Parametric Population Pharmacokinetics Model Repository of Rifampicin: Model-Informed Individualized Therapy

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.