Highly Promiscuous Small Molecules from Biological Screening Assays Include Many Pan-Assay Interference Compounds but Also Candidates for Polypharmacology

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Non-equilibrium modalities of inhibition: Characterizing irreversible inhibition for the ErbB receptor family members

Most drug target interactions for clinically approved small-molecules are non-equilibrium slow-onset, tight-binding or irreversible in nature, with pronounced element of time-dependence of inhibition. Analysis of such modality of inhibition requires a continuous enzyme kinetic measurement that can yield complete progress curves and an automated high-throughput analysis pipeline. Given the increasing emphasis on designing non-equilibrium modes of inhibiting an enzyme target (especially irreversible), the above specified pipeline for data generation and analysis is essential for extracting parameters to guide decisions in early drug discovery. In this manuscript, the methodology and data analysis protocol from our irreversible inhibitor characterization campaigns for the ErbB receptor family members is presented. Guidance is provided on the appropriate design of assay to generate quality data, setting up the analysis and estimation of inactivation rate (kinact) and the pseudo-equilibrium binding affinity (KI) constant (or their ratio kinact/KI) in a high-throughput manner for the inhibitor interacting with the protein target of interest.

Polyphenolic promiscuity, inflammation-coupled selectivity: Whether PAINs filters mask an antiviral asset

The Covid-19 pandemic has elicited much laboratory and clinical research attention on vaccines, mAbs, and certain small-molecule antivirals against SARS-CoV-2 infection. By contrast, there has been comparatively little attention on plant-derived compounds, especially those that are understood to be safely ingested at common doses and are frequently consumed in the diet in herbs, spices, fruits and vegetables. Examining plant secondary metabolites, we review recent elucidations into the pharmacological activity of flavonoids and other polyphenolic compounds and also survey their putative frequent-hitter behavior. Polyphenols, like many drugs, are glucuronidated post-ingestion. In an inflammatory milieu such as infection, a reversion back to the active aglycone by the release of β-glucuronidase from neutrophils and macrophages allows cellular entry of the aglycone. In the context of viral infection, virions and intracellular virus particles may be exposed to promiscuous binding by the polyphenol aglycones resulting in viral inhibition. As the mechanism's scope would apply to the diverse range of virus species that elicit inflammation in infected hosts, we highlight pre-clinical studies of polyphenol aglycones, such as luteolin, isoginkgetin, quercetin, quercetagetin, baicalein, curcumin, fisetin and hesperetin that reduce virion replication spanning multiple distinct virus genera. It is hoped that greater awareness of the potential spatial selectivity of polyphenolic activation to sites of pathogenic infection will spur renewed research and clinical attention for natural products antiviral assaying and trialing over a wide array of infectious viral diseases.

Computational Literature-based Discovery for Natural Products Research: Current State and Future Prospects

Literature-based discovery (LBD) mines existing literature in order to generate new hypotheses by finding links between previously disconnected pieces of knowledge. Although automated LBD systems are becoming widespread and indispensable in a wide variety of knowledge domains, little has been done to introduce LBD to the field of natural products research. Despite growing knowledge in the natural product domain, most of the accumulated information is found in detached data pools. LBD can facilitate better contextualization and exploitation of this wealth of data, for example by formulating new hypotheses for natural product research, especially in the context of drug discovery and development. Moreover, automated LBD systems promise to accelerate the currently tedious and expensive process of lead identification, optimization, and development. Focusing on natural product research, we briefly reflect the development of automated LBD and summarize its methods and principal data sources. In a thorough review of published use cases of LBD in the biomedical domain, we highlight the immense potential of this data mining approach for natural product research, especially in context with drug discovery or repurposing, mode of action, as well as drug or substance interactions. Most of the 91 natural product-related discoveries in our sample of reported use cases of LBD were addressed at a computer science audience. Therefore, it is the wider goal of this review to introduce automated LBD to researchers who work with natural products and to facilitate the dialogue between this community and the developers of automated LBD systems.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Identification of Chemical and Pharmacological Chaperones for Correction of Trafficking-Deficient Mutant Cyclic Nucleotide-Gated A3 Channels

