Handling the Hurdles on the Way to Anti-tuberculosis Drug Development

Target-Directed Dynamic Combinatorial Chemistry Affords Binders of Mycobacterium tuberculosis IspE

In the search for new antitubercular compounds, we leveraged target-directed dynamic combinatorial chemistry (tdDCC) as an efficient hit-identification method. In tdDCC, the target selects its own binders from a dynamic library generated in situ, reducing the number of compounds that require synthesis and evaluation. We combined a total of 12 hydrazides and six aldehydes to generate 72 structurally diverse N-acylhydrazones. To amplify the best binders, we employed anti-infective target 4-diphosphocytidyl-2C-methyl-d-erythritol kinase (IspE) from Mycobacterium tuberculosis (Mtb). We successfully validated the use of tdDCC as hit-identification method for IspE and optimized the analysis of tdDCC hit determination. From the 72 possible N-acylhydrazones, we synthesized 12 of them, revealing several new starting points for the development of IspE inhibitors as antibacterial agents.

5-Fluoroindole Reduces the Bacterial Burden in a Murine Model of Mycobacterium tuberculosis Infection

Tuberculosis is a disease caused by a single pathogen that leads to a death toll estimated to be more than a million per year. Mycobacterium tuberculosis (Mtb), which affects mainly the lungs, spreads by airborne transmission when infectious respiratory particles from an infected human enter the respiratory tract of another person. Despite diagnosis and treatment being well established, the rise of cases of patients infected with Mtb strains with multidrug resistance to the antibiotics used in the regimen against the disease is alarming. Indole used as a core molecule has been described as a promising structure to treat several diseases. 5-Fluoroindole (5-FI) compound, evaluated in the free base and in the hydrochloride (5-FI.HCl) forms, inhibited the growth of pan-sensitive Mtb H37Rv strain in the same range (4.7-29.1 μM) of clinical isolates that have resistance to at least two first-line drugs. Although 5-FI showed no cytotoxicity in Vero and HepG2 cells, high permeability (2.4.10-6 cm/s) in the PAMPA assay, and high metabolic stability (Clint 9.0 mL/min/kg) in rat liver microsomes, limited solubility at plasmatic and intestinal pH values prompted formation and employment of its salt form (5-FI.HCl). Although the 5-FI.HCl compound showed increased solubility at pH values of 7.4 and 9.1 and increased stability in aqueous solutions, data for intrinsic clearance (Clint = 48 mL/min/kg) and a half-life (t 1/2 = 12 min) showed decreased metabolic stability. As 5-FI.HCl showed both good absorption and ability to reach the systemic circulation of animals without the need to use vehicles containing cosolvents or surfactants, it was chosen to evaluate its effectiveness in the model of tuberculosis in mice. The in vivo results showed the concentration of the compound in plasma increasing within 30 min in the systemic circulation and the capacity of reducing the Mtb burden in the lungs at the concentration of 200 μmol/kg after 21 days of infection, with no toxicity in mice.

Computational Insight into Substrate-Induced Conformational Changes in Methionyl-tRNA Synthetase of Mycobacterium Tuberculosis

Tuberculosis caused by Mycobacterium tuberculosis (M.tb) has killed millions worldwide. Antibiotic resistance leads to the ineffectiveness of the current therapies. Aminoacyl tRNA synthetase (aaRS) class of proteins involved in protein synthesis are promising bacterial targets for developing new therapies. Here, we carried out a systematic comparative study on the aaRS sequences from M.tb and human. We listed important M.tb aaRS that could be explored as potential M.tb targets alongside the detailed conformational space analysis of methionyl-tRNA synthetase (MetRS) in apo- and substrate-bound form, which is among the proposed targets. Understanding the conformational dynamics is central to the mechanistic understanding of MetRS, as the substrate binding leads to the conformational changes causing the reaction to proceed. We performed the most complete simulation study of M.tb MetRS for 6 microseconds (2 systems × 3 runs × 1 microsecond) in the apo and substrate-bound states. Interestingly, we observed differential features, showing comparatively large dynamics for the holo simulations, whereas the apo structures became slightly compact with reduced solvent exposed area. In contrast, the ligand size decreased significantly in holo structures possibly to relax ligand conformation. Our findings correlate with experimental studies, thus validating our protocol. Adenosine monophosphate moiety of the substrate exhibited quite higher fluctuations than the methionine. His21 and Lys54 were found to be the important residues forming prominent hydrogen bond and salt-bridge interactions with the ligand. The ligand-protein affinity decreased during simulations as computed by MMGBSA analysis over the last 500 ns trajectories, which indicates the conformational changes upon ligand binding. These differential features could be further explored for designing new M.tb inhibitors.

