Evaluation of a Bayesian hierarchical pharmacokinetic-pharmacodynamic model for predicting parasitological outcomes in Phase 2 studies of new antimalarial drugs
- Author(s)
- Tully, MK; Dini, S; Flegg, JA; McCarthy, JS; Price, DJ; Simpson, JA;
- Details
- Publication Year 2024-08-13,Volume 68,Issue #9,Page e0086324
- Journal Title
- Antimicrobial Agents and Chemotherapy
- 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 regimens. 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 eight volunteers who received cipargamin 7 days after inoculation with malaria parasites. The cipargamin profiles were generated from a two-compartment PK model and parasite profiles from a previously published biologically informed PD model. One thousand PK-PD data sets of eight 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 were 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 data set using the estimated PK parameters. Posterior predictive checks demonstrate that our PK-PD model adequately captures the 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 be evaluated in Phase 3 trials.
- Publisher
- ASM
- Keywords
- Bayesian methods; antimalarial; pharmacokinetic–pharmacodynamic modeling; simulation
- Research Division(s)
- Infectious Diseases And Immune Defence
- PubMed ID
- 39136464
- Publisher's Version
- https://doi.org/10.1128/aac.00863-24
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-08-23 02:54:10
Last Modified: 2024-10-03 09:19:52