RESUMO
Rationale: Standardized dosing of antitubercular drugs contributes to a substantial incidence of toxicities, inadequate treatment response, and relapse, in part due to variable drug concentrations achieved. SNPs in the NAT2 (N-acetyltransferase-2) gene explain the majority of interindividual pharmacokinetic variability of isoniazid (INH). However, an obstacle to implementing pharmacogenomic-guided dosing is the lack of a point-of-care assay. Objectives: To develop and test a NAT2 classification algorithm, validate its performance in predicting isoniazid clearance, and develop a prototype pharmacogenomic assay. Methods: We trained random forest models to predict NAT2 acetylation genotype from unphased SNP data using a global collection of 8,561 phased genomes. We enrolled 48 patients with pulmonary tuberculosis, performed sparse pharmacokinetic sampling, and tested the acetylator prediction algorithm accuracy against estimated INH clearance. We then developed a cartridge-based multiplex quantitative PCR assay on the GeneXpert platform and assessed its analytical sensitivity on whole blood samples from healthy individuals. Measurements and Main Results: With a 5-SNP model trained on two-thirds of the data (n = 5,738), out-of-sample acetylation genotype prediction accuracy on the remaining third (n = 2,823) was 100%. Among the 48 patients with tuberculosis, predicted acetylator types were 27 (56.2%) slow, 16 (33.3%) intermediate, and 5 (10.4%) rapid. INH clearance rates were lowest in predicted slow acetylators (median 14.5 L/h), moderate in intermediate acetylators (median 40.3 L/h), and highest in fast acetylators (median 53.0 L/h). The cartridge-based assay accurately detected all allele patterns directly from 25 µl of whole blood. Conclusions: An automated pharmacogenomic assay on a platform widely used globally for tuberculosis diagnosis could enable personalized dosing of INH.