Cleo Demeester 1,2, Raphaëlle Lesage 1, Henry Enzama 3, Catriona Waitt 3,4, Collen Masimirembwa 5, Marco Siccardi 1
1 ESQlabs (Saterland, Germany), 2 MPSlabs, ESQlabs GmbH (Saterland, Germany), 3 Infectious Diseases Institute, Makerere University (Kampala, Uganda), 4 University of Liverpool (Liverpool, United Kingdom), 5 African Institute of Biomedical Science and Technology (AiBST) (Harare, Zimbabwe)
Introduction: Sub‑Saharan Africa (SSA) faces an outsized burden of infectious diseases but continues to be underrepresented in pharmacokinetic (PK) studies. Approximately 67% of the global HIV burden and over 80% of tuberculosis-related deaths occur in SSA [1-3]. Despite the high genetic diversity in African populations, most dose recommendations are derived from studies conducted in non-African populations [4]. The lack of region-specific pharmacogenomic and PK reference data contributes to uncertainty in dose selection and increased risk of adverse drug reactions for therapies used in HIV, tuberculosis, malaria, and other high-burden diseases. Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic framework that addresses this gap, as its predictions can support dosing regimens. However, an informed virtual SSA population is needed, which is not yet available open-source. This study aims to develop and validate an open‑source SSA population database to support PBPK modeling.
Methods: As part of a collaborative project, a unique analysis of liver samples from 100 individuals has been conducted before, accompanied by phenotype mapping. Additionally, anthropometric, anatomical, and physiological data for the SSA population have been collected previously. In the current study, these data were used to inform physiological and anthropometric parameter distribution, genomic diversity, liver enzyme abundance, and liver enzyme variability. The focus was on known African genetic variants in cytochrome P450 (CYP)2B6, CYP2D6, and CYP3A4. These data were then integrated into a database to create virtual populations within the open-source software PK-Sim® (OSP-Suite [5]). Genomic diversity was mapped to phenotypes and translated into variability in enzyme abundance distributions. Efavirenz was selected as a clinically relevant probe compound to validate the population integration into the PBPK model. PBPK model predictions for the SSA population were generated and compared with those for the European population.
Results: The datasets were successfully integrated into a usable PBPK database for population simulations in PK-Sim® (OSP-Suite[5]). Anatomical and physiological variability was incorporated through distributions of height and weight, informed by a height-to-age relationship. Upon population generation, organ volume distributions were scaled based on the mean-to-target height ratio, and plausibility checks were performed for each individual. Genotype-informed metabolic phenotypes were accounted for via liver enzyme abundance variability. Individuals were sampled to match the incidence rates of phenotypes. This enabled a mechanistic representation of African-specific variability in drug disposition. The population was implemented as a modular component that can be combined with compound-specific PBPK, disease, or safety models within a unified open-source modeling ecosystem. This SSA population modular component was tested by performing PBPK predictions specific for efavirenz, which showed agreement with observed exposure data when SSA-genotype and enzyme variability were included.
Conclusion: This study developed an open-source SSA population database and population-generation method that can be used for PBPK simulations to support predictive modeling in this underrepresented population. The population database was validated by improving the prediction of efavirenz kinetics. This framework supports more informed dose selection and contributes to improving the safety and effectiveness of pharmacotherapy in African populations.
References:
[1] UNAIDS. Global HIV & AIDS statistics — Fact sheet. UNAIDS; 2023.
[2] World Health Organization. Global Tuberculosis Report 2023. WHO; 2023
[3] Vollset SE et al. (2024) Lancet 403(10440):2204–56
[4] Twesigomwe D et al. (2025) Annu Rev Genomics Hum Genet;26(1):321-349
[5] Lippert, J et al. (2019) CPT:PSP (12):878-882
Reference: PAGE 34 (2026) Abstr 11896 [www.page-meeting.org/?abstract=11896]
Poster: Clinical Applications