High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv

A high-throughput response to the SARS-CoV-2 pandemic

Four years after the beginning of the COVID-19 pandemic, it is important to reflect on the events that have occurred during that time and the knowledge that has been gained. The response to the pandemic was rapid and highly resourced; it was also built upon a foundation of decades of federally funded basic and applied research. Laboratories in government, pharmaceutical, academic, and non-profit institutions all played roles in advancing pre-2020 discoveries to produce clinical treatments. This perspective provides a summary of how the development of high-throughput screening methods in a biosafety level 3 (BSL-3) environment at Southern Research Institute (SR) contributed to pandemic response efforts. The challenges encountered are described, including those of a technical nature as well as those of working under the pressures of an unpredictable virus and pandemic.

Identification and structure-activity relationships for a series of N, N-disubstituted 2-aminobenzothiazoles as potent inhibitors of S. aureus

An internal collection of commercial and synthetically derived small molecule compounds was screened against several drug-resistant bacterial pathogens. Compound 1, a known N, N-disubstituted 2-aminobenzothiazole, was found to be a potent inhibitor of Staphylococcus aureus and several associated clinically relevant strains of methicillin-resistant S. aureus suggesting a possible novel mechanism of inhibition. It failed to show activity in any of the Gram-negative pathogens it was tested in. Evaluation in Escherichia coli BW25113 and Pseudomonas aeruginosa PAO1, as well as in their respective hyperporinated and efflux pump-deletion mutants revealed that activity in Gram-negative bacteria is diminished because this benzothiazole scaffold is a substrate for bacterial efflux pumps. Several analogs of 1 were synthesized to generate basic structure-activity relationships for the scaffold which highlighted that the N-propyl imidazole moiety was critical for the observed antibacterial activity.

Identification of 2-Amino Benzothiazoles with Bactericidal Activity against Mycobacterium tuberculosis

We identified an amino-benzothiazole scaffold from a whole-cell screen against recombinant Mycobacterium tuberculosis under expressing the essential signal peptidase LepB. The seed molecule had 2-fold higher activity against the LepB hypomorph. Through a combination of purchase and chemical synthesis, we explored the structure-activity relationship for this series; 34 analogs were tested for antitubercular activity and for cytotoxicity against eukaryotic cells. We identified molecules with improved potency and reduced cytotoxicity. However, molecules did not appear to target LepB directly and did not inhibit protein secretion. Key compounds showed good permeability, low protein binding, and lack of CYP inhibition, but metabolic stability was poor with short half-lives. The seed molecule showed good bactericidal activity against both replicating and nonreplicating bacteria, as well as potency against intracellular M. tuberculosis in murine macrophages. Overall, the microbiological properties of the series are attractive if metabolic stability can be improved, and identification of the target could assist in the development of this series. IMPORTANCE Mycobacterium tuberculosis, the causative agent of tuberculosis, is a serious global health problem requiring the development of new therapeutics. We previously ran a high-throughput screen and identified a series of compounds with antitubercular activity. In this paper, we test analogs of our hit molecules for activity against M. tuberculosis, as well as for activity against eukaryotic cells. We identified molecules with improved selectivity. Our molecules killed both replicating and nonreplicating bacteria but did not work by targeting protein secretion.

Human Kinase IGF1R/IR Inhibitor Linsitinib Controls the In Vitro and Intracellular Growth of Mycobacterium tuberculosis

ATP provides energy in the biosynthesis of cellular metabolites as well as regulates protein functions through phosphorylation. Many ATP-dependent enzymes are antibacterial and anticancer targets including human kinases acted on by most of the successful drugs. In search of new chemotherapeutics for tuberculosis (TB), we screened repurposing compounds against the essential glutamine synthase (GlnA1) of Mycobacterium tuberculosis (Mtb) and identified linsitinib, a clinical-stage drug originally targeting kinase IGF1R/IR as a potent GlnA1 inhibitor. Linsitinib has direct antimycobacterial activity. Biochemical, molecular modeling, and target engagement analyses revealed the inhibition is ATP-competitive and specific in Mtb. Linsitinib also improves autophagy flux in both Mtb-infected and uninfected THP1 macrophages, as demonstrated by the decreased p-mTOR and p62 and the increased lipid-bound LC3B-II and autophagosome forming puncta. Linsitinib-mediated autophagy reduces intracellular growth of wild-type and isoniazid-resistant Mtb alone or in combination with bedaquiline. We have demonstrated that an IGF-IR/IR inhibitor can potentially be used to treat TB. Our study reinforces the concept of targeting ATP-dependent enzymes for novel anti-TB therapy.

