caused by a singular genetic defect, and using a proprietary in-house technology, create a drug that can “edit” out the abnormality so that it becomes a normal, functional gene—potentially, in a single treatment. . .
. . .Editas, in theory, could use this system to create a drug that could cure any number of genetic diseases via a one-time fix, and be more flexible than gene therapy or other techniques used to cure a disease on the genetic level. But even so, the challenges, just like gene therapy, are significant. Editas has to figure out a way to safely and effectively deliver a gene-editing drug into the body, something Bitterman acknowledges is one of the big hills the company has to climb.
This is all very exciting stuff. But personally, I don't do gene editing, being an organic chemist and a small-molecule therapeutics guy. So what does all this progress mean for someone like me (or for the companies that employ people like me?) Well, for one thing, it is foretelling the eventual doom of the what we can call the Genzyme model, treating rare metabolic disorders with few patients but high cost-per-patient. A lot of companies are targeting (or trying to target) that space these days, and no wonder. Their business model is still going to be safe for some years, but honestly, I'd have to think that eventually someone is going to get this gene-editing thing to work. You'd have to assume that it will be harder than it looks; most everything is harder than it looks. And regulatory agencies are not going to be at their speediest when it comes to setting up trials for this kind of thing. But a lot of people with a lot of intelligence, a lot of persistence, and an awful lot of money are going after this, and I have to think that someone is going to succeed. Gene editing, Moderna's mRNA work - we're going to rewrite the genome to suit ourselves, and sooner than later. The reward will be treatments that previous eras would have had to ascribe to divine intervention, a huge step forward in Francis Bacon's program of "the effecting of all things possible".
The result will also be a lot of Schumpeterian "creative destruction" as some existing business models dissolve. And that's fine - I think that business models should always be subject to that selection pressure. As a minor side benefit, these therapies might finally (but probably won't) shut up the legion of people who go on about how drug companies aren't interested in cures, just endlessly profitable treatments. It never seems to occur to them that cures are hard, nor that someone might actually come along with one.
+ TrackBacks (0) | Category: Biological News
January 28, 2014
Here's a look at some very interesting research on HIV (and a repurposed compound) that I was unable to comment on here. As for the first line of that post, well, I doubt it, but I like to think of myself as rich in spirit. Or something.
+ TrackBacks (0) | Category: Biological News | Infectious Diseases
January 14, 2014
Here's a good paper on the design of stapled peptides, with an emphasis on what's been learned about making them cell-penetrant. It's also a specific rebuttal to a paper from Genentech (the Okamoto one referenced below) detailing problems with earlier reported stapled peptides:
In order to maximize the potential for success in designing stapled peptides for basic research and therapeutic development, a series of important considerations must be kept in mind to avoid potential pitfalls. For example, Okamoto et al. recently reported in ACS Chemical Biology that a hydrocarbon-stapled BIM BH3 peptide (BIM SAHB) manifests neither improved binding activity nor cellular penetrance compared to an unmodified BIM BH3 peptide and thereby caution that peptide stapling does not necessarily enhance affinity or biological activity. These negative results underscore an important point about peptide stapling: insertion of any one staple at any one position into any one peptide to address any one target provides no guarantee of stapling success. In this particular case, it is also noteworthy that the Walter and Eliza Hall Institute (WEHI) and Genentech co-authors based their conclusions on a construct that we previously reported was weakened by design to accomplish a specialized NMR study of a transient ligand−protein interaction and was not used in cellular studies because of its relatively low α-helicity, weak binding activity, overall negative charge, and diminished cellular penetrance. Thus, the Okamoto et al. report provides an opportunity to reinforce key learnings regarding the design and application of stapled peptides, and the biochemical and biological activities of discrete BIM SAHB peptides.
