Intrahost modeling of artemisinin resistance in Plasmodium falciparum

Abstract

Artemisinin-resistant Plasmodium falciparum malaria has emerged in western Cambodia. Resistance is characterized by prolonged in vivo parasite clearance times (PCTs) following artesunate treatment. The biological basis is unclear. The hypothesis that delayed parasite clearance results from a stage-specific reduction in artemisinin sensitivity of the circulating young asexual parasite ring stages was examined. A mathematical model was developed, describing the intrahost parasite stage-specific pharmacokinetic-pharmacodynamic relationships. Model parameters were estimated using detailed pharmacokinetic and parasite clearance data from 39 patients with uncomplicated falciparum malaria treated with artesunate from Pailin (western Cambodia) where artemisinin resistance was evident and 40 patients from Wang Pha (northwestern Thailand) where efficacy was preserved. The mathematical model reproduced the observed parasite clearance for each patient with an accurate goodness of fit (rmsd: 0.03-0.67 in log(10) scale). The parameter sets that provided the best fits with the observed in vivo data consist of a highly conserved concentration-effect relationship for the trophozoite and schizont parasite stages, but a variable relationship for the ring stages. The model-derived assessment suggests that the efficacy of artesunate on ring stage parasites is reduced significantly in Pailin. This result supports the hypothesis that artemisinin resistance mainly reflects reduced ring-stage susceptibility and predicts that doubling the frequency of dosing will accelerate clearance of artemisinin-resistant parasites.