Bioactive Aurones, Indanones, and Other Hemiindigoid Scaffolds: Medicinal Chemistry and Photopharmacology Perspectives

Hemiindigoids comprise a range of natural and synthetic scaffolds that share the same aromatic hydrocarbon backbone as well as promising biological and optical properties. The encouraging therapeutic potential of these scaffolds has been unraveled by many studies over the past years and uncovered representants with inspiring pharmacophoric features such as the acetylcholinesterase inhibitor donezepil and the tubulin polymerization inhibitor indanocine. In this review, we summarize the last advances in the medicinal potential of hemiindigoids, with a special attention to molecular design, structure-activity relationship, ligand-target interactions, and mechanistic explanations covering their effects. As their strong fluorogenic potential and photoswitch behavior recently started to be highlighted and explored in biology, giving rise to the development of novel fluorescent probes and photopharmacological agents, we also discuss these properties in a medicinal chemistry perspective.

Machine Learning Models for Mycobacterium tuberculosisIn Vitro Activity: Prediction and Target Visualization

Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths per year. There is still a need to develop novel treatments for patients infected with Mycobacterium tuberculosis (Mtb). There have been many large-scale phenotypic screens that have led to the identification of thousands of new compounds. Yet, there is very limited investment in TB drug discovery which points to the need for new methods to increase the efficiency of drug discovery against Mtb. We have used machine learning approaches to learn from the public Mtb data, resulting in many data sets and models with robust enrichment and hit rates leading to the discovery of new active compounds. Recently, we have curated predominantly small-molecule Mtb data and developed new machine learning classification models with 18 886 molecules at different activity cutoffs. We now describe the further validation of these Bayesian models using a library of over 1000 molecules synthesized as part of EU-funded New Medicines for TB and More Medicines for TB programs. We highlight molecular features which are enriched in these active compounds. In addition, we provide new regression and classification models that can be used for scoring compound libraries or used to design new molecules. We have also visualized these molecules in the context of known molecular targets and identified clusters in chemical property space, which may aid in future target identification efforts. Finally, we are also making these data sets publicly available, representing a significant increase to the available Mtb inhibition data in the public domain.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Evaluation of small molecule kinase inhibitors as novel antimicrobial and antibiofilm agents

Antibiotic resistance is a global and pressing concern. Our current therapeutic arsenal is increasingly limited as bacteria are developing resistance at a rate that far outpaces our ability to create new treatments. Novel approaches to treating and curing bacterial infections are urgently needed. Bacterial kinases have been increasingly explored as novel drug targets and are poised for development into novel therapeutic agents to combat bacterial infections. This review describes several general classes of bacterial kinases that play important roles in bacterial growth, antibiotic resistance, and biofilm formation. General features of these kinase classes are discussed and areas of particular interest for the development of inhibitors will be highlighted. Small molecule kinase inhibitors are described and organized by phenotypic effect, spotlighting particularly interesting inhibitors with novel functions and potential therapeutic benefit. Finally, we provide our perspective on the future of bacterial kinase inhibition as a viable strategy to combat bacterial infections and overcome the pressures of increasing antibiotic resistance.

