# 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.

## Key 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.