Evaluation of Substituted Pyrazole-Based Kinase Inhibitors in One Decade (2011-2020): Current Status and Future Prospects.

Pyrazole has been recognized as a pharmacologically important privileged scaffold whose derivatives produce almost all types of pharmacological activities and have attracted much attention in the last decades. Of the various pyrazole derivatives reported as potential therapeutic agents, this article focuses on pyrazole-based kinase inhibitors. Pyrazole-possessing kinase inhibitors play a crucial role in various disease areas, especially in many cancer types such as lymphoma, breast cancer, melanoma, cervical cancer, and others in addition to inflammation and neurodegenerative disorders. In this article, we reviewed the structural and biological characteristics of the pyrazole derivatives recently reported as kinase inhibitors and classified them according to their target kinases in a chronological order. We reviewed the reports including pyrazole derivatives as kinase inhibitors published during the past decade (2011-2020).

SARS-CoV-2 entry inhibitors by dual targeting TMPRSS2 and ACE2: An in silico drug repurposing study.

The coronavirus disease (COVID-19) is spreading between human populations mainly through nasal droplets. Currently, the vaccines have great hope, but it takes years for testing its efficacy in human. As there is no specific drug treatment available for COVID-19 pandemic, we explored in silico repurposing of drugs with dual inhibition properties by targeting transmembrane serine protease 2 (TMPRSS2) and human angiotensin-converting enzyme 2 (ACE2) from FDA-approved drugs. The TMPRSS2 and ACE2 dual inhibitors in COVID-19 would be a novel antiviral class of drugs called "entry inhibitors." For this purpose, approximately 2800 US-FDA approved drugs were docked using a virtual docking tool with the targets TMPRSS2 and ACE2. The best-fit drugs were selected as per docking scores and visual outcomes. Later on, drugs were selected on the basis of molecular dynamics simulations. The drugs alvimopan, arbekacin, dequalinum, fleroxacin, lopinavir, and valrubicin were shortlisted by visual analysis and molecular dynamics simulations. Among these, lopinavir and valrubicin were found to be superior in terms of dual inhibition. Thus, lopinavir and valrubicin have the potential of dual-target inhibition whereby preventing SARS-CoV-2 entry to the host. For repurposing of these drugs, further screening in vitro and in vivo would help in exploring clinically.

Substitutions at H134 and in the 430-loop region in influenza B neuraminidases can confer reduced susceptibility to multiple neuraminidase inhibitors.

With the introduction of the influenza specific neuraminidase inhibitors (NAIs) in 1999, there were concerns about the emergence and spread of resistant viruses in the community setting. Surveillance and testing of community isolates for their susceptibility to the NAIs was initially carried out by the Neuraminidase Inhibitor Susceptibility Network (NISN) and has subsequently been taken on by the global WHO influenza network laboratories. During the NISN surveillance, we identified two Yamagata lineage influenza B viruses with amino acid substitutions of H134Y (B/Auckland/2/2001) or W438R (B/Yokohama/12/2005) which had slightly elevated IC50 values for zanamivir and/or oseltamivir, but not sufficiently to be characterized as mild outliers at the time. As it has now been well demonstrated that mixed populations can mask the true magnitude of resistance of a mutant, we re-examined both of these isolates by plaque purification to see if the true susceptibilities were being masked due to mixed populations. Results confirmed that the B/Auckland isolate contained both wild type and H134Y mutant populations, with mutant IC50 values > 250 nM for both oseltamivir and peramivir in the enzyme inhibition assay. The B/Yokohama isolate also contained both wild type and W438R mutant populations, the latter now demonstrating IC50 values > 400 nM for zanamivir, oseltamivir and peramivir. In addition, plaque purification of the B/Yokohama isolate identified viruses with other single neuraminidase substitutions H134Y, H134R, H431R, or T436P. H134R and H431R viruses had IC50 values > 400 nM and >250 nM respectively against all three NAIs. All changes conferred much greater resistance to peramivir than to zanamivir, and less to oseltamivir, and affected the kinetics of binding and dissociation of the NAIs. Most affected affinity (Km) for the MUNANA substrate, but some had decreased while others had increased affinity. Despite resistance in the enzyme assay, no reduced susceptibility was seen in plaque reduction assays in MDCK cells for any of the mutant viruses. None of these substitutions was in the active site. Modelling suggests that these substitutions affect the 150 and 430-loop regions described for influenza A NAs, suggesting they may also be important for substrate and inhibitor binding for influenza B NAs.