QcrB in Mycobacterium tuberculosis: The new drug target of antitubercular agents

Drug-resistance in mycobacterial infections is a major global health problem that leads to high mortality and socioeconomic pressure in developing countries around the world. From finding new targets to discovering novel chemical scaffolds, there is an urgent need for the development of better approaches for the cure of tuberculosis. Recently, energy metabolism in mycobacteria, particularly the oxidative phosphorylation pathway of cellular respiration, has emerged as a novel target pathway in drug discovery. New classes of antibacterials which target oxidative phosphorylation pathway either by interacting with a protein or any step in the pathway of oxidative phosphorylation can combat dormant mycobacterial infections leading to shortening of tuberculosis chemotherapy. Adenosine triphosphate synthase is one such recently discovered target of the newly approved antitubercular drug bedaquiline. Cytochrome bcc is another new target of the antitubercular drug candidate Q203, currently in phase II clinical trial. Research suggests that b subunit of cytochrome bcc, QcrB, is the target of Q203. The review article describes the structure, function, and importance of targeting QcrB throwing light on all chemical classes of QcrB inhibitors discovered to date. An understanding of the structure and function of validated targets and their inhibitors would enable the development of new chemical entities.

Inhibitors of pantothenate synthetase of Mycobacterium tuberculosis - a medicinal chemist perspective

Tuberculosis (TB), one of the most prevalent infections, is on the rise today. Although there are drugs available in the market to combat this lethal disorder, there are several shortcomings with the current drug regimen, such as prolonged treatment period, drug resistance, high cost, etc. Hence, it is inevitable for the current researchers across the globe to embark on new strategies for TB drug discovery, which will yield highly active low cost drugs with a shorter treatment period. To achieve this, novel strategies need to be adopted to discover new drugs. Pantothenate Synthetase (PS) is one such striking drug target in Mycobacterium tuberculosis (MTB). It was observed that the pantothenate biosynthetic pathway is crucial for the pathogenicity of MTB. Pantothenate is absent in mammals and needs to be obtained from dietary sources. Hence, the pantothenate biosynthesis pathway is an impending target for emerging new therapeutics to treat TB. Worldwide, several approaches have been implemented by researchers in the quest for these inhibitors such as high-throughput screening, simulating the reaction intermediate pantoyl adenylate, use of vibrant combinatorial chemistry, hybridization approach, virtual screening of databases, inhibitors based on the crystal structure of MTB PS, etc. The present review recapitulates current developments in PS inhibitors, important analogues of numerous metabolic intermediates, and newly established inhibitors with innumerable chemical structures.

Identification of Anti-tuberculosis Compounds From Aurone Analogs

The emergence of multidrug-resistant Mycobacterium tuberculosis (Mtb) strains has made tuberculosis (TB) control more difficult. Aurone derivatives have demonstrated promising anti-bacterial activities, but their effects against Mtb have not been thoroughly determined. In this study, we aimed to develop anti-TB compounds from aurone analogs. We used a fluorescent protein tdTomato labeled Mtb CDC1551 strain to screen 146 synthesized aurone derivatives for effective anti-TB compounds. The 9504, 9505, 9501, 9510, AA2A, and AA8 aurones inhibited the growth of Mtb with minimal inhibitory concentrations of 6.25, 12.5, 25, 25, 25, and 50 μM, respectively. We also examined cytotoxicities of the six leads against the human liver cell line HepG2, the primate kidney cell line Vero and human monocyte THP-1 derived macrophages. Three of the aurone leads (9504, 9501, and 9510) showed low cytotoxic effects on all three cell lines and high Mtb inhibitory efficacy (selectivity index > 10). Aurone 9504, 9501, AA2A, or AA8 significantly reduced the Mtb load in the lungs of infected mice after a 12-days treatment. We determined that the aurone leads inhibit Mtb chorismate synthase, an essential enzyme for aromatic acid synthesis. Our studies demonstrate the promise of synthetic aurones as novel anti-TB therapeutics.

