Mycobacterial drug discovery

Mycobacterium tuberculosis is the causative pathogen of the pulmonary disease tuberculosis. Despite the availability of effective treatment programs, there is a global pursuit of new anti-tubercular agents to respond to the developing threat of drug resistance, in addition to reducing the extensive duration of chemotherapy and any associated toxicity. The route to mycobacterial drug discovery can be considered from two directions: target-to-drug and drug-to-target. The former approach uses conventional methods including biochemical assays along with innovative computational screens, but is yet to yield any drug candidates to the clinic, with a high attrition rate owing to lack of whole cell activity. In the latter approach, compound libraries are screened for efficacy against the bacilli or model organisms, ensuring whole cell activity, but here subsequent target identification is the rate-limiting step. Advances in a variety of scientific fields have enabled the amalgamation of aspects of both approaches in the development of novel drug discovery tools, which are now primed to accelerate the discovery of novel hits and leads with known targets and whole cell activity. This review discusses these traditional and innovative techniques, which are widely used in the quest for new anti-tubercular compounds

Denovo Drug Design, Synthesis and Biological Activity Evaluation of Certain Novel Benzothiazole Derivativesa

The present work was focused on the insilico studies, synthesis and evaluation of benzothiazole derivatives as DprE1 inhibitors. Step I: LITERATURE REVIEW: Literature review showed that benzothiazoles are having potent antitubercular and antibacterial activity. Step II: IDENTIFICATION OF DRUG TARGET AND SELECTION OF LEAD: The drug target was identified as DprE1 and the lead moiety was selected as benzothiazole based on the extensive literature survey. Step III: LEAD OPTIMISATION: Lead optimisation was done by observing in silico ADME studies and computation of drug like properties. All the ligands had showed druglikness and acceptable pharmacokinetic parameter. Hence would be further studied for biological activities. Step IV: DOCKING: Docking studies were carried out using Schrodinger Release 2020-3 Life Science Suite. Docking study was done for 25 lead compounds which showed best binding energy with the DprE1 enzyme. The compounds B1-B8 showed good binding score when compared to standard BTZ-043. Step V: SYNTHESIS: In the present work, eight new compounds with good docking score were synthesised. Synthesis involves the formation of N=CH bond followed by thiazolidinone formation by cyclisation using thiolactic acid. Step VI: CHARACTERISATION: All the synthesized compounds were characterised by melting point, Rf values, percentage yield and solubility. The structural determination was done by spectral studies including UV, IR, 1H NMR and Mass spectroscopy. Step VII: ANTIMICROBIAL ACTIVITES: 1. Antitubercular activity: The compounds B3, B5, B6, B7 and B8 showed good antitubercular activity. These compounds showed better binding (binding energy around -7.0 to -8.0 kcal/mol) towards the target enzyme DprE1 in docking study. 2. Antibacterial activity: The compounds B4, B6 and B8 showed antibacterial activity against gram-positive Staphylococcus aureus and gram-negative Escherichia coli. CONCLUSION: The present study had proved to be a tool in minimising tedious drug discovery process and delivers new drug candidate more quickly. • DprE1, critical enzyme for the cell wall synthesis of Mycobacterium tuberculosis was chosen after the review of literature. • A database of 25 molecules with high probability of inhibiting the target DprE1 was chosen by making changes to a known inhibitor scaffold i.e., aryl substituted benzothiazole nucleus was chosen for the study. • The in silico ADME studies and drug likeness score established for the 25 compounds are proven to be pharmacokinetically active. The binding energies obtained from the docking studies for all 25 compounds further confirmed the synthesis of lead compounds. • Using the developed schemes, only eight benzothiazole derivatives were prepared in good yield and their structures were established based on spectral data. • The synthesized compounds were evaluated for their antitubercular and antibacterial activity. The antitubercular study establishes a good correlation with the docking score. • Few of the synthesized compounds also showed good to moderate antibacterial properties. • Five synthesized derivatives (B3, B5, B6, B7 and B8) showed moderate antitubercular activity compared to the standards at concentration of 25μg/ml. Three synthesized derivatives (B1, B2 and B4) exhibited antitubercular activity at a concentration of 50μg/ml. • It is concluded that the synthesized benzothiazole derivatives might effectively inhibit the chosen target DprE1 which is essential for the Mycobacterial tuberculosis and act as a potential lead moiety

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its “End TB” strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for—early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB

Computational de-orphaning of Mycobacterium tuberculosis targets

Tuberculosis (TB) continues to be a major health hazard worldwide due to the resurgence of drug discovery strains of Mycobacterium tuberculosis (Mtb) and co-infection. For decades drug discovery has concentrated on identifying ligands for ~10 Mtb targets, hence most of the identified essential proteins are not utilised in TB chemotherapy. Here computational techniques were used to identify ligands for the orphan Mtb proteins. These range from ligand-based and structure-based virtual screening modelling the proteome of the bacterium. Identification of ligands for most of the Mtb Proteins will provide novel TB drugs and targets and hence address drug resistance, toxicity and the duration of TB treatment

