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

Abstract 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 Footnotes ↵† Co-senior authors CONFLICT OF INTEREST: All authors declared no competing interests for this work. FUNDING: 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). Data Availability No new datasets are presented in this research. Code to perform the analyses is available at https://github.com/M-Tully/pkpd_model_cip