Yuan Pétermann1, Bibie Said2,3, Margaretha Sariko4, Yann Thoma5, Stellah Mpagama2, Chantal Csajka1,6,7, Monia Guidi1,6,8
1Centre for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, 2Kibong'oto Infectious Diseases Hospita, 3The Nelson Mandela African Institution of Science and Technology, 4Kilimanjaro Clinical Research Institute, 5School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, 6 Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, 7School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 8Service of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne
Introduction: Rifampicin remains one of the most critical drugs in the treatment of tuberculosis (TB) (1). Yet its clinical efficacy, dependent of the drug exposure, is frequently compromised by substantial inter-individual variability (IIV) and complex pharmacokinetic (PK) leading to subtherapeutic exposition and unfavourable outcomes (1). In this context, Therapeutic Drug Monitoring (TDM) emerges as a pivotal strategy: by measuring individuals rifampicin peak plasma concentrations, TDM enables dose adjustments to the recommended peak concentration of 8-24 mg/L (2). Yet, rifampicin is known to exhibit a saturable elimination and an autoinduction (AI) of its metabolic pathways. This process is further influenced by genetic polymorphisms of the organic anion-transporting polypeptide 1B1 (OATP1B1), which plays a key role in rifampicin metabolism and exhibits varying expression across ethnic groups (1). Notably, Tanzanians have been associated with OATP1B1 polymorphisms linked to lower rifampicin exposure. These combined factors significantly complicate drug exposure predictions and thus hinder timely dosing optimization for patients in need (1,3). The TuberXpert project aims to establish the foundations of TDM practice in Tanzanian TB care, by providing a clinical decision support system for rifampicin dosing optimization based on a model-informed precision dosing software, Tucuxi (4,5). This study aimed to develop a population pharmacokinetic (popPK) model for rifampicin PK in TB patients, capturing both saturable elimination and the AI process. Methods: Rifampicin plasma concentrations data were prospectively collected from TB patients under first-line anti-TB combination therapies followed at Kibong’oto Infectious Disease Hospital (KIDH) in Tanzania. Patients were sampled at day 1 to 3 and day 12-16 after treatment initiation. In addition, rifampicin retrospective data from patients in South Africa (HIGHRIF1 (HR1) study) were retrieved. Rifampicin concentrations sampled at day 7 and 14 were included in the popPK analyses. A classical stepwise analysis was performed in MonolixSuite™ (version 2024R1) to compare compartmental models featuring both linear and non-linear absorption and elimination. IIV, inter-study (ISV) to assess ethnical genetical differences, and inter-occasion variability (IOV) in PK parameters were tested sequentially. Different residual error models were evaluated to characterize the unexplained variability (RUV), with a distinct error model per study. Model performance was rigorously evaluated through standard internal validation techniques. Results: The popPK analysis was performed on a total of 2223 rifampicin plasma concentrations sampled from 134 patients. The median (range) administered daily dose across both studies was 1200 (450 – 3000) mg. Rifampicin PK was best described by a one-compartment popPK model with linear absorption (rate of 2.64 h-1) and a lag time of approximately 0.32 h improved the model (p < 0.001). A saturable Michaelis-Menten elimination with a maximal elimination rate of 124 mg/h for the HR1 population and 419 mg/h for the Tanzanian cohort was estimated, with a half-saturation constant of 27.5 mg/L (p << 0.001). Incorporating a time-dependent Emax function revealed significant differing amplitudes of AI (p << 0.001), with Emax values of 3.31 for HR1 and 0.461 for TuberXpert patients. The estimated AI half-life of 157 h indicates that steady state is reached in roughly one month. The final model estimates an AI of 3.76-fold in the South African group and of 1.38-fold in the Tanzanian population. The volume of distribution was of 41 L. As expected, adding IIV across all PK parameters substantially improved the model. A proportional error of 25% (CV%) for TuberXpert and 23% (CV%) for HR1 best characterized the intra-individual variability. Visual predictive checks and non-parametric bootstrap method demonstrated good performance and reliability of the model. Conclusions: This study captured the non-linear popPK model of rifampicin with both a saturable elimination and a time-dependent AI. The observed ISV may be partly attributed to differences in the prevalence of OATP1B1 polymorphisms. The marked IIV leading to potential subtherapeutic exposition underscores the need for personalized dosing of rifampicin via TDM to optimize treatment outcomes in TB patients.
1. Petermann YJ, Said B, Cathignol AE, Sariko ML, Thoma Y, Mpagama SG, et al. State of the art of real-life concentration monitoring of rifampicin and its implementation contextualized in resource-limited settings: the Tanzanian case. JAC Antimicrob Resist [Internet]. 2024 Dec 14 [cited 2025 Jan 16];6(6):dlae182. Available from: https://academic.oup.com/jacamr/article-pdf/6/6/dlae182/61220089/dlae182.pdf 2. Buclin T, Thoma Y, Widmer N, André P, Guidi M, Csajka C, et al. The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib. Front Pharmacol [Internet]. 2020 Mar 3;11:177. Available from: http://dx.doi.org/10.3389/fphar.2020.00177 3. Svensson RJ, Aarnoutse RE, Diacon AH, Dawson R, Gillespie SH, Boeree MJ, et al. A Population Pharmacokinetic Model Incorporating Saturable Pharmacokinetics and Autoinduction for High Rifampicin Doses. Clin Pharmacol Ther [Internet]. 2018 Apr;103(4):674–83. Available from: http://dx.doi.org/10.1002/cpt.778 4. Dubovitskaya A, Buclin T, Schumacher M, Aberer K, Thoma Y. TUCUXI: An Intelligent System for Personalized Medicine from Individualization of Treatments to Research Databases and Back. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics [Internet]. New York, NY, USA: Association for Computing Machinery; 2017 [cited 2023 Oct 26]. p. 223–32. (ACM-BCB ’17). Available from: https://doi.org/10.1145/3107411.3107439 5. Thoma Y, Cathignol AE, Pétermann YJ, Sariko ML, Said B, Csajka C, et al. Toward a clinical decision support system for monitoring therapeutic antituberculosis medical drugs in Tanzania (project TuberXpert): Protocol for an algorithm’ development and implementation. JMIR Res Protoc [Internet]. 2024 Oct 21;13:e58720. Available from: https://www.researchprotocols.org/2024/1/e58720/
Reference: PAGE 33 (2025) Abstr 11625 [www.page-meeting.org/?abstract=11625]
Poster: Drug/Disease Modelling - Infection