Gehang Ju,1– 3 Xin Liu,3,4 Meng Gu,4 Lulu Chen,3,5 Xintong Wang,3,5 Chao Li,3,6 Nan Yang,3,6 Gufen Zhang,3,6 Chenchen Zhang,7 Xiao Zhu,4 Qingfeng He,4,* Dongsheng Ouyang1– 3,5,* 

1Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 2Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China; 3Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China; 4Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China; 5Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China; 6Phamark Data Technology Co., Ltd., Changsha, People’s Republic of China; 7School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Dongsheng Ouyang; Qingfeng He, Email 801940@csu.edu.cn; qf_he@fudan.edu.cn

Introduction: Rifampicin is a crucial first-line anti-tuberculosis drug that has been extensively studied through population pharmacokinetic (popPK) analyses. This study aims to construct a comprehensive rifampicin popPK model repository to support model-informed individualized therapy.
Methods: A systematic review was conducted using PubMed, Web of Science, and Embase databases up to September 2023 to retrieve popPK model articles on rifampicin. Extracted data included basic information, dosing regimens, sampling strategies, model parameters, and covariate details. Non-English studies, non-parametric models, and duplicates were excluded. The repository was built using R package mrgsolve, and a Shiny application was developed for simulation and individualized dosing predictions.
Results: A total of 29 studies were included in the rifampicin model repository: 23 on adults, 5 on pediatrics, 1 on both populations, and 1 on pregnant women. Most rifampicin popPK models were one-compartment linear elimination models, with transit compartment or lagged absorption models improving drug absorption fitting. An allometric growth model based on fat-free mass (FFM) might improved model fit. Postmenstrual age (PMA) significantly impacted elimination in pediatric patients. All models underwent internal validation, with three studies validated externally. Significant variations in exposure predictions were observed among models, indicating challenges in achieving therapeutic targets under standard treatment.
Discussion: The model repository provides a comprehensive resource for exploring various models and their application in different populations, supporting individualized rifampicin therapy. Further research is needed for special populations and to determine whether weight or FFM is more rational for dosing. External validation is essential for model development.

Keywords: rifampicin, model-informed precision dosing, population pharmacokinetics