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.”

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.

What makes molecules aggregate?

The propensity for some small molecules to form aggregates in water has bedeviled fragment-finding efforts for decades. Indeed, the phenomenon was not fully recognized until early this century. Although plenty of tools are available for detecting aggregates, I still see too many papers that omit these crucial quality controls. As annoying as aggregation can be in activity assays, in certain cases it could actually be useful for formulating drugs. There has been speculation that the good oral bioavailability of venetoclax is due to aggregation. But despite computational methods to predict aggregation, the structural features of molecules that cause them to aggregate are still not well understood. In a new open-access Nature Comm. paper, Daniel Heller and collaborators at Memorial Sloan Kettering Cancer Center and elsewhere provide some answers.

 

The researchers had previously published an article describing how indocyanine green (ICG) could be used to stabilize and visualize aggregates, and they applied the same technique to examine the aggregation potential of a small set of fragments. Benzoic acid and 2-napthoic acid did not aggregate, while 4-phenylbenzoic acid did. Intrigued, the researchers tested a set of 14 4-substituted biphenyl fragments and found that those containing both a hydrogen bond donor and acceptor, such as acids, sulfonamides, amides, and ureas, could aggregate, while those containing only donors (aniline) or acceptors (nitrile) did not.

 

Fourier transform infrared spectroscopy was used to examine the stretching region of the carbonyl of 4-phenylbenzoic acid in various states: in an aqueous aggregate, in solution in either t-butanol or DMSO, or in the solid state. Interestingly, the aggregate most resembled the solid state, consistent with close-packed self-assembly as opposed to free in solution.

 

From all this, the researchers hypothesized that a combination of aromatic groups and hydrogen bond donors and acceptors was necessary for aggregation. However, having these features does not mean aggregation is inevitable. Neither 3-phenylbenzoic acid nor 2-phenylbenzoic acid formed aggregates, with the former precipitating while the latter remained completely soluble. These three phenylbenzoic acid isomers behave very differently despite the fact that they have the same calculated logP values, and the suggestion is that the latter two molecules are less able to form pi-pi stacking interactions that lead to stable aggregation.

 

Next the researchers examined the approved drug sorafenib, which had previously been shown to aggregate. This was confirmed, and the aggregates were characterized with a battery of biophysical methods including dynamic light scattering, transmission electron microscopy, and X-ray scattering, along with molecular dynamics simulations. The conclusion is that sorafenib forms amorphous aggregates whose assembly is driven by a combination of pi-pi stacking and hydrogen-bonding. A series of sorafenib analogs was synthesized, and those that could not form strong intermolecular hydrogen bonds were less prone to aggregation.

 

All of this is fascinating from a molecular assembly viewpoint and will help to explain and predict which compounds are likely to aggregate, for better or for worse. But as of now, experimental assessment is still best practice for any new compound.

NMR for SAR: All about the ligand

In last week’s post we described a free online tool for predicting bad behavior of compounds in various assays. But as we noted, you often get what you pay for, and computational methods can’t (yet) take the place of experimentation. In a new (open-access) J. Med. Chem. paper, Steven LaPlante and collaborators at NMX and INRS describe a roadmap for discovering, validating, and advancing weak fragments. They call it NMR by SAR

 

Unlike SAR by NMR, the grand-daddy of fragment-finding techniques which involves protein-detected NMR, NMR for SAR focuses heavily on the ligand. The researchers illustrate the process by finding ligands for the protein HRAS, for which drug discovery has lagged in comparison to its sibling KRAS.

 

The researchers started by screening the G12V mutant form of HRAS in its inactive (GDP-bound) state. They screened their internal library of 461 fluorinated fragments in pools of 11-15 compounds (each at ~0.24 mM) using ¹⁹F NMR. An initial screen at 15 µM protein produced a very low hit rate, so the protein concentration was increased to 50 µM. After deconvolution, two hits confirmed, one of which was NMX-10001.

 

The affinity of the compound was found to be so low that ¹H NMR experiments could not detect binding. Thus, the researchers kept to fluorine NMR to screen for commercial analogs. They used ¹⁹F-detected versions of differential line width (DLW) and CPMG experiments to rank affinities, and the latter technique was also used to test for compound aggregation using methodology we highlighted in 2019. Indeed, the researchers have developed multiple tools for detecting aggregators, such as those we wrote about in 2022.

