Pharmacogenomics and mutation informatics: correlation of NAT2 mutations and isoniazid acetylation rate

Tuberculosis (TB) drug resistance poses a major global health challenge. The first-line antitubercular prodrug isoniazid (INH) is metabolized by N-acetyltransferase 2 (NAT2) and activated by catalase peroxidase (KatG) to inhibit enoyl-acyl carrier protein reductase (InhA) in the mycolic acid biosynthesis pathway. Genetic variations in NAT2 are associated with the formation of slow and fast acetylators, influencing drug efficacy and toxicity. Despite significant advances that have clarified key aspects of NAT2-mediated isoniazid metabolism, the complete spectrum of mechanisms governing isoniazid deactivation and their broader implications for treatment efficacy and resistance evolution remain to be fully elucidated. In this review, we discuss the pharmacokinetics (PK), pharmacodynamics (PD), dosing regimens, and pharmacogenomics of isoniazid, along with the role of artificial intelligence (AI)/machine learning (ML) in its personalized use. In addition, we analyze NAT2 mutations and their impact on acetylation rates using bioinformatics. These insights collectively advance our understanding of genotype-driven variability in isoniazid response, aiding the development of personalized therapy.