Towards Model-Informed Precision Dosing of High-Dose Rifampicin: A Parsimonious Population Pharmacokinetic Model Capturing Saturation and Autoinduction in Tanzanian and South African Patients with Tuberculosis.

BACKGROUND: Rifampicin, a cornerstone of tuberculosis treatment, exhibits complex pharmacokinetics with variable absorption, saturable elimination and time-dependent autoinduction, together with marked inter- and intra-individual variability in drug exposure. These properties challenge standard weight-based dosing and underscore the need for individualized strategies, particularly in high-burden settings like Tanzania. While therapeutic drug monitoring (TDM) supported by model-informed precision dosing (MIPD) is being implemented, few of the existing population pharmacokinetic (popPK) models includes Tanzanians and are parsimonious. OBJECTIVE: This study aimed to develop a parsimonious popPK model of rifampicin that successfully captures both its autoinductive and saturable elimination components, achieving predictive performance comparable to more complex reference models described in the literature. METHODS: Rifampicin concentrations data from 42 Tanzanian and 92 South African patients, covering doses up to 50 mg/kg over 2 weeks were used to develop the model, which was further benchmarked against literature reference models. RESULTS: The chosen model employed a one-compartment structure with Michaelis-Menten elimination and a time-dependant Emax-based autoinduction function. The final model demonstrated individual Bayesian predictive performance comparable to two complex reference popPK models, and confirmed substantial inter-individual variability in exposure, with exposures differing by up to fivefold within the same weight-based dosing cohort. CONCLUSION: These findings support the implementation of MIPD and TDM to improve efficacy and safety in TB treatment rather than relying on a uniform dosing approach based on bodyweight. This model provides a robust foundation for individualized dosing and the deployment of clinical decision support tools in high TB burden settings.