Trafficking deficiency caused by missense mutations is a well known phenomenon that occurs for mutant, misfolded proteins. Typically, the misfolded protein is retained by the protein quality-control system and degraded by the endoplasmic reticulum-associated protein degradation pathway and thus does not reach its destination, although residual function of the protein may be preserved. Chemical and pharmacological chaperones can improve the targeting of trafficking-deficient proteins and thus may be promising candidates for therapeutic applications. Here, we report the application of a cellular bioassay based on the bioluminescent calcium reporter aequorin to quantify surface expression of mutant CNGA3 channels associated with the autosomal recessively inherited retinal disease achromatopsia. A screening of 77 compounds enabled the identification of effective chemical and pharmacological chaperones that result in a 1.5- to 4.8-fold increase of surface expression of mutant CNGA3. Using selected compounds, we confirmed that the rescue of the defective trafficking is not limited to a single mutation in CNGA3. Active compounds and our structure-activity correlated data for the dihydropyridine compound class may provide valuable information for developing a treatment of the trafficking defect in achromatopsia. SIGNIFICANCE STATEMENT: This study describes a novel luminescence-based assay to detect the surface expression of mutant trafficking-deficient CNGA3 channels based on the calcium-sensitive photoprotein aequorin. Using this assay for a compound screening, this study identifies novel chemical and pharmacological chaperones that restore the surface localization of mutant trafficking-deficient CNGA3 channels. The results from this work may serve as starting point for the development of potent compounds that rescue trafficking deficiencies in the autosomal recessively inherited retinal disease achromatopsia.

Evolution of Novartis' Small Molecule Screening Deck Design

This article summarizes the evolution of the screening deck at the Novartis Institutes for BioMedical Research (NIBR). Historically, the screening deck was an assembly of all available compounds. In 2015, we designed a first deck to facilitate access to diverse subsets with optimized properties. We allocated the compounds as plated subsets on a 2D grid with property based ranking in one dimension and increasing structural redundancy in the other. The learnings from the 2015 screening deck were applied to the design of a next generation in 2019. We found that using traditional leadlikeness criteria (mainly MW, clogP) reduces the hit rates of attractive chemical starting points in subset screening. Consequently, the 2019 deck relies on solubility and permeability to select preferred compounds. The 2019 design also uses NIBR's experimental assay data and inferred biological activity profiles in addition to structural diversity to define redundancy across the compound sets.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

On the Conformational Dynamics of β-Amyloid Forming Peptides: A Computational Perspective

Understanding the conformational dynamics of proteins and peptides involved in important functions is still a difficult task in computational structural biology. Because such conformational transitions in β-amyloid (Aβ) forming peptides play a crucial role in many neurological disorders, researchers from different scientific fields have been trying to address issues related to the folding of Aβ forming peptides together. Many theoretical models have been proposed in the recent years for studying Aβ peptides using mathematical, physicochemical, and molecular dynamics simulation, and machine learning approaches. In this article, we have comprehensively reviewed the developmental advances in the theoretical models for Aβ peptide folding and interactions, particularly in the context of neurological disorders. Furthermore, we have extensively reviewed the advances in molecular dynamics simulation as a tool used for studying the conversions between polymorphic amyloid forms and applications of using machine learning approaches in predicting Aβ peptides and aggregation-prone regions in proteins. We have also provided details on the theoretical advances in the study of Aβ peptides, which would enhance our understanding of these peptides at the molecular level and eventually lead to the development of targeted therapies for certain acute neurological disorders such as Alzheimer's disease in the future.

In Silico Design and Analysis of a Kinase-Focused Combinatorial Library Considering Diversity and Quality

A structurally diverse, high-quality, and kinase-focused database plays a critical role in finding hits or leads in kinase drug discovery. Here, we propose a workflow for designing a virtual kinase-focused combinatorial library using existing structures. Based on the analysis of known protein kinase inhibitors (PKIs), detailed fragment optimization, fragment selection, fragment linking, and a molecular filtering scheme were defined. Quick recognition of core fragments that can possibly form dual hydrogen bonds with the hinge region of the ATP-pocket was proposed. Furthermore, three diversity and four quality metrics were chosen for compound library analysis, which can be applied to databases with over 30 million structures. Compared with 13 commercial libraries, our protocol demonstrates a special advantage in terms of good skeleton diversity, acceptable fingerprint diversity, balanced scaffold distribution, and high quality, which can work well not only on existing PKIs, but also on four chosen commercial libraries. Overall, the strategy can greatly facilitate the expansion of a desirable chemical space for kinase drug discovery.

