Population pharmacokinetics model of pyrazinamide to optimize tuberculosis treatment: An interethnic cohort study of diabetes mellitus effect on drug exposure

초록

Diabetes mellitus (DM), a common comorbidity in tuberculosis (TB) patients, can alter the pharmacokinetics (PK) of TB drugs. Additionally, clinical and demographic differences may contribute to interethnic PK variability. However, current WHO-recommended doses for pyrazinamide (PZA) do not account for those factors. We aimed to evaluate factors related to interindividual variability (IIV) and interethnic differences in the PK of PZA between Korean and Indonesian patients with TB. Demographics, clinical characteristics, and PZA concentrations obtained from hospitals in both countries were used for model establishment. Population PK models were developed using the nonlinear mixed-effect method. A Monte Carlo simulation was performed sequentially to evaluate optimal PZA dosing strategies. A one-compartment model with allometric scaling adequately described the PK of PZA. Internal validation of the model showed good performance. No significant interethnic differences in PK parameters were observed. There were 23% and 26% increases in apparent clearance (CL/F) of Indonesian patients with DM (CL/F 3.18&#x2009;L/h) and Korean patients aged&#x2009;>&#x2009;60 years with DM (CL/F 3.5). PZA doses of 1000-1250&#x2009;mg for patients of bodyweight&#x2009;<&#x2009;40&#x2009;kg and 1250&#x2009;mg for older patients with DM in this weight band had a 90% probability of attaining the target AUC0-24&#x2009;&#x2265;&#x2009;363 mg&#xb7;h/L These findings indicate that DM strongly influenced IIV, particularly in older patients. We recommend higher PZA doses for patients <40&#x2009;kg and older patients with DM in this weight band. Our model provides a basis for implementing model-informed precision dosing-based therapeutic drug monitoring in both ethnicities. Copyright: © 2026 Jayanti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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