Ivermectin postmortem: PAINful experiences with a good drug

Ivermectin is a miracle drug. It cures infections caused by several types of parasitic roundworms, including those that cause river blindness and elephantiasis, two highly unpleasant diseases. Half of the 2015 Nobel Price for Physiology or Medicine was awarded to researchers who discovered the drug.

 

More recently, ivermectin was touted as a treatment for SARS-CoV-2. Unfortunately, after more than 90 human clinical trials, the preponderance of evidence shows that it is ineffective against COVID-19. A new (open-access) Perspective in J. Med. Chem. by Olaf Andersen, Jayme Dahlin, and collaborators at Weill Cornell Medicine, the National Institutes of Health, University of North Carolina Chapel Hill, and University of California San Francisco explores why it proved so misleading, with lessons for other drug repurposing efforts.

 

In 2011, ivermectin was one of 480 compounds tested in a biochemical assay at 50 µM and appeared to disrupt the interaction between HIV integrase and a mammalian protein involved in viral trafficking. In April 2020, low micromolar concentrations of ivermectin were reported to have anti-SARS-CoV-2 activity in a cellular assay. Given a terrifying new disease with no treatments, an approved drug that showed even tenuous activity looked like a lifeline. Researchers around the world began studying ivermectin. PubMed citations jumped from 459 in 2019 to 734 in 2021.

 

The new paper examines some of this past work and dives deeply into the biological effects of ivermectin. The molecule is poorly soluble in water (around 1-2 µM) and partitions into cellular membranes, where it activates a chloride channel receptor in worms, paralyzing them. At this it is quite potent: when taken as directed, human plasma concentrations of ivermectin are around 60 nM, but because the drug is highly protein bound the free concentration is only around 6 nM.

 

The astute reader may notice that the apparent antiviral activity was observed at low micromolar concentrations of ivermectin, three orders of magnitude higher than physiologically relevant, and the initial biochemical assay was conducted at an even higher concentration. In fact, that work was done using an AlphaScreen, which is notoriously sensitive to artifacts. The new paper demonstrates that ivermectin forms aggregates at low micromolar concentrations, and that these aggregates interfere with the AlphaScreen. (We previously wrote about how many of the early reports of compounds active against SARS-CoV-2 proteins were in fact aggregators.)

 

If ivermectin perturbs membrane proteins in worms, what does it do in human cells? The researchers tested the drug against panels of G protein-coupled receptors (GPCRs) and found that in one assay format it inhibited more than a quarter of 168 GPCRs at 10 µM, much higher than physiologically relevant but comparable to the in vitro experiments with SARS-CoV-2. Further studies revealed that ivermectin changes the properties of membranes at low micromolar concentrations, as assessed by multiple methods including electrophysiological assays. Several ivermectin analogs were also tested and found to have similar activity, consistent with nonspecific effects. Impressively, these experiments were done blinded to the experimenter.

 

You might think that messing with membranes would not be good for cells, and you would be right. The researchers found that ivermectin decreased cell viability at low micromolar concentrations in a variety of assay formats through multiple mechanisms, both cytotoxic and apoptotic. Importantly, the concentrations at which ivermectin was active against cells were similar to the concentrations where it showed activity against SARS-CoV-2. The researchers also analyzed 766 PubChem assays and found that ivermectin is active in nearly a third of those assessing cellular toxicity. Killing a host cell is certainly one way of killing a virus, but likely not a useful one.

 

In summary, the original data suggesting that ivermectin is a developable antiviral agent was flawed. The researchers describe this as “a saga of the damage that can be done by assay interference compounds” and a “cautionary tale for the dangers of ‘pandemic exceptionalism.’” They continue:

 

The fact that a repurposed drug is well-characterized clinically, or that there is an ongoing pandemic, may justify performing clinical and mechanistic experiments in parallel, but not skipping mechanistic studies, where the key experiments could have been done in a matter of a few weeks/months.

