Bioinformatics approach to prioritize known drugs towards repurposing for tuberculosis

Impact of bedaquiline regimen on the treatment success rates of multidrug-resistant tuberculosis patients in Egypt

Bedaquiline (BDQ), an innovative anti-tuberculous (TB) agent, has attracted attention for its potential effectiveness against drug-resistant TB. This study investigated the impact of BDQ-containing regimens on treatment success rates among multi-drug resistant tuberculosis (MDR-TB) patients in Egypt. We conducted a prospective cohort study that included all adult non-pregnant patients treated in MDR-TB centers in Egypt from April 1, 2020, to June 30, 2021, with follow-up extended until December 31, 2022. The study compared patients prescribed BDQ according to national protocols with those receiving conventional treatments for MDR-TB. Treatment success rates, mortality rates, and adverse events were analyzed using descriptive statistics, chi-square tests, logistic regression, and Kaplan-Meier survival curves. Adjustment for potential confounders was conducted using propensity score matching and Cox-hazard regressions. A total of 84 patients were included in this study. The median age of the study participants was 39 years; 22.6% were women, 57.1% were unemployed or housewives, and 1.2% had human immunodeficiency virus (HIV). Regarding the treatment regimen, 67.8% were exposed to BDQ-based treatment. Among the 55 patients (65.5%) with treatment success, a significantly higher success rate was observed in the BDQ group (73.7%) compared to the conventional group (48.1%), P = 0.042. Additionally, the incidence of skin discoloration was significantly higher in the BDQ group compared to the conventional group (38.6% versus 0.0%, P < 0.001). Despite the lower mortality incidence in the BDQ-group (14.0% versus 22.2% in the conventional group), the Kaplan-Meier survival analysis revealed no excess mortality associated with the BDQ-group, with a hazard ratio (HR) of 0.62 (95% CI 0.21-1.78, P = 0.372). Propensity score matching, while considering factors such as lesion site, diabetes mellitus, hepatitis C virus, and smoking, revealed a significant increase in the success rate associated with BDQ inclusion, with an HR of 6.79 (95% CI 1.8-25.8). In conclusion, BDQ is an effective and tolerable medication for treating MDR-TB, associated with lower mortality rates compared to conventional treatment.

An overview of current strategies and future prospects in drug repurposing in tuberculosis

A large number of the population faces mortality as an effect of tuberculosis (TB). The line of treatment in the management of TB faces a jolt with ever-increasing multi-drug resistance (DR) cases. Further, the drugs engaged in the treatment of TB are associated with different toxicities, such as renal and hepatic toxicity. Different combinations are sought for effective anti-tuberculosis (anti-TB) effects with a decrease in toxicity. In this regard, drug repurposing has been very promising in improving the efficacy of drugs by enhancement of bioavailability and widening the safety margin. The success in drug repurposing lies in specified binding and inhibition of a particular target in the drug molecule. Different drugs have been repurposed for various ailments like cancer, Alzheimer’s disease, acquired immunodeficiency syndrome (AIDS), hair loss, etc. Repurposing in anti-TB drugs holds great potential too. The use of whole-cell screening assays and the availability of large chemical compounds for testing against Mycobacterium tuberculosis poses a challenge in this development. The target-based discovery of sites has emerged in the form of phenotypic screening as ethionamide R (EthR) and malate synthase inhibitors are similar to pharmaceuticals. In this review, the authors have thoroughly described the drug repurposing techniques on the basis of pharmacogenomics and drug metabolism, pathogen-targeted therapy, host-directed therapy, and bioinformatics approaches for the identification of drugs. Further, the significance of repurposing of drugs elaborated on large databases has been revealed. The role of genomics and network-based methods in drug repurposing has been also discussed in this article.

In Silico Identification of Possible Inhibitors for Protein Kinase B (PknB) of Mycobacterium tuberculosis

With tuberculosis still being one of leading causes of death in the world and the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), researchers have been seeking to find further therapeutic strategies or more specific molecular targets. PknB is one of the 11 Ser/Thr protein kinases of Mtb and is responsible for phosphorylation-mediated signaling, mainly involved in cell wall synthesis, cell division and metabolism. With the amount of structural information available and the great interest in protein kinases, PknB has become an attractive target for drug development. This work describes the optimization and application of an in silico computational protocol to find new PknB inhibitors. This multi-level computational approach combines protein-ligand docking, structure-based virtual screening, molecular dynamics simulations and free energy calculations. The optimized protocol was applied to screen a large dataset containing 129,650 molecules, obtained from the ZINC/FDA-Approved database, Mu.Ta.Lig Virtual Chemotheca and Chimiothèque Nationale. It was observed that the most promising compounds selected occupy the adenine-binding pocket in PknB, and the main interacting residues are Leu17, Val26, Tyr94 and Met155. Only one of the compounds was able to move the active site residues into an open conformation. It was also observed that the P-loop and magnesium position loops change according to the characteristics of the ligand. This protocol led to the identification of six compounds for further experimental testing while also providing additional structural information for the design of more specific and more effective derivatives.

