ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens

Rethinking 'academic' drug discovery: the Manchester Institute perspective

The contraction in research within pharma has seen a renaissance in drug discovery within the academic setting. Often, groups grow organically from academic research laboratories, exploiting a particular area of novel biology or new technology. However, increasingly, new groups driven by industrial staff are emerging with demonstrable expertise in the delivery of medicines. As part of a strategic review by Cancer Research UK (CR-UK), the drug discovery team at the Manchester Institute was established to translate novel research from the Manchester cancer research community into drug discovery programmes. From a standing start, we have taken innovative approaches to solve key issues faced by similar groups, such as hit finding and target identification. Herein, we share our lessons learnt and successful strategies.

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.

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.

Cheminformatics models based on machine learning approaches for design of USP1/UAF1 abrogators as anticancer agents

Cancer cells have upregulated DNA repair mechanisms, enabling them survive DNA damage induced during repeated rapid cell divisions and targeted chemotherapeutic treatments. Cancer cell proliferation and survival targeting via inhibition of DNA repair pathways is currently a very promiscuous anti-tumor approach. The deubiquitinating enzyme, USP1 is known to promote DNA repair via complexing with UAF1. The USP1/UAF1 complex is responsible for regulating DNA break repair pathways such as trans-lesion synthesis pathway, Fanconi anemia pathway and homologous recombination. Thus, USP1/UAF1 inhibition poses as an efficient anti-cancer strategy. The recently made available high throughput screen data for anti USP1/UAF1 activity prompted us to compute bioactivity predictive models that could help in screening for potential USP1/UAF1 inhibitors having anti-cancer properties. The current study utilizes publicly available high throughput screen data set of chemical compounds evaluated for their potential USP1/UAF1 inhibitory effect. A machine learning approach was devised for generation of computational models that could predict for potential anti USP1/UAF1 biological activity of novel anticancer compounds. Additional efficacy of active compounds was screened by applying SMARTS filter to eliminate molecules with non-drug like features. The structural fragment analysis was further performed to explore structural properties of the molecules. We demonstrated that modern machine learning approaches could be efficiently employed in building predictive computational models and their predictive performance is statistically accurate. The structure fragment analysis revealed the structures that could play an important role in identification of USP1/UAF1 inhibitors.

Promiscuous 2-aminothiazoles (PrATs): a frequent hitting scaffold

We have identified a class of molecules, known as 2-aminothiazoles (2-ATs), as frequent-hitting fragments in biophysical binding assays. This was exemplified by 4-phenylthiazol-2-amine being identified as a hit in 14/14 screens against a diverse range of protein targets, suggesting that this scaffold is a poor starting point for fragment-based drug discovery. This prompted us to analyze this scaffold in the context of an academic fragment library used for fragment-based drug discovery (FBDD) and two larger compound libraries used for high-throughput screening (HTS). This analysis revealed that such "promiscuous 2-aminothiazoles" (PrATs) behaved as frequent hitters under both FBDD and HTS settings, although the problem was more pronounced in the fragment-based studies. As 2-ATs are present in known drugs, they cannot necessarily be deemed undesirable, but the combination of their promiscuity and difficulties associated with optimizing them into a lead compound makes them, in our opinion, poor scaffolds for fragment libraries.

Parallel worlds of public and commercial bioactive chemistry data

The availability of structures and linked bioactivity data in databases is powerfully enabling for drug discovery and chemical biology. However, we now review some confounding issues with the divergent expansions of public and commercial sources of chemical structures. These are associated with not only expanding patent extraction but also increasingly large vendor collections amassed via different selection criteria between SciFinder from Chemical Abstracts Service (CAS) and major public sources such as PubChem, ChemSpider, UniChem, and others. These increasingly massive collections may include both real and virtual compounds, as well as so-called prophetic compounds from patents. We address a range of issues raised by the challenges faced resolving the NIH probe compounds. In addition we highlight the confounding of prior-art searching by virtual compounds that could impact the composition of matter patentability of a new medicinal chemistry lead. Finally, we propose some potential solutions.