 

This J. Med. Chem. paper is a meticulous, comprehensive study; with 71 pages of supporting information there is far more to cover than I can do justice to in a blog post. The paper also includes a useful flowchart for derisking nonspecific membrane perturbation. It is well worth reading, particularly for those new to drug discovery. As Richard Feynman warned, “the first principle is that you must not fool yourself -- and you are the easiest person to fool.”

Fragments vs CYP125 and CYP142 for M. tuberculosis

Although 2020 and 2021 were baleful exceptions, tuberculosis is normally the world’s deadliest infectious disease. The pathogen Mycobacterium tuberculosis (Mtb) makes its home inside macrophages, the very cells that normally destroy microorganisms. Worse, some strains have become resistant to approved drugs. In a recent open-access J. Med. Chem. paper, Madeline Kavanagh, Kirsty McLean, and collaborators at University of Manchester, University of Cambridge, and elsewhere explore a new mechanism to fight this ancient disease.

 

An important nutrient source Mtb exploits inside human cells is cholesterol, which bacteria oxidize with the cytochrome P450 enzyme CYP125. A second enzyme, CYP142, is also present in some strains and is functionally redundant. Thus, the researchers set out to make a dual inhibitor.

 

Mtb has some 20 CYPs, and the Cambridge researchers have been studying them for a long time: we wrote about their work on CYP121 in 2016 and their work on CYP126 in 2014. All these enzymes contain a heme cofactor, and much is known about targeting the bound iron. However, some ligands are promiscuous, hitting human P450 enzymes, or they are rapidly effluxed out of cells. Thus, the researchers built a fragment library of just 80 likely heme binders but excluded particularly promiscuous moieties, such as imidazoles. The library was screened using UV-vis spectroscopy; ligands that bind to the heme group cause a red-shift in the λ_max. Only four hits were found for CYP125, while a dozen were found for CYP142, including three of the four CYP125 hits. Compound 1a had modest affinity for CYP125 and low micromolar affinity for CYP142.

 

Compound 1a was soaked into crystals of CYP142, and interestingly two molecules bound at the active site: one coordinating to the iron atom as expected, the other binding near the entrance of the active site. This suggested a linking or merging strategy, so the researchers made small libraries based on compound 1a and tested these against the two enzymes. Compound 5m was the most potent against both. Crystal structures of this molecule bound to both CYP125 and CYP142 confirmed that the pyridine nitrogen maintained its interaction with the heme iron, while the added bit nicely filled the space previously occupied by the second copy of compound 1a.

 

Functional assays revealed that compound 5m inhibited both enzymes with nanomolar activity, comparable to their affinities. It also inhibited the growth of Mtb grown on media containing cholesterol as the sole source of carbon. More impressively, it even inhibited the growth of Mtb in standard media spiked with just low concentrations of cholesterol. Oddly though, it also inhibited the growth of Mtb grown on media not containing cholesterol, albeit at a higher concentration, suggesting perhaps other targets. But one reason tuberculosis is so hard to treat is that the bacteria persist inside human cells. Encouragingly, compound 5m inhibited the growth of Mtb in human macrophages at low micromolar concentrations, and it  did not show cytotoxicity up to 50 micromolar concentration.

 

Unfortunately, compound 5m did show cytotoxicity to human HepG2 cells, and it also inhibited several human P450 enzymes at high nanomolar concentrations, which could cause drug-drug interactions. Also, selectivity against other MTb P450 enzymes is unclear. Finally, no in vitro ADME data are reported. Nonetheless, this is a nice fragment to lead story, and compound 5m could be used – cautiously – as a chemical probe to study Mtb biology.

The Chemical Probes Portal turns ten. Use it!

Last week we highlighted a new tool to computationally predict whether a molecule might aggregate, thereby causing false positives. This doesn’t necessarily mean the molecules are bad (after all, some approved drugs aggregate), but it’s all too easy to screen molecules under inappropriate conditions. This brings up the topic of chemical probes, and as it happens the Chemical Probes Portal turns ten years old this year, as celebrated in a Cancer Cell Commentary by Susanne Müller, Domenico Sanfelice, and Paul Workman and a blog post by Ben Kolbington at the Institute of Cancer Research.

