Isoniazid Population Pharmacokinetics and Dose Recommendation for Korean Patients With Tuberculosis Based on Target Attainment Analysis.
Yong-Soon ChoTae Won JangHyo-Jung KimJee Youn OhHyun-Kyung LeeHye Kyeong ParkJong-Lyul GhimNguyen Phuoc LongYumi ParkYoung-Kyung ChoiPhuong Thi Thu NguyenJae-Gook Shinnull nullPublished in: Journal of clinical pharmacology (2021)
The wide variability of isoniazid (INH) pharmacokinetics is mainly attributed to the trimodal N-acetyltransferase 2 (NAT2) acetylator phenotype, that is, rapid, intermediate, and slow. Consequently, a uniform INH dose in current clinical practice may lead to treatment failure and emergence of drug resistance. There is a lack of studies on specific doses of INH for different NAT2 acetylator phenotypes among tuberculosis (TB) patients. Therefore, we aimed to provide insight into the optimal dosing of INH for each NAT2 acetylator phenotype with respect to the probability of achieving a pharmacokinetic (PK)/pharmacodynamic target. PK, the NAT2 genotype, and clinical data were collected in a multicenter prospective cohort study conducted at 13 clinical centers in Korea. Population PK modeling and simulation were carried out. Data from 454 TB patients were divided into a training data set and a test data set at a ratio of 4 to 1. The PK of the training data were best described by a 2-compartment model with allometric scaling for body size effect. Importantly, NAT2 acetylator phenotypes significantly affected the apparent clearance. Our model, which provided better predictive performance compared with previously published models, was evaluated by external validation using the test set. The simulation for assessing target efficacy and toxicity indicated that the best INH dosing regimens for Korean tuberculosis patients were once-daily doses of 400, 300, and 200 mg for rapid, intermediate, and slow acetylators, respectively. In conclusion, our study provides a step forward in precision dosing for antituberculosis management.
Keyphrases
- mycobacterium tuberculosis
- end stage renal disease
- ejection fraction
- newly diagnosed
- electronic health record
- chronic kidney disease
- big data
- randomized controlled trial
- peritoneal dialysis
- clinical trial
- clinical practice
- systematic review
- machine learning
- physical activity
- deep learning
- oxidative stress
- hepatitis c virus
- smoking cessation
- patient reported
- double blind