Computational prediction and validation of an expert's evaluation of chemical probes

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines

Background. Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of traditional Chinese medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of traditional Chinese medicines and accurate in-silico cheminformatics models for data mining for antitubercular agents and computational filters to prioritize molecules has prompted us to search for potential hits from these datasets.

Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization.

Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients.

Detailed analysis and follow-up studies of a high-throughput screening for indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors

Indoleamine 2,3-dioxygenase 1 (IDO1) is a key regulator of immune responses and therefore an important therapeutic target for the treatment of diseases that involve pathological immune escape, such as cancer. Here, we describe a robust and sensitive high-throughput screen (HTS) for IDO1 inhibitors using the Prestwick Chemical Library of 1200 FDA-approved drugs and the Maybridge HitFinder Collection of 14,000 small molecules. Of the 60 hits selected for follow-up studies, 14 displayed IC50 values below 20 μM under the secondary assay conditions, and 4 showed an activity in cellular tests. In view of the high attrition rate we used both experimental and computational techniques to identify and to characterize compounds inhibiting IDO1 through unspecific inhibition mechanisms such as chemical reactivity, redox cycling, or aggregation. One specific IDO1 inhibitor scaffold, the imidazole antifungal agents, was chosen for rational structure-based lead optimization, which led to more soluble and smaller compounds with micromolar activity.

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.

Exploring the biological promiscuity of high-throughput screening hits through DFT calculations

The goal of this study is the understanding of biologically promiscuous compounds (frequent hitters) in HTS outcomes through their chemical behavior estimated via reactivity descriptors. Chemical reactivity is often an undesirable property due to the lack in biological selectivity of compounds comprised in HTS libraries. In this study the reactivity indexes have been computed within the DFT formalism, at different levels of theory, for two classes of representative compounds compiled from PubChem database, one comprising frequent hitters and the second one comprising rare hitters (biologically more selective compounds). We found that frequent hitters exert increased reactivity, mainly due to their electrophilic character, compared to the more selective class of compounds.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

A cell-free fluorometric high-throughput screen for inhibitors of Rtt109-catalyzed histone acetylation

The lysine acetyltransferase (KAT) Rtt109 forms a complex with Vps75 and catalyzes the acetylation of histone H3 lysine 56 (H3K56ac) in the Asf1-H3-H4 complex. Rtt109 and H3K56ac are vital for replication-coupled nucleosome assembly and genotoxic resistance in yeast and pathogenic fungal species such as Candida albicans. Remarkably, sequence homologs of Rtt109 are absent in humans. Therefore, inhibitors of Rtt109 are hypothesized as potential and minimally toxic antifungal agents. Herein, we report the development and optimization of a cell-free fluorometric high-throughput screen (HTS) for small-molecule inhibitors of Rtt109-catalyzed histone acetylation. The KAT component of the assay consists of the yeast Rtt109-Vps75 complex, while the histone substrate complex consists of full-length Drosophila histone H3-H4 bound to yeast Asf1. Duplicated assay runs of the LOPAC demonstrated day-to-day and plate-to-plate reproducibility. Approximately 225,000 compounds were assayed in a 384-well plate format with an average Z' factor of 0.71. Based on a 3σ cut-off criterion, 1,587 actives (0.7%) were identified in the primary screen. The assay method is capable of identifying previously reported KAT inhibitors such as garcinol. We also observed several prominent active classes of pan-assay interference compounds such as Mannich bases, catechols and p-hydroxyarylsulfonamides. The majority of the primary active compounds showed assay signal interference, though most assay artifacts can be efficiently removed by a series of straightforward counter-screens and orthogonal assays. Post-HTS triage demonstrated a comparatively small number of confirmed actives with IC₅₀ values in the low micromolar range. This assay, which utilizes five label-free proteins involved in H3K56 acetylation in vivo, can in principle identify compounds that inhibit Rtt109-catalyzed H3K56 acetylation via different mechanisms. Compounds discovered via this assay or adaptations thereof could serve as chemical probes or leads for a new class of antifungals targeting an epigenetic enzyme.

