A mechanistic model quantifies artemisinin-induced parasite growth retardation in blood-stage Plasmodium falciparum infection

Related Staff:

A mechanistic model quantifies artemisinin-induced parasite growth retardation in blood-stage Plasmodium falciparum infection. Pengxing Caoa, Nectarios Klonisb, Sophie Zaloumisc, David S. Khouryd, Deborah Cromerd, Miles P. Davenportd, Leann Tilleyb, Julie A. Simpsonc, James M. McCaw ace,. https://doi.org/10.1016/j.jtbi.2017.07.017


Abstract

Falciparum malaria is a major parasitic disease causing widespread morbidity and mortality globally. Artemisinin derivatives—the most effective and widely-used antimalarials that have helped reduce the burden of malaria by 60% in some areas over the past decade—have recently been found to induce growth retardation of blood-stage Plasmodium falciparum when applied at clinically relevant concentrations. To date, no model has been designed to quantify the growth retardation effect and to predict the influence of this property on in vivo parasite killing. Here we introduce a mechanistic model of parasite growth from the ring to trophozoite stage of the parasite’s life cycle, and by modelling the level of staining with an RNA-binding dye, we demonstrate that the model is able to reproduce fluorescence distribution data from in vitro experiments using the laboratory 3D7 strain. We quantify the dependence of growth retardation on drug concentration and identify the concentration threshold above which growth retardation is evident. We estimate that the parasite life cycle is prolonged by up to 10 hours. We illustrate that even such a relatively short delay in growth may significantly influence in vivo parasite dynamics, demonstrating the importance of considering growth retardation in the design of optimal artemisinin-based dosing regimens.

a School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia

b Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, Victoria, Australia

c Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

d Infection Analytics Program, Kirby Institute, UNSW Australia, Kensington, New South Wales, Australia

e Modelling and Simulation, Infection and Immunity Theme, Murdoch Childrens Research Institute, The Royal Children’s Hospital, Parkville, Victoria, Australia

Also in this section