Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with individual pharmacokinetic profiles. These are crucial to improving the treatment outcome of the patients, particularly for those with complex comorbidity and a high risk of treatment failure. Despite the actual benefits of TDM at the bedside, conventional TDM encounters several hurdles related to laborious, time-consuming, and costly processes. Herein, we review the current practice of TDM and discuss the main obstacles that impede it from successful clinical implementation. Moreover, we propose a semi-automated TDM approach to further enhance precision medicine for TB management.

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.

Clofazimine [N,5-bis(4-chlorophenyl)-3-[(propane-2-yl)rimino]-3,5-dihydrophenazin-2-amine] is an antimycobacterial agent used as a second-line antituberculosis (anti-TB) drug. Nonetheless, little information is known about the metabolic routes of clofazimine, and the enzymes involved in metabolism.

Objectives: This study was performed to develop a population pharmacokinetic model of pyrazinamide for Korean tuberculosis (TB) patients and to explore and identify the influence of demographic and clinical factors, especially geriatric diabetes mellitus (DM), on the pharmacokinetics (PK) of pyrazinamide (PZA).Methods: PZA concentrations at random post-dose points, demographic characteristics, and clinical information were collected in a multicenter prospective TB cohort study from 18 hospitals in Korea. Data obtained from 610 TB patients were divided into training and test datasets at a 4:1 ratio. A population PK model was developed using a nonlinear mixed-effects method.Results: A one-compartment model with allometric scaling for body size effect adequately described the PK of PZA. Geriatric patients with DM (age >70 years) were identified as a significant covariate, increasing the apparent clearance of PZA by 30% (geriatric patients with DM: 5.73 L/h; others: 4.50 L/h), thereby decreasing the area under the concentration–time curve from 0 to 24 h by a similar degree compared with other patients (geriatric patients with DM: 99.87 μg h/mL; others: 132.3 μg h/mL). Our model was externally evaluated using the test set and provided better predictive performance compared with the previously published model.Conclusion: The established population PK model sufficiently described the PK of PZA in Korean TB patients. Our model will be useful in therapeutic drug monitoring to provide dose optimization of PZA, particularly for geriatric patients with DM and TB.