 

Ligand concentrations were measured by NMR, which sometimes differed from the assumed concentrations. As the researchers note, these differences, which are normally not measured experimentally, can lead to errors in ranking the affinities of compounds. The researchers also examined the 1D spectra of the proteins to assess whether compounds caused dramatic changes via pathological mechanisms, such as precipitation.

 

The researchers turned to protein-detected 2D NMR for orthogonal validation and to determine the binding sites of their ligands. These experiments revealed that the compounds bind in a shallow pocket that has previously been targeted by several groups (see here for example). Optimization of their initial hit ultimately led to NMX-10095, which binds to the protein with low double digit micromolar affinity. This compound also blocked SOS-mediated nucleotide exchange and was cytotoxic, albeit at high concentrations.

I do wish the researchers had measured the affinity of their molecules towards other RAS isoforms as this binding pocket is conserved, and inhibiting all RAS activity in cells is generally toxic. Moreover, the best compound is reminiscent of a series reported by Steve Fesik back in 2012.

 

But this specific example is less important than the clear description of an NMR-heavy assay cascade that weeds out artifacts in the quest for true binders. The strategy is reminiscent of the “validation cross” we mentioned back in 2016. Perhaps someday computational methods will advance to the point where “wet” experiments become an afterthought. But in the meantime, this paper provides a nice set of tools to find and rigorously validate even weak binders.

A Liability Predictor for avoiding artifacts?

False positives and artifacts are a constant source of irritation – and worse – in compound screening. We’ve written frequently about small molecule aggregation as well as generically reactive molecules that repeatedly come up as screening hits. It is possible to weed these out experimentally, but this can entail considerable effort, and for particularly difficult targets, false positives may dominate. Indeed, there may be no true hits at all, as we noted in this account of a five-year and ultimately fruitless hunt for prion protein binders.

 

A computational screen to rapidly assess small molecule hits as possible artifacts would be nice, and in fact several have been developed. Among the most popular are computational filters for pan-assay interference compounds, or PAINs. However, as Pete Kenny and others have pointed out, these were developed using data from a limited number of screens in one particular assay format. Now Alexander Tropsha and collaborators at University of North Carolina Chapel Hill and the National Center for Advancing Translational Science (NCATS) at the NIH have provided a broader resource in a new J. Med. Chem. paper.

 

The researchers experimentally screened around 5000 compounds, taken from the NCATS Pharmacologically Active Chemical Toolbox, in four different assays: a fluorescence-based thiol reactivity assay, an assay for redox activity, a firefly luciferase (FLuc) assay, and a nanoluciferase (NLux) assay. The latter two assays are commonly used in cell-based screens to measure gene transcription. The thiol reactivity assay yielded around 1000 interfering compounds, while the other three assays each produced from 97 to 142. Interestingly, there was little overlap among the problematic compounds.

 

These data were used to develop quantitative structure-interference relationship (QSIR) models. The NCATS library of nearly 64,000 compounds was virtually screened, and around 200 compounds were tested experimentally for interference in the four assays, with around half predicted to interfere and the other half predicted not to interfere. The researchers had also previously built a computational model to predict aggregation, and this – along with the four models discussed here – have been combined into a free web-based “Liability Predictor.”

 

So how well does it work? The researchers calculated the sensitivity, specificity, and balanced accuracy for each of the models and state that “they can detect around 55%-80% of interfering compounds.”

 

This sounded encouraging, so naturally I took it for a spin. Unfortunately, my mileage varied. Or, to pile on the metaphors, lots of wolves successfully passed themselves off as sheep. Iniparib was recognized correctly as a possible thiol interference compound. On the other hand, the known redox cycler toxoflavin was predicted not to be a redox cycler – with 97.12% confidence. Similarly, curcumin, which can form adducts with thiols as well as aggregate and redox cycle, was pronounced innocent. Quercetin was recognized as possibly thiol-reactive, but its known propensity to aggregate was not. Weirdly, Walrycin B, which the researchers note interferes with all the assays, got a clean bill of health. Perhaps the online tool is still being optimized.