The Collaborative Drug Discovery (CDD) database

The broad goals of Collaborative Drug Discovery (CDD) are to enable a collaborative "cloud-based" tool to be used to bring together neglected disease researchers and other researchers from usually separate areas, to collaborate and to share compounds and drug discovery data in the research community, which will ultimately result in long-term improvements in the research enterprise and health care delivery. This chapter briefly introduces CDD software and describes applications in antimalarial and tuberculosis research.

Fragment-based drug discovery using NMR spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy has evolved into a powerful tool for fragment-based drug discovery over the last two decades. While NMR has been traditionally used to elucidate the three-dimensional structures and dynamics of biomacromolecules and their interactions, it can also be a very valuable tool for the reliable identification of small molecules that bind to proteins and for hit-to-lead optimization. Here, we describe the use of NMR spectroscopy as a method for fragment-based drug discovery and how to most effectively utilize this approach for discovering novel therapeutics based on our experience.

Cheminformatics aspects of high throughput screening: from robots to models: symposium summary

The "Cheminformatics aspects of high throughput screening (HTS): from robots to models" symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting in Denver, Colorado during the fall of 2011. This symposium brought together researchers from high throughput screening centers and molecular modelers from academia and industry to discuss the integration of currently available high throughput screening data and assays with computational analysis. The topics discussed at this symposium covered the data-infrastructure at various academic, hospital, and National Institutes of Health-funded high throughput screening centers, the cheminformatics and molecular modeling methods used in real world examples to guide screening and hit-finding, and how academic and non-profit organizations can benefit from current high throughput screening cheminformatics resources. Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on "Accurate Kinase virtual screening: biochemical, cellular and selectivity", "Selective, privileged and promiscuous chemical patterns in high-throughput screening" and "Visualizing and exploring relationships among HTS hits using network graphs".

Synthesis and structure-activity relationships of 2-amino-3-carboxy-4-phenylthiophenes as novel atypical protein kinase C inhibitors

Recent evidence suggests atypical protein kinase C (aPKC) isoforms are required for both TNF- and VEGF-induced breakdown of the blood-retinal barrier (BRB) and endothelial permeability to 70kDa dextran or albumin. A chemical library screen revealed a series of novel small molecule phenylthiophene based inhibitors of aPKC isoforms that effectively block permeability in cell culture and in vivo. In an effort to further elucidate the structural requirements of this series of inhibitors, we detail in this study a structure-activity relationship (SAR) built on screening hit 1, which expands on our initial pharmacophore model. The biological activity of our analogues was evaluated in models of bona fide aPKC-dependent signaling including NFκB driven-gene transcription as a marker for an inflammatory response and VEGF/TNF-induced vascular endothelial permeability. The EC50 for the most efficacious inhibitors (6, 32) was in the low nanomolar range in these two cellular assays. Our study demonstrates the key structural elements that confer inhibitory activity and highlights the requirement for electron-donating moieties off the C-4 aryl moiety of the 2-amino-3-carboxy-4-phenylthiophene backbone. These studies suggest that this class has potential for further development into small molecule aPKC inhibitors with therapeutic efficacy in a host of diseases involving increased vascular permeability and inflammation.

Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv)

Background

Mycobacterium tuberculosis (M.tb) is the causative agent of tuberculosis, killing ~1.7 million people annually. The remarkable capacity of this pathogen to escape the host immune system for decades and then to cause active tuberculosis disease, makes M.tb a successful pathogen. Currently available anti-mycobacterial therapy has poor compliance due to requirement of prolonged treatment resulting in accelerated emergence of drug resistant strains. Hence, there is an urgent need to identify new chemical entities with novel mechanism of action and potent activity against the drug resistant strains.

