A Preclinical Candidate Targeting Mycobacterium tuberculosis KasA

The Mycobacterium tuberculosis Cell Wall: An Alluring Drug Target for Developing Newer Anti-TB Drugs-A Perspective

The Mycobacterium cell wall is a capsule-like structure comprising of various layers of biomolecules such as mycolic acid, peptidoglycans, and arabinogalactans, which provide the Mycobacteria a sort of cellular shield. Drugs like isoniazid, ethambutol, cycloserine, delamanid, and pretomanid inhibit cell wall synthesis by inhibiting one or the other enzymes involved in cell wall synthesis. Many enzymes present across these layers serve as potential targets for the design and development of newer anti-TB drugs. Some of these targets are currently being exploited as the most druggable targets like DprE1, InhA, and MmpL3. Many of the anti-TB agents present in clinical trials inhibit cell wall synthesis. The present article covers a systematic perspective of developing cell wall inhibitors targeting various enzymes involved in cell wall biosynthesis as potential drug candidates for treating Mtb infection.

Synthesis, Molecular Docking, Molecular Dynamic Simulation Studies, and Antitubercular Activity Evaluation of Substituted Benzimidazole Derivatives

Tuberculosis, also known as TB, is a widespread bacterial infection that remains a significant global health issue. This study focuses on conducting a thorough investigation into the synthesis, evaluation of anti-Tb activity, molecular docking, and molecular dynamic simulation of substituted benzimidazole derivatives. A series of twelve substituted benzimidazole derivatives (1-12) were successfully synthesized, employing a scaffold consisting of electron-withdrawing and electron-donating groups. The newly synthesized compounds were defined by their FTIR, 1H NMR, and mass spectra. The microplate Alamar blue assay (MABA) was used to evaluate the antimycobacterial activity of the synthesized compound against Mycobacterium tuberculosis (Mtb). Compounds 7 (MIC = 0.8 g/mL) and 8 (MIC = 0.8 g/mL) demonstrated exceptional potential to inhibit M. tuberculosis compared to the standard drug (isoniazid). In addition, the synthesized compounds were docked with the Mtb KasA protein (PDB ID: 6P9K), and the results of molecular docking and molecular dynamic simulation confirmed the experimental results, as compounds 7 and 8 exhibited the highest binding energy of -7.36 and -7.17 kcal/mol, respectively. The simulation results such as the RMSD value, RMSF value, radius of gyration, and hydrogen bond analysis illustrated the optimum potential of compounds 7 and 8 to inhibit the M. tuberculosis strain. Hydrogen bond analysis suggested that compound 7 has greater stability and affinity towards the KasA protein compared to compound 8. Moreover, both compounds (7 and 8) were safe for acute inhalation and cutaneous sensitization. These two compounds have the potential to be potent M. tuberculosis inhibitors.

The use of Mycobacterium tuberculosis H37Ra-infected immunocompetent mice as an in vivo model of persisters

Persistence of Mycobacterium tuberculosis (Mtb) is one of the challenges to successful treatment of tuberculosis (TB). In vitro models of non-replicating Mtb are used to test the efficacy of new molecules against Mtb persisters. The H37Ra strain is attenuated for growth in macrophages and mice. We validated H37Ra-infected immunocompetent mice for testing anti-TB molecules against slow/non-replicating Mtb in vivo. Swiss mice were infected intravenously with H37Ra and monitored for CFU burden and histopathology for a period of 12 weeks. The bacteria multiplied at a slow pace reaching a maximum load of ∼106 in 8-12 weeks depending on the infection dose, accompanied by time and dose-dependent histopathological changes in the lungs. Surprisingly, four-weeks of treatment with isoniazid-rifampicin-ethambutol-pyrazinamide combination caused only 0.4 log10 and 1 log10 reduction in CFUs in lungs and spleen respectively. The results show that ∼40 % of the H37Ra bacilli in lungs are persisters after 4 weeks of anti-TB therapy. Isoniazid/rifampicin monotherapy also showed similar results. A combination of bedaquiline and isoniazid reduced the CFU counts to <200 (limit of detection), compared to ∼5000 CFUs by isoniazid alone. The study demonstrates an in vivo model of Mtb persisters for testing new leads using a BSL-2 strain.

