Evaluation of a Bayesian hierarchical pharmacokinetic-pharmacodynamic model for predicting parasitological outcomes in Phase 2 studies of new antimalarial drugs

The rise of multidrug resistant malaria requires accelerated development of novel antimalarial drugs. Pharmacokinetic-pharmacodynamic (PK-PD) models relate blood antimalarial drug concentrations with the parasite-time profile to inform dosing regiments. We performed a simulation study to assess the utility of a Bayesian hierarchical mechanistic PK-PD model for predicting parasite-time profiles for a Phase 2 study of a new antimalarial drug, cipargamin. We simulated cipargamin concentration- and malaria parasite-profiles based on a Phase 2 study of 8 volunteers who received cipargamin 7 days after inoculation with malaria parasites. The cipargamin profiles were generated from a 2-compartment PK model and parasite profiles from a previously published biologically informed PD model. One-thousand PK-PD datasets of 8 patients were simulated, following the sampling intervals of the Phase 2 study. The mechanistic PK-PD model was incorporated in a Bayesian hierarchical framework and the parameters estimated. Population PK model parameters describing absorption, distribution and clearance were estimated with minimal bias (mean relative bias ranged from 1.7 to 8.4%). The PD model was fitted to the parasitaemia profiles in each simulated dataset using the estimated PK parameters. Posterior predictive checks demonstrate that our PK-PD model successfully captures both the pre- and post-treatment simulated PD profiles. The bias of the estimated population average PD parameters was low-moderate in magnitude. This simulation study demonstrates the viability of our PK-PD model to predict parasitological outcomes in Phase 2 volunteer infection studies. This work will inform the dose-effect relationship of cipargamin, guiding decisions on dosing regimens to evaluate in Phase 3 trials.