You may be able to detect the sound of teeth gritting together in that paragraph. The authors (Loren Walensky of Dana-Farber, and colleagues from Dana-Farber, Albert Einstein, Chicago, and Yale), point out that the Genentech paper took a peptide that's about 21% helical, and used a staple modification that took it up to about 39% helical, which they say is not enough to guarantee anything. They also note that when you apply this technique, you're necessarily altering two amino acids at a minimum (to make them "stapleable"), as well as adding a new piece across the surface of the peptide helix, so these changes have to be taken into account when you compare binding profiles. Some binding partners may be unaffected, some may be enhanced, and some may be wiped out.
It's the Genentech team's report of poor cellular uptake that you can tell is the most irritating feature of their paper to these authors, and from the way they make their points, you can see why:
The authors then applied this BIM SAHBA (aa 145−164) construct in cellular studies and observed no biological activity, leading to the conclusion that “BimSAHB is not inherently cell-permeable”. However, before applying stapled peptides in cellular studies, it is very important to directly measure cellular uptake of fluorophore-labeled SAHBs by a series of approaches, including FACS analysis, confocal microscopy, and fluorescence scan of electrophoresed lysates from treated cells, as we previously reported. Indeed, we did not use the BIM SAHBA (aa 145−164) peptide in cellular studies, specifically because it has relatively low α-helicity, weakened binding activity, and overall negative charge (−2), all of which combine to make this particular BIM SAHB construct a poor candidate for probing cellular activity. As indicated in our 2008 Methods in Enzymology review, “anionic species may require sequence modification (e.g., point mutagenesis, sequence shift) to dispense with negative charge”, a strategy that emerged from our earliest studies in 2004 and 2007 to optimize the cellular penetrance of stapled BID BH3 and p53 peptides for cellular and in vivo analyses and also was applied in our 2010 study involving stapled peptides modeled after the MCL-1 BH3 domain. In our 2011 Current Protocols in Chemical Biology article, we emphasized that “based on our evaluation of many series of stapled peptides, we have observed that their propensity to be taken up by cells derives from a combination of factors, including charge, hydrophobicity, and α-helical structure, with negatively charged and less structured constructs typically requiring modification to achieve cell penetrance. . .
They go on to agree with the Genentech group that the peptide they studied has poor uptake into cells, but the tell-us-something-we-don't-know tone comes through pretty clearly, I'd say. The paper goes on to detail several other publications where these authors worked out the behavior of BIM BH3 stapled peptides, saying that "By assembling our published documentation of the explicit sequence compositions of BIM SAHBs and their distinct properties and scientific applications, as also summarized in Figure 1, we hope to resolve any confusion generated by the Okamoto et al. study".
They do note that the Genentech (Okamoto) paper did use one of their optimized peptides in a supplementary experiment, which shows that they were aware of the different possibilities. That one was apparently showed no effects on the viability of mouse fibroblasts, but this new paper says that a closer look (at either their own studies or at the published literature) would have shown them that the cells were actually taking up the peptide, but were relatively resistant to its effects, which actually helps establish something of a therapeutic window.
This is a pretty sharp response, and it'll be interesting to see if the Genentech group has anything to add in their defense. Overall, the impression is that stapled peptides can indeed work, and do have potential as therapeutic agents (and are in the clinic being tested as such), but that they need careful study along the way to make sure of their properties, their pharmacokinetics, and their selectivity. Just as small molecules do, when you get down to it.
+ TrackBacks (0) | Category: Biological News | Cancer | Chemical Biology
January 13, 2014
Here's a paper from a few weeks back that I missed during the holidays: work from the Sinclair labs at Harvard showing a new connection between SIRT1 and aging, this time through a mechanism that no one had appreciated. I'll appreciate, in turn, that that opening sentence is likely to divide its readers into those who will read on and those who will see the words "SIRT1" or "Sinclair" and immediate seek their entertainment elsewhere. I feel for you, but this does look like an interesting paper, and it'll be worthwhile to see what comes of it.
Here's the Harvard press release, which is fairly detailed, in case you don't have access to Cell. The mechanism they're proposing is that as NAD+ levels decline with age, this affects SIRT1 function to the point that it no longer constains HIF-1. Higher levels of HIF-1, in turn, disrupt pathways between the nucleus and the mitochondia, leading to lower levels of mitochondria-derived proteins, impaired energy generation, and cellular signs of aging.
