Revolutionizing TB Treatment with AI: Herbal and Chemical Synergies
Year: 2025
Type: article
Abstract: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains one of the leading causes of infectious disease-related deaths worldwide. The growing prevalence of multidrug-resistant (MDRTB) and extensively drug-resistant strains (XDR-TB) highlights the urgent need for alternative and integrative treatment strategies. While Directly Observed Treatment, Short-course (DOTS) has been the global standard, its limitations such as long treatment duration, toxicity and patient noncompliance have driven research toward novel solutions. This study proposes an AI-driven hybrid approach that synergizes conventional anti-TB drugs with phytochemically potent herbal compounds to formulate a next-generation therapy: TB-X ultimate. This research integrates pathogenic insights, including M.TB immune evasion and virulence mechanisms, with cutting-edge technologies like CRISPR-based functional genomics and AI-enhanced network pharmacology. Machine learning models were employed to predict synergistic interactions, filter toxic compounds, and identify herbal agents (e.g., Curcuma longa, Azadirachta indica, Pipernigrum) that can enhance the efficacy of rifampicin, isoniazid and other frontline drugs. Through systematic reviews, molecular docking, and in-silico validation, the proposed method demonstrates how combining ethno pharmacological knowledge with computational precision can accelerate drug discovery and reduce resistance development. This interdisciplinary framework paves the way for preclinical validation, clinical protocol design and eventual therapeutic deployment. By harnessing the strengths of both modern pharmacotherapy and traditional medicine, this proposed model offers a promising, patient-friendly and sustainable solution for global TB control.
Authors: R Sarika, S Vaishnavi, V Shirley Theresa, C Sabarishwaran, R Vasanth, D Ramana, D Ramana, E Shapna Rani, L Muruganandam
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6968525593
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