Isoxazole carboxylic acid methyl ester-based urea and thiourea derivatives as promising antitubercular agents

In our continued efforts to find potential chemotherapeutics active against drug-resistant (DR) Mycobacterium tuberculosis (Mtb), causative agent of Tuberculosis (TB) and to curb the current burdensome treatment regimen, herein we describe the synthesis and biological evaluation of urea and thiourea variants of 5-phenyl-3-isoxazolecarboxylic acid methyl esters as promising anti-TB agent. Majority of the tested compounds displayed potent in vitro activity not only against drug-susceptible (DS) Mtb H37Rv but also against drug-resistant (DR) Mtb. Cell viability test against Vero cells deemed these compounds devoid of significant toxicity. 3,4-Dichlorophenyl derivative (MIC 0.25 µg/mL) and 4-chlorophenyl congener (MIC 1 µg/mL) among urea and thiourea libraries respectively exhibited optimum potency. Lead optimization resulted in the identification of 1,4-linked analogue of 3,4-dichlorophenyl urea derivative demonstrating improved selectivity. Further, in silico study complemented with previously proposed prodrug like attributes of isoxazole esters. Taken together, this molecular hybridization approach presents a new chemotype having potential to be translated into an alternate anti-Mtb agent.

Identification of anti-Mycobacterium tuberculosis agents targeting the interaction of bacterial division proteins FtsZ and SepF

Tuberculosis (TB) is one of the deadly diseases caused by Mycobacterium tuberculosis (Mtb), which presents a significant public health challenge. Treatment of TB relies on the combination of several anti-TB drugs to create shorter and safer regimens. Therefore, new anti-TB agents working by different mechanisms are urgently needed. FtsZ, a tubulin-like protein with GTPase activity, forms a dynamic Z-ring in cell division. Most of FtsZ inhibitors are designed to inhibit GTPase activity. In Mtb, the function of Z-ring is modulated by SepF, a FtsZ binding protein. The FtsZ/SepF interaction is essential for FtsZ bundling and localization at the site of division. Here, we established a yeast two-hybrid based screening system to identify inhibitors of FtsZ/SepF interaction in M. tuberculosis. Using this system, we found compound T0349 showing strong anti-Mtb activity but with low toxicity to other bacteria strains and mice. Moreover, we have demonstrated that T0349 binds specifically to SepF to block FtsZ/SepF interaction by GST pull-down, fluorescence polarization (FP), surface plasmon resonance (SPR) and CRISPRi knockdown assays. Furthermore, T0349 can inhibit bacterial cell division by inducing filamentation and abnormal septum. Our data demonstrated that FtsZ/SepF interaction is a promising anti-TB drug target for identifying agents with novel mechanisms.

Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds

The cell wall of Mycobacterium tuberculosis and related organisms has a very complex and unusual organization that makes it much less permeable to nutrients and antibiotics, leading to the low activity of many potential antimycobacterial drugs against whole-cell mycobacteria compared to their isolated molecular biotargets. The ability to predict and optimize the cell wall permeability could greatly enhance the development of novel antitubercular agents. Using an extensive structure-permeability dataset for organic compounds derived from published experimental big data (5371 compounds including 2671 penetrating and 2700 non-penetrating compounds), we have created a predictive classification model based on fragmental descriptors and an artificial neural network of a novel architecture that provides better accuracy (cross-validated balanced accuracy 0.768, sensitivity 0.768, specificity 0.769, area under ROC curve 0.911) and applicability domain compared with the previously published results.