Inhibition of urease activity by different compounds provides insight into the modulation and association of bacterial nickel import and ureolysis.

The nickel-dependent urease enzyme is responsible for the hydrolysis of urea to ammonia and carbon dioxide. A number of bacteria produce urease (ureolytic bacteria) and are associated with various infectious diseases and ammonia emissions from agriculture. We report the first comprehensive comparison of the inhibition of urease activity by compounds analysed under the same conditions. Thus, 71 commercially available compounds were screened for their anti-ureolytic properties against both the ureolytic bacterium Klebsiella pneumoniae and purified jack bean urease. Of the tested compounds, 30 showed more than 25% inhibition of the ureolytic activity of Klebsiella pneumoniae or jack bean urease, and among these, carbon disulfide, N-phenylmaleimide, diethylenetriaminepentaacetic acid, sodium pyrrolidinedithiocarbamate, 1,2,4-butanetricarboxylic acid, tannic acid, and gallic acid have not previously been reported to possess anti-ureolytic properties. The diverse effects of metal ion chelators on ureolysis were investigated using a cellular nickel uptake assay. Ethylenediaminetetraacetic acid (EDTA) and dimethylglyoxime (DMG) clearly reduced the nickel import and ureolytic activity of cells, oxalic acid stimulated nickel import but reduced the ureolytic activity of cells, 1,2,4-butanetricarboxylic acid strongly stimulated nickel import and slightly increased the ureolytic activity of cells, while L-cysteine had no effect on nickel import but efficiently reduced the ureolytic activity of cells.

Defining Stage-Specific Activity of Potent New Inhibitors of Cryptosporidium parvum Growth In Vitro.

Cryptosporidium parvum and Cryptosporidium hominis have emerged as major enteric pathogens of infants in the developing world, in addition to their known importance in immunocompromised adults. Although there has been recent progress in identifying new small molecules that inhibit Cryptosporidium sp. growth in vitro or in animal models, we lack information about their mechanism of action, potency across the life cycle, and cidal versus static activities. Here, we explored four potent classes of compounds that include inhibitors that likely target phosphatidylinositol 4 kinase (PI4K), phenylalanine-tRNA synthetase (PheRS), and several potent inhibitors with unknown mechanisms of action. We utilized monoclonal antibodies and gene expression probes for staging life cycle development to define the timing of when inhibitors were active during the life cycle of Cryptosporidium parvum grown in vitro These different classes of inhibitors targeted different stages of the life cycle, including compounds that blocked replication (PheRS inhibitors), prevented the segmentation of daughter cells and thus blocked egress (PI4K inhibitors), or affected sexual-stage development (a piperazine compound of unknown mechanism). Long-term cultivation of C. parvum in epithelial cell monolayers derived from intestinal stem cells was used to distinguish between cidal and static activities based on the ability of parasites to recover from treatment. Collectively, these approaches should aid in identifying mechanisms of action and for designing in vivo efficacy studies based on time-dependent concentrations needed to achieve cidal activity.IMPORTANCE Currently, nitazoxanide is the only FDA-approved treatment for cryptosporidiosis; unfortunately, it is ineffective in immunocompromised patients, has varied efficacy in immunocompetent individuals, and is not approved in infants under 1 year of age. Identifying new inhibitors for the treatment of cryptosporidiosis requires standardized and quantifiable in vitro assays for assessing potency, selectivity, timing of activity, and reversibility. Here, we provide new protocols for defining which stages of the life cycle are susceptible to four highly active compound classes that likely inhibit different targets in the parasite. We also utilize a newly developed long-term culture system to define assays for monitoring reversibility as a means of defining cidal activity as a function of concentration and time of treatment. These assays should provide valuable in vitro parameters to establish conditions for efficacious in vivo treatment.