Epigenetic drug discovery: systematic assessment of chemical space

Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 10⁶ ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 10⁶ compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.

The diverse mechanisms and anticancer potential of naphthoquinones

Cancer is one of the leading causes of death around the world and although the different clinical approaches have helped to increase survival rates, incidence is still high and so its mortality. Chemotherapy is the only approach which is systemic, reaching cancer cells in all body tissues and the search for new potent and selective drugs is still an attractive field within cancer research. Naphthoquinones, natural and synthetic, have garnered much attention in the scientific community due to their pharmacological properties, among them anticancer action, and potential therapeutic significance. Many mechanisms of action have been reported which also depend on structural differences among them. Here, we describe some of the most relevant mechanisms of action reported so far for naphthoquinones and highlight novel targets which are being described in the literature. Furthermore, we gather some of the most impressive efforts done by researchers to harness the anticancer properties of these compounds through specifically designed structural modifications.

Computational advances in combating colloidal aggregation in drug discovery

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

Promiscuous Ligands from Experimentally Determined Structures, Binding Conformations, and Protein Family-Dependent Interaction Hotspots

Compound promiscuity is often attributed to nonspecific binding or assay artifacts. On the other hand, it is well-known that many pharmaceutically relevant compounds are capable of engaging multiple targets in vivo, giving rise to polypharmacology. To explore and better understand promiscuous binding characteristics of small molecules, we have searched X-ray structures (and very few qualifying solution structures) for ligands that bind to multiple distantly related or unrelated target proteins. Experimental structures of a given ligand bound to different targets represent high-confidence data for exploring promiscuous binding events. A total of 192 ligands were identified that formed crystallographic complexes with proteins from different families and for which activity data were available. These "multifamily" compounds included endogenous ligands and were often more polar than other bound compounds and active in the submicromolar range. Unexpectedly, many promiscuous ligands displayed conserved or similar binding conformations in different active sites. Others were found to conformationally adjust to binding sites of different architectures. A comprehensive analysis of ligand-target interactions revealed that multifamily ligands frequently formed different interaction hotspots in binding sites, even if their bound conformations were similar, thus providing a rationale for promiscuous binding events at the molecular level of detail. As a part of this work, all multifamily ligands we have identified and associated activity data are made freely available.

Identification of Inhibitors of the dNTP Triphosphohydrolase SAMHD1 Using a Novel and Direct High-Throughput Assay

The dNTP triphosphohydrolase SAMHD1 is a regulator of cellular dNTP pools. Given its central role in nucleotide metabolism, SAMHD1 performs important functions in cellular homeostasis, cell cycle regulation, and innate immunity. It therefore represents a high-profile target for small molecule drug design. SAMHD1 has a complex mechanism of catalytic activation that makes the design of an activating compound challenging. However, an inhibitor of SAMHD1 could serve multiple therapeutic roles, including the potentiation of antiviral and anticancer drug regimens. The lack of high-throughput screens that directly measure SAMHD1 catalytic activity has impeded efforts to identify inhibitors of SAMHD1. Here we describe a novel high-throughput screen that directly measures SAMHD1 catalytic activity. This assay results in a colorimetric end point that can be read spectrophotometrically and utilizes bis(4-nitrophenyl) phosphate as the substrate and Mn²⁺ as the activating cation that facilitates catalysis. When used to screen a library of Food and Drug Administration-approved drugs, this HTS identified multiple novel compounds that inhibited SAMHD1 dNTPase activity at micromolar concentrations.

Design, synthesis, and evaluation of chalcone analogues incorporate α,β-Unsaturated ketone functionality as anti-lung cancer agents via evoking ROS to induce pyroptosis

