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

TL;DR

This paper evaluates a model that predicts how a new malaria drug affects parasite levels in patients, helping to design better dosing regimens.

Contribution

A Bayesian hierarchical PK-PD model is validated for predicting parasitological outcomes in early-stage antimalarial drug trials.

Findings

The Bayesian PK-PD model accurately simulated drug concentration and parasite profiles with minimal bias.

Posterior predictive checks confirmed the model's ability to capture simulated parasite profiles.

The model can guide dosing decisions for Phase 3 trials of cipargamin.

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.

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