Very interestingly, these effects were reversed (on a cellular/biomarker level) by one-week treatment of aging mice with NMN (nicotine mononucleotide edit: fixed typo), a precursor to NAD. That's kind of a brute-force approach to the problem, but a team from Washington U. recently showed extremely similar effects in aging diabetic rodents supplemented with NMN, done for exactly the same NAD-deficiency reasons. I would guess that the NMN is flying off the shelves down at the supplement stores, although personally I'll wait for some more in vivo work before I start taking it with my orange juice in the mornings.
Now, whatever you think of sirtuins (and of Sinclair's work with them), this work is definitely not crazy talk. Mitochondria function has long been a good place to look for cellular-level aging, and HIF-1 is an interesting connection as well. As many readers will know, that acronym stands for "hypoxia inducible factor" - the protein was originally seen to be upregulated when cells were put under low-oxygen stress. It's a key regulatory switch for a number of metabolic pathways under those conditions, but there's no obvious reason for it to be getting more active just because you're getting older. Some readers may have encountered it as an oncology target - there are a number of tumors that show abnormal HIF activity. That makes sense, on two levels - the interiors of solid tumors are notoriously oxygen-poor, so that would at least be understandable, but switching on HIF under normal conditions is also bad news. It promotes glycolysis as a metabolic pathway, and stimulates growth factors for angiogenesis. Both of those are fine responses for a normal cell that needs more oxygen, but they're also the behavior of a cancer cell showing unrestrained growth. (And those cells have their tradeoffs, too, such as a possible switch between metastasis and angiogenesis, which might also have a role for HIF).
There's long been speculation about a tradeoff between aging and cellular prevention of carcinogenicity. In this case, though, we might have a mechanism where our interests on on the same side: overactive HIF (under non-hypoxic conditions) might be a feature of both cancer cells and "normally" aging ones. I put that word in quotes because (as an arrogant upstart human) I'm not yet prepared to grant that the processes of aging that we undergo are the ones that we have to undergo. My guess is that there's been very little selection pressure on lifespan, and that what we've been dealt is the usual evolutionary hand of cards: it's a system that works well enough to perpetuate the species and beyond that who cares?
Well, we care. Biochemistry is a wonderful, heartbreakingly intricate system whose details we've nowhere near unraveled, and we often mess it up when we try to do anything to it, anyway. But part of what makes us human is the desire (and now the ability) to mess around with things like this when we think we can benefit. Not looking at the mechanisms of aging seems to me like not looking at the mechanisms of, say, diabetes, or like letting yourself die of a bacterial infection when you could take an antibiotic. Just how arrogant that attitude is, I'm not sure yet. I think we'll eventually get the chance to find out. All this recent NAD work suggests that we might get that chance sooner than later. Me, I'm 51. Speed the plow.
+ TrackBacks (0) | Category: Aging and Lifespan | Biological News | Diabetes and Obesity
December 4, 2013
Here's some work that gets right to the heart of modern drug discovery: how are we supposed to deal with the variety of patients we're trying to treat? And the variety in the diseases themselves? And how does that correlate with our models of disease?
This new paper, a collaboration between eight institutions in the US and Europe, is itself a look at two other recent large efforts. One of these, the Cancer Genome Project, tested 138 anticancer drugs against 727 cell lines. Its authors said at the time (last year) that "By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies". The other study, the Cancer Cell Line Encyclopedia, tested 24 drugs against 1,036 cell lines. That one appeared at about the same time, and its authors said ". . .our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens."