 

We first wrote about the Chemical Probes Portal in July 2015, when it contained just 7 compounds. When we returned in 2023 it contained more than 500 compounds, and by the end of last year the number was up to 803. As of today it lists 1174 probes for 622 targets. Nearly a third of the probes also have chemically related inactive controls. These seem like large numbers, but the the human genome conservatively encodes for some 20,000 proteins, and the ambitious Target 2035 initiative seeks chemical probes for all of them.

 

The new paper emphasizes that the standards are in some ways higher for chemical probes than for approved drugs: “whereas probes principally require a high degree of selectivity, drugs need ‘only’ to be safe and effective and may often hit several targets.” Dimethyl fumarate comes to mind as a highly promiscuous covalent modifier that is nonetheless a useful drug for multiple sclerosis and psoriasis.

 

Even when a compound hits a target of interest, that doesn’t mean any biological effects observed are due to the target, particularly when the readout is cell death. The researchers note that TH588 was originally reported as a potent inhibitor of MTH1, but it actually kills cancer cells by binding to tubulin, a fact not always mentioned by chemical suppliers. Another study found that ten clinical compounds were still active in cells even when their putative target was knocked out using CRISPR.

 

The tone of the Commentary is pragmatic, emphasizing that for new or difficult targets, it may be difficult to find good chemical probes. For example, LY294002 is mentioned as a “pathfinder tool” that was useful to explore the biology around the PI3 kinase family but has now been superseded by more selective molecules.

 

Unfortunately, not everyone seems to have gotten the message. Curcumin, which as we noted can aggregate, form nonselective covalent adducts, fluoresce, and generate reactive oxygen species, appears in >2600 PubMed publications – just in the past year. What a waste.

 

If you’re exploring the biology of a target, please check the Portal to see whether there are good probes. If you’re reading (or reviewing!) a paper that reports small molecule studies, please check to see whether the probe has been assessed - especially to see if it shows up as one of more than 250 Unsuitables. And if you’re interested in participating, please consider reviewing or even hosting a Probe Hackathon.

Can machine learning help you avoid SCAMs?

Among the many types of artifacts that can fool screens and derail efforts to find leads, small colloidally aggregating molecules (SCAMs) are particularly pernicious. As we discussed way back in 2009, these molecules can form aggregates in aqueous buffer that interfere with a variety of assays, leading to wasted resources and embarrassing publications.

 

The problem is that there isn’t necessarily anything wrong with the molecules per se, and even many approved drugs can form aggregates. Thus, it is difficult to predict whether any given molecule will be a troublemaker. In a new (open-access) Angew. Chem. Int. Ed. paper, Pascal Friederich, Rebecca Davis, and collaborators at Karlsruhe Institute of Technology and University of Manitoba Winnipeg explore whether machine learning can help.

 

The researchers built a Multi-Explanation Graph Attention Network, or MEGAN, which is accessible through a simple web interface. Rather than a homicidal doll, this MEGAN represents atoms as nodes and bonds as edges in a graph, similar to the Fragment Network we wrote about here. MEGAN was trained on a set of 12,338 aggregators and 177,048 non-aggregating molecules. Importantly, the researchers used explainable AI (xAI), which colors portions of the molecule according to their importance for (non)aggregation.

 

Testing MEGAN on a set of 1500 aggregators and 1500 non-aggregators, none of which were included in the training set, yielded an accuracy of 82%. Given that most molecules don’t aggregate, a model biased towards non-aggregators would be expected to have a high accuracy, and to account for this the researchers assessed the “F1” score, which was similarly impressive.

 

 

The researchers provide several examples in which subtle variations transform a molecule from a non-aggregator to an aggregator, and show that MEGAN correctly predicts these. Furthermore, it “shows its work,” highlighting the chemical features underlying the prediction. For example, 9H-pyrido[3,4-b]indole is predicted with 92% confidence not to be an aggregator.

