Development of population pharmacokinetics model of isoniazid in Indonesian patients with tuberculosis

• Published in: International journal of infectious diseases 2022-04, Vol.117, p.8-14
• Main Authors: Soedarsono, Soedarsono, Jayanti, Rannissa Puspita, Mertaniasih, Ni Made, Kusmiati, Tutik, Permatasari, Ariani, Indrawanto, Dwi Wahyu, Charisma, Anita Nur, Yuliwulandari, Rika, Long, Nguyen Phuoc, Choi, Young-Kyung, Hoa, Pham Quang, Hoa, Pham Vinh, Cho, Yong-Soon, Shin, Jae-Gook
• Format: Article
• Language: English
• Subjects: Antitubercular Agents - pharmacokinetics; Antitubercular Agents - therapeutic use; Arylamine N-Acetyltransferase - genetics; Bayes Theorem; Genotype; Humans; Indonesia; Isoniazid; Isoniazid - pharmacokinetics; Isoniazid - therapeutic use; Population Pharmacokinetics; Therapeutic Drug Monitoring; Tuberculosis; Tuberculosis - drug therapy; Tuberculosis - genetics
• ISSN: 1201-9712; 1878-3511
• DOI: 10.1016/j.ijid.2022.01.003

•This study reports the first population pharmacokinetics (PK) model for isoniazid (INH) in Indonesian patients with tuberculosis (TB).•The PK of INH in this population is significantly different from other populations.•N-acetyltransferase 2 (NAT2) acetylator was reconfirmed as significant covariate of variability in INH PK. No population pharmacokinetics (PK) model of isoniazid (INH) has been reported for the Indonesian population with tuberculosis (TB). Therefore, we aimed to develop a population PK model to optimize pharmacotherapy of INH on the basis of therapeutic drug monitoring (TDM) implementation in Indonesian patients with TB. INH concentrations, N-acetyltransferase 2 (NAT2) genotypes, and clinical data were collected from Dr. Soetomo General Academic Hospital, Indonesia. A nonlinear mixed-effect model was used to develop and validate the population PK model. A total of 107 patients with TB (with 153 samples) were involved in this study. A one-compartment model with allometric scaling for bodyweight effect described well the PK of INH. The NAT2 acetylator phenotype significantly affected INH clearance. The mean clearance rates for the rapid, intermediate, and slow NAT2 acetylator phenotypes were 55.9, 37.8, and 17.7 L/h, respectively. Our model was well-validated through visual predictive checks and bootstrapping. We established the population PK model for INH in Indonesian patients with TB using the NAT2 acetylator phenotype as a significant covariate. Our Bayesian forecasting model should enable optimization of TB treatment for INH in Indonesian patients with TB.