Well, will they? As the latest paper shows, the two earlier efforts overlap to the extent of 15 drugs, 471 cell lines, 64 genes and the expression of 12,153 genes. How well do they match up? Unfortunately, the answer is "Not too well at all". The discrepancies really come out in the drug sensitivity data. The authors tried controlling for all the variables they could think of - cell line origins, dosing protocols, assay readout technologies, methods of estimating IC50s (and/or AUCs), specific mechanistic pathways, and so on. Nothing really helped. The two studies were internally consistent, but their cross-correlation was relentlessly poor.
It gets worse. The authors tried the same sort of analysis on several drugs and cell lines themselves, and couldn't match their own data to either of the published studies. Their take on the situation:
Our analysis of these three large-scale pharmacogenomic studies points to a fundamental problem in assessment of pharmacological drug response. Although gene expression analysis has long been seen as a source of ‘noisy’ data, extensive work has led to standardized approaches to data collection and analysis and the development of robust platforms for measuring expression levels. This standardization has led to substantially higher quality, more reproducible expression data sets, and this is evident in the CCLE and CGP data where we found excellent correlation between expression profiles in cell lines profiled in both studies.
The poor correlation between drug response phenotypes is troubling and may represent a lack of standardization in experimental assays and data analysis methods. However, there may be other factors driving the discrepancy. As reported by the CGP, there was only a fair correlation (rs < 0.6) between camptothecin IC50 measurements generated at two sites using matched cell line collections and identical experimental protocols. Although this might lead to speculation that the cell lines could be the source of the observed phenotypic differences, this is highly unlikely as the gene expression profiles are well correlated between studies.
Although our analysis has been limited to common cell lines and drugs between studies, it is not unreasonable to assume that the measured pharmacogenomic response for other drugs and cell lines assayed are also questionable. Ultimately, the poor correlation in these published studies presents an obstacle to using the associated resources to build or validate predictive models of drug response. Because there is no clear concordance, predictive models of response developed using data from one study are almost guaranteed to fail when validated on data from another study, and there is no way with available data to determine which study is more accurate. This suggests that users of both data sets should be cautious in their interpretation of results derived from their analyses.
"Cautious" is one way to put it. These are the sorts of testing platforms that drug companies are using to sort out their early-stage compounds and projects, and very large amounts of time and money are riding on those decisions. What if they're gibberish? A number of warning sirens have gone off in the whole biomarker field over the last few years, and this one should be so loud that it can't be ignored. We have a lot of issues to sort out in our cell assays, and I'd advise anyone who thinks that their own data are totally solid to devote some serious thought to the possibility that they're wrong.
Here's a Nature News summary of the paper, if you don't have access. It notes that the authors of the two original studies don't necessarily agree that they conflict! I wonder if that's as much a psychological response as a statistical one. . .
+ TrackBacks (0) | Category: Biological News | Cancer | Chemical Biology | Drug Assays
November 20, 2013
Double Nobelist Frederick Sanger has died at 95. He is, of course, the pioneer in both protein and DNA sequencing, and he lived to see these techniques, revised and optimized beyond anyone's imagining, become foundations of modern biology.
When he and his team determined the amino acid sequence of insulin in the 1950s, no one was even sure if proteins had definite sequences or not. That work, though, established the concept for sure, and started off the era of modern protein structural studies, whose importance to biology, medicine, and biochemistry is completely impossible to overstate. The amount of work needed to sequence a protein like insulin was ferocious - this feat was just barely possible given the technology of the day, and that's even with Sanger's own inventions and insights (such as Sanger's reagent) along the way. He received a well-deserved Nobel in 1958 for having accomplished it.
In the 1970s, he made fundamental advances in sequencing DNA, such as the dideoxy chain-termination method, again with effects which really can't be overstated. This led to a share of a second chemistry Nobel in 1980 - he's still only double laureate in chemistry, and every bit of that recognition was deserved.
+ TrackBacks (0) | Category: Biological News | Chemical News
November 12, 2013
Nature Biotechnology is making it known that they're open to publishing studies with negative results. The occasion is their publication of this paper, which is an attempt to replicate the results of this work, published last year in Cell Research. The original paper, from Chen-Yu Zhang of Nanjing University, reported that micro-RNAs (miRNAs) from ingested plants could be taken up into the circulation of rodents, and (more specifically) that miRNA168a from rice could actually go on to modulate gene expression in the animals themselves. This was a very interesting (and controversial) result, with a lot of implications for human nutrition and for the use of transgenic crops, and it got a lot of press at the time.