 

 

Just adding a methyl group flips the odds in favor of aggregation to 92%.

 

Exploring the molecular features that lead to aggregation can reveal general trends, such as rigid, “flat” molecules with moieties that can serve either as hydrogen bond donors or acceptors. This is consistent with a paper we discussed last year, though unfortunately the researchers do not cite it.

 

To further assess the tool, it was tested against a set of drugs that had been characterized as aggregators or non-aggregators. MEGAN correctly classified 15 of 30 aggregators and 24 of 28 non-aggregators. In contrast, a different program caught only 2 of the aggregators. The researchers note that most of the training data for MEGAN came from a single screen in phosphate buffer at pH 7, and aggregation can be very dependent on buffer components and pH.

 

Practical Fragments has previously highlighted other aggregation predictors, most notably Aggregator Advisor and Liability Predictor. As for any computational model, the old chestnut “trust but verify” applies. MEGAN appears to be a useful tool, but please run physical experiments if the molecule is important.

They may be cons, but they’re our CONS

Practical Fragments has written repeatedly about various assay artifacts (vide infra). Different technologies are susceptible to different interference mechanisms, making general rules difficult. Earlier this year we wrote about the Metal Ion Interference Set, or MIIS: a collection of a dozen salts that could be used to assess the sensitivity of assays to metal contaminants. In a recent open-access JACS Au paper, Huabin Hu (Uppsala University), Jonathan Baell (Monash University), and collaborators extend the concept to small molecules.

 

The researchers have compiled a Collection Of useful Nuisance compounds, or CONS, perhaps with a nod to “Chemical con artists foil drug discovery” published a decade ago, which we highlighted here. The 103 members of the CONS are divided into three categories.

 

The first set contains five aggregators: molecules that have been shown to form colloidal clusters that non-specifically interfere with biological assays, as discussed here.

 

The largest set, at 67 members, consists of PAINS, or pan-assay interference compounds, which we first wrote about in 2010. These are themselves divided into various subcategories: non-specific electrophiles such as curcumin and an isothiazolone, redox cyclers such as quinones, contaminants such as the decomposition products of certain fused tetrahydroquinolines, miscellaneous, metal chelators, and additional mechanisms including optical interference and singlet oxygen quenchers, which are particularly problematic in AlphaScreen assays.  

 

The last set consists of 31 compounds that can cause problems in phenotypic assays. Some of these non-specifically disrupt cell membranes. Others have well-defined but toxic effects, such as interfering with tubulin or intercalating into DNA. Such bioactivity is not always a bad thing: some of these molecules, such as topotecan and colchicine, are approved drugs, but it’s useful to be aware of whether these types of activities will affect your assay.

 

One criticism of the PAINS concept is that it lumps together multiple mechanisms. (Pete Kenny wrote about this recently.) Another criticism is that, by focusing on chemical substructures, true hits may be unfairly deprioritized based on structure alone. What’s nice about the CONS list is that the potentially interfering mechanisms of each molecule are documented and categorized so they can be considered when establishing an assay. For example, you may not care whether a compound interferes in a phenotypic assay if you are performing a screen on an isolated enzyme.

 

The entire set of compounds is available from Enamine, and additional vendors are provided in a supplementary table. If you’re doing a lot of assays, particularly on new targets and mechanisms, it may be worth testing the CONS to understand what kinds of false positives might occur.

Spacial Scores: new metrics for measuring molecular complexity

Molecular complexity is one of the theoretical underpinnings for fragment-based drug discovery. Mike Hann and colleagues proposed two decades ago that very simple molecules may not have enough features to bind tightly to any proteins, whereas highly functionalized molecules may have extraneous spinach that keeps them from binding to any proteins. Fragments, being small and thus less complex, are in a sweet spot: just complex enough.