Scorpion venomics: a 2019 overview.

Introduction: A few scorpions are dangerous to humans. Their medical relevance was the initial driving force for venom research. By classical biochemistry and molecular cloning, several venom peptides and their coding transcripts were characterized, mainly those related to toxins. The discovery of other components with novel activities and potential applications has revitalized the interest in the field in the last decade and a half. Nontoxic scorpion species have also attracted major interest.Areas covered: Advances in the identification of scorpion venom components via high-throughput venomics (genomics, transcriptomics and proteomics) up to 2019 are summarized. A classification system for venom-related transcripts and proteins, together with an intuitive systematic nomenclature for RNAseq-generated transcripts are proposed. Venom components classified as Na+, K+, Ca2+, Cl- and TRP channel toxins, enzymes, protease inhibitors, host defense peptides and other peptidic molecules are briefly reviewed, giving a comprehensive picture of the venom.Expert opinion: Modern high-throughput technologies applied to scorpion venom studies have resulted in a dramatic increase in both, the number and diversity of available sequences, leading to a deeper understanding of the composition of scorpion venoms. Still, many newly-discovered venom constituents remain to be characterized, to complete the puzzle of scorpion venoms.

Phytoestrogens and their synthetic analogues as substrate mimic inhibitors of CYP1B1.

Phytoestrogens are class of natural compounds that shares structural similarity with estrogen and has the capacity to alter the fertilization in mammals. Till early 1990s, the natural phytoestrogens as well as their synthetic analogues were explored for their fertility modulating activity. During late 1990s, two findings renewed the interest on phytoestrogens as means to control hormone induced cancer: (i) revelation of overexpression of CYP1B1 in breast & ovarian cancer and (ii) protection offered by alphanapthoflavone (ANF) against hormone induced cancer. The objective of the review is to summarize the CYP1B1 inhibitory activity of phytoestrogens and their synthetic analogues reported till date. This review is an attempt to classify phytoestrogens and their synthetic analogues on their chemical architecture rather than simply by their chemical class (flavones, stilbenes etc.). This provides a broader sense to cluster many chemical classes under a particular chemical architecture/framework. Accordingly, we divided the phytoestrogen into three different classes based on two aryl groups (Ar) separated by linker (X), which may be either cyclic (c) or linear (l). The number in subscript to X denotes number of atoms: (i) Ar-cX4-Ar, (ii) Ar-lX3-Ar and (iii) Ar-lX2-Ar. This provides an opportunity to cluster flavones, quinolines and quinazolinones under Ar-cX4-Ar class, while biphenyl ureas and chalcones under Ar-lX3-Ar class. We believe in coming years many chemical scaffolds may be clustered under this framework.

Structure-Based Optimization of a Novel Class of Aldehyde Dehydrogenase 1A (ALDH1A) Subfamily-Selective Inhibitors as Potential Adjuncts to Ovarian Cancer Chemotherapy.

Aldehyde dehydrogenase (ALDH) activity is commonly used as a marker to identify cancer stem-like cells. The three ALDH1A isoforms have all been individually implicated in cancer stem-like cells and in chemoresistance; however, which isoform is preferentially expressed varies between cell lines. We sought to explore the structural determinants of ALDH1A isoform selectivity in a series of small-molecule inhibitors in support of research into the role of ALDH1A in cancer stem cells. An SAR campaign guided by a cocrystal structure of the HTS hit CM39 (7) with ALDH1A1 afforded first-in-class inhibitors of the ALDH1A subfamily with excellent selectivity over the homologous ALDH2 isoform. We also discovered the first reported modestly selective single isoform 1A2 and 1A3 inhibitors. Two compounds, 13g and 13h, depleted the CD133+ putative cancer stem cell pool, synergized with cisplatin, and achieved efficacious concentrations in vivo following IP administration. Compound 13h additionally synergized with cisplatin in a patient-derived ovarian cancer spheroid model.

Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors.

