Abstract
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.
Highlights
Robert Koch identified the etiological agent of tuberculosis (TB) as Mycobacterium tuberculosis (Mtb) [1]
There were other recent studies, where developments of TB drugs based on computer-aided drug design (CADD) were extensively appraised with respect to specific targets using in silico approaches [21,22], this review provides insight into the most recent developments on the various available resources used in TB drug design and the inclusion and contributions of these resources to the development of new effective therapeutics against Mtb
The most often utilized computational approaches in structure-based drug design (SBDD) include structure-based virtual screening (SBVS), molecular docking, and molecular dynamics (MD) simulations, which are all examples of applicable techniques
Summary
Robert Koch identified the etiological agent of tuberculosis (TB) as Mycobacterium tuberculosis (Mtb) [1]. Phenotypic screening efforts using commercial vendor libraries evolved toward identifying compounds that inhibit Mtb development [6,7,8] This intervention gives a ray of hope in the search for new therapeutics against Mtb. The urgency to end the Mtb epidemic requires improvement in diagnostic tools and the efficacy of therapeutics used in treating TB in diagnosed patients. Target drug discovery begins with identifying and studying enzymes or proteins necessary for the growth and development of the pathogen Researchers screen these proteins against some chemicals or compounds in libraries for potency and inhibitory effect leading to drug candidate identification using computer software after learning the accurate details of the target and lead molecule. Advances in drug discovery involve using computational analysis to identify and validate vulnerable targets, which leads to the emergence of new therapeutics; they are used in preclinical trials, drastically altering the drug development pipeline. TB disease affects 5.7 million men, 3.2 million women, and 1.1 million children with 9% of this population infected with HIV in 2018 [27]
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