But other researchers in the field were not buying these results, and this new paper (from miRagen Therapeutics and Monsanto) reports that they cannot replicated the Nanjing work at all. Here's their rationale for doing the repeat:
The naturally occurring RNA interference (RNAi) response has been extensively reported after feeding double-stranded RNA (dsRNA) in some invertebrates, such as the model organism Caenorhabditis elegans and some agricultural pests (e.g., corn rootworm and cotton bollworm). Yet, despite responsiveness to ingested dsRNA, a recent survey revealed substantial variation in sensitivity to dsRNA in other Caenorhabditis nematodes and other invertebrate species. In addition, despite major efforts in academic and pharmaceutical laboratories to activate the RNA silencing pathway in response to ingested RNA, the phenomenon had not been reported in mammals until a recent publication by Zhang et al. in Cell Research. This report described the uptake of plant-derived microRNAs (miRNA) into the serum, liver and a few other tissues in mice following consumption of rice, as well as apparent gene regulatory activity in the liver. The observation provided a potentially groundbreaking new possibility that RNA-based therapies could be delivered to mammals through oral administration and at the same time opened a discussion on the evolutionary impact of environmental dietary nucleic acid effects across broad phylogenies. A recently reported survey of a large number of animal small RNA datasets from public sources has not revealed evidence for any major plant-derived miRNA accumulation in animal samples. Given the number of questions evoked by these analyses, the limited success with oral RNA delivery for pharmaceutical development, the history of safe consumption for dietary small RNAs and lack of evidence for uptake of plant-derived dietary small RNAs, we felt further evaluation of miRNA uptake and the potential for cross-kingdom gene regulation in animals was warranted to assess the prevalence, impact and robustness of the phenomenon.
They believe that the expression changes that the original team noted in their rodents were due to the dietary changes, not to the presence of rice miRNAs, which they say that they cannot detect. Now, at this point, I'm going to exit the particulars of this debate. I can imagine that there will be a lot of hand-waving and finger-pointing, not least because these latest results come partly from Monsanto. You have only to mention that company's name to an anti-GMO activist, in my experience, to induce a shouting fit, and it's a real puzzle why saying "DeKalb" or "Pioneer Hi-Bred" doesn't do the same. But it's Monsanto who take the heat. Still, here we have a scientific challenge, which can presumably be answered by scientific means: does rice miRNA get into the circulation and have an effect, or not?
What I wanted to highlight, though, is another question that might have occurred to anyone reading the above. Why isn't this new paper in Cell Research, if they published the original one? Well, the authors apparently tried them, only to find their work rejected because (as they were told) "it is a bit hard to publish a paper of which the results are largely negative". That is a silly response, verging on the stupid. The essence of science is reproducibility, and if some potentially important result can't be replicated, then people need to know about it. The original paper had very big implications, and so does this one.
Note that although Cell Research is published out of Shanghai, it's part of the Nature group of journals. If two titles under the same publisher can't work something like this out, what hope is there for the rest of the literature? Congratulations to Nature Biotechnology, though, for being willing to publish, and for explicitly stating that they are open to replication studies of important work. Someone should be.
+ TrackBacks (0) | Category: Biological News | The Scientific Literature
October 31, 2013
Laura Helmuth has a provocative piece up at Slate with the title "Watch Francis Collins Lunge For a Nobel Prize". She points out that the NIH and the Smithsonian are making a big deal out of celebrating the "10th anniversary of the sequencing of the human genome", even though many people seem to recall the big deal being in 2001 - not 2003. Yep, that was when the huge papers came out in Science and Nature with all the charts and foldouts, and the big press conferences and headlines. February of 2001.