 

But what does it mean for one molecule to be more complex than another? Most chemists would agree that pyridine is more complex than methane, but is it more complex than benzene? To decide, you need a numerical metric, and there are plenty to choose from. The problem, as we discussed in 2017, is that they don’t correlate with one another, so it is not clear which one(s) to choose. In a new (open access) J. Med. Chem. paper, Adrian Krzyzanowski, Herbert Waldmann and colleagues at the Max Planck Institute Dortmund have provided another. (Derek Lowe also recently covered this paper.)

 

The researchers propose the Spacial Score, or SPS. This is calculated based on four molecular parameters for each atom in a given molecule. The term h is dependent on atom hybridization: 1 for sp-, 2 for sp²-, 3 for sp³-hybrized atoms, and 4 for all others. Stereogenic centers are assigned an s value of 2, while all other atoms are assigned a value of 1. Atoms that are part of non-aromatic rings are also assigned an r value of 2; those that are part of an aromatic ring or linear chain are set to 1. Finally, the n score is set to the number of heavy-atom neighbors.

 

For each atom in a molecule, h is multiplied by s, r, and n². The SPS is calculated by summing the individual scores for all the atoms in a molecule. Because there is no upper limit, and because it is nice to be able to compare molecules of the same size, the researchers also define the nSPS, or normalized SPS, which is simply the SPS divided by the number of non-hydrogen atoms in the molecule. Although SPS can be calculated manually, the process is tedious and the researchers have kindly provided code to automate the process. Having defined SPS, the researchers compare it to other molecular complexity metrics, including the simple fraction  of sp³ carbons in a molecule, Fsp³, which we wrote about in 2009. 

 

The researchers next calculated nSPS for four sets of molecules including drugs, a screening library from Enamine, natural products, and so-called “dark chemical matter,” library compounds that have not hit in numerous screens. The results are equivocal. For example, the nSPS for dark chemical matter is very similar to that for drugs. On the other hand, natural products tend to have higher nSPS scores than drugs, as expected. Interestingly, the average nSPS score for compounds in the GDB-17 database, consisting of theoretical molecules having up to 17 atoms, is also quite high.

 

The researchers assessed whether nSPS correlated with biological properties, and found that compounds with lower nSPS tended to have lower potencies against fewer proteins, as predicted by theory. That said, this analysis was based on binning compounds into a small number of categories, and as Pete Kenny has repeatedly warned, this can lead to spurious trends.

 

The same issue of J. Med. Chem. carries an analysis of the paper by Tudor Oprea and Cristian Bologa, both at University of New Mexico. This contextualizes the work and confirms that drugs do not seem to be getting more complex over time, as measured by nSPS. This may seem odd, though Oprea and Cristian note that by “normalizing” for size, nSPS misses the increasing molecular weight of drugs.

 

This observation also raises other questions, such as the fact that SPS explicitly excludes element identity. Coming back to benzene and pyridine, both have identical SPS and nSPS, which does not seem chemically intuitive. One could quibble more: why square the value of n in the calculation of SPS? Why allow s to be only 1 and 2, as opposed to 1 and 5?

 

In the end I did enjoy reading this paper, and I do think having some metric of molecular complexity might be valuable. I’m just not sure where SPS will fit in with all the existing and conflicting metrics, and how such metrics can lead to practical applications.

PAINS made painless

Practical Fragments has several entries on pan-assay interference compounds, or PAINS: see here for an introduction, here for their (mis)incorporation into a fragment library, here for the sad results of such misincorporations, here for a much longer review, and here for something that will hopefully bring a smile to your face after reading the previous tales of woe.

But artifacts are not only (or even primarily!) restricted to fragments, so Michael Walters at The University of Minnesota has established a blog devoted to PAINS (www.htspains.com).

As with any blog, success depends to a large degree on engagement with the broader community, so please check it out – and leave comments!

