Integrating chemical, genetic, and feasibility assessments for anti-tubercular target validation

Despite the approval of two first-in-class anti-tuberculars over the past two decades, the global burden of tuberculosis (TB) remains unacceptably high, in part due to the emergence and spread of drug-resistant strains of Mycobacterium tuberculosis (Mtb). This review summarizes advances and ongoing challenges in anti-TB drug discovery, focusing on identifying and validating novel targets. Highlighted is a framework developed by the TB Drug Accelerator (TBDA) consortium for target validation in Mtb. Two computational platforms, DAIKON and PARSNIP, allow the systematic evaluation of targets across multiple dimensions, including chemical validation, genetic essentiality, vulnerability, and the feasibility to identify drug-like molecules for a target of interest. Case studies of Pks13 and NadE illustrate how these parameters guide target prioritization and risk assessment. By integrating these metrics, the framework enables dynamic, transparent target ranking, supporting development of both pan-TB and treatment-shortening regimens. This paradigm is adaptable to other bacterial pathogens and is designed to improve evidence-based decision-making in antibacterial drug discovery.

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Publication Details

Journal
EMBO Molecular Medicine
Published
2026-04-07
DOI
https://doi.org/10.1038/s44321-026-00415-7
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrating chemical, genetic, and feasibility assessments for anti-tubercular target validation

Shree Rath, Paridhi Sukheja, Carl Nathan, Sandeep R. Ghorpade et al.
EMBO Molecular Medicine
Computational Drug Discovery Methods
article

Integrating chemical, genetic, and feasibility assessments for anti-tubercular target validation

Shree Rath, Paridhi Sukheja, Carl Nathan, Sandeep R. Ghorpade, Dirk Schnappinger, Inna V. Krieger, Case W. McNamara, Sari Rasheed, Nader Fotouhi, Steven J. Berthel, Jeremy M. Rock, Xuelin Bian, Valerie Mizrahi, Dirk A. Lamprecht, Saswati Panda, Kelsey Briggs, Laura A T Cleghorn, Peter Warner, Rolf Müller, Helena I M Boshoff, Tanya Parish, James C Sacchettini, Kyu Y Rhee
article en

Abstract

Despite the approval of two first-in-class anti-tuberculars over the past two decades, the global burden of tuberculosis (TB) remains unacceptably high, in part due to the emergence and spread of drug-resistant strains of Mycobacterium tuberculosis (Mtb). This review summarizes advances and ongoing challenges in anti-TB drug discovery, focusing on identifying and validating novel targets. Highlighted is a framework developed by the TB Drug Accelerator (TBDA) consortium for target validation in Mtb. Two computational platforms, DAIKON and PARSNIP, allow the systematic evaluation of targets across multiple dimensions, including chemical validation, genetic essentiality, vulnerability, and the feasibility to identify drug-like molecules for a target of interest. Case studies of Pks13 and NadE illustrate how these parameters guide target prioritization and risk assessment. By integrating these metrics, the framework enables dynamic, transparent target ranking, supporting development of both pan-TB and treatment-shortening regimens. This paradigm is adaptable to other bacterial pathogens and is designed to improve evidence-based decision-making in antibacterial drug discovery.

EMBO Molecular Medicine
Gates (United States) (US), Scripps Research Institute (US), Gates Foundation (US), University of Cape Town (ZA), University of Dundee (GB), Liverpool School of Tropical Medicine (GB), Cornell University (US), TB Alliance (US), National Institute of Allergy and Infectious Diseases (US), Panorama Research (United States) (US), Helmholtz Institute for Pharmaceutical Research Saarland (DE), Weill Cornell Medicine (US), Rockefeller University (US), Texas A&M University (US)
U.S. Department of Health and Human Services, Bill and Melinda Gates Foundation, South African Medical Research Council, National Institutes of Health, Medical Research Council
Peace, Justice and strong institutions
Openalex Percentile: Top 24%
Computational Drug Discovery Methods
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