Chalcone, a natural structure, demonstrates many pharmacological activities including anticancer, and one promising mechanism is to modulate the generation of ROS. It has been known that pyroptosis is associated with anticancer effects, whereas there is fewer researches about ROS-mediated pyroptosis triggered by chemotherapy drugs. Moreover, incorporation of a α,β-unsaturated ketone unit into chalcone may be an effective strategy for development of chemotherapy drugs. Hence, a number of chalcone analogues bearing a α,β-unsaturated ketone were synthesized from chalcone analogues 1 with modest anticancer activities as the lead compound. Structure-activity relationship (SAR) studies confirmed the function of α,β-unsaturated ketone to improve anticancer activity. Notably, compound 8, bearing a α,β-unsaturated ketone, is the most potent inhibitor of cancer, with IC₅₀ values on NCI-H460, A549 and H1975 cells of 2.3 ± 0.3, 3.2 ± 0.0 and 5.7 ± 1.4 μM, respectively. Besides, 8 showed antiproliferative ability against NCI-H460 cells in a time- and concentration-dependent manner through modulating ROS to induce caspase-3-mediated pyroptosis, and displayed a better safety profile in vivo. Overall, these results demonstrated that compound 8 is a candidate agent and a potential lead compound for development of chemotherapy drugs, and can be used as a probe to further examine the mechanism of ROS-dependent pyroptosis.

Rational application of drug promiscuity in medicinal chemistry

‘Drug promiscuity’ refers to a drug that can act on multiple molecular targets, exhibiting similar or different pharmacological effects. Drugs may interact with unwanted targets, leading to off-target effects (one of the main reasons for side effects). Thus, intervention to prevent off-target effects in the early stages of drug discovery could reduce the risk of failure. The conversion between target and off-target effects is important for drug repurposing. Drug repurposing strategies could reduce research and development costs. This review details the research progress in the rational application of drug promiscuity for the discovery of multi-target drugs, drug repurposing and improving druggability in medicinal chemistry over the last 5 years.

X-ray-Structure-Based Identification of Compounds with Activity against Targets from Different Families and Generation of Templates for Multitarget Ligand Design

Compounds with multitarget activity (promiscuity) are increasingly sought in drug discovery. However, promiscuous compounds are often viewed controversially in light of potential assay artifacts that may give rise to false-positive activity annotations. We have reasoned that the strongest evidence for true multitarget activity of small molecules would be provided by experimentally determined structures of ligand-target complexes. Therefore, we have carried out a systematic search of currently available X-ray structures for compounds forming complexes with different targets. Rather unexpectedly, 1418 such crystallographic ligands were identified, including 702 that formed complexes with targets from different protein families (multifamily ligands). About half of these multifamily ligands originated from the medicinal chemistry literature, making it possible to consider additional target annotations and search for analogues. From 168 distinct series of analogues containing one or more multifamily ligands, 133 unique analogue-series-based scaffolds were isolated that can serve as templates for the design of new compounds with multitarget activity. As a part of our study, all of the multifamily ligands we have identified and the analogue-series-based scaffolds are made freely available.

Polypharmacology of conformationally locked methanocarba nucleosides

A single molecular scaffold can be adapted to interact with diverse targets, either separately or simultaneously. Nucleosides and nucleotides in which ribose is substituted with bicyclo[3.1.0]hexane are an example of a versatile drug-like scaffold for increasing selectivity at their classical targets: kinases, polymerases, adenosine and P2 receptors. Also, by applying structure-based functional group manipulations, rigidified adenosine derivatives can be repurposed to satisfy pharmacophoric requirements of various GPCRs, ion channels, enzymes and transporters, initially detected as off-target activities. Recent examples include 5HT2B serotonin receptor antagonists and novel dopamine transporter allosteric modulators. This directable target diversity establishes rigid nucleosides as privileged scaffolds.

Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity

A large-scale statistical analysis of hit rates of extensively assayed compounds is presented to provide a basis for a further assessment of assay interference potential and multi-target activities. A special feature of this investigation has been the inclusion of compound series information in activity analysis and the characterization of analog series using different parameters derived from assay statistics. No prior knowledge of compounds or targets was taken into consideration in the data-driven study of analog series. It was anticipated that taking large volumes of activity data, assay frequency, and assay overlap information into account would lead to statistically sound and chemically meaningful results. More than 6000 unique series of analogs with high hit rates were identified, more than 5000 of which did not contain known interference candidates, hence providing ample opportunities for follow-up analyses from a medicinal chemistry perspective.

Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity

A large-scale statistical analysis of hit rates of extensively assayed compounds is presented to provide a basis for a further assessment of assay interference potential and multi-target activities. A special feature of this investigation has been the inclusion of compound series information in activity analysis and the characterization of analog series using different parameters derived from assay statistics. No prior knowledge of compounds or targets was taken into consideration in the data-driven study of analog series. It was anticipated that taking large volumes of activity data, assay frequency, and assay overlap information into account would lead to statistically sound and chemically meaningful results. More than 6000 unique series of analogs with high hit rates were identified, more than 5000 of which did not contain known interference candidates, hence providing ample opportunities for follow-up analyses from a medicinal chemistry perspective.

How Frequently Are Pan-Assay Interference Compounds Active? Large-Scale Analysis of Screening Data Reveals Diverse Activity Profiles, Low Global Hit Frequency, and Many Consistently Inactive Compounds

Undetected pan-assay interference compounds (PAINS) with false-positive activities in assays often propagate through medicinal chemistry programs and compromise their outcomes. Although a large number of PAINS have been classified, often on the basis of individual studies or chemical experience, little has been done so far to systematically assess their activity profiles. Herein we report a large-scale analysis of the behavior of PAINS in biological screening assays. More than 23 000 extensively tested compounds containing PAINS substructures were detected, and their hit rates were determined. Many consistently inactive compounds were identified. The hit frequency was low overall, with median values of two to five hits for PAINS tested in hundreds of assays. Only confined subsets of PAINS produced abundant hits. The same PAINS substructure was often found in consistently inactive and frequently active compounds, indicating that the structural context in which PAINS occur modulates their effects.

Chemistry-based molecular signature underlying the atypia of clozapine

The central nervous system is functionally organized as a dynamic network of interacting neural circuits that underlies observable behaviors. At higher resolution, these behaviors, or phenotypes, are defined by the activity of a specific set of biomolecules within those circuits. Identification of molecules that govern psychiatric phenotypes is a major challenge. The only organic molecular entities objectively associated with psychiatric phenotypes in humans are drugs that induce psychiatric phenotypes and drugs used for treatment of specific psychiatric conditions. Here, we identified candidate biomolecules contributing to the organic basis for psychosis by deriving an in vivo biomolecule-tissue signature for the atypical pharmacologic action of the antipsychotic drug clozapine. Our novel in silico approach identifies the ensemble of potential drug targets based on the drug's chemical structure and the region-specific gene expression profile of each target in the central nervous system. We subtracted the signature of the action of clozapine from that of a typical antipsychotic, chlorpromazine. Our results implicate dopamine D4 receptors in the pineal gland and muscarinic acetylcholine M1 (CHRM1) and M3 (CHRM3) receptors in the prefrontal cortex (PFC) as significant and unique to clozapine, whereas serotonin receptors 5-HT_2A in the PFC and 5-HT_2C in the caudate nucleus were common significant sites of action for both drugs. Our results suggest that D4 and CHRM1 receptor activity in specific tissues may represent underappreciated drug targets to advance the pharmacologic treatment of schizophrenia. These findings may enhance our understanding of the organic basis of psychiatric disorders and help developing effective therapies.

Discovery of hyaluronidase inhibitors from natural products and their mechanistic characterization under DMSO-perturbed assay conditions

The present study discovered four novel hyaluronan-degrading enzyme (hyaluronidase) inhibitors including chikusetsusaponins and catechins through the activity-guided separation of Panax japonicus and Prunus salicina, respectively. Although the discovery resulted in identification of usual frequent hitters, subsequent mechanistic characterizations under our DMSO-perturbed assay conditions and related protocols revealed that chikusetusaponin IV would serve as an aggregating and non-specific binding inhibitor, while (-)-epicatechin would interact specifically with enzyme at the catalytic site or more likely at a kind of catechin-binding site with a relatively week inhibitory activity. The latter description might provide a possible explanation for the well-known fact that a series of catechin have been described as frequent hitters in biological assays with a moderate activity. Thus, the present study demonstrated a practical and robust methodology to characterize initial screening hits mechanistically molecule-by-molecule in the early stage of natural product-based drug discovery.

Activity profiles of analog series containing pan assay interference compounds

Shown is the distribution of activity profiles (color-coded bars) of analog series containing PAINS substructures in a heatmap.

Identification and analysis of promiscuity cliffs formed by bioactive compounds and experimental implications

For three promiscuity cliffs (enclosed), cliff compounds, their promiscuity degrees (PDs), and color-coded substitution sites are shown. Comparison of these cliffs suggests the design of a new analog to further explore promiscuity.