 

At this point, perhaps the Liability Predictor is best treated as a cautionary tool: molecules that come up with a warning should be singled out for particular interrogation, but passing does not mean the molecule is innocent. Laudably, the researchers have made all the underlying data and models publicly available for others to build on, and I hope this happens. But for now, it seems that no computational tool can substitute for experimental (in)validation of hits.

The Chemical Probes Portal at Eight

Back in 2015, Practical Fragments highlighted a new resource calling itself “The Chemical Probes Portal.” At the time it included just seven probes, and my post concluded, “I hope this takes off. Understanding the natural world is hard enough even with well-behaved reagents and carefully controlled experiments.”

 

Well, take off it has, as illustrated by a new (open access) paper in Nucleic Acids Res. by Susanne Müller (Goethe University Frankfurt), Bissan Al-Lazikani (MD Anderson Cancer Center), Paul Workman (Institute of Cancer Research), and collaborators.

 

The paper notes that “the widespread use of small molecule compounds that are claimed as chemical probes but are lacking sufficient quality, especially being inadequately selective for the desired target or even broadly promiscuous in behavior, has resulted in many erroneous conclusions in the biomedical literature.” As an antidote, the Portal is an “expert review-based public resource to empower chemical probe assessment, selection, and use.”

 

Any scientist can suggest a potential probe, and these are then internally reviewed and curated. Assuming enough public information is available about the molecule, probes are then sent to three members of a Scientific Expert Review Panel for further vetting. Reviewers rate probes from one to four stars for use in cellular and/or animal models and recommend relevant concentration ranges. Importantly, reviewers can also include comments to highlight off-targets, lack of certain data, oral bioavailability, or anything else.

 

From a mere seven probes in 2015 the Portal has grown to include more than 500 molecules covering more than 400 protein targets in about 100 protein families. About two thirds of the probes have three or more stars, meaning they are recommended. The Portal is very easy to use and can be searched by probe or protein. Laudably, all the data can also be easily downloaded in bulk.

 

In addition to the chemical probes, the Portal also contains around 250 “Historical Compounds” that have been described in the literature but “are not recommended to be used to study the function of specific proteins as they are seriously flawed.” These include molecules such as gossypol, a known aggregator that has been reported as an inhibitor of multiple proteins, and curcumin. If you see a molecule used as a probe in the literature, it’s worth checking to see whether it shows up in the Portal.

 

The Chemical Probes Portal features heavily in a Conversation between Cheryl Arrowsmith (Structural Genomics Consortium) and Paul Workman published (open access) last year in Nat. Commun. The researchers concisely define chemical probes as “small-molecule modulators to interrogate the functions of their target proteins, as opposed to protein location, or other physical properties.” Importantly, they differentiate chemical probes from drugs. “Drugs don’t necessarily need to be as selective as high-quality chemical probes. They just need to get the job done on the disease and be safe to use. In fact, many drugs act on multiple targets as part of their therapeutic mechanism.” I have frequently heard people make comments such as, “this is just a probe, not a drug,” but a good probe should actually be more selective than many drugs.

 

That said, you do want a drug to actually hit the target of interest. The researchers highlight iniparib, a putative PARP inhibitor that made it all the way to phase 3 clinical trials for breast cancer and was tested in >2500 cancer patients. It failed. Moreover, that failure cast a pall over the field which likely delayed the development of actual PARP inhibitor drugs.

 

The researchers also discuss aggregators, which are still being reported uncritically in the literature, along with PAINS. “Such compounds should never be considered further or used as chemical probes. They should be excluded from compound libraries. Yet many are sold by commercial vendors as chemical probes and widely used.”

 

This statement raised the hackles of Pete Kenny. In a recently published critique, he states: “it is asserted in the conversation that commercial vendors are selling compounds as chemical probes that are unfit for purpose and I strongly recommend that anybody making such assertions should carefully examine the supporting evidence.”

 

Dear reader, please try the following experiment. Enter “iniparib supplier” in your favorite search engine and see what comes up. For me, the first 10 results include several that describe it as a PARP inhibitor. I won’t link to them here because I don’t want to encourage traffic to their sites. (This is also part of the reason Practical Fragments has discontinued PAINS shaming, as it only increases the profile of sloppy or harmful papers.)