Results

This study describes novel computational models developed for predicting inhibitors against both replicative and non-replicative phase of drug-tolerant M.tb under carbon starvation stage. These models were trained on highly diverse dataset of 2135 compounds using four classes of binary fingerprint namely PubChem, MACCS, EState, SubStructure. We achieved the best performance Matthews correlation coefficient (MCC) of 0.45 using the model based on MACCS fingerprints for replicative phase inhibitor dataset. In case of non-replicative phase, Hybrid model based on PubChem, MACCS, EState, SubStructure fingerprints performed better with maximum MCC value of 0.28. In this study, we have shown that molecular weight, polar surface area and rotatable bond count of inhibitors (replicating and non-replicating phase) are significantly different from non-inhibitors. The fragment analysis suggests that substructures like hetero_N_nonbasic, heterocyclic, carboxylic_ester, and hetero_N_basic_no_H are predominant in replicating phase inhibitors while hetero_O, ketone, secondary_mixed_amine are preferred in the non-replicative phase inhibitors. It was observed that nitro, alkyne, and enamine are important for the molecules inhibiting bacilli residing in both the phases. In this study, we introduced a new algorithm based on Matthews correlation coefficient called MCCA for feature selection and found that this algorithm is better or comparable to frequency based approach.

Conclusion

In this study, we have developed computational models to predict phase specific inhibitors against drug resistant strains of M.tb grown under carbon starvation. Based on simple molecular properties, we have derived some rules, which would be useful in robust identification of tuberculosis inhibitors. Based on these observations, we have developed a webserver for predicting inhibitors against drug tolerant M.tb H37Rv available at http://crdd.osdd.net/oscadd/mdri/.

Locating sweet spots for screening hits and evaluating pan-assay interference filters from the performance analysis of two lead-like libraries

The efficiency of automated compound screening is heavily influenced by the design and the quality of the screening libraries used. We recently reported on the assembly of one diverse and one target-focused lead-like screening library. Using data from 15 enzyme-based screenings conducted using these libraries, their performance was investigated. Both libraries delivered screening hits across a range of targets, with the hits distributed across the entire chemical space represented by both libraries. On closer inspection, however, hit distribution was uneven across the chemical space, with enrichments observed in octants characterized by compounds at the higher end of the molecular weight and lipophilicity spectrum for lead-like compounds, while polar and sp³-carbon atom rich compounds were underrepresented among the screening hits. Based on these observations, we propose that screening libraries should not be evenly distributed in lead-like chemical space but be enriched in polar, aliphatic compounds. In conjunction with variable concentration screening, this could lead to more balanced hit rates across the chemical space and screening hits of higher ligand efficiency will be captured. Apart from chemical diversity, both screening libraries were shown to be clean from any pan-assay interference (PAINS) behavior. Even though some compounds were flagged to contain PAINS structural motifs, some of these motifs were demonstrated to be less problematic than previously suggested. To maximize the diversity of the chemical space sampled in a screening campaign, we therefore consider it justifiable to retain compounds containing PAINS structural motifs that were apparently clean in this analysis when assembling screening libraries.

High-throughput identification of promiscuous inhibitors from screening libraries with the use of a thiol-containing fluorescent probe

Testing small molecules for their ability to modify cysteine residues of proteins in the early stages of drug discovery is expected to accelerate our ability to develop more selective drugs with lesser side effects. In addition, this approach also enables the rapid evaluation of the mode of binding of new drug candidates with respect to thiol reactivity and metabolism by glutathione. Herein, we describe the development of a fluorescence-based high-throughput assay that allows the identification of thiol-reactive compounds. A thiol-containing fluorescent probe, MSTI, was synthesized and used to evaluate small molecules from the Library of Pharmacologically Active Compounds (LOPAC) collection of bioactive molecules. LOPAC compounds that are known to react with sulfur nucleophiles were identified with this assay, for example, irreversible protease inhibitors, nitric oxide-releasing compounds, and proton-pump inhibitors. The results confirm that both electrophilic and redox reactive compounds can be quickly identified in a high-throughput manner, enabling the assessment of screening libraries with respect to thiol-reactive compounds.