Mucosal exposure to non-tuberculous mycobacteria elicits B cell-mediated immunity against pulmonary tuberculosis

Bacille Calmette-Guerin (BCG) is the only licensed vaccine against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB) disease. However, BCG has limited efficacy, necessitating the development of better vaccines. Non-tuberculous mycobacteria (NTMs) are opportunistic pathogens present ubiquitously in the environment. TB endemic countries experience higher exposure to NTMs, but previous studies have not elucidated the relationship between NTM exposure and BCG efficacy against TB. Therefore, we develop a mouse model (BCG + NTM) to simulate human BCG immunization regime and continuous NTM exposure. BCG + NTM mice exhibit superior and prolonged protection against pulmonary TB, with increased B cell influx and anti-Mtb antibodies in serum and airways, compared with BCG alone. Notably, spatial transcriptomics and immunohistochemistry reveal that BCG + NTM mice formed B cell aggregates with features of germinal center development, which correlate with reduced Mtb burden. Our studies suggest a direct relationship between NTM exposure and TB protection, with B cells playing a crucial role.

Combined Bioinformatics and Combinatorial Chemistry Tools to Locate Drug-Able Anti-TB Phytochemicals: A Cost-Effective Platform for Natural Product-Based Drug Discovery

Based on extensive experimental studies, a huge number of phytochemicals showed potential activity against tuberculosis (TB) at a lower minimum inhibitory concentration (MIC) and fewer toxicity profiles. However, these promising drugs have not been able to convert from 'lead' to 'mainstream' due to inadequate drug-ability profiles. Thus, early drug-prospective analyses are required at the primary stage to accelerate natural-product-based drug discovery with limited resources and time. In the present study, we have selected seventy-three potential anti-TB phytochemicals (MIC value ≤10 μg/mL) and assessed the drug-ability profiles using bioinformatics and combinatorial chemistry tools, systematically. Primarily, the molecular docking study was done against two putative drug targets, catalase-peroxidase enzyme (katG) and RNA polymerase subunit-β (rpoB) of Mycobacterium tuberculosis (Mtb) using AutoDock 4.2 software. Further, assessed the drug-ability score from Molsoft, toxicity profiles from ProTox, pharmacokinetics from SwisADME, hierarchical cluster analysis (HCA) by ChemMine tools and frontier molecular orbitals (FMOs) with Avogadro and structural activity relationships (SAR) analysis with ChemDraw 18.0 software. Above analyses indicated that, lower MIC exhibited anti-TB phytochemicals, abietane, 12-demethylmulticaulin exhibited poor docking and drug-ability scores, while tiliacorinine, 2-nortiliacorinine showed higher binding energy and drug-ability profiles. Overall, tiliacorinine, 2-nortiliacorinine, 7α-acetoxy-6β-hydroxyroyleanone (AHR), (2S)-naringenin and isovachhalcone were found as the most active and drug-able anti-TB candidates from 73 candidates. Phytochemicals are always a vital source of mainstream drugs, but the MIC value of a phytochemical is not sufficient for it to be promoted. An ideal drug-ability profile is therefore essential for achieving clinical success, where advanced bioinformatics tools help to assess and analyse that profile. Additionally, several natural pharmacophores found in existing anti-TB drugs in SAR analyses also provide crucial information for developing potential anti-TB drug. As a conclusion, combined bioinformatics and combinatorial chemistry are the most effective strategies to locate potent-cum-drug-able candidates in the current drug-development module.

Bioevaluation of quinoline-4-carbonyl derivatives of piperazinyl-benzothiazinones as promising antimycobacterial agents

The quinoline moiety remains a privileged antitubercular (anti-TB) pharmacophore, whereas 8-nitrobenzothiazinones are emerging potent antimycobacterial agents with two investigational candidates in the clinical pipeline. Herein, we report the synthesis and bioevaluation of 30 piperazinyl-benzothiazinone-based quinoline hybrids as prospective anti-TB agents. Preliminary evaluation revealed 24/30 compounds exhibiting substantial activity (minimum inhibitory concentration [MIC] = 0.06-1 µg/ml) against Mycobacterium tuberculosis (Mtb) H37Rv. Cytotoxicity analysis against Vero cells found these to be devoid of any significant toxicity, with the majority displaying a selectivity index of >80. Furthermore, potent nontoxic compounds, when screened against clinical isolates of drug-resistant Mtb strains, demonstrated equipotent inhibition with MIC values of 0.03-0.25 µg/ml. A time-kill study identified a lead compound exhibiting concentration-dependent bactericidal activity, with 10× MIC completely eliminating Mtb bacilli within 7 days. Along with acceptable aqueous solubility and microsomal stability, the optimum active compounds of the series manifested all desirable traits of a promising antimycobacterial candidate.