 

Pete goes on to write: “I would strongly advise against making statements that a compound is unfit for use as a chemical probe unless the assertion is supported by measured data in the public domain for the compound in question.”

 

Frankly, I don’t understand Pete’s position, which I parodied here. Life is short and biology is complicated, so why waste time with dirty or inadequately characterized reagents? For me, everything is an artifact until proven otherwise. And the Chemical Probes Portal goes a long way towards demonstrating whether a particular probe is fit for purpose.

An epidemic of aggregators, and suggestions for cures

COVID-19 has been with us for over two years now. While the human effects have been unquestionably negative, for science it has been the best of times and the worst of times. The development of remarkably effective vaccines in less than a year stands as a triumph of twenty-first century medicine, as does the discovery of nirmatrelvir, a covalent inhibitor of the SARS-CoV-2 main protease M^pro (also called 3CL-Pro). But there is a lot of junk-science out there too, as illuminated in a recent J. Med. Chem. paper by Brian Shoichet and colleagues at University of California San Francisco.

 

Before vaccines and custom-built drugs were developed, labs everywhere started screening all the compounds they could get against targets relevant for COVID-19. The most popular molecules to test were approved drugs, the idea being that if any of these turned out to be effective they could immediately be put to use.

 

One of the most common artifacts in screening is caused by aggregation: small molecules can form colloids that non-specifically inhibit a variety of different assays. This phenomenon has been understood for more than two decades; Practical Fragments wrote about it back in 2009. Unfortunately, many labs ignore it.

 

The UCSF lab investigated 56 drugs that had been reported in 12 papers as inhibitors against two targets relevant for SARS-CoV-2, including 3CL-Pro. The molecules were characterized in multiple assays: particle formation and clean autocorrelation curves in dynamic light scattering (DLS), inhibition of an aggregation-sensitive enzyme in the absence of detergent but no inhibition in the presence of detergent, and a high Hill slope in the dose-response curve. Nineteen molecules, four of them fragment-sized, were positive in most of these assays, clearly indicating aggregation. (Interestingly, several of these gave reasonable Hill slopes (<1.4), and the researchers suggest this be a “soft criterion.”) Another 14 molecules gave more ambiguous results, such as forming particles by DLS but not inhibiting the sentinel enzyme.

 

OK, so maybe the molecules are aggregators, but perhaps they also act legitimately? Unfortunately, of the 12 drugs reported in the literature to inhibit 3CL-Pro, only two inhibited the enzyme in the presence of detergent, and one of these was five-fold less potent than reported. And as the researchers point out, detergent is not a magic elixir, and sometimes only right-shifts the onset of aggregation. Moreover, of the 19 molecules conclusively found to be aggregators, detergent was not included for 15 of them in the original publications. Brian may be too polite to write this, but channeling my inner Teddy, I would argue that the authors are negligent for failing to test for aggregation, as are the editors and reviewers who allowed these papers to be published.

 

And the problem is not confined to the COVID-19 literature. The researchers examined a commercial library of 2336 FDA-approved drugs, 73 of which are known aggregators. Another 356 were flagged in the very useful Aggregation Advisor tool (see here), and 6 of 15 experimentally evaluated tested positive in all the aggregation assays.

 

How do you avoid being misled by these artifacts? An extensive suite of tools for assessing aggregation is provided in a recent Nat. Protoc. paper by Steven LaPlante and colleagues at Université du Québec and NMX. The procedures are described in sufficient detail that they “can be easily performed by graduate students and even undergraduate students.”

 

Most of the focus is on various NMR techniques, such as one we wrote about here. The easiest is an NMR dilution assay, in which a 20 mM solution of a compound in DMSO is serially diluted into aqueous buffer at concentrations from 200 to 12 µM. If the number, shape, shift, or intensities of the NMR resonances changes, aggregation is likely.

 

Another assay involves testing compounds in the absence and presence of various detergents, including NP40, Triton, SDS, CHAPS, Tween 20, and Tween 80. Again, changes in the NMR spectra suggest aggregation.

