Genomic Diagnostics for Drug-Resistant Mycobacterium tuberculosis: Computational Prediction of Antimicrobial Resistance

Tuberculosis remains a leading cause of infectious disease mortality, and the continued emergence of drug-resistant Mycobacterium tuberculosis strains threatens the effectiveness of standard treatment regimens. Culture-based antibiotic susceptibility testing remains the clinical reference standard for resistance determination but typically requires six to eight weeks, delaying initiation of optimized therapy for patients with drug-resistant disease. Whole-genome sequencing-based approaches provide a rapid alternative for predicting antimicrobial resistance directly from genomic data and are increasingly being incorporated into diagnostic workflows. This survey reviews computational approaches for genomic resistance prediction in Mycobacterium tuberculosis, focusing on two major classes of methods: catalog-based tools that identify established resistance-conferring variants, and de novo machine learning approaches that infer resistance from genome-wide sequence features. We examine the strengths and limitations of these approaches with respect to interpretability, scalability, computational requirements, and concordance with phenotypic testing. We further discuss emerging directions in quantitative minimum inhibitory concentration prediction, challenges in pyrazinamide susceptibility testing, and the limited availability of resistant isolates for newer and repurposed drugs used in multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis treatment regimens. Continued expansion of paired phenotypic and genomic datasets, standardized minimum inhibitory concentration testing protocols, and rigorous lineage-aware evaluation frameworks will be essential for improving the clinical reliability and global deployment of genomic resistance prediction for tuberculosis diagnostics.