Development of a population pharmacokinetic model of pyrazinamide to guide personalized therapy: impacts of geriatric and diabetes mellitus on clearance

Front Pharmacol. 2023 May 26:14:1116226. doi: 10.3389/fphar.2023.1116226. eCollection 2023.

Abstract

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.

Keywords: diabetes mellitus; geriatric; population pharmacokinetics; pyrazinamide; therapeutic drug monitoring; tuberculosis.

Grants and funding

This study was supported by a National Research Foundation of Korea (NRF) grant (No. 2018R1A5A2021242) funded by the Korean government (MSIT).