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Peer-reviewed veterinary case report

Preclinical approaches in tuberculosis drug development research: Bridging experimental tools and clinical translation.

Journal:
The Indian journal of tuberculosis
Year:
2026
Authors:
Gaurav, Akriti et al.
Affiliation:
Amity Institute of Biotechnology · India
Species:
rodent

Abstract

BACKGROUND: Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to pose a serious global health threat, especially with the rise of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains. Progress in developing new therapeutics relies on preclinical models that closely reproduce human disease features and treatment responses. OBJECTIVES: The purpose of this review is to examine the range of preclinical models currently employed in TB research and highlighting their strengths, limitations, and translational relevance in drug discovery and development. METHODS: We evaluated conventional two-dimensional (2D) macrophage systems, advanced three-dimensional (3D) cultures, lung organoids, and animal models such as mice, guinea pigs, rabbits, non-human primates, and cattle. Their ability to capture key aspects of TB pathology including granuloma formation, immune mechanisms, and pharmacokinetics was analysed. Alternative hosts, including Galleria mellonella and Caenorhabditis elegans, were also considered for their role in early-stage screening. Attention was given to infection routes, biosafety requirements, and regulatory considerations. RESULTS: No single model fully reflects the human condition. Simpler in vitro and invertebrate models are cost-efficient and useful for preliminary screening, whereas mammalian systems provide greater disease complexity and translational insights. A comparative framework is presented to support model selection at different stages of drug development. Emerging approaches, such as humanized mouse models, hold promise for bridging gaps between preclinical findings and clinical outcomes. CONCLUSION: A phased, and integrative use of preclinical models can improve predictive accuracy, reduce attrition in drug pipelines, and accelerate the development of effective anti-TB agents. This review provides a practical guide for selecting models that enhance the robustness of TB drug discovery.

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Original publication: https://pubmed.ncbi.nlm.nih.gov/42031451/