So why the "tenth anniversary" stuff this year? Well, 2003 is the year that the NIH team published its more complete version of the genome. That's the anniversary they've chosen to remember. If you start making a big deal out of 2001, you have to start making a big deal out of the race between that group and the Celera group - and you start having to, you know, share credit. Now, I make no claims for Craig Venter's personality or style. But I don't see how it can be denied that he and his group vastly sped up the sequencing of the genome, and arrived at a similar result in far less time than the NIH consortium. The two drafts of the sequence were published simultaneously, even though there seems to have been a lot of elbow-throwing by the NIH folks to keep that from happening.
The NIH has been hosting anniversary events all year, but the most galling anniversary claim is made in an exhibit that opened this year at the Smithsonian’s National Museum of Natural History, the second-most-visited museum in the world. (Dang that Louvre.) It’s called “Genome: Unlocking Life’s Code,” and the promotional materials claim, “It took nearly a decade, three billion dollars, and thousands of scientists to sequence the human genome in 2003.” (Disclosure: I worked for Smithsonian magazine while the exhibition, produced in partnership with the NIH, was being planned, and I consulted very informally with the curators. That is, we had lunch and I warned them they were being played.) To be clear, I’m delighted that the Smithsonian has an exhibit on the human genome. And I’m a huge fan of the NIH. (To its credit, the NIH did host an anniversary symposium in 2011.) But the Smithsonian exhibit enshrines the 2003 date in the country’s museum of record and minimizes the great drama and triumph of 2001.
Celebrating 2003 rather than 2001 as the most important date in the sequencing of the human genome is like celebrating the anniversary of the final Apollo mission rather than the first one to land on the moon. . .
No one is well served by pretending that things happened otherwise, or that 2003 is somehow the date of the "real" human genome. The race was on to publish in 2001, and the headlines were in 2001, and all the proclamations that the genome had at least been sequenced were in February of 2001. If, from some perspectives, that makes for a messier story, oh well. If we stripped all the messy stories out of the history books, what would be left?
Update: Matthew Herper has more on this. He's not as down on the NIH as Helmuth is, but he has some history lessons of his own.
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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.
+ TrackBacks (0) | Category: Biological News | In Silico | The Central Nervous System
October 7, 2013
This year's Medicine Nobel is one that's been anticipated for some time. James Rothman of Yale, Randy W. Schekman of Berkeley, and Thomas C. Südhof of Stanford are cited for their fundamental discoveries in vesicular trafficking, and I can't imagine anyone complaining that it wasn't deserved. (The only controversy would be thanks, once again, to the "Rule of Three" in Alfred Nobel's will. Richard Scheller of Genentech has won prizes with Südhof and with Scheller for his work in the same field).
Here's the Nobel Foundation's scientific summary, and as usual, it's a good one. Vesicles are membrane-enclosed bubbles that bud off from cellular compartments and transport cargo to other parts of the cell (or outside it entirely), where they merge with another membrane and release their contents. There's a lot of cellular machinery involved on both the sending and receiving end, and that's what this year's winners worked out.
As it turns out, there are specific proteins (such as the SNAREs) imbedded in intracellular membranes that work as an addressing system: "tie up the membrane around this point and send the resulting globule on its way", or "stick here and start the membrane fusion process". This sort of thing is going on constantly inside the cell, and the up-to-the-surface-and-out variation is particularly noticeably in neurons, since they're constantly secreting neurotransmitters into the synapse. That latter process turned out to be very closely tied to signals like local calcium levels, which gives it the ability to be turned on and off quickly.
As the Nobel summary shows, a lot of solid cell biology had to be done to unravel all this. Scheckman looked for yeast cells that showed obvious mutations in their vesicle transport and tracked down what proteins had been altered. Rothman started off with a viral infection system that produced a lot of an easily-trackable protein, and once he'd identified others that helped to move it around, he used these as affinity reagents to find what bound to them in turn. This work dovetailed very neatly with the proteins that Scheckman's lab had identified, and suggested (as you'd figure) that this machinery was conserved across many living systems. Südhof then extended this work into the neurotransmitter area, discovering the proteins involved in the timing signals that are so critical in those cells, and demonstrating their function by generating mouse knockout models along the way.