 

The researchers note that “no one technique can detect all the types of aggregates that exist; thus, a combination of strategies is necessary.” Indeed, the various techniques can distinguish different types of aggregates which can vary in size and polydispersity. On a lemons-to-lemonade note, these “nano-entities” might even be useful for “drug delivery, anti-aggregates, cell penetrators and bioavailability enhancers.”

 

We live in the age of wisdom and the age of foolishness. As scientists – and as people – it is our responsibility to aspire to the former by being aware of “unknown knowns,” such as aggregation. And perhaps, by even taking advantage of the weird phenomena that can occur with small molecules in water.

Eighteenth Annual Discovery on Target Meeting

Last week CHI held Discovery on Target – virtually of course. There were 20 tracks over three days and more than 650 attendees, down from 1100+ last year. Because the event was more fragmented (pun intended) than the recent DDC, which had at most four parallel tracks, the Q&As and discussions seemed smaller, though that could have just been the ones I attended. On the other hand, one of the huge advantages of the format is being able to watch concurrently scheduled talks later. More thoughts on virtual conferences are here, and if you have not already done so please take our poll on the right side of the page.

 

This conference has always been more biology-focused than DDC, with tracks on antibodies, immunology, NASH, gene therapy, disease modeling, and fibrosis, among others. But there were also plenty of talks on targets and methodology, which is where I’ll focus most of this post. Please add your highlights and thoughts in the comments.

 

Julien Orts (ETH Zurich) presented an update on his NMR² method, which uses information from intermolecular NOEs to computationally determine protein-ligand structures without requiring full NMR assignment of the protein. We wrote about this technique in 2017 and at the time we questioned how applicable it would be to fragments due to low affinities, multiple binding modes, and fewer contacts. As it turns out, very: Julien described successes with proteins including HDM2, DsbA, bromodomains, and Pin1. Even with as few as 10-12 intermolecular NOEs he has been able to get good agreement with crystal structures. Currently he is applying this approach to SARS-CoV-2 proteins as part of the COVID-19-NMR project.

 

William Pomerantz (University of Minnesota Twin Cities) also presented NMR techniques. He is particularly known for his protein-observed ¹⁹F (PrOF) NMR screening, in which fluorinated tyrosine and tryptophan residues are introduced into proteins. Ligand binding changes the chemical shifts of the fluorine atoms, and by varying the concentration of the fragment, accurate dissociation constants can be determined. In early work, a screen of 930 fragments in pools of 5 against BRD4 took 11 hours (and another 11 hours for deconvolution) and provided multiple hits. We’ve covered some of his more recent work using shapely fragments here, and in unpublished work he has been screening the dual-domain construct of BRD4 and finding fragments that are ten-fold selective for one over the other bromodomain. He is further improving throughput by screening two proteins simultaneously.

 

Rounding out NMR, Andrew Petros (AbbVie) presented a beautiful fragment-to-lead success story on TNFα, a trimeric cytokine that has been the subject of numerous (often unsuccessful) lead-discovery efforts. A 2-dimensional NMR screen of 18,000 fragments gave just 11 hits. Crystallography of one showed two copies binding in close proximity, and linking these ultimately led to a low nanomolar binder. The series showed high clearance and no oral bioavailability, so they performed additional screens to identify different fragments that were ultimately advanced to potent compounds with animal efficacy. I look forward to reading the paper when it is published.

 

Finding fragments is important, but so is avoiding artifacts, the subject of a talk by Samantha Allen (Janssen). Around 2% of screening compounds can form small molecule aggregates that can interfere with assays, and if these aren’t weeded out they can quickly overwhelm an assay. Samantha described the use of resonance waveguide grating (RWG) technology, as used in the Corning Epic BT. This label-free technology is similar to SPR, but RWG can be run in 384 or 1536 well plates. Samantha showed that RWG compares favorably to dynamic light scattering for detecting aggregates. It is also 4-5 times faster and less prone to false-positives.

 

Covalent fragments were a theme of last month’s DDC, and they were prominent here as well. Four years ago we highlighted work out of Ben Cravatt’s lab doing covalent fragment screening in cells, but this was a rather time-consuming process. Steve Gygi (Harvard) has streamlined activity-based protein profiling and was able to screen 288 fragments in just 7 days and identify more than 1500 modified cysteine residues.