Molecule Property Analyses of Active Compounds for Mycobacterium tuberculosis

Tuberculosis (TB) continues to claim the lives of around 1.7 million people per year. Most concerning are the reports of multidrug drug resistance. Paradoxically, this global health pandemic is demanding new therapies when resources and interest are waning. However, continued tuberculosis drug discovery is critical to address the global health need and burgeoning multidrug resistance. Many diverse classes of antitubercular compounds have been identified with activity in vitro and in vivo. Our analyses of over 100 active leads are representative of thousands of active compounds generated over the past decade, suggests that they come from few chemical classes or natural product sources. We are therefore repeatedly identifying compounds that are similar to those that preceded them. Our molecule-centered cheminformatics analyses point to the need to dramatically increase the diversity of chemical libraries tested and get outside of the historic Mtb property space if we are to generate novel improved antitubercular leads.

Azaaurones as Potent Antimycobacterial Agents Active against MDR- and XDR-TB

Herein we report the screening of a small library of aurones and their isosteric counterparts, azaaurones and N-acetylazaaurones, against Mycobacterium tuberculosis. Aurones were found to be inactive at 20 μm, whereas azaaurones and N-acetylazaaurones emerged as the most potent compounds, with nine derivatives displaying MIC₉₉ values ranging from 0.4 to 2.0 μm. In addition, several N-acetylazaaurones were found to be active against multidrug-resistant (MDR) and extensively drug-resistant (XDR) clinical M. tuberculosis isolates. The antimycobacterial mechanism of action of these compounds remains to be determined; however, a preliminary mechanistic study confirmed that they do not inhibit the mycobacterial cytochrome bc1 complex. Additionally, microsomal metabolic stability and metabolite identification studies revealed that N-acetylazaaurones are deacetylated to their azaaurone counterparts. Overall, these results demonstrate that azaaurones and their N-acetyl counterparts represent a new entry in the toolbox of chemotypes capable of inhibiting M. tuberculosis growth.

Large-scale chemical-genetics yields new M. tuberculosis inhibitor classes

New antibiotics are needed to combat rising levels of resistance, with new Mycobacterium tuberculosis (Mtb) drugs having the highest priority. However, conventional whole-cell and biochemical antibiotic screens have failed. Here we develop a strategy termed PROSPECT (primary screening of strains to prioritize expanded chemistry and targets), in which we screen compounds against pools of strains depleted of essential bacterial targets. We engineered strains that target 474 essential Mtb genes and screened pools of 100-150 strains against activity-enriched and unbiased compound libraries, probing more than 8.5 million chemical-genetic interactions. Primary screens identified over tenfold more hits than screening wild-type Mtb alone, with chemical-genetic interactions providing immediate, direct target insights. We identified over 40 compounds that target DNA gyrase, the cell wall, tryptophan, folate biosynthesis and RNA polymerase, as well as inhibitors that target EfpA. Chemical optimization yielded EfpA inhibitors with potent wild-type activity, thus demonstrating the ability of PROSPECT to yield inhibitors against targets that would have eluded conventional drug discovery.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

A multifaceted approach to identify non-specific enzyme inhibition: Application to Mycobacterium tuberculosis shikimate kinase

Single dose high-throughput screening (HTS) followed by dose-response evaluations is a common strategy for the identification of initial hits for further development. Early identification and exclusion of false positives is a cost-saving and essential step in early drug discovery. One of the mechanisms of false positive compounds is the formation of aggregates in assays. This study evaluates the mechanism(s) of inhibition of a set of 14 compounds identified previously as actives in Mycobacterium tuberculosis (Mt) cell culture screening and in vitro actives in Mt shikimate kinase (MtSK) assay. Aggregation of hit compounds was characterized using multiple experimental methods, LC-MS, ¹HNMR, dynamic light scattering (DLS), transmission electron microscopy (TEM), and visual inspection after centrifugation for orthogonal confirmation. Our results suggest that the investigated compounds containing oxadiazole-amide and aminobenzothiazole moieties are false positive hits and non-specific inhibitors of MtSK through aggregate formation.

