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.