PAINS: Relevance to Tool Compound Discovery and Fragment-Based Screening

Pan assay interference compounds (PAINS) are readily discovered in any bioassay and can appear to give selective and optimisable hits. The most common PAINS can be readily recognised by their structure. However, there are compounds that closely resemble PAINS that are not specifically recognised by the PAINS filters. In addition, highly reactive compounds are not encoded for in the PAINS filters because they were excluded from the high-throughput screening (HTS) library used to develop the filters and so were never present to provide indicting data. A compounding complication in the area is that very occasionally a PAINS compound may serve as a viable starting point for progression. Despite such an occasional example, the literature is littered with an overwhelming number of examples of compounds that fail to progress and were probably not optimisable in the first place, nor useful tool compounds. Thus it is with great caution and diligence that compounds possessing a known PAINS core should be progressed through to medicinal chemistry optimisation, if at all, as the chances are very high that the hits will be found to be non-progressable, often after a significant waste of resources.

Broad coverage of commercially available lead-like screening space with fewer than 350,000 compounds

In establishing what we propose is the globe's highest quality collection of available screening compounds, it is convincingly shown that the globe's pool of such compounds is extremely shallow and can be represented by fewer than 350,000 compounds. To support our argument, we discuss and fully disclose our extensive battery of functional group filters. We discuss the use of PAINS filters and also show the effect of similarity exclusion on structure-activity relationships. We show why limited analogue representation requires screening at higher concentrations to capture hit classes for difficult targets that otherwise may be prosecuted unsuccessfully. We construct our arguments in a structurally focused manner to be most useful to medicinal chemists, the key players in drug discovery.

Shaping a screening file for maximal lead discovery efficiency and effectiveness: elimination of molecular redundancy

High Throughput Screening (HTS) is a successful strategy for finding hits and leads that have the opportunity to be converted into drugs. In this paper we highlight novel computational methods used to select compounds to build a new screening file at Pfizer and the analytical methods we used to assess their quality. We also introduce the novel concept of molecular redundancy to help decide on the density of compounds required in any region of chemical space in order to be confident of running successful HTS campaigns.

ZINC: a free tool to discover chemistry for biology

ZINC is a free public resource for ligand discovery. The database contains over twenty million commercially available molecules in biologically relevant representations that may be downloaded in popular ready-to-dock formats and subsets. The Web site also enables searches by structure, biological activity, physical property, vendor, catalog number, name, and CAS number. Small custom subsets may be created, edited, shared, docked, downloaded, and conveyed to a vendor for purchase. The database is maintained and curated for a high purchasing success rate and is freely available at zinc.docking.org.

Drug discovery for a new generation of covalent drugs

INTRODUCTION

The design of target-specific covalent inhibitors is conceptually attractive because of increased biochemical efficiency through covalency and increased duration of action that outlasts the pharmacokinetics of the agent. Although many covalent inhibitors have been approved or are in advanced clinical trials to treat indications such as cancer and hepatitis C, there is a general tendency to avoid them as drug candidates because of concerns regarding immune-mediated toxicity that can arise from indiscriminate reactivity with off-target proteins.

AREAS COVERED

The review examines potential reason(s) for the excellent safety record of marketed covalent agents and advanced clinical candidates for emerging therapeutic targets. A significant emphasis is placed on proteomic techniques and chemical/biochemical reactivity assays that aim to provide a systematic rank ordering of pharmacologic selectivity relative to off-target protein reactivity of covalent inhibitors.