Mycobacterium tuberculosis: Pathogenesis and therapeutic targets

Tuberculosis (TB) remains a significant public health concern in the 21st century, especially due to drug resistance, coinfection with diseases like immunodeficiency syndrome (AIDS) and coronavirus disease 2019, and the lengthy and costly treatment protocols. In this review, we summarize the pathogenesis of TB infection, therapeutic targets, and corresponding modulators, including first-line medications, current clinical trial drugs and molecules in preclinical assessment. Understanding the mechanisms of Mycobacterium tuberculosis (Mtb) infection and important biological targets can lead to innovative treatments. While most antitubercular agents target pathogen-related processes, host-directed therapy (HDT) modalities addressing immune defense, survival mechanisms, and immunopathology also hold promise. Mtb's adaptation to the human host involves manipulating host cellular mechanisms, and HDT aims to disrupt this manipulation to enhance treatment effectiveness. Our review provides valuable insights for future anti-TB drug development efforts.

Target Identification in Anti-Tuberculosis Drug Discovery

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that, although preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance, and uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limiting factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bacterial proteins are targeted during the biosynthesis of the cell wall and the energetic metabolism of two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and explains how chemical probes for protein profiling enabled the identification of new targets and biomarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Anti-tuberculosis drug development via targeting the cell envelope of Mycobacterium tuberculosis

Mycobacterium tuberculosis possesses a dynamic cell envelope, which consists of a peptidoglycan layer, a mycolic acid layer, and an arabinogalactan polysaccharide. This envelope possesses a highly complex and unique structure representing a barrier that protects and assists the growth of M. tuberculosis and allows its adaptation to the host. It regulates the immune response of the host cells, causing their damage. Therefore, the cell envelope of M. tuberculosis is an attractive target for vaccine and drug development. The emergence of multidrug-resistant as well as extensively drug resistant tuberculosis and co-infection with HIV prevented an effective control of this disease. Thus, the discovery and development of new drugs is a major keystone for TB treatment and control. This review mainly summarizes the development of drug enzymes involved in the biosynthesis of the cell wall in M. tuberculosis, and other potential drug targets in this pathway, to provide more effective strategies for the development of new drugs.

Mycobacterium tuberculosis KasA as a drug target: Structure-based inhibitor design

Recent studies have reported the β-ketoacyl-acyl carrier protein KasA as a druggable target for Mycobacterium tuberculosis. This review summarizes the current status of major classes of KasA inhibitors with an emphasis on significant contributions from structure-based design methods leveraging X-ray crystal structures of KasA alone and in complex with inhibitors. The issues addressed within each inhibitor class are discussed while detailing the characterized interactions with KasA and structure-activity relationships. A critical analysis of these findings should lay the foundation for new KasA inhibitors to study the basic biology of M. tuberculosis and to form the basis of new antitubercular molecules of clinical significance with activity against drug-sensitive and drug-resistant infections.

Indole: A promising scaffold for the discovery and development of potential anti-tubercular agents

