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

Area of Science:

• Pharmacokinetics and Drug Development
• Computational Biology and Modeling
• Infectious Disease Research

Background:

• Malaria poses a significant global health threat, necessitating novel therapeutic strategies due to increasing drug resistance.
• Physiologically-based pharmacokinetic (PBPK) modeling is crucial for optimizing anti-malarial drug development, reducing costs, and mitigating risks.

Purpose of the Study:

• To develop and validate a virtual malaria population simulating pathophysiological changes in acute uncomplicated malaria.
• To assess the impact of these simulated physiological changes on the pharmacokinetics (PK) of key anti-malarial drugs.

Main Methods:

• Literature-derived parameters were used to model alterations in plasma proteins, renal function, hepatic enzymes, blood flow, and gastric emptying.
• A dynamic function was implemented to represent the progression of these physiological changes during infection and treatment.
• The virtual population was employed for PK predictions of quinine, dihydroartemisinin, amodiaquine, and desethylamodiaquine.

Main Results:

• Sensitivity analyses revealed that plasma protein levels significantly influenced PK exposure, followed by hepatic enzyme abundance and blood flow.
• Estimated glomerular filtration rate (eGFR) demonstrated a minimal impact on PK predictions.
• The model successfully predicted PK variations for drugs with different metabolic pathways and protein binding.

Conclusions:

• The developed virtual malaria population serves as a proof-of-concept for improving PK predictions during acute malaria.
• This translational framework can accelerate the development of new anti-malarial therapies by de-risking the drug development process.