In silico and in vitro study of Mycobacterium tuberculosis H37Rv uncharacterized protein (RipD): an insight on tuberculosis therapeutics

Tuberculosis caused by Mycobacterium tuberculosis (Mtb) is responsible for the highest global health problem, with the deaths of millions of people. With prevalence of multiple drug resistance (MDR) strains and extended therapeutic times, it is important to discover small molecule inhibitors against novel hypothetical proteins of the pathogen. In this study, a virtual screening protocol was carried out against MtbH37Rv hypothetical protein RipD (Rv1566c) for the identification of potential small molecule inhibitors. The 3D model of the protein structure binding site was used for virtual screening (VS) of inhibitors from the Pathogen Box, followed by its validation through a molecular docking study. The stability of the protein-ligand complex was assessed using a 150 ns molecular dynamics simulation. MM-PBSA and MM-GBSA are the two approaches that were used to perform the trajectory analysis and determine the binding free energies, respectively. The ligand binding was observed to be stable across the entire time frame with an approximate binding free energy of -22.9916 kcal/mol. The drug-likeness of the inhibitors along with a potential anti-tuberculosis compound was validated by ADMET prediction software. Furthermore, a CFU inhibition assay was used to validate the best hit compound's in vitro inhibitory efficacy against a non-pathogenic Mycobacterium smegmatis MC2155 under low nutrient culture conditions. The study reported that the compound proposed in our study (Pathogen Box ID: MMV687700) will be useful for the identification of potential inhibitors against Mtb in future.

Development of Predictive Classification Models for Whole Cell Antimycobacterial Activity of Benzothiazinones

Nitrobenzothiazinones (BTZs) are a very potent class of antibiotics against Mycobacterium tuberculosis. However, relationships between their structural properties and whole cell activity remain poorly predictable. Herein, we present the synthesis and antimycobacterial evaluation of a diverse set of BTZs. High potency was predominantly achieved by piperidine and piperazine substitutions, whereupon three compounds were identified as promising candidates, showing preferable metabolic stability. Lack of correlation between potency and calculated binding energies suggested that target inhibition is not the only requirement to obtain suitable antimycobacterial agents. In contrast, prediction of whole cell activity class was successfully accomplished by extensively validated machine learning models. The performance of the superior model was further verified by >70% correct class predictions for a large set of reported BTZs. Our generated model is thus a key prerequisite to streamline lead optimization endeavors, particularly regarding the improvement of overall hit rates in whole cell antimycobacterial assays.

Synthesis and Antimycobacterial Evaluation of N-(4-(Benzyloxy)benzyl)-4-aminoquinolines

Tuberculosis remains a global health problem that affects millions of people around the world. Despite recent efforts in drug development, new alternatives are required. Herein, a series of 27 N-(4-(benzyloxy)benzyl)-4-aminoquinolines were synthesized and evaluated for their ability to inhibit the M. tuberculosis H37Rv strain. Two of these compounds exhibited minimal inhibitory concentrations (MICs) similar to the first-line drug isoniazid. In addition, these hit compounds were selective for the bacillus with no significant change in viability of Vero and HepG2 cells. Finally, chemical stability, permeability and metabolic stability were also evaluated. The obtained data show that the molecular hits can be optimized aiming at the development of drug candidates for tuberculosis treatment.

Recent progress on the prospective application of machine learning to structure-based virtual screening

As more bioactivity and protein structure data become available, scoring functions (SFs) using machine learning (ML) to leverage these data sets continue to gain further accuracy and broader applicability. Advances in our understanding of the optimal ways to train and evaluate these ML-based SFs have introduced further improvements. One of these advances is how to select the most suitable decoys (molecules assumed inactive) to train or test an ML-based SF on a given target. We also review the latest applications of ML-based SFs for prospective structure-based virtual screening (SBVS), with a focus on the observed improvement over those using classical SFs. Finally, we provide recommendations for future prospective SBVS studies based on the findings of recent methodological studies.