Indole-containing small molecules have been reported to have diverse pharmacological activities. The aromatic heterocyclic scaffold, which resembles various protein structures, has received attention from organic and medicinal chemists. Exploration of indole derivatives in drug discovery has rapidly yielded a vast array of biologically active compounds with broad therapeutic potential. Nature is the major source of indole scaffolds, but various classical and advanced synthesis methods for indoles have also been reported. One-pot synthesis is widely considered an efficient approach in synthetic organic chemistry and has been used to synthesize some indole compounds. The rapid emergence of drug-resistant tuberculosis is a major challenge to be addressed. Identifying novel targets and drug candidates for tuberculosis is therefore crucial. Researchers have extensively explored indole derivatives as potential anti-tubercular agents or drugs. Indole scaffolds containing the novel non-covalent (decaprenylphosphoryl-β-D-ribose2'-epimerase) DprE1 inhibitor 1,4-azaindole is currently in clinical trials to treat Mycobacterium tuberculosis. In addition, DG167 indazole sulfonamide with potent anti-tubercular activity is undergoing early-stage development in preclinical studies. Indole bearing cationic amphiphiles with high chemical diversity have been reported to depolarize and disrupt the mycobacterial membrane. Some indole-based compounds have potential inhibitory activities against distinct anti-tubercular targets, including the inhibition of cell wall synthesis, replication, transcription, and translation, as summarized in the graphical abstract. The success of computer-aided drug design in the fields of cancer and anti-viral drugs has accelerated in silico studies in antibacterial drug development. This review describes the sources of indole scaffolds, the potential for novel indole derivatives to serve as anti-tubercular agents, in silico findings, and proposed actions to facilitate the design of novel compounds with anti-tubercular activity.

Rickettsia Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection

Rickettsia is a genus of Gram-negative bacteria that has for centuries caused large-scale morbidity and mortality. In recent years, the resurgence of rickettsial diseases as a major cause of pyrexias of unknown origin, bioterrorism concerns, vector movement, and concerns over drug resistance is driving a need to identify novel treatments for these obligate intracellular bacteria. Utilizing an uvGFP plasmid reporter, we developed a screen for identifying anti-rickettsial small molecule inhibitors using Rickettsia canadensis as a model organism. The screening data were utilized to train a Bayesian model to predict growth inhibition in this assay. This two-pronged methodology identified anti-rickettsial compounds, including duartin and JSF-3204 as highly specific, efficacious, and noncytotoxic compounds. Both molecules exhibited in vitro growth inhibition of R. prowazekii, the causative agent of epidemic typhus. These small molecules and the workflow, featuring a high-throughput phenotypic screen for growth inhibitors of intracellular Rickettsia spp. and machine learning models for the prediction of growth inhibition of an obligate intracellular Gram-negative bacterium, should prove useful in the search for new therapeutic strategies to treat infections from Rickettsia spp. and other obligate intracellular bacteria.

Tuberculosis Drug Discovery: Challenges and New Horizons

Over the past 2000 years, tuberculosis (TB) has claimed more lives than any other infectious disease. In 2020 alone, TB was responsible for 1.5 million deaths worldwide, comparable to the 1.8 million deaths caused by COVID-19. The World Health Organization has stated that new TB drugs must be developed to end this pandemic. After decades of neglect in this field, a renaissance era of TB drug discovery has arrived, in which many novel candidates have entered clinical trials. However, while hundreds of molecules are reported annually as promising anti-TB agents, very few successfully progress to clinical development. In this Perspective, we critically review those anti-TB compounds published in the last 6 years that demonstrate good in vivo efficacy against Mycobacterium tuberculosis. Additionally, we highlight the main challenges and strategies for developing new TB drugs and the current global pipeline of drug candidates in clinical studies to foment fresh research perspectives.

An Outline of the Latest Crystallographic Studies on Inhibitor-Enzyme Complexes for the Design and Development of New Therapeutics against Tuberculosis

The elucidation of the structure of enzymes and their complexes with ligands continues to provide invaluable insights for the development of drugs against many diseases, including bacterial infections. After nearly three decades since the World Health Organization’s (WHO) declaration of tuberculosis (TB) as a global health emergency, Mycobacterium tuberculosis (Mtb) continues to claim millions of lives, remaining among the leading causes of death worldwide. In the last years, several efforts have been devoted to shortening and improving treatment outcomes, and to overcoming the increasing resistance phenomenon. The structural elucidation of enzyme-ligand complexes is fundamental to identify hot-spots, define possible interaction sites, and elaborate strategies to develop optimized molecules with high affinity. This review offers a critical and comprehensive overview of the most recent structural information on traditional and emerging mycobacterial enzymatic targets. A selection of more than twenty enzymes is here discussed, with a special emphasis on the analysis of their binding sites, the definition of the structure–activity relationships (SARs) of their inhibitors, and the study of their main intermolecular interactions. This work corroborates the potential of structural studies, substantiating their relevance in future anti-mycobacterial drug discovery and development efforts.