FBLD 2012

Fragment-based Lead Discovery 2012 concluded today in San Francisco. This is the fourth in an illustrious series of conferences that started in San Diego in 2008, moved to York in 2009, and then to Philadelphia in 2010. These meetings set a high bar in terms of quality, and I think it’s fair to say that FBLD 2012 has lived up to its predecessors. With 39 talks, 16 vendors, over 50 posters, and close to 250 attendees I won’t attempt a comprehensive summary, so please weigh in if you were there.

Pete Kenny is always ahead of the curve, and so despite his absence from the conference he did put together a nice preview. He worried that the big pharma talks might be “strategy-heavy and results-light,” but happily this was not the case. In particular, Jane Withka gave a very detailed talk on Pfizer’s carefully constructed fragment library (see also here). One statistic that caught my eye is that, after screening this library against 21 proteins, 44% of the 2500+ fragments have hit at least one target – considerably higher than the 33% that has been seen in several organizations. Whether this is because of a better library or simply more screens remains unclear.

Fragment validation – or the lack thereof – and fragment promiscuity were also frequently recurring topics. Peter Kutchukian from Novartis observed that frequent hitters are not always problematic: fragments that hit multiple targets were actually more likely to produce co-crystal structures than more selective fragments.

There were lots of cool approaches for finding fragments; one particularly impressive advance was presented by SensiQ in a workshop before the main conference. Their surface plasmon resonance (SPR) instrument operates as expected; what sets it apart is a cunningly designed injection method in which the sample compound flows through a long capillary before entering the flow cell. A concentration gradient forms within the capillary, and by controlling this gradient the user can run a full dose-response curve over several orders of magnitude without having to pre-dilute the sample. Instead of doing a primary screen at a fixed concentration and then following up on active compounds, dissociation constants can be determined directly from a primary screen.

Protein flexibility is a subject dear to my heart, but not typically observed in biophysical fragment-finding techniques besides crystallography and protein-detected NMR. Josh Salafsky described Biodesy’s second-harmonic generation technology to specifically find molecules that cause conformational changes. More surprisingly, Beactica’s Helena Danielson argued that SPR could be used to observe conformational changes in membrane proteins. Although I’m no SPR expert, I've only seen the technique being used to detect changes in mass, so it will be fun to see how this develops.

Fluorine NMR looks like it’s finally coming into its own; Brad Jordan discussed how this has become a standard screening technique at Amgen, and both Nino Campobasso (GlaxoSmithKline) and Stephan Zech (Ariad) mentioned that their companies are using it too.

In addition to the method talks there were plenty of hit-to-lead and success stories, some of which have been covered on Practical Fragments, and some of which will be as the publications come out.

If you weren’t able to make it this year, FBLD 2014 is tentatively planned to be held in Basel, Switzerland. And if you can’t wait that long for your next fragment fix, there is at least one more relevant event this year and several already taking shape for 2013 – details to come shortly.

A decade of molecular complexity

Molecular complexity is one of the key reasons why fragment-based lead discovery should work. As described in 2001 by Mike Hann and colleagues at GlaxoSmithKline, the idea is that very small, simple molecules are likely to be able to bind to many different sites on many different proteins; think of the water molecule as being an extreme example of this. As molecules become larger and more complex, they are less likely to bind to any given site on a protein, though if they are complementary to a site the potency will be greater. Similarly, more complex molecules are more likely to have a single binding mode than smaller, less-decorated molecules, which could assume multiple orientations at a single site. These intuitive ideas were supported by a simple computational model that suggested that there is an optimum complexity where molecules would be simple enough that they would bind to several different targets (and thus be useful in a screening collection) while still being complex enough to bind in single, defined orientations with sufficient potency to permit detection. Mike Hann and Andrew Leach now have a new paper in Current Opinion in Chemical Biology that analyzes how this idea has weathered the past decade.

