Predicting Optimal Antimalarial Drug Combinations from a Standardized Plasmodium falciparum Humanized Mouse Model

Area of Science:

• * Infectious Diseases
• * Parasitology
• * Pharmacology

Background:

• * Urgent need for novel antimalarial drug combinations to combat drug-resistant Plasmodium falciparum.
• * Existing treatments face challenges due to emerging parasite resistance.
• * Malaria control and eradication efforts require innovative therapeutic strategies.

Purpose of the Study:

• * Evaluate a standardized humanized mouse model (PfalcHuMouse) for selecting optimal antimalarial drug combinations.
• * Compare different therapeutic response variables for assessing drug combination efficacy in vivo.
• * Determine the most reliable method for estimating individual drug contributions within combinations.

Main Methods:

• * Utilized a robust and reproducible Plasmodium falciparum (Pf) replication model in humanized mice (PfalcHuMouse).
• * Compared parasite clearance, recrudescence (parasite regrowth), and cure as therapeutic response variables.
• * Formalized and validated the Day of Recrudescence (DoR) as a quantitative measure of parasite burden.
• * Employed multivariate statistical modeling and pharmacokinetic/pharmacodynamic (PK/PD) analysis.

Main Results:

• * Demonstrated robust and reproducible P. falciparum replication in the PfalcHuMouse model.
• * Established a log-linear relationship between the Day of Recrudescence (DoR) and parasite numbers.
• * Found that measuring parasite killing (cure) as a function of drug exposure is essential for estimating individual drug contributions.
• * Identified ferroquine plus artefenomel and piperaquine plus artefenomel combinations in historical data.

Conclusions:

• * The PfalcHuMouse model is a validated tool for evaluating antimalarial drug combinations.
• * Parasite killing (cure) is the most informative variable for assessing drug contributions in vivo.
• * Pharmacometric modeling using parasite killing data aids in selecting optimal antimalarial drug combinations.
• * This approach supports malaria control and eradication strategies.