A genome-wide structure-based survey of nucleotide binding proteins in M. tuberculosis

Nucleoside tri-phosphates (NTP) form an important class of small molecule ligands that participate in, and are essential to a large number of biological processes. Here, we seek to identify the NTP binding proteome (NTPome) in M. tuberculosis (M.tb), a deadly pathogen. Identifying the NTPome is useful not only for gaining functional insights of the individual proteins but also for identifying useful drug targets. From an earlier study, we had structural models of M.tb at a proteome scale from which a set of 13,858 small molecule binding pockets were identified. We use a set of NTP binding sub-structural motifs derived from a previous study and scan the M.tb pocketome, and find that 1,768 proteins or 43% of the proteome can theoretically bind NTP ligands. Using an experimental proteomics approach involving dye-ligand affinity chromatography, we confirm NTP binding to 47 different proteins, of which 4 are hypothetical proteins. Our analysis also provides the precise list of binding site residues in each case, and the probable ligand binding pose. As the list includes a number of known and potential drug targets, the identification of NTP binding can directly facilitate structure-based drug design of these targets.

Identification of the anti-mycobacterial functional properties of piperidinol derivatives

Background and Purpose

Tuberculosis (TB) remains a major global health threat and is now the leading cause of death from a single infectious agent worldwide. The current TB drug regimen is inadequate, and new anti‐tubercular agents are urgently required to be able to successfully combat the increasing prevalence of drug‐resistant TB. The purpose of this study was to investigate a piperidinol compound derivative that is highly active against the Mycobacterium tuberculosis bacillus.

Experimental Approach

The antibacterial properties of the piperidinol compound and its corresponding bis‐Mannich base analogue were evaluated against M. smegmatis and Gram‐negative organisms. Cytotoxicity studies were undertaken in order to determine the selectivity index for these compounds. Spontaneous resistant mutants of M. smegmatis were generated against the piperidinol and corresponding bis‐Mannich base lead derivatives and whole genome sequencing employed to determine the genetic modifications that lead to selection pressure in the presence of these compounds.

Key Results

The piperidinol and the bis‐Mannich base analogue were found to be selective for mycobacteria and rapidly kill this organism with a cytotoxicity selectivity index for mycobacteria of >30‐fold. Whole genome sequencing of M. smegmatis strains resistant to the lead compounds led to the identification of a number of single nucleotide polymorphisms indicating multiple targets.

Conclusion and Implications

Our results indicate that the piperidinol moiety represents an attractive compound class in the pursuit of novel anti‐tubercular agents.

Linked Articles

This article is part of a themed section on Drug Metabolism and Antibiotic Resistance in Micro‐organisms. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.14/issuetoc

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development.

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public-private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project.

Non-classical transpeptidases yield insight into new antibacterials

Bacterial survival requires an intact peptidoglycan layer, a three-dimensional exoskeleton that encapsulates the cytoplasmic membrane. Historically, the final steps of peptidoglycan synthesis are known to be carried out by D,D-transpeptidases, enzymes that are inhibited by the β-lactams, which constitute >50% of all antibacterials in clinical use. Here, we show that the carbapenem subclass of β-lactams are distinctly effective not only because they inhibit D,D-transpeptidases and are poor substrates for β-lactamases, but primarily because they also inhibit non-classical transpeptidases, namely the L,D-transpeptidases, which generate the majority of linkages in the peptidoglycan of mycobacteria. We have characterized the molecular mechanisms responsible for inhibition of L,D-transpeptidases of Mycobacterium tuberculosis and a range of bacteria including ESKAPE pathogens, and used this information to design, synthesize and test simplified carbapenems with potent antibacterial activity.

Learning from the past for TB drug discovery in the future

Tuberculosis drug discovery has shifted in recent years from a primarily target-based approach to one that uses phenotypic high-throughput screens. As examples of this, through our EU-funded FP7 collaborations, New Medicines for Tuberculosis was target-based and our more-recent More Medicines for Tuberculosis project predominantly used phenotypic screening. From these projects we have examples of success (DprE1) and failure (PimA) going from drug to target and from target to drug, respectively. It is clear that we still have much to learn about the drug targets and the complex effects of the drugs on Mycobacterium tuberculosis. We propose a more integrated approach that learns from earlier drug discovery efforts that could help to move drug discovery forward.

