In-silico functional prediction of novel tuberculosis pharmacogenetic variants and NAT2 phenotype prediction in African populations

Tuberculosis (TB) remains a major public health challenge, exerting profound socio-economic burdens and causing debilitating illness in approximately 2.5 million individuals across Africa annually. Optimized large-scale treatment regimens, such as NAT2 -genotype adjusted dosing, could improve patient outcomes and strengthen healthcare systems. However, fully addressing the complexity of multi-drug TB treatment responses requires consideration of the entire pharmacogenomic (PGx) landscape, particularly within African populations, which are both genetically diverse and critically understudied. In this study, we predict NAT2 genotypes and phenotypes in specific African populations, and we extend TB PGx research beyond well-established biomarkers. Current bioinformatic prediction tools were used to evaluate individual- and population-specific variation in genotype and next-generation sequencing data from 2,143 individuals across 20 African population groups, spanning ten PGx genes associated with multi-drug TB treatment and response. Most predicted functionally deleterious variants occurred at low frequencies (MAF < 0.01) and were observed in only one of the 20 populations. The Khomani and Nama populations had a distinctly higher proportion of NAT2 fast metabolizer phenotypes than other African populations, indicating a lower risk of INH overexposure and possibly different dosage requirements in these groups. These findings highlight both the potential and current limitations of functional prediction for absorption, distribution, metabolism and excretion (ADME) variants, and the transferability of their predictive value between African population groups. With the increasing accessibility of next-generation sequencing, alongside the development of comprehensive databases capturing African variation and advances in computational algorithms, the cumulative impact of genetic variation on TB drug response can be more accurately captured, thereby informing precision treatment strategies.