A central tenet of the molecular complexity model is that more complex molecules should be less promiscuous (bind to fewer protein targets) than less complex molecules. Although defining complexity is itself complex, the authors summarize a number of studies that examine promiscuity as a function of various molecular properties that could be used as proxies for complexity. Interestingly, many of these studies find that as molecular weight or – especially – lipophilicity increases, promiscuity actually increases, an apparent contradiction of the complexity model. Indeed, Hann and Leach present internal data showing that, for a given molecular weight, promiscuity increases with increasing lipophilicity.

The authors consider several explanations for this, such as the notion that larger, lipophilic molecules may not need to be perfectly complementary to a protein: one portion could bind, while the rest of the molecule remains unbound. One explanation that the authors don’t address but that could account for much of the discrepancy is the validity of the measurements from the studies surveyed. Practical Fragments has previously discussed the issue of aggregation artifacts, which can occur even at nanomolar concentration – well below the 10 micromolar cutoff used in many of the cited studies. Indeed, Brian Shoichet has commented that the majority of hits from HTS screens could be artifacts, and an alarming proportion of "active molecules" in published work are also bogus. Thus, the apparent promiscuity of more lipophilic compounds may reflect merely assay artifacts, not true binding.

In other words, I propose at least two kinds of promiscuity. “Legitimately promiscuous” compounds actually bind to multiple proteins in a one-to-one defined fashion. Perhaps these are rare, in line with the complexity model. “Apparently promiscuous” compounds simply interfere with the assay, whether through aggregation, fluorescence artifacts, or other PAINful mechanisms. Given how many discovery programs get side-tracked by these phenomena, these compounds are likely to vastly outnumber legitimately promiscuous molecules, thus distorting the results of data-mining exercises.

There is plenty more in the paper than can be summarized here, and if this piques your interest Mike Hann will be discussing both molecular complexity as well as molecular obesity at the SLAS webinar series starting next month.

Small but PAINful

We’ve written previously about the phenomenon of compound aggregation, and how this can lead to false positives in high-concentration screening (see also here). Unfortunately, aggregation is not the only thing that can trip you up. Earlier this year Jonathan Baell and Georgina Holloway published a description and systematic catalog of “Pan Assay Interference Compounds,” or PAINS, and Baell has now followed up on this with a Perspective in the October issue of Future Medicinal Chemistry. (For those of you without journal access, he has also summarized some of this material here.)

PAINS are compounds that frequently show up as screening hits, but that act through non-specific mechanisms such as covalent attachment to proteins or generation of hydrogen peroxide. Sometimes it is not the compound itself that is problematic but a breakdown product or leftover reactant; most readers with experience in hit-to-lead discovery will experience a painful sense of déjà vu reading about hits that did not confirm when the compound was resynthesized or repurified. This problem is exacerbated when trifluoracetic acid (TFA, which is toxic to cells and can accelerate compounds’ decomposition) is used in reverse-phase purification of compounds; apparently Pfizer has systematically repurified hundreds of thousands of their screening compounds to remove traces of TFA.

The problem with PAINS is that they may show convincing biochemical and even cell based activity, but mechanistically be useless for further advancement to drugs or even chemical probes. Unfortunately this does not prevent them from being published, where they are picked up by other researchers and identified as “privileged pharmacophores” by computational chemists. This leads to more (wasted) research and more (useless) publications in a vicious circle Baell terms “pollution of the scientific literature.”

Although the focus of the Perspective is high-throughput screening, fragments can be PAINS too, as the sampling below shows.

Compound 1 is obviously a Michael acceptor and compound 2 is unsurprisingly reactive with cysteine, but the problems with compound 3 are less obvious, and compound 4 generates hydrogen peroxide in the presence of reducing agents and oxygen – a mechanism it likely took some time to track down.

One of the fun aspects of reading this paper is that Baell is not shy about calling out high-profile publications that are likely to report false positives, though he readily admits that he too has been misled. The question is what to do: using computational filters to get rid of the worst offenders only eliminates 5-12% of commercial compounds, but more stringent screens can eliminate upwards of 95%!

