Michael A. Walters

Chemistry: Chemical con artists foil drug discovery

Academic researchers, drawn into drug discovery without appropriate guidance, are doing muddled science. When biologists identify a protein that contributes to disease, they hunt for chemical compounds that bind to the protein and affect its activity. A typical assay screens many thousands of chemicals. ‘Hits’ become tools for studying the disease, as well as starting points in the hunt for treatments. But many hits are artefacts — their activity does not depend on a specific, drug-like interaction between molecule and protein. A true drug inhibits or activates a protein by fitting into a binding site on the protein. Artefacts have subversive reactivity that masquerades as drug-like binding and yields false signals across a variety of assays. These molecules — pan-assay interference compounds, or PAINS — have defined structures, covering several classes of compound (see ‘Worst offenders’). But biologists and inexperienced chemists rarely recognize them. Instead, such compounds are reported as having promising activity against a wide variety of proteins. Time and research money are consequently wasted in attempts to optimize the activity of these compounds. Chemists make multiple analogues of apparent hits hoping to improve the ‘fit’ between protein and compound. Meanwhile, true hits with real potential are neglected. Until the past decade or so, screening Chemical con artists foil drug discovery

The promise and peril of chemical probes

Chemical probes are powerful reagents with increasing impacts on biomedical research. However, probes of poor quality or that are used incorrectly generate misleading results. To help address these shortcomings, we will create a community-driven wiki resource to improve quality and convey current best practice.

The Essential Medicinal Chemistry of Curcumin

Curcumin is a constituent (up to ∼5%) of the traditional medicine known as turmeric. Interest in the therapeutic use of turmeric and the relative ease of isolation of curcuminoids has led to their extensive investigation. Curcumin has recently been classified as both a PAINS (pan-assay interference compounds) and an IMPS (invalid metabolic panaceas) candidate. The likely false activity of curcumin in vitro and in vivo has resulted in >120 clinical trials of curcuminoids against several diseases. No double-blinded, placebo controlled clinical trial of curcumin has been successful. This manuscript reviews the essential medicinal chemistry of curcumin and provides evidence that curcumin is an unstable, reactive, nonbioavailable compound and, therefore, a highly improbable lead. On the basis of this in-depth evaluation, potential new directions for research on curcuminoids are discussed.

PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS.

Significant resources in early drug discovery are spent unknowingly pursuing artifacts and promiscuous bioactive compounds, while understanding the chemical basis for these adverse behaviors often goes unexplored in pursuit of lead compounds. Nearly all the hits from our recent sulfhydryl-scavenging high-throughput screen (HTS) targeting the histone acetyltransferase Rtt109 were such compounds. Herein, we characterize the chemical basis for assay interference and promiscuous enzymatic inhibition for several prominent chemotypes identified by this HTS, including some pan-assay interference compounds (PAINS). Protein mass spectrometry and ALARM NMR confirmed these compounds react covalently with cysteines on multiple proteins. Unfortunately, compounds containing these chemotypes have been published as screening actives in reputable journals and even touted as chemical probes or preclinical candidates. Our detailed characterization and identification of such thiol-reactive chemotypes should accelerate triage of nuisance compounds, guide screening library design, and prevent follow-up on undesirable chemical matter.

Mitigating risk in academic preclinical drug discovery

The number of academic drug discovery centres has grown considerably in recent years, providing new opportunities to couple the curiosity-driven research culture in academia with rigorous preclinical drug discovery practices used in industry. To fully realize the potential of these opportunities, it is important that academic researchers understand the risks inherent in preclinical drug discovery, and that translational research programmes are effectively organized and supported at an institutional level. In this article, we discuss strategies to mitigate risks in several key aspects of preclinical drug discovery at academic drug discovery centres, including organization, target selection, assay design, medicinal chemistry and preclinical pharmacology.

Specific inhibition of p97/VCP ATPase and kinetic analysis demonstrate interaction between D1 and D2 ATPase domains.

The p97 AAA (ATPase associated with diverse cellular activities), also called VCP (valosin-containing protein), is an important therapeutic target for cancer and neurodegenerative diseases. p97 forms a hexamer composed of two AAA domains (D1 and D2) that form two stacked rings and an N-terminal domain that binds numerous cofactor proteins. The interplay between the three domains in p97 is complex, and a deeper biochemical understanding is needed in order to design selective p97 inhibitors as therapeutic agents. It is clear that the D2 ATPase domain hydrolyzes ATP in vitro, but whether D1 contributes to ATPase activity is controversial. Here, we use Walker A and B mutants to demonstrate that D1 is capable of hydrolyzing ATP and show for the first time that nucleotide binding in the D2 domain increases the catalytic efficiency (kcat/Km) of D1 ATP hydrolysis 280-fold, by increasing kcat 7-fold and decreasing Km about 40-fold. We further show that an ND1 construct lacking D2 but including the linker between D1 and D2 is catalytically active, resolving a conflict in the literature. Applying enzymatic observations to small-molecule inhibitors, we show that four p97 inhibitors (DBeQ, ML240, ML241, and NMS-873) have differential responses to Walker A and B mutations, to disease-causing IBMPFD mutations, and to the presence of the N domain binding cofactor protein p47. These differential effects provide the first evidence that p97 cofactors and disease mutations can alter p97 inhibitor potency and suggest the possibility of developing context-dependent inhibitors of p97.

