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.

Avoiding will-o’-the-wisps: aggregation artifacts in activity assays

The phenomenon of aggregation is the drug hunter’s quicksand. A prerequisite for using biochemical assays to study fragments – or any low-affinity molecules – is an ability to sort activity from artifact. Many small molecules, even bona fide drugs, form aggregates in aqueous solution, and these aggregates can non-specifically interfere with biochemical assays. There are several ways to expose these promiscuous inhibitors (see list below), but even with vigilance, researchers can inadvertently stumble onto a route lit by will-o’-the-wisps. The most recent issue of J. Med. Chem. provides a particularly insidious example from Brian Shoichet, Adam Renslo, and colleagues at UCSF.

The researchers were looking for noncovalent inhibitors of cruzain, a popular protease target for Chagas’ disease. After a virtual screen of commercial lead-like compounds, 17 molecules were purchased and tested in enzymatic assays, and compound 1 (below) inhibited cruzain, albeit weakly. However, the compound looked like the real deal: it showed no time-dependence; it was active in the presence of detergent; and Lineweaver-Burk plots revealed that it was mechanistically competitive.

The researchers thus turned to medicinal chemistry, replacing the ester group of compound 1 with an oxadiazole bioisostere and swapping the aryl group for a substituted pyrazole, ultimately arriving at molecules such as compound 21, more than two orders of magnitude more potent than the starting molecule.



So far, so standard: similar stories appear every week in J. Med. Chem., Bioorg. Med. Chem. Lett., ChemMedChem, and other journals, and it would not have been surprising to see this published with a title like “Discovery of a high affinity inhibitor of cruzain.” Only in this case, the researchers became suspicious: most of the molecules were not active against the targeted protozoa, and many of the dose-response curves had unusually steep Hill slopes, a tell-tale sign of aggregation. Looking more closely at their protocol, the researchers also realized that the concentration of non-ionic detergent in their assays was ten-fold lower than they had thought. D’oh!

A series of tests confirmed that, despite interpretable and rationalizable SAR, the series had been optimized for aggregation-based inhibition: compound 21, with an IC50 of 200 nM in buffer containing 0.001% of the detergent Triton X-100, showed no inhibition whatsoever in 0.01% Triton X-100. The compound also inhibited AmpC beta-lactamase, an enzyme particularly sensitive to aggregators, and this inhibition could be reversed with detergent. Finally, dynamic light scattering (DLS) revealed the presence of particles (or aggregates) in aqueous solutions of compound 21.

But the tale gets even more twisted. Some of the aggregators show legitimate, competitive binding to cruzain under high-detergent conditions, albeit at much higher concentrations (with IC50s above 40 micromolar). Conversely, compound 1 actually shows noncompetitive behavior in low-detergent conditions, though again only at fairly high concentrations. In other words, promiscuous inhibitors can behave legitimately under sufficiently stringent conditions, and legitimate inhibitors can behave promiscuously under less stringent conditions.

What’s especially sobering is how easy this promiscuity would have been to overlook: many molecules with good activity in biochemical assays don’t show any effects in cells, and it is easy to ignore steep slopes in inhibition assays. How many of those “Discovery of a high affinity inhibitor of Hot Target X” papers actually report promiscuous inhibitors? The authors, who have been researching this problem for a long time, end on a justifiably paranoid note:

The cautionary contribution of this study is to point out that even within a clear SAR series, one is never entirely free from the concern that non-stoichiometric, artifactual mechanisms are contributing to the inhibition one observes.

This is a serious problem, both for the researchers doing the original work and for anyone trying to follow up on the results. But one can take precautions, summarized below and described more fully here:

• Add non-ionic detergent to the assay (Triton-X 100, Tween-20, CHAPS, others)
• Increase protein concentration – this should have no effect on genuine binders (within limits)
• Characterize the mechanism of inhibition (competitive, noncompetitive, or uncompetitive): competitive inhibitors are normally not promiscuous
• Centrifuge your samples and retest them – this can sometimes remove aggregators
• Examine your samples with DLS or flow cytometry – aggregators can sometimes be directly observed as 50-1000 nm particles
• Look closely at your dose-response curve - unusually steep slopes can signal aggregation

And of course, biophysical methods such as SPR, NMR, and X-ray crystallography can provide more information than biochemical assays and reveal stoichiometric (and – in the case of SPR – superstoichiometric) binding.

Difficulty sorting true low-affinity binders from false positives stymied fragment-based approaches for decades, and in fact the nature of promiscuous inhibition caused by aggregation wasn’t even characterized until earlier this century. We now have techniques to sort deceptive aggregation from true but faint affinity. Let’s make sure these tools are consistently used.