Baell calls for better stewardship of the scientific literature:

Journals therefore have a responsibility here because if misleading information is published, unintentional though it may be, it is more likely to propagate and be taken as fact by others who may then initiate flawed research projects. Research resources are too precious for this to be acceptable.

I agree whole-heartedly with this, though given what I see published on a weekly basis I despair of this happening any time soon. Ultimately of course the onus is on researchers to carefully follow up and fully understand the mechanism of their hits, particularly those with dubious structures: don’t contribute to the pollution yourself, and don’t let friends (or papers you review) pollute!

FBLD 2010

The last major fragment event of this year is over, but it ends on a high note: FBLD 2010 has remained true to its predecessors in bringing together a great group of fragment enthusiasts in a Gordon-Conference-like environment. With 30 talks and even more posters I won’t attempt to be comprehensive or even representative, but will instead just pick out a few themes. Those of you who were there, please chime in with your own observations.

One of the themes was the shape of chemical space, and what makes a good binder. Jean-Louis Reymond, who has been systematically enumerating all stable molecules containing carbon, nitrogen, oxygen, and a few other atoms, has already published up to 13 heavy atoms but is now expanding his analysis to molecules containing up to 17. The issue of whether more attention should be given to three-dimensional fragments was discussed, with Ken Brameld reporting that fragments in crystal structures at Roche and the protein data bank contain fewer “flat” compounds than does the ZINC database of commercial molecules. However, analyzing 150,000 molecules with MW < 300 that had been screened in 40-100 high-throughput screens at Roche did not show any shape differences between the 50,000 molecules that showed up in at least one screen and those that didn’t. Interestingly, this ratio also came up in a talk by Tony Giannetti of Genentech, who said that across 13 screens 36% of their fragments hit at least one protein, while the rest didn’t hit any. Vernalis has found similar results; is there any way to enrich for the productive binders?

While FBLD 2009 had a strong computational theme, a major thrust of this conference was using biophysics to detect and confirm fragment binding. Tony discussed best-practices in SPR, and noted that since small molecules are “brighter” in NMR assays than proteins it is possible to find even very tiny fragments, including a 6 heavy-atom compound with a Kd of 600 micromolar. Tony also described the use of SPR for weeding out badly behaved compounds. Spookily, he noted that promiscuity is a function of compound, protein, and buffer, so it is not possible to weed out bad actors in a library before screening: one compound that was promiscuous against 8 targets bound legitimately and gave a crystal structure with a ninth. Adam Renslo of the University of California San Francisco described how easy it is to be misled by such phenomena. Glyn Williams described how Astex uses biophysical techniques to detect problem compounds, and noted that oxidizers can be particularly insidious – a trend that will likely continue as people explore novel heterocycles.

Glyn also presented a fascinating if slightly depressing discussion of ligand efficiency. As many have found, it can be challenging to maintain ligand efficiency during the course of fragment optimization. Yet even this goal is too modest. A fragment pays about 4.2 kcal/mol in binding energy when it binds to a protein due to loss of rotational and translational entropy; since this enropy cost is only paid once, atoms added to this molecule do not have this liability . Thus, merely maintaining ligand efficiency means that the atoms being added are binding less efficiently. This point was also emphasized by Colin Groom of the Cambridge Crystallographic Data Centre.

Membrane proteins are increasingly being targeted by fragment-based methods, as recently discussed on this site, and both Gregg Siegal of ZoBio and Rebecca Rich of the University of Utah presented progress against GPCRs.

There was general agreement that many approaches can find fragments, and that using several orthogonal methods is a good way to separate the true binders from the chaff, but a continuing challenge is what to do next. The last two sessions were devoted to chemical follow-up strategies and success stories. Some of these have been at least partially covered on Practical Fragments (for example here, here, and here) but there were a number of unpublished examples too – we’ll try to discuss these individually as they emerge.

If you missed this or the previous two conferences you’ll have another chance in 2012, when the meeting will be held in my fair city of San Francisco. And if you can’t wait that long, there are at least two fragment conferences scheduled for next year – details to come shortly.