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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Australian National Health and Medical Research Council (NHMRC) Leadership Investigator Grants (#1196068) to JAS and and (#2016396) to JSM, the Australian Centre for Research Excellence in Malaria Elimination (#2024622) and a NHMRC Synergy Grant (#2018654). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes No new datasets are presented in this research. Code to perform the analyses is available at https://github.com/M-Tully/pkpd\_model\_cip