Prothionamide Dose Optimization Using Population Pharmacokinetics for Multidrug-Resistant Tuberculosis Patients

Hwi Yeol Yun, Min Jung Chang, Heeyoon Jung, Vincent Chang, Qianwen Wang, Natasha Strydom, Young Ran Yoon, Radojka M. Savic

Research output: Contribution to journalArticlepeer-review


Prothionamide, a second-line drug for multidrug-resistant tuberculosis (MDR-TB), has been in use for a few decades. However, its pharmacokinetic (PK) profile remains unclear. This study aimed to develop a population PK model for prothionamide and then apply the model to determine the optimal dosing regimen for MDR-TB patients. Multiple plasma samples were collected from 27 MDR-TB patients who had been treated with prothionamide at 2 different study hospitals. Prothionamide was administered according to the weight-band dose regimen (500 mg/day for weight <50 kg and 750 mg/day for weight >50 kg) recommended by the World Health Organization. The population PK model was developed using nonlinear mixed-effects modeling. The probability of target attainment, based on systemic exposure and MIC, was used as a response target. Fixed-dose regimens (500 or 750 mg/day) were simulated to compare the efficacies of various dosing regimens. PK profiles adequately described the two-compartment model with first-order elimination and the transit absorption compartment model with allometric scaling on clearance. All dosing regimens had effectiveness >90% for MIC values <0.4 mg/mL in 1.0-log kill target. However, a fixed dose of 750 mg/day was the only regimen that achieved the target resistance suppression of ≥90% for MIC values of <0.2 mg/mL. In conclusion, fixed-dose prothionamide (750 mg/day), regardless of weight-band, was appropriate for adult MDR-TB patients with weights of 40 to 67 kg.

Original languageEnglish
JournalAntimicrobial agents and chemotherapy
Issue number9
Publication statusPublished - 2022 Sept

Bibliographical note

Funding Information:
This study was funded by Chungnam National University, as well as a grant by the Institute for Information and Communications Technology Planning and Evaluation, funded by the government of the Republic of Korea (MSIT; no. 2020-0-01441; Artificial Intelligence Convergence Research Center, Chungnam National University, South Korea). No. RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A2C1010929).

Publisher Copyright:
© 2022 American Society for Microbiology. All Rights Reserved.

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Pharmacology (medical)
  • Infectious Diseases


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