Summer Research Fellowship Programme of India's Science Academies

Exploring Chemical-Biology Space for Predicting Drug Repurposing Candidates for Non-tubercular Mycobacteria

Priyanka Rani Paul

Lucknow University, Lucknow, Uttar Pradesh 226007

Dr. Anshu Bhardwaj

Institute of Microbial Technology, sector 39A, Chandigarh, 160036


The non-tubercular mycobacterium can be classified into the apid-growing (RGM- M. chelonae, M. fortuitum, M. abscessus) the slow-growing bacterium (M. kansasii, M. simiae, M. xenopi). RGM take 7 days to grow in subculture whereas slow growers usually take 2-3 weeks. The available data on NTM-Pulmonary Disease (NTM-PD) prevalence comes from studies done in some countries of Asia, such as Japan, South Korea, India, China, Thailand and Taiwan. The information from a population-based study on NTM epidemiology suggests that there has been a rise in prevalence of NTM-PD. In Asian countries, the Mycobacterium avium complex was responsible for about 68% of cases of NTM-PD, and in some parts of Asia RGM were the main cause of NTM-PD. In order to explore a new target and chemical space for predicting potential drug-repurposing candidates for NTMs, we have designed a combined evidence-based strategy. Our in-house developed platform, RepTB, is used as the first tool for predicting potential drug-target pairs for NTM species that are prevalent in India. The targets initially found from Drugbank database were very few and did not have any predicted drug, as per the existing RepTB predictions. Our next approach is to include more databases and prediction methods like the Drug repurposing hub and BindingDB to increase our search space for drug-target or ligand-target interactions. Another approach will be to find homology of the NTM pan-proteome with MTB targets present in Drugbank to identify potential inhibitors as starting points for drug repurposing library that may be evaluated further.

Keywords: Non-tubercular mycobacterium, antimicrobial resistance, Drug repurposing, Drug-target interactions

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