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

Three Pectin Methylesterase Inhibitors Protect Cell Wall Integrity for Arabidopsis Immunity to Botrytis.

Infection by necrotrophs is a complex process that starts with the breakdown of the cell wall (CW) matrix initiated by CW-degrading enzymes and results in an extensive tissue maceration. Plants exploit induced defense mechanisms based on biochemical modification of the CW components to protect themselves from enzymatic degradation. The pectin matrix is the main CW target of Botrytis cinerea, and pectin methylesterification status is strongly altered in response to infection. The methylesterification of pectin is controlled mainly by pectin methylesterases (PMEs), whose activity is posttranscriptionally regulated by endogenous protein inhibitors (PMEIs). Here, AtPMEI10, AtPMEI11, and AtPMEI12 are identified as functional PMEIs induced in Arabidopsis (Arabidopsis thaliana) during B. cinerea infection. AtPMEI expression is strictly regulated by jasmonic acid and ethylene signaling, while only AtPMEI11 expression is controlled by PME-related damage-associated molecular patterns, such as oligogalacturonides and methanol. The decrease of pectin methylesterification during infection is higher and the immunity to B. cinerea is compromised in pmei10, pmei11, and pmei12 mutants with respect to the control plants. A higher stimulation of the fungal oxalic acid biosynthetic pathway also can contribute to the higher susceptibility of pmei mutants. The lack of PMEI expression does not affect hemicellulose strengthening, callose deposition, and the synthesis of structural defense proteins, proposed as CW-remodeling mechanisms exploited by Arabidopsis to resist CW degradation upon B. cinerea infection. We show that PME activity and pectin methylesterification are dynamically modulated by PMEIs during B. cinerea infection. Our findings point to AtPMEI10, AtPMEI11, and AtPMEI12 as mediators of CW integrity maintenance in plant immunity.

Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: A systematic route for designing selective SphK inhibitors.

Accurate and robust classification models for describing and predicting the activity of 330 chemicals that are sphingosine kinase 1 (SphK1) and/or sphingosine kinase 2 (SphK2) inhibitors were derived. The classification models developed in this work assist in finding selective subspaces in chemical space occupied by particular groups of SphK inhibitors. A combination of a genetic algorithm (GA) and a counter propagation artificial neural network (CPANN) was utilized to select the most efficient subsets of the molecular descriptors. The optimized models in this work reasonably separate active inhibitors of SphK1 from active SphK2 inhibitors. Generally, the CPANN models in this work were used to classify the compounds according to their therapeutic targets and activities. The simplicity of the chosen descriptors and their relative importance sheds some light on the structural features necessary to induce selective inhibitory activity to the studied molecules. The areas under the receiver operating characteristic (ROC) curves for the GA-CPANN models in this work were 0.934 and 0.922 for active SphK1 and SphK2 inhibitors, respectively. Generally, the results in this work suggest some important molecular features and pharmacophores that could help medicinal chemists develop selective and potent SphK inhibitors.

Matrix-Based Activity Pattern Classification as a Novel Method for the Characterization of Enzyme Inhibitors Derived from High-Throughput Screening.

One of the central questions in the characterization of enzyme inhibitors is determining the mode of inhibition (MOI). Classically, this is done with a number of low-throughput methods in which inhibition models are fitted to the data. The ability to rapidly characterize the MOI for inhibitors arising from high-throughput screening in which hundreds to thousands of primary inhibitors may need to be characterized would greatly help in lead selection efforts. Here we describe a novel method for determining the MOI of a compound without the need for curve fitting of the enzyme inhibition data. We provide experimental data to demonstrate the utility of this new high-throughput MOI classification method based on nonparametric analysis of the activity derived from a small matrix of substrate and inhibitor concentrations (e.g., from a 4S × 4I matrix). Lists of inhibitors from four different enzyme assays are studied, and the results are compared with the previously described IC50-shift method for MOI classification. The MOI results from this method are in good agreement with the known MOI and compare favorably with those from the IC50-shift method. In addition, we discuss some advantages and limitations of the method and provide recommendations for utilization of this MOI classification method.

