Soedarsono Soedarsono1, Rannissa Puspita Jayanti2, Ni Made Mertaniasih3, Tutik Kusmiati4, Ariani Permatasari4, Dwi Wahyu Indrawanto5, Anita Nur Charisma5, Rika Yuliwulandari6, Nguyen Phuoc Long2, Young-Kyung Choi7, Pham Quang Hoa2, Pham Vinh Hoa2, Yong-Soon Cho2, Jae-Gook Shin8. 1. Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia; Tuberculosis Study Group, Universitas Airlangga, Surabaya 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya 60131, Indonesia. Electronic address: ssoedarsono@gmail.com. 2. Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea. 3. Tuberculosis Study Group, Universitas Airlangga, Surabaya 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya 60131, Indonesia; Department of Clinical Microbiology, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia. 4. Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia; Tuberculosis Study Group, Universitas Airlangga, Surabaya 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya 60131, Indonesia. 5. Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya 60131, Indonesia. 6. Department of Pharmacology, Faculty of Medicine, YARSI University, Jakarta 10510, Indonesia; Genetic Research Center, YARSI Research Institute, YARSI University, Jakarta 10510, Indonesia. 7. Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Republic of Korea. 8. Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea; Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan 47392, Republic of Korea. Electronic address: phshinjg@inje.ac.kr.
Abstract
OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.