 

Dan Nomura (UC Berkeley) continued the theme with a wide-ranging presentation using chemoproteomics to discover covalent ligands for a variety of targets, including new E3 ligases, which can be used for developing targeted protein degraders. (Shameless plug/disclosure: Dan Nomura is a founder of my company, Frontier Medicines, and we are actively hiring across multiple positions and levels.)

 

Targeted protein degraders such as PROTACs were the subject of one track at last year’s DoT meeting, and this year two sequential tracks were devoted to the topic. As I suggested in 2018, fragments could be ideal starting points given that high affinity is not always necessary. This year, Stewart Fisher confirmed that he and his colleagues at C4 Therapeutics often “detune” chemical matter, lowering the binding affinity to get efficient degraders. That doing so can improve physicochemical properties is a nice bonus.

 

Finally, although not directly fragment-related, William Kaelin (Dana Farber Cancer Institute) gave an inspiring talk on the discovery and development of MK-6482, an allosteric HIF2α inhibitor in late-stage clinical trials for cancer linked to Von Hippel-Lindau disease; data released just last week shows durable responses in patients with kidney cancer. The science itself was lovely, but he reminded us of the ultimate stakes: “It’s not about what journal your paper is published in or whether you can fool reviewer 3, it’s about whether you publish things that are true and robust and can be built upon by others.”

 

Words to live by.

Failing honorably and openly on PrP

Many of the posts on Practical Fragments – and indeed much of what appears in the literature – describe successes. This is obvious in the list of fragment-derived clinical compounds, and discoveries of high-affinity tool molecules or even advanceable fragment hits make up a large share of the 750+ posts on this blog. But of course, most of what we do in science fails, and such failures can also be informative. Eric Minikel and 25 collaborators from the Broad Institute and multiple other organizations have just published an illustrative case study on bioRxiv. (Eric also has a detailed and eloquent blog post of his own about the work.)

The researchers describe a five-year effort to find small-molecule binders of the prion protein, PrP, which misfolds and forms aggregates that lead to neurodegeneration. The hope was that binders could be turned into PrP stabilizers or perhaps even degraders. PrP has been studied for decades and there are plenty of literature reports of small molecules that seem to interact with the target, but none of these have been convincingly validated. Moreover, the crystallographic structure of the protein does not reveal attractive binding pockets.

Fragment-based methods have succeeded for other difficult targets, so the researchers performed STD and ¹⁹F NMR screens. Of 6630 pooled fragments, 238 initial hits were retested by STD NMR, leading to 80 hits that were then assessed by two-dimensional (TROSY) NMR. This led to a single hit, a substituted benzimidazole. Unfortunately the binding site could not be determined: chemical shift perturbations were spread across the protein. Differential scanning fluorimetry (DSF) showed the molecule caused a slight decrease in thermal stability. Both of these results suggest some sort of pathological mechanism, but the researchers did multiple experiments to rule out aggregation. A dose-response suggested a dissociation constant well above 1 mM, and none of 54 analogs tested proved any better. Soaking crystals of PrP with 20 of these was also unsuccessful.

Next, the researchers performed a thermal shift assay of just over 30,000 compounds (not necessarily fragments), which yielded both stabilizers and destabilizers. Unfortunately, none of 93 tested by two-dimensional NMR (HSQC) revealed any sign of binding. A DNA-encoded library (DEL) screen of 256,000 macrocycles also didn’t yield any confirmed hits (though more on that below), nor did a computational screen of just under 7 million molecules.

Given this experience, the researchers cautiously conclude that their “results may hint toward relative rarity of PrP binders in chemical space.” They do suggest several alternative approaches, such as screening the more biologically-relevant membrane-bound form of the protein. It may also be worth doing a high-concentration crystallographic screen. Finally, the researchers note that one of the DEL scaffolds “was judged to be a likely covalent binder and was not pursued further.” This decision may be worth revisiting. Indeed, covalent approaches have led to clinical compounds against another formerly undruggable target, KRAS.

According to the blog post, the researchers have pivoted to oligonucleotide therapeutics, where they seem to be making good progress. This makes sense, and I wish them luck. But I hope someone returns to PrP itself with new tools. If they do, the assays established and described here will prove invaluable.