The essential roles of chemistry in high-throughput screening triage

It is increasingly clear that academic high-throughput screening (HTS) and virtual HTS triage suffers from a lack of scientists trained in the art and science of early drug discovery chemistry. Many recent publications report the discovery of compounds by screening that are most likely artifacts or promiscuous bioactive compounds, and these results are not placed into the context of previous studies. For HTS to be most successful, it is our contention that there must exist an early partnership between biologists and medicinal chemists. Their combined skill sets are necessary to design robust assays and efficient workflows that will weed out assay artifacts, false positives, promiscuous bioactive compounds and intractable screening hits, efforts that ultimately give projects a better chance at identifying truly useful chemical matter. Expertise in medicinal chemistry, cheminformatics and purification sciences (analytical chemistry) can enhance the post-HTS triage process by quickly removing these problematic chemotypes from consideration, while simultaneously prioritizing the more promising chemical matter for follow-up testing. It is only when biologists and chemists collaborate effectively that HTS can manifest its full promise.

Mutant Prevention Concentrations for Single-Step Fluoroquinolone-Resistant Mutants of Wild-Type, Efflux-Positive, or ParC or GyrA Mutation-Containing Streptococcus pneumoniae Isolates

ABSTRACT Three fluoroquinolone-susceptible and five fluoroquinolone-resistant (two with ParC Ser79Phe mutations, one with a GyrA Ser81Phe mutation, and two that were efflux positive) Streptococcus pneumoniae isolates were exposed to one, two, four, eight, and sixteen times the MICs of ciprofloxacin, gatifloxacin, gemifloxacin, levofloxacin, and moxifloxacin. Mutational frequencies were calculated at each multiple of the MIC for which growth was observed. Mutant prevention concentrations (MPCs) and the multiple of the MIC at the MPC (MPMIC) were evaluated. All resulting mutants were sequenced for quinolone resistance-determining region changes in GyrA and ParC and were evaluated for reserpine-sensitive efflux. The MPC order was generally ciprofloxacin > levofloxacin > gatifloxacin > moxifloxacin > gemifloxacin. The MPMIC order varied depending on the genetic constitution of the original isolates from which the mutants were generated. For those mutants created from fluoroquinolone-susceptible isolates (those that had wild-type ParC and GyrA and were efflux negative), the MPMIC order was ciprofloxacin = moxifloxacin > gemifloxacin > levofloxacin > gatifloxacin. The MPMICs of each fluoroquinolone for mutants created from isolates with a ParC mutation (with wild-type GyrA and efflux negative) were similar. A similar occurrence was observed with the mutants created from the efflux-positive isolates (with wild-type ParC and GyrA). The MPMIC order for the mutants created from the isolate with a GyrA mutation (with wild-type ParC and efflux negative) was ciprofloxacin = gemifloxacin > levofloxacin = moxifloxacin > gatifloxacin. Gatifloxacin, levofloxacin, and moxifloxacin may be intrinsically more able to prevent the development of resistance by fluoroquinolone-susceptible isolates, isolates that are efflux positive, or isolates that carry a GyrA mutation. However, once a ParC mutation is present, the MPC increases dramatically for all fluoroquinolones.

Assessing synthetic accessibility of chemical compounds using machine learning methods.

With de novo rational drug design, scientists can rapidly generate a very large number of potentially biologically active probes. However, many of them may be synthetically infeasible and, therefore, of limited value to drug developers. On the other hand, most of the tools for synthetic accessibility evaluation are very slow and can process only a few molecules per minute. In this study, we present two approaches to quickly predict the synthetic accessibility of chemical compounds by utilizing support vector machines operating on molecular descriptors. The first approach, RSsvm, is designed to identify the compounds that can be synthesized using a specific set of reactions and starting materials and builds its model by training on the compounds identified as synthetically accessible or not by retrosynthetic analysis. The second approach, DRsvm, is designed to provide a more general assessment of synthetic accessibility that is not tied to any set of reactions or starting materials. The training set compounds for this approach are selected from a diverse library based on the number of other similar compounds within the same library. Both approaches have been shown to perform very well in their corresponding areas of applicability with the RSsvm achieving a receiver operator characteristic score of 0.952 in cross-validation experiments and the DRsvm achieving a score of 0.888 on an independent set of compounds. Our implementations can successfully process thousands of compounds per minute.

How to Triage PAINS-Full Research.

Nonspecific bioactivity and assay artifacts have gained increasing attention in recent years. This focus has arisen primarily from the publication of a set of chemical substructures, termed pan assay interference compounds (PAINS), which are associated with promiscuous bioactivity and assay interference in real and virtual high-throughput screening (HTS) campaigns. Despite an increasing awareness in the HTS and medicinal chemistry communities about the liabilities of these compounds, articles featuring PAINS and PAINS-like compounds are still being published. In this perspective, we describe some of the factors we believe are driving this resource-sapping trend. We also provide what we hope are helpful insights that may lead to the earlier recognition of these generally nontranslatable compounds, thus preventing the propagation of PAINS-full costly research.