Computational Deorphaning of <em>Mycobacterium tuberculosis</em> Targets

Tuberculosis (TB) continues to be a major health hazard worldwide due to the resurgence of drug discovery strains of Mycobacterium tuberculosis (Mtb) and co-infection. For decades drug discovery has concentrated on identifying ligands for ~10 Mtb targets, hence most of the identified essential proteins are not utilised in TB chemotherapy. Here computational techniques were used to identify ligands for the orphan Mtb proteins. These range from ligand-based and structure-based virtual screening modelling the proteome of the bacterium. Identification of ligands for most of the Mtb proteins will provide novel TB drugs and targets and hence address drug resistance, toxicity and the duration of TB treatment

Discovery and development of novel salicylate synthase (MbtI) furanic inhibitors as antitubercular agents

We report on the virtual screening, synthesis, and biological evaluation of new furan derivatives targeting Mycobacterium tuberculosis salicylate synthase (MbtI). A receptor-based virtual screening procedure was applied to screen the Enamine database, identifying two compounds, I and III, endowed with a good enzyme inhibitory activity. Considering the most active compound I as starting point for the development of novel MbtI inhibitors, we obtained new derivatives based on the furan scaffold. Among the SAR performed on this class, compound 1a emerged as the most potent MbtI inhibitor reported to date (Ki = 5.3 μM). Moreover, compound 1a showed a promising antimycobacterial activity (MIC99 = 156 μM), which is conceivably related to mycobactin biosynthesis inhibition

Biophysical and structural studies of novel F420-dependent oxidoreductases in Mycobacterium tuberculosis

The Mycobacterium tuberculosis (Mtb) genome encodes hundreds of proteins with unknown or putative function based on sequence similarity to characterised proteins. Incomplete knowledge of Mtb functional genomics undermines efforts to understand the complex biological repertoire of this deadly human pathogen. While bioinformatics provides useful clues about gene function, biochemical characterisation of gene products remains essential to accurate annotation, particularly in cases where functional redundancy is suggested based on the presence of multiple homologous proteins. The Mtb genome includes 7 genes which encode conserved hypothetical proteins with unknown function that are annotated as “pyridoxine 5’-phosphate (PNPOx)-like” proteins based on structural similarity: Rv2607, Rv2991, Rv1155, Rv2074, Rv3369c, Rv1875, Rv0121c. PNPOx is a flavin mononucleotide (FMN)-dependent enzyme involved in the biosynthesis of the crucial cofactor, pyridoxal 5’- phosphate (PLP). The work presented in this thesis shows that while Rv2607 exhibits canonical PNPOx activity, Rv1155 and Rv2991 do not have affinity for FMN as do other members of the PNPOx class. They instead bind tightly to the unusual flavin coenzyme F420 by isothermal calorimetry (ITC), saturation transfer difference (STD) NMR, and X-ray crystallography. Although recent studies have drawn attention to F420 due to its role in activating the promising anti-tuberculosis drug PA-824, little is known about the function of this coenzyme in Mtb metabolism. In order to identify the substrate recognition properties of Rv1155 and Rv2991 respectively, a novel application of fragment-based approaches was employed. A library of fragments was designed based on known ligands to flavoenzymes and screened for binding to Rv1155 and Rv2991 in the presence and absence of F420 using STD NMR and differential scanning fluorimetry. Rv1155 and Rv2991 show affinity for distinct classes of fragments. Using a computational method known as “virtual fragment linking,” fragment binding data was used to predict and test potential substrates. This approach to probing structure-function relationships may serve as a generalisable method for exploring functional diversity within a structural class of proteins.This work was supported by the National Institutes of Health-Oxford-Cambridge Scholars Progra

Covalent inhibitors of LgtC: a blueprint for the discovery of non-substrate-like inhibitors for bacterial glycosyltransferases

Non-substrate-like inhibitors of glycosyltransferases are sought after as chemical tools and potential lead compounds for medicinal chemistry, chemical biology and drug discovery. Here, we describe the discovery of a novel small molecular inhibitor chemotype for LgtC, a retaining α-1,4-galactosyltransferase involved in bacterial lipooligosaccharide biosynthesis. The new inhibitors, which are structurally unrelated to both the donor and acceptor of LgtC, have low micromolar inhibitory activity, comparable to the best substrate-based inhibitors. We provide experimental evidence that these inhibitors react covalently with LgtC. Results from detailed enzymological experiments with wild-type and mutant LgtC suggest the non-catalytic active site residue Cys246 as a likely target residue for these inhibitors. Analysis of available sequence and structural data reveals that non-catalytic cysteines are a common motif in the active site of many bacterial glycosyltransferases. Our results can therefore serve as a blueprint for the rational design of non-substrate-like, covalent inhibitors against a broad range of other bacterial glycosyltransferases