Targeting Mycobacterium tuberculosis response to environmental cues for the development of effective antitubercular drugs

Sensing and response to environmental cues, such as pH and chloride (Cl⁻), is critical in enabling Mycobacterium tuberculosis (Mtb) colonization of its host. Utilizing a fluorescent reporter Mtb strain in a chemical screen, we have identified compounds that dysregulate Mtb response to high Cl⁻ levels, with a subset of the hits also inhibiting Mtb growth in host macrophages. Structure–activity relationship studies on the hit compound “C6,” or 2-(4-((2-(ethylthio)pyrimidin-5-yl)methyl)piperazin-1-yl)benzo[d]oxazole, demonstrated a correlation between compound perturbation of Mtb Cl⁻ response and inhibition of bacterial growth in macrophages. C6 accumulated in both bacterial and host cells, and inhibited Mtb growth in cholesterol media, but not in rich media. Subsequent examination of the Cl⁻ response of Mtb revealed an intriguing link with bacterial growth in cholesterol, with increased transcription of several Cl⁻-responsive genes in the simultaneous presence of cholesterol and high external Cl⁻ concentration, versus transcript levels observed during exposure to high external Cl⁻ concentration alone. Strikingly, oral administration of C6 was able to inhibit Mtb growth in vivo in a C3HeB/FeJ murine infection model. Our work illustrates how Mtb response to environmental cues can intersect with its metabolism and be exploited in antitubercular drug discovery.

Responding to environmental cues such as pH and chloride is critical in enabling Mycobacterium tuberculosis to colonize its host. A chemical screen using an M. tuberculosis strain bearing a fluorescent reporter identifies a compound that perturbs the bacterial response to chloride and inhibits its growth in a murine infection model.

Random Forest Model Prediction of Compound Oral Exposure in the Mouse

An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.

Computational Approaches to Identify Molecules Binding to Mycobacterium tuberculosis KasA

Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) and is a deadly disease resulting in the deaths of approximately 1.5 million people with 10 million infections reported in 2018. Recently, a key condensation step in the synthesis of mycolic acids was shown to require β-ketoacyl-ACP synthase (KasA). A crystal structure of KasA with the small molecule DG167 was recently described, which provided a starting point for using computational structure-based approaches to identify additional molecules binding to this protein. We now describe structure-based pharmacophores, docking and machine learning studies with Assay Central as a computational tool for the identification of small molecules targeting KasA. We then tested these compounds using nanoscale differential scanning fluorimetry and microscale thermophoresis. Of note, we identified several molecules including the Food and Drug Administration (FDA)-approved drugs sildenafil and flubendazole with K _d values between 30-40 μM. This may provide additional starting points for further optimization.

Morphological profiling of tubercle bacilli identifies drug pathways of action

Morphological profiling is a method to classify target pathways of antibacterials based on how bacteria respond to treatment through changes to cellular shape and spatial organization. Here we utilized the cell-to-cell variation in morphological features of Mycobacterium tuberculosis bacilli to develop a rapid profiling platform called Morphological Evaluation and Understanding of Stress (MorphEUS). MorphEUS classified 94% of tested drugs correctly into broad categories according to modes of action previously identified in the literature. In the other 6%, MorphEUS pointed to key off-target activities. We observed cell wall damage induced by bedaquiline and moxifloxacin through secondary effects downstream from their main target pathways. We implemented MorphEUS to correctly classify three compounds in a blinded study and identified an off-target effect for one compound that was not readily apparent in previous studies. We anticipate that the ability of MorphEUS to rapidly identify pathways of drug action and the proximal cause of cellular damage in tubercle bacilli will make it applicable to other pathogens and cell types where morphological responses are subtle and heterogeneous.

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae

PURPOSE

To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.

METHODS

Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >10⁵ drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits.

RESULTS

Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae.

CONCLUSIONS

This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web. Graphical Abstract.