Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Coumarin hybrids: dual-target candidates for future antimicrobial and antitubercular therapies

AIMS

This study aimed to synthesize, characterize, and evaluate the antimicrobial and antitubercular activities of two novel series of coumarin-based derivatives (Series 5 and Series 9), focusing on their structure-activity relationship (SAR) and molecular docking interactions with key bacterial enzymes.

MATERIALS & METHODS

Series 5 (5a-5j) and Series 9 (9a-9t) compounds were synthesized and characterized using spectroscopic techniques. Their antimicrobial and antitubercular activities were evaluated against Mycobacterium tuberculosis, Staphylococcus aureus, Bacillus subtilis, and E. coli. IC₅₀ values were determined, and molecular docking studies were conducted to assess binding interactions with M. tuberculosis enoyl-ACP reductase (InhA) and E. coli DNA gyrase B.

RESULTS

Series 5 compounds exhibited moderate activity, with 5f, 5 g, 5i, and 5j showing notable inhibition. Series 9 derivatives displayed superior dual-target inhibition, with 9t, 9c, 9a, 9b, and 9p achieving >90% inhibition against S. aureus and B. subtilis. The lowest IC₅₀ against M. tuberculosis was observed for 9c (1.50 µg/mL), followed by 9a (2.84 µg/mL) and 9b (2.73 µg/mL). Molecular docking confirmed strong binding interactions, correlating with observed biological activities.

CONCLUSIONS

Series 9 compounds, particularly 9t, 9c, and 9a, demonstrate high potential as dual-target antimicrobial drug candidates. Further optimization may enhance their therapeutic efficacy.

A Review of In Silico and In Vitro Approaches in the Fight Against Carbapenem-Resistant Enterobacterales

OBJECTIVES

The rise in carbapenem-resistant Enterobacterales (CRE) has reinforced the global quest for developing effective therapeutics. Traditional drug discovery approaches have been inadequate in overcoming this challenge due to their resource and time constraints.

METHODS

English literature was searched by structured queries related to our review between January 1, 2020, and December 31, 2024.

RESULTS

The key resistance mechanisms in CRE, such as enzymatic hydrolysis, decreased permeability, and efflux pump overexpression, have been examined in this review. Computational technologies have become pivotal in discovering novel antimicrobial agents with improved accuracy and efficiency. Besides this, the review highlights the advances in structure- and ligand-based drug discovery approaches for identifying potential drugs against CRE. Recent studies demonstrating the use of such in silico techniques to develop targeted drugs against CRE have also been explored. Moreover, this review also underscores the significance of integrating both in silico and in vitro techniques to counter resistance in Enterobacterales, supported by the latest studies. However, these promising computational technologies have a few major drawbacks, such as a lack of standardized parameterization, potentially false positives, and the complexity of effective clinical translations. The drug regulatory barriers also restrict the progress of new antimicrobials for market approval.

CONCLUSION

The use of computational technologies for antimicrobial inhibitor discovery is gaining popularity, and it can be expedited by refining computational techniques and integrating them with reliable in vitro validation. The use of innovative hybrid in silico and in vitro technologies is the need of the hour to tackle CRE and mitigate the global threat of antimicrobial resistance.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

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.

Inhibitory effect of lupeol, quercetin, and solasodine on Rhizopus oryzae: A molecular docking and dynamic simulation study

BACKGROUND

Mucormycosis is an infection caused by fungi belonging to the order Mucorales. Rhizopus oryzae is one of the most prevalent organisms identified in mucormycosis patients. Because it spreads quickly through the blood vessels, this opportunistic illness has an exceptionally high fatality rate, even when vigorous treatment is administered. Nonetheless, it has a high tolerance to antifungal medicines, limiting treatment options. As a result, improved methods for preventing and treating mucormycosis are desperately needed. Hence, this study was aimed at assessing the effect of lupeol, quercetin, and solasodine against mucormycosis based on computational approaches.

METHODS

The Rhizopus oryzae RNA-dependent RNA polymerase (RdRp) was the target for the design of drugs against the deadly mucormycosis. The three-dimensional structure of the RdRp was modelled with a Swiss model and validated using PROCHECK, VERIFY 3D, and QMEAN. Using the Schrodinger maestro module, a molecular docking study was performed between RdRp and the antimicrobial phytochemicals lupeol, quercetin, and solasodine. A molecular dynamics (MD) simulation study was used to assess the stability and interaction of the RdRp with these phytochemicals.

RESULTS

The RdRp protein binds strongly to lupeol (-7.2 kcal/mol), quercetin (-9.1 kcal/mol), and solasodine (-9.6 kcal/mol), according to molecular docking assessment based on the lowest binding energy, confirmation, and bond interaction. Simulations suggest that lupeol, quercetin, and solasodine complexes with RdRp and showed stable confirmation with minimal fluctuation throughout the 200 nanoseconds based on the RMSD and RMSF trajectory assessments.

CONCLUSION

The molecular docking and MD simulation investigation improved our understanding of phytochemical-RdRp interactions. Due to its high affinity for RdRp, solasodine may be a better treatment option for mucormycosis.

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.