Chemical Genetics Uncovers Novel Inhibitors of Lignification, Including p-Iodobenzoic Acid Targeting CINNAMATE-4-HYDROXYLASE.

Plant secondary-thickened cell walls are characterized by the presence of lignin, a recalcitrant and hydrophobic polymer that provides mechanical strength and ensures long-distance water transport. Exactly the recalcitrance and hydrophobicity of lignin put a burden on the industrial processing efficiency of lignocellulosic biomass. Both forward and reverse genetic strategies have been used intensively to unravel the molecular mechanism of lignin deposition. As an alternative strategy, we introduce here a forward chemical genetic approach to find candidate inhibitors of lignification. A high-throughput assay to assess lignification in Arabidopsis (Arabidopsis thaliana) seedlings was developed and used to screen a 10-k library of structurally diverse, synthetic molecules. Of the 73 compounds that reduced lignin deposition, 39 that had a major impact were retained and classified into five clusters based on the shift they induced in the phenolic profile of Arabidopsis seedlings. One representative compound of each cluster was selected for further lignin-specific assays, leading to the identification of an aromatic compound that is processed in the plant into two fragments, both having inhibitory activity against lignification. One fragment, p-iodobenzoic acid, was further characterized as a new inhibitor of CINNAMATE 4-HYDROXYLASE, a key enzyme of the phenylpropanoid pathway synthesizing the building blocks of the lignin polymer. As such, we provide proof of concept of this chemical biology approach to screen for inhibitors of lignification and present a broad array of putative inhibitors of lignin deposition for further characterization.

Characterization of two distinct structural classes of selective aldehyde dehydrogenase 1A1 inhibitors.

Aldehyde dehydrogenases (ALDH) catalyze the irreversible oxidation of aldehydes to their corresponding carboxylic acid. Alterations in ALDH1A1 activity are associated with such diverse diseases as cancer, Parkinson's disease, obesity, and cataracts. Inhibitors of ALDH1A1 could aid in illuminating the role of this enzyme in disease processes. However, there are no commercially available selective inhibitors for ALDH1A1. Here we characterize two distinct chemical classes of inhibitors that are selective for human ALDH1A1 compared to eight other ALDH isoenzymes. The prototypical members of each structural class, CM026 and CM037, exhibit submicromolar inhibition constants but have different mechanisms of inhibition. The crystal structures of these compounds bound to ALDH1A1 demonstrate that they bind within the aldehyde binding pocket of ALDH1A1 and exploit the presence of a unique glycine residue to achieve their selectivity. These two novel and selective ALDH1A1 inhibitors may serve as chemical tools to better understand the contributions of ALDH1A1 to normal biology and to disease states.

1-Phenylsulfinyl-3-(pyridin-3-yl)naphthalen-2-ols: a new class of potent and selective aldosterone synthase inhibitors.

1-Phenylsulfinyl-3-(pyridin-3-yl)naphthalen-2-ols and related compounds were synthesized and evaluated for inhibition of aldosterone synthase (CYP11B2), a potential target for cardiovascular diseases associated with elevated plasma aldosterone levels like congestive heart failure and myocardial fibrosis. Introduction of substituents at the phenylsulfinyl moiety and changes of the substitution pattern at the naphthalene core were examined. Potent compounds were further examined for selectivity versus other important steroidogenic CYP enzymes, i.e. the highly homologous 11ß-hydroxylase (CYP11B1), CYP17 and CYP19. The most potent compound (IC50 = 14 nM) discovered was the meta-trifluoromethoxy derivative 11, which also exhibited excellent selectivity toward CYP11B1 (SF = 415), and showed no inhibition of CYP17 and CYP19.

Structure-activity relationship study of trifluoromethylketone inhibitors of insect juvenile hormone esterase: comparison of several classification methods.

