Parasite-Host Dynamics throughout Antimalarial Drug Development Stages Complicate the Translation of Parasite Clearance

Abstract

Ensuring continued success against malaria depends on a pipeline of new antimalarials. Antimalarial drug development utilizes pre-clinical murine and experimental human malaria infection studies to evaluate drug efficacy. A sequential approach is typically adapted, with results from each stage, informing the design of the next stage of development. The validity of this approach depends on confidence that results from murine malarial studies predict the outcome of clinical trials in humans. Parasite clearance rates following treatment are key parameters of drug efficacy. To investigate the validity of forward predictions, we developed a suite of mathematical models to capture parasite growth and drug clearance along the drug development pathway and estimated parasite clearance rates. When comparing the three infection experiments, we identified different relationships of parasite clearance with dose, and different maximum parasite clearance rates: in P. berghei-NMRI mouse infections we estimated a maximum parasite clearance rate of 0.2 [1/h]; in P. falciparum-SCID mouse infections 0.05 [1/h]; while in human volunteer infection studies with P. falciparum, we found a maximum parasite clearance rate of 0.12 [1/h] and 0.18 [1/h] after treatment with OZ439 and MMV048, respectively. Sensitivity analysis revealed that host-parasite driven processes account for up to 25% of variance in parasite clearance for medium-high doses of antimalarials. Although there are limitations in translating parasite clearance rates across these experiments, they provide insight into characterising key parameters of drug action and dose response, and assist in decision-making regarding dosage for further drug development.

Parasite Viability as a Measure of In Vivo Drug Activity in Preclinical and Early Clinical Antimalarial Drug Assessment

“…We also found that these estimates do not influence the clearance half-life of nonviable parasites, which is equal to 3.76 h (95% CI, 3.26, 4.34) and 7.08 h (95% CI, 6.19, 8.11) in humans and mice, respectively. The latter observation is consistent with observations of slower parasite clearance after drug treatment in the NSG mouse model compared with human infection ( 27 ). Together, these findings suggest that although parasite viability provides a number of consistent observations between the dynamics of artesunate activity in mice and humans, differences in these infection models remain, which could not be completely reconciled by assessing parasite viability.…”

SARS-CoV-2-specific CD4+ T cells are associated with long-term persistence of neutralizing antibodies

“…We fit the model to the data using a Bayesian hierarchical modeling method, which is a well-established method for estimating biological parameters and has been used in many quantitative studies. 36 – 39 Briefly, Bayesian inference is a statistical method to update our existing knowledge about the distribution of the biological parameter of interest (referred to as the prior distribution) by integrating the information from a set of novel data (by a likelihood function). The updated distribution of the parameter is called the posterior distribution, which is what we would like to obtain to provide an estimate for the parameter of interest θ.…”

Combined Transcriptome and Proteome Profiling for Role of pfEMP1 in Antimalarial Mechanism of Action of Dihydroartemisinin

“…Second, because the mechanism of action of the drug in the body is more complex than that in vitro , the in vitro single drug sensitivity test cannot reflect the synergy or addition of the drugs in the combined chemotherapy. Third, interactions of parasite-host and antimalarial drug-host are vital to the efficacy of the drugs during the in vivo study ( 40 – 43 ).…”