The importance of all these processes to living systems can't be overstated. Eukaryotic cells have to be compartmentalized to function; there's too much going on for everything to be in the same stew pot all at the same time. So a system for "mailing" materials between those regions is vital. And in the same way, cells have to communicate with others, releasing packets of signaling molecules under very tight supervision, and that's done through many of the same mechanisms. You can trace the history of our understanding of these things through years of Nobel awards, and there will surely be more.
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September 18, 2013
Does it matter how a drug works, if it works? PTC Therapeutics seems bent on giving everyone an answer to that question, because there sure seem to be a lot of questions about how ataluren (PTC124), their Duchenne Muscular Dystrophy (DMD) therapy, acts. This article at Nature Biotechnology does an excellent job explaining the details.
Premature "stop" codons in the DNA of DMD patients, particularly in the dystrophin gene, are widely thought to be one of the underlying problems in the disease. (The same mechanism is believed to operate in many other genetic-mutation-driven conditions as well. Ataluren is supposed to promote "read-through" of these to allow the needed protein to be produced anyway. That's not a crazy idea at all - there's been a lot of thought about ways to do that, and several aminoglycoside antibiotics have been shown to work through that mechanism. Of that class, gentamicin has been given several tries in the clinic, to ambiguous effect so far.
So screening for a better enhancer of stop codon read-through seems like it's worth a shot for a disease with so few therapeutic options. PTC did this using a firefly luciferase (Fluc) reporter assay. As with any assay, there are plenty of opportunities to get false positives and false negatives. Firefly luciferase, as a readout, suffers from instability under some conditions. And if its signal is going to wink out on its own, then a compound that stabilizes it will look like a hit in your assay system. Unfortunately, there's no particular market in humans for a compound that just stabilizes firefly luciferase.
That's where the argument is with ataluren. Papers have appeared from a team at the NIH detailing trouble with the FLuc readout. That second paper (open access) goes into great detail about the mechanism, and it's an interesting one. FLuc apparently catalyzes a reaction between PTC124 and ATP, to give a new mixed anhydride adduct that is a powerful inhibitor of the enzyme. The enzyme's normal mechanism involves a reaction between luciferin and ATP, and since luciferin actually looks like something you'd get in a discount small-molecule screening collection, you have to be alert to something like this happening. The inhibitor-FLuc complex keeps the enzyme from degrading, but the new PTC124-derived inhibitor itself is degraded by Coenzyme A - which is present in the assay mixture, too. The end result is more luciferase signal that you expect versus the controls, which looks like a hit from your reporter gene system - but isn't. PTC's scientists have replied to some of these criticisms here.
Just to add more logs to the fire, other groups have reported that PTC124 seems to be effective in restoring read-through for similar nonsense mutations in other genes entirely. But now there's another new paper, this one from a different group at Dundee, claiming that ataluren fails to work through its putative mechanism under a variety of conditions, which would seem to call these results into question as well. Gentamicin works for them, but not PTC124. Here's the new paper's take-away:
In 2007 a drug was developed called PTC124 (latterly known as Ataluren), which was reported to help the ribosome skip over the premature stop, restore production of functional protein, and thereby potentially treat these genetic diseases. In 2009, however, questions were raised about the initial discovery of this drug; PTC124 was shown to interfere with the assay used in its discovery in a way that might be mistaken for genuine activity. As doubts regarding PTC124's efficacy remain unresolved, here we conducted a thorough and systematic investigation of the proposed mechanism of action of PTC124 in a wide array of cell-based assays. We found no evidence of such translational read-through activity for PTC124, suggesting that its development may indeed have been a consequence of the choice of assay used in the drug discovery process.