Juvenile hormone esterase (JHE) plays a key role in the development and metamorphosis of holometabolous insects. Its inhibitors could possibly be targeted for insect control. Conversely, JHE may also be involved in endocrine disruption by xenobiotics, resulting in detrimental effects in beneficial insects. There is therefore a need to know the structural characteristics of the molecules able to monitor JHE activity, and to develop SAR and QSAR studies to estimate their effectiveness. For a large diverse population of 181 trifluoromethylketones (TFKs) - the most potent JHE inhibitors known to date - we recently proposed a binary classification (active/inactive) using a support vector machine and Codessa structural descriptors. We have now examined, using the same data set and with the same descriptors, the applicability and performance of five other machine learning approaches. These have been shown able to handle high dimensional data (with descriptors possibly irrelevant or redundant) and to cope with complex mechanisms, but without delivering explicit directly exploitable models. Splitting the data into five batches (training set 80%, test set 20%) and carrying out leave-one-out cross-validation, led to good results of comparable performance, consistent with our previous support vector classifier (SVC) results. Accuracy was greater than 0.80 for all approaches. A reduced set of 15 descriptors common to all the investigated approaches showed good predictive ability (confirmed using a three-layer perceptron) and gives some clues regarding a mechanistic interpretation.

[The basic functions of inosine 5'-monophosphate dehydrogenase and its application in drug discovery].

Inosine 5'-monophosphate dehydrogenase (IMPDH) is a key enzyme of de novo GMP biosynthesis. The expression and activity of IMPDH can be affected by diseases and physiological process. It is the drug target for anticancer, antiviral, antimicrobial and immunosuppressive therapeutics. Not only catalytic action but the other biological functions of IMPDH also play an important role in diseases. The basic functions, mechanism of catalysis, classification of inhibitors, biological functions and the latest advances to IMPDH will be illustrated in this review. It is expected to be helpful to the discovery of new inhibitors and biological functions of IMPDH.

Human DNA ligases: a comprehensive new look for cancer therapy.

Living organisms belonging to all three domains of life, viz., eubacteria, archaeabacteria, and eukaryotes encode one or more DNA ligases. DNA ligases are indispensable in various DNA repair and replication processes and a deficiency or an inhibition of their activity can lead to accumulation of DNA damage and strand breaks. DNA damage, specially strand breaks at unsustainable levels can lead to replication block and/or cell death. DNA ligases as potential anticancer targets have been realized only recently. There is enough rationale to suggest that ligases have a tremendous potential for novel therapeutics including anticancer and antibacterial therapy, specially when the world is facing acute problems of drug resistance and chemotherapy failure, with an immediate need for new therapeutic targets. Here, we review the current state of the art in the development of human ligase inhibitors, their structures, molecular mechanisms, physiological effects, and their potential in future cancer therapy. Citing examples, we focus on strategies for improving the activity and specificity of existing and novel inhibitors by using structure-based rational approaches. In the end, we describe potential new sites on the ligase I protein that can be targeted for the development of novel inhibitors. This is the first comprehensive review to compile all known human ligase inhibitors and to provide a rationale for the further development of ligase inhibitors for cancer therapy.

Building predictive models for mechanism-of-action classification from phenotypic assay data sets.

Compound mechanism-of-action information can be critical for drug development decisions but is often challenging for phenotypic drug discovery programs. One concern is that compounds selected by phenotypic screening will have a previously known but undesirable target mechanism. Here we describe a useful method for assigning mechanism class to compounds and bioactive agents using an 84-feature signature from a panel of primary human cell systems (BioMAP systems). For this approach, a reference data set of well-characterized compounds was used to develop predictive models for 28 mechanism classes using support vector machines. These mechanism classes encompass safety and efficacy-related mechanisms, include both target-specific and pathway-based classes, and cover the most common mechanisms identified in phenotypic screens, such as inhibitors of mitochondrial and microtubule function, histone deacetylase, and cAMP elevators. Here we describe the performance and the application of these predictive models in a decision scheme for triaging phenotypic screening hits using a previously published data set of 309 environmental chemicals tested as part of the Environmental Protection Agency's ToxCast program. By providing quantified membership in specific mechanism classes, this approach is suitable for identification of off-target toxicity mechanisms as well as enabling target deconvolution of phenotypic drug discovery hits.