In Silico Approach for Phytocompound-Based Drug Designing to Fight Efflux Pump-Mediated Multidrug-Resistant Mycobacterium tuberculosis

Tuberculosis (TB), caused by the bacteria Mycobacterium tuberculosis, is one of the principal causes of death in the world despite the existence of a significant number of antibiotics aimed against it. This is mainly due to the drug resistance mechanisms present in the bacterium, which leads to multidrug-resistant tuberculosis (MDR-TB). Additionally, the development of new antibiotics has become limited over the years. Although there are various drug resistance mechanisms present, efflux pumps are of utmost importance because they extrude out several dissimilar antitubercular drugs out of the cell. There are many efflux pump proteins present in Mycobacterium tuberculosis. Therefore, blocking these efflux pumps by inhibitors can raise the efficacy of the existing antibiotics and may also pave the path for the discovery and synthesis of new drugs. Plant compounds can act as a resource for the development of efflux pump inhibitors (EPIs), which may eventually replace or augment the current therapeutic options. This is mainly because plants have been traditionally used for ages for food or treatment and are considered safe with little or no side effects. Various computational tools are available which are used for the virtual screening of a large number of phytocompounds within a short span of time. This review aims to highlight the mechanism and appearance of drug resistance in Mycobacterium tuberculosis with emphasis on efflux pumps along with the significance of phytochemicals as inhibitors of these pumps and their screening strategy by computational approaches.

Pangenome Analysis of Mycobacterium tuberculosis Reveals Core-Drug Targets and Screening of Promising Lead Compounds for Drug Discovery

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is one of the leading causes of human deaths globally according to the WHO TB 2019 report. The continuous rise in multi- and extensive-drug resistance in M. tuberculosis broadens the challenges to control tuberculosis. The availability of a large number of completely sequenced genomes of M. tuberculosis has provided an opportunity to explore the pangenome of the species along with the pan-phylogeny and to identify potential novel drug targets leading to drug discovery. We attempt to calculate the pangenome of M. tuberculosis that comprises a total of 150 complete genomes and performed the phylo-genomic classification and analysis. Further, the conserved core genome (1251 proteins) is subjected to various sequential filters (non-human homology, essentiality, virulence, physicochemical parameters, and pathway analysis) resulted in identification of eight putative broad-spectrum drug targets. Upon molecular docking analyses of these targets with ligands available at the DrugBank database shortlisted a total of five promising ligands with projected inhibitory potential; namely, 2′deoxy-thymidine-5′-diphospho-alpha-d-glucose, uridine diphosphate glucose, 2′-deoxy-thymidine-beta-l-rhamnose, thymidine-5′-triphosphate, and citicoline. We are confident that with further lead optimization and experimental validation, these lead compounds may provide a sound basis to develop safe and effective drugs against tuberculosis disease in humans.

Current advances on the development of BET inhibitors: insights from computational methods

Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.

In silico tools to study molecular targets of neglected diseases: inhibition of TcSir2rp3, an epigenetic enzyme of Trypanosoma cruzi

There is a growing interest to study and address neglected tropical diseases (NTD). To this end, in silico methods can serve as the bridge that connects academy and industry, encouraging the development of future treatments against these diseases. This chapter discusses current challenges in the development of new therapies, available computational methods and successful cases in computer-aided design with particular focus on human trypanosomiasis. Novel targets are also discussed. As a case study, we identify amentoflavone as a potential inhibitor of TcSir2rp3 (sirtuine) from Trypanosoma cruzi (20.03 μM) with a workflow that integrates chemoinformatic approaches, molecular modeling, and theoretical affinity calculations, as well as in vitro assays.

RepTB: a gene ontology based drug repurposing approach for tuberculosis

Tuberculosis (TB) is the world’s leading infectious killer with 1.8 million deaths in 2015 as reported by WHO. It is therefore imperative that alternate routes of identification of novel anti-TB compounds are explored given the time and costs involved in new drug discovery process. Towards this, we have developed RepTB. This is a unique drug repurposing approach for TB that uses molecular function correlations among known drug-target pairs to predict novel drug-target interactions. In this study, we have created a Gene Ontology based network containing 26,404 edges, 6630 drug and 4083 target nodes. The network, enriched with molecular function ontology, was analyzed using Network Based Inference (NBI). The association scores computed from NBI are used to identify novel drug-target interactions. These interactions are further evaluated based on a combined evidence approach for identification of potential drug repurposing candidates. In this approach, targets which have no known variation in clinical isolates, no human homologs, and are essential for Mtb’s survival and or virulence are prioritized. We analyzed predicted DTIs to identify target pairs whose predicted drugs may have synergistic bactericidal effect. From the list of predicted DTIs from RepTB, four TB targets, namely, FolP1 (Dihydropteroate synthase), Tmk (Thymidylate kinase), Dut (Deoxyuridine 5′-triphosphate nucleotidohydrolase) and MenB (1,4-dihydroxy-2-naphthoyl-CoA synthase) may be selected for further validation. In addition, we observed that in some cases there is significant chemical structure similarity between predicted and reported drugs of prioritized targets, lending credence to our approach. We also report new chemical space for prioritized targets that may be tested further. We believe that with increasing drug-target interaction dataset RepTB will be able to offer better predictive value and is amenable for identification of drug-repurposing candidates for other disease indications too.