Pharmacogenomic insights into tuberculosis treatment shows the NAT2 genetic variants linked to hepatotoxicity risk: a systematic review and meta-analysis.

Background: Tuberculosis (TB) patients undergoing anti-tuberculosis treatment often face serious adverse drug reactions, such as hepatotoxicity. Genetic variants of the N-acetyltransferase 2 (NAT2) gene have been linked to an increased risk of these toxic events.

Objective: This study aims to provide a comprehensive evaluation of the evidence linking NAT2 genetic variants to anti-tuberculosis drug-related hepatotoxicity (ATDH).

Method: A comprehensive review and meta-analysis was performed by accessing databases such as PubMed, Scopus, and Web of Science. A total of 24 articles were incorporated into the dataset. Meta-analyses were conducted to gather estimates of the association between the slow acetlylators (SA) genotype and ATDH. The studies were stratified by ethnicity, regimen, genotyping methods, criteria for liver toxicity, and dosage. Also, meta-analysis for the specific SA type that was most likely responsible for the ATDH was also conducted.

Results: The included studies showed individuals with a slow NAT2 acetylator had a significantly greater risk of experiencing hepatotoxicity ATDH (odds ratio [OR] 2.52 (95% CI: 1.95-3.27; p value < 0.001) compared to individuals with other types of acetylator (i.e., rapid and immediate). Among individuals with slow acetylator NAT2*5/7, NAT2*5/6, and NAT2*6/6 genotypes, there is a greater likelihood of association compared to other variations.

Conclusion: Our meta-analysis confirms a significant association between slow NAT2 acetylator and increased hepatotoxicity risk. The findings from the present underscore the potential of pharmacogenomic testing to improve TB treatment outcomes. By identifying patients with the slow acetylator NAT2 genotype, healthcare providers can predict an increased risk of anti-tuberculosis drug-induced hepatotoxicity. This allows for personalized treatment strategies, such as adjusting drug dosages or selecting alternative therapies, to minimize adverse effects and optimize efficacy.