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015)

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t_1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtbin vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t_1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitroMtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivoMtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t_1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app.

Mycobacterium tuberculosis High-Throughput Screening

High-throughput screening is a valuable way to identify hit compounds that combined with a robust medicinal chemistry program could lead to the identification of new antibiotics. Here, we discuss our method for screening large compound libraries with virulent Mycobacterium tuberculosis, possibly one of the more difficult bacteria to use because of its slow growth and assignment to Biosafety Level-3 by the CDC and NIH. The principles illuminated here, however, are relevant to the execution of most bacteria high-throughput screens.

Synthesis and Cytotoxic Evaluation of Pyran, Dihydropyridine and Thiophene Derivatives of 3-Acetylcoumarin

A series of coumarin analogues bearing 4H-pyran rings 2a-d, 11a-d and 1,4-dihydropyridine rings 3a-d, 12a-d at position 3 were synthesized starting from either 3-acetyl coumarin (1) or the coumarin acetohydrazide derivative 4. Condensation of 3-acetylcoumarin (1) with 2-cyanoacetohydrazide afforded 2-cyano-N'-{1-[2-oxo-2H-chromen-3-yl]ethylidene}acetohydrazide (4). Reaction of compound 4 with elemental sulfur and either malononitrile or ethyl cyanoacetate afforded the thiophene derivatives 8 and 9, respectively. The structures of the newly synthesized compounds were confirmed on the basis of their spectral data and elemental analyses. All synthesized compounds were screened for their in vitro anticancer activity against six human cancer cell lines and normal fibroblasts. Several compounds showed potent inhibition with an IC50 value of ˂870 nM. Compound 3d exhibited equivalent cytotoxic effect as the standard CHS 828 against a breast cancer cell line (IC50 value=18 nM). Normal fibroblast cells (WI38) were affected to a much lesser extent (IC50 value >10000 nM).

Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis

There is an urgent need to identify new treatments for tuberculosis (TB), a major infectious disease caused by Mycobacterium tuberculosis (Mtb), which results in 1.5 million deaths each year. We have targeted two essential enzymes in this organism that are promising for antibacterial therapy and reported to be inhibited by naphthoquinones. ThyX is an essential thymidylate synthase that is mechanistically and structurally unrelated to the human enzyme. DNA gyrase is a DNA topoisomerase present in bacteria and plants but not animals. The current study set out to understand the structure-activity relationships of these targets in Mtb using a combination of cheminformatics and in vitro screening. Here, we report the identification of new Mtb ThyX inhibitors, 2-chloro-3-(4-methanesulfonylpiperazin-1-yl)-1,4-dihydronaphthalene-1,4-dione) and idebenone, which show modest whole-cell activity and appear to act, at least in part, by targeting ThyX in Mtb.

Adapting high-throughput screening methods and assays for biocontainment laboratories

High-throughput screening (HTS) has been integrated into the drug discovery process, and multiple assay formats have been widely used in many different disease areas but with limited focus on infectious agents. In recent years, there has been an increase in the number of HTS campaigns using infectious wild-type pathogens rather than surrogates or biochemical pathogen-derived targets. Concurrently, enhanced emerging pathogen surveillance and increased human mobility have resulted in an increase in the emergence and dissemination of infectious human pathogens with serious public health, economic, and social implications at global levels. Adapting the HTS drug discovery process to biocontainment laboratories to develop new drugs for these previously uncharacterized and highly pathogenic agents is now feasible, but HTS at higher biosafety levels (BSL) presents a number of unique challenges. HTS has been conducted with multiple bacterial and viral pathogens at both BSL-2 and BSL-3, and pilot screens have recently been extended to BSL-4 environments for both Nipah and Ebola viruses. These recent successful efforts demonstrate that HTS can be safely conducted at the highest levels of biological containment. This review outlines the specific issues that must be considered in the execution of an HTS drug discovery program for high-containment pathogens. We present an overview of the requirements for HTS in high-level biocontainment laboratories.