EXPERT OPINION

While tactics to examine selective covalent modification of the pharmacologic target are broadly applicable in drug discovery, it is unclear whether the output from such studies can prospectively predict idiosyncratic immune-mediated drug toxicity. Opinions regarding an acceptable threshold of protein reactivity/body burden for a toxic electrophile and a non-toxic electrophilic covalent drug have not been defined. Increasing confidence in proteomic and chemical/biochemical reactivity screens will require a retrospective side-by-side profiling of marketed covalent drugs and electrophiles known to cause deleterious toxic effects via non-selective covalent binding.

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.

Lead-oriented synthesis: a new opportunity for synthetic chemistry

The pharmaceutical industry remains solely reliant on synthetic chemistry methodology to prepare compounds for small-molecule drug discovery programmes. The importance of the physicochemical properties of these molecules in determining their success in drug development is now well understood but we present here data suggesting that much synthetic methodology is unintentionally predisposed to producing molecules with poorer drug-like properties. This bias may have ramifications to the early hit- and lead-finding phases of the drug discovery process when larger numbers of compounds from array techniques are prepared. To address this issue we describe for the first time the concept of lead-oriented synthesis and the opportunity for its adoption to increase the range and quality of molecules used to develop new medicines.

Growing PAINS in academic drug discovery

In a recent article it was argued that compounds published as drug leads by academic laboratories commonly contain functionality that identifies them as nonspecific 'pan-assay interference compounds' (PAINS). The article raises broad questions about why best practices for hit and lead qualification that are well known in industry are not more widely employed in academia, as well as about the role of journals in publishing manuscripts that report drug leads of little potential value. Barriers to adoption of best practices for some academic drug-discovery researchers include knowledge gaps and infrastructure deficiencies, but they also arise from fundamental differences in how academic research is structured and how success is measured. Academic drug discovery should not seek to become identical to commercial pharmaceutical research, but we can do a better job of assessing and communicating the true potential of the drug leads we publish, thereby reducing the wastage of resources on nonviable compounds.

Targeting oncogenic protein-protein interactions by diversity oriented synthesis and combinatorial chemistry approaches

We are currently witnessing a decline in the development of efficient new anticancer drugs, despite the salient efforts made on all fronts of cancer drug discovery. This trend presumably relates to the substantial heterogeneity and the inherent biological complexity of cancer, which hinder drug development success. Protein-protein interactions (PPIs) are key players in numerous cellular processes and aberrant interruption of this complex network provides a basis for various disease states, including cancer. Thus, it is now believed that cancer drug discovery, in addition to the design of single-targeted bioactive compounds, should also incorporate diversity-oriented synthesis (DOS) and other combinatorial strategies in order to exploit the ability of multi-functional scaffolds to modulate multiple protein-protein interactions (biological hubs). Throughout the review, we highlight the chemistry driven approaches to access diversity space for the discovery of small molecules that disrupt oncogenic PPIs, namely the p53-Mdm2, Bcl-2/Bcl-xL-BH3, Myc-Max, and p53-Mdmx/Mdm2 interactions.

Rational methods for the selection of diverse screening compounds

Traditionally a pursuit of large pharmaceutical companies, high-throughput screening assays are becoming increasingly common within academic and government laboratories. This shift has been instrumental in enabling projects that have not been commercially viable, such as chemical probe discovery and screening against high-risk targets. Once an assay has been prepared and validated, it must be fed with screening compounds. Crafting a successful collection of small molecules for screening poses a significant challenge. An optimized collection will minimize false positives while maximizing hit rates of compounds that are amenable to lead generation and optimization. Without due consideration of the relevant protein targets and the downstream screening assays, compound filtering and selection can fail to explore the great extent of chemical diversity and eschew valuable novelty. Herein, we discuss the different factors to be considered and methods that may be employed when assembling a structurally diverse compound collection for screening. Rational methods for selecting diverse chemical libraries are essential for their effective use in high-throughput screens.

