Development of a Disease‐Specific Virtual Malaria Population for Physiologically‐Based Pharmacokinetic Modeling

ABSTRACT Malaria remains a major global health challenge, with an urgent need for new therapies to combat the evolving resistance to anti‐malarial treatments. Physiologically‐based pharmacokinetic (PBPK) modeling offers a promising approach to accurately predict PK, optimize dosing strategies, reduce development time and cost, and de‐risk the development of novel anti‐malarial compounds. In this study, we developed and validated a virtual malaria population, reflecting the pathophysiological changes associated with acute uncomplicated malaria infections. Key alterations included elevated plasma α1‐acid glycoprotein (+118%), reduced plasma albumin (−16.8%), decreased estimated glomerular filtration rate (−10%), reduced hepatic enzyme abundance (−26% to −42%), increased blood flow (+40%), and prolonged gastric emptying time (+~45 min), with parameter magnitudes systematically obtained from the literature. A dynamic function was incorporated to describe the evolution of these biological changes during the acute infection and treatment. The virtual population was used for PK predictions of quinine, dihydroartemisinin, amodiaquine, and desethylamodiaquine, which differ in metabolic pathways and plasma protein binding characteristics. Sensitivity analyses indicated that plasma protein levels had the largest impact on PK exposure, followed by enzyme abundance and blood flow, whereas eGFR contributed minimally. The developed virtual malaria population provides a proof‐of‐concept translational framework that may improve PK predictions during the acute infection and support the development of novel anti‐malarial therapies.