Thiophenecarboxamide Derivatives Activated by EthA Kill Mycobacterium tuberculosis by Inhibiting the CTP Synthetase PyrG

To combat the emergence of drug-resistant strains of Mycobacterium tuberculosis, new antitubercular agents and novel drug targets are needed. Phenotypic screening of a library of 594 hit compounds uncovered two leads that were active against M. tuberculosis in its replicating, non-replicating, and intracellular states: compounds 7947882 (5-methyl-N-(4-nitrophenyl)thiophene-2-carboxamide) and 7904688 (3-phenyl-N-[(4-piperidin-1-ylphenyl)carbamothioyl]propanamide). Mutants resistant to both compounds harbored mutations in ethA (rv3854c), the gene encoding the monooxygenase EthA, and/or in pyrG (rv1699) coding for the CTP synthetase, PyrG. Biochemical investigations demonstrated that EthA is responsible for the activation of the compounds, and by mass spectrometry we identified the active metabolite of 7947882, which directly inhibits PyrG activity. Metabolomic studies revealed that pharmacological inhibition of PyrG strongly perturbs DNA and RNA biosynthesis, and other metabolic processes requiring nucleotides. Finally, the crystal structure of PyrG was solved, paving the way for rational drug design with this newly validated drug target.

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Two compounds activated by EthA kill M. tuberculosis through PyrG inhibition

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EthA metabolite is active against PyrG and M. tuberculosis growth

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Definition of the mechanism of activation and validation of PyrG as a new drug target

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL

In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.

Interaction of ATP with a small heat shock protein from Mycobacterium leprae: effect on its structure and function

Adenosine-5'-triphosphate (ATP) is an important phosphate metabolite abundantly found in Mycobacterium leprae bacilli. This pathogen does not derive ATP from its host but has its own mechanism for the generation of ATP. Interestingly, this molecule as well as several antigenic proteins act as bio-markers for the detection of leprosy. One such bio-marker is the 18 kDa antigen. This 18 kDa antigen is a small heat shock protein (HSP18) whose molecular chaperone function is believed to help in the growth and survival of the pathogen. But, no evidences of interaction of ATP with HSP18 and its effect on the structure and chaperone function of HSP18 are available in the literature. Here, we report for the first time evidences of "HSP18-ATP" interaction and its consequences on the structure and chaperone function of HSP18. TNP-ATP binding experiment and surface plasmon resonance measurement showed that HSP18 interacts with ATP with a sub-micromolar binding affinity. Comparative sequence alignment between M. leprae HSP18 and αB-crystallin identified the sequence 49KADSLDIDIE58 of HSP18 as the Walker-B ATP binding motif. Molecular docking studies revealed that β4-β8 groove/strands as an ATP interactive region in M. leprae HSP18. ATP perturbs the tertiary structure of HSP18 mildly and makes it less susceptible towards tryptic cleavage. ATP triggers exposure of additional hydrophobic patches at the surface of HSP18 and induces more stability against chemical and thermal denaturation. In vitro aggregation and thermal inactivation assays clearly revealed that ATP enhances the chaperone function of HSP18. Our studies also revealed that the alteration in the chaperone function of HSP18 is reversible and is independent of ATP hydrolysis. As the availability and binding of ATP to HSP18 regulates its chaperone function, this functional inflection may play an important role in the survival of M. leprae in hosts.

Identification of Bacillus anthracis PurE inhibitors with antimicrobial activity

N(5)-carboxy-amino-imidazole ribonucleotide (N(5)-CAIR) mutase (PurE), a bacterial enzyme in the de novo purine biosynthetic pathway, has been suggested to be a target for antimicrobial agent development. We have optimized a thermal shift method for high-throughput screening of compounds binding to Bacillus anthracis PurE. We used a low ionic strength buffer condition to accentuate the thermal shift stabilization induced by compound binding to Bacillus anthracis PurE. The compounds identified were then subjected to computational docking to the active site to further select compounds likely to be inhibitors. A UV-based enzymatic activity assay was then used to select inhibitory compounds. Minimum inhibitory concentration (MIC) values were subsequently obtained for the inhibitory compounds against Bacillus anthracis (ΔANR strain), Escherichia coli (BW25113 strain, wild-type and ΔTolC), Francisella tularensis, Staphylococcus aureus (both methicillin susceptible and methicillin-resistant strains) and Yersinia pestis. Several compounds exhibited excellent (0.05-0.15μg/mL) MIC values against Bacillus anthracis. A common core structure was identified for the compounds exhibiting low MIC values. The difference in concentrations for inhibition and MIC suggest that another enzyme(s) is also targeted by the compounds that we identified.