Now this is a mess, and it's complicated still more by the not-so-impressive performance of PTC124 in the clinic. Here's the Nature Biotechnology article's summary:
In 2008, PTC secured an upfront payment of $100 million from Genzyme (now part of Paris-based Sanofi) in return for rights to the product outside the US and Canada. But the deal was terminated following lackluster data from a phase 2b trial in DMD. Subsequently, a phase 3 trial in cystic fibrosis also failed to reach statistical significance. Because the drug showed signs of efficacy in each indication, however, PTC pressed ahead. A phase 3 trial in DMD is now underway, and a second phase 3 trial in cystic fibrosis will commence shortly.
It should be noted that the read-through drug space has other players in it as well. Prosensa/GSK and Sarepta are in the clinic with competing antisense oligonucleotides targeting a particular exon/mutation combination, although this would probably taken them into other subpopulations of DMD patients than PTC is looking to treat.
If they were to see real efficacy, PTC could have the last laugh here. To get back to the first paragraph of this post, if a compound works, well, the big argument has just been won. The company has in vivo data to show that some gene function is being restored, as well they should (you don't advance a compound to the clinic just on the basis of in vitro assay numbers, no matter how they look). It could be that the compound is a false positive in the original assay but manages to work through some other mechanism, although no one knows what that might be.
But as you can see, opinion is very much divided about whether PTC124 works at all in the real clinical world. If it doesn't, then the various groups detailing trouble with the early assays will have a good case that this compound never should have gotten as far as it did.
+ TrackBacks (0) | Category: Biological News | Business and Markets | Drug Assays | Drug Development
September 6, 2013
At C&E News, Lisa Jarvis has an excellent writeup on Warp Drive Bio and the whole idea of "cryptic natural products" (last blogged on here). As the piece makes clear, not everyone even is buying into the idea that there's a lot of useful-but-little-expressed natural product chemical matter out there, but since there could be, I'm glad that someone's looking.
Yet not everyone looked at the abundant gene clusters and saw a sea of drug candidates. The biosynthetic pathways defined by these genes are turned off most of the time. That inactivity caused skeptics to wonder how genome miners could be so sure they carried the recipes for medicinally important molecules.
Researchers pursuing genomics-based natural products say the answer lies in evolution and the environment. “These pathways are huge,” says Gregory L. Challis, a professor of chemical biology at the University of Warwick, in Coventry, England. With secondary metabolites encoded by as many as 150 kilobases of DNA, a bacterium would have to expend enormous amounts of energy to make each one.
Because they use so much energy, these pathways are turned on only when absolutely necessary. Traditional “grind and find” natural products discovery means taking bacteria out of their natural habitat—the complex communities where they communicate and compete for resources—and growing each strain in isolation. In this artificial setting, bacteria have no reason to expend energy to make anything other than what they need to survive.
“I absolutely, firmly believe that these compounds have a strong role to play in the environment in which these organisms live,” says Challis, who also continues to pursue traditional approaches to natural products. “Of course, not all bioactivities will be relevant to human medicine and agriculture, but many of them will be.”
The article also mentions that Novartis is working in this area, which I hadn't realized, as well as a couple of nonprofit groups. If there's something there, at any kind of reasonable hit rate, presumably one of these teams will find it?
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September 5, 2013
If you haven't heard of CRISPR, you must not have to mess around with gene expression. And not everyone does, true, but we sure do count on that sort of thing in biomedical research. And this is a very useful new technique to do it:
In 2007, scientists from Danisco, a Copenhagen-based food ingredient company now owned by DuPont, found a way to boost the phage defenses of this workhouse microbe. They exposed the bacterium to a phage and showed that this essentially vaccinated it against that virus (Science, 23 March 2007, p. 1650). The trick has enabled DuPont to create heartier bacterial strains for food production. It also revealed something fundamental: Bacteria have a kind of adaptive immune system, which enables them to fight off repeated attacks by specific phages.
That immune system has suddenly become important for more than food scientists and microbiologists, because of a valuable feature: It takes aim at specific DNA sequences. I