Non-stoichiometric inhibition in biochemical high-throughput screening

INTRODUCTION

Over the last 2 decades, high-throughput screening (HTS) has become one of the key strategies for the generation of new leads. Non-stoichiometric inhibition is one of the most extensively studied mechanisms responsible for the large percentage of hit compounds from biochemical screens that cannot be developed into leads. Therefore, HTS hit lists need to be sorted rapidly and efficiently into stoichiometrically binding inhibitors and compounds that affect enzyme activity non-stoichiometrically.

AREAS COVERED

This article explores the non-stoichiometric inhibition of enzymatic activity in biochemical screens, particularly by compound aggregation, and the authors explain the terminology they use to describe such compound behavior. The paper then provides a short historical overview of both academic and industrial research on compound aggregation specifically. Finally, the article discusses the implications for industrial drug discovery and the measures that can be taken to identify non-stoichiometric and aggregating inhibitors early in this process.

EXPERT OPINION

The most pragmatic approach in a lead finding campaign is to focus on the early identification of selective and stoichiometric inhibitors. The combination of multiple approaches (assessing both activity and binding) allows the enrichment of stoichiometric inhibitors at each stage of the flowchart.

Application of NMR and molecular docking in structure-based drug discovery

Drug discovery is a complex and costly endeavor, where few drugs that reach the clinical testing phase make it to market. High-throughput screening (HTS) is the primary method used by the pharmaceutical industry to identify initial lead compounds. Unfortunately, HTS has a high failure rate and is not particularly efficient at identifying viable drug leads. These shortcomings have encouraged the development of alternative methods to drive the drug discovery process. Specifically, nuclear magnetic resonance (NMR) spectroscopy and molecular docking are routinely being employed as important components of drug discovery research. Molecular docking provides an extremely rapid way to evaluate likely binders from a large chemical library with minimal cost. NMR ligand-affinity screens can directly detect a protein-ligand interaction, can measure a corresponding dissociation constant, and can reliably identify the ligand binding site and generate a co-structure. Furthermore, NMR ligand affinity screens and molecular docking are perfectly complementary techniques, where the combination of the two has the potential to improve the efficiency and success rate of drug discovery. This review will highlight the use of NMR ligand affinity screens and molecular docking in drug discovery and describe recent examples where the two techniques were combined to identify new and effective therapeutic drugs.

Observations on screening-based research and some concerning trends in the literature

Academic drug discovery is being accompanied by a plethora of publications that report screening hits as good starting points for drug discovery or as useful tool compounds, whereas in many cases this is not so. These compounds may be protein-reactive but can also interfere in bioassays via a number of other means, and it can be very hard to prove early on that they represent false starts. This, for instance, makes it difficult for journals in their assessment of manuscripts submitted for publication. Wider awareness and recognition of these problematic compounds will help the academic drug-discovery community focus on and publish genuinely optimizable screening hits. This will be of general benefit.

Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99% of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1% of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone.

Apparent activity in high-throughput screening: origins of compound-dependent assay interference

Expansive compound collections made up of structurally heterogeneous chemicals, the activities of which are largely undefined, present challenging problems for high-throughput screening (HTS). Foremost is differentiating whether the activity for a given compound in an assay is directed against the targeted biology, or is the result of surreptitious compound activity involving the assay detection system. Such compound interference can be especially difficult to identify if it is reproducible and concentration-dependent - characteristics generally attributed to compounds with genuine activity. While reactive chemical groups on compounds were once thought to be the primary source of compound interference in assays used in HTS, recent work suggests that other factors, such as compound aggregation, may play a more significant role in many assay formats. Considerable progress has been made to profile representative compound libraries in an effort to identify chemical classes susceptible to producing compound interference, such as compounds commonly found to inhibit the reporter enzyme firefly luciferase. Such work has also led to the development of practices that have the potential to significantly reduce compound interference, for example, through the addition of non-ionic detergent to assay buffer to reduce aggregation-based inhibition.