Biochemical and structural characterization of mycobacterial aspartyl-tRNA synthetase AspS, a promising TB drug target

The human pathogen Mycobacterium tuberculosis is the causative agent of pulmonary tuberculosis (TB), a disease with high worldwide mortality rates. Current treatment programs are under significant threat from multi-drug and extensively-drug resistant strains of M. tuberculosis, and it is essential to identify new inhibitors and their targets. We generated spontaneous resistant mutants in Mycobacterium bovis BCG in the presence of 10× the minimum inhibitory concentration (MIC) of compound 1, a previously identified potent inhibitor of mycobacterial growth in culture. Whole genome sequencing of two resistant mutants revealed in one case a single nucleotide polymorphism in the gene aspS at ⁵³⁵GAC>⁵³⁵AAC (D179N), while in the second mutant a single nucleotide polymorphism was identified upstream of the aspS promoter region. We probed whole cell target engagement by overexpressing either M. bovis BCG aspS or Mycobacterium smegmatis aspS, which resulted in a ten-fold and greater than ten-fold increase, respectively, of the MIC against compound 1. To analyse the impact of inhibitor 1 on M. tuberculosis AspS (Mt-AspS) activity we over-expressed, purified and characterised the kinetics of this enzyme using a robust tRNA-independent assay adapted to a high-throughput screening format. Finally, to aid hit-to-lead optimization, the crystal structure of apo M. smegmatis AspS was determined to a resolution of 2.4 Å.

Bigger data, collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

Purification and characterization of the Staphylococcus aureus bacillithiol transferase BstA

BACKGROUND

Gram-positive bacteria in the phylum Firmicutes synthesize the low molecular weight thiol bacillithiol rather than glutathione or mycothiol. The bacillithiol transferase YfiT from Bacillus subtilis was identified as a new member of the recently discovered DinB/YfiT-like Superfamily. Based on structural similarity using the Superfamily program, we have determined 30 of 31 Staphylococcus aureus strains encode a single bacillithiol transferase from the DinB/YfiT-like Superfamily, while the remaining strain encodes two proteins.

METHODS

We have cloned, purified, and confirmed the activity of a recombinant bacillithiol transferase (henceforth called BstA) encoded by the S. aureus Newman ORF NWMN_2591. Moreover, we have studied the saturation kinetics and substrate specificity of this enzyme using in vitro biochemical assays.

RESULTS

BstA was found to be active with the co-substrate bacillithiol, but not with other low molecular weight thiols tested. BstA catalyzed bacillithiol conjugation to the model substrates monochlorobimane, 1-chloro-2,4-dinitrobenzene, and the antibiotic cerulenin. Several other molecules, including the antibiotic rifamycin S, were found to react directly with bacillithiol, but the addition of BstA did not enhance the rate of reaction. Furthermore, cells growing in nutrient rich medium exhibited low BstA activity.

CONCLUSIONS

BstA is a bacillithiol transferase from S. aureus that catalyzes the detoxification of cerulenin. Additionally, we have determined that bacillithiol itself might be capable of directly detoxifying electrophilic molecules.

GENERAL SIGNIFICANCE

BstA is an active bacillithiol transferase from S. aureus Newman and is the first DinB/YfiT-like Superfamily member identified from this organism. Interestingly, BstA is highly divergent from B. subtilis YfiT.

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0

BACKGROUND

We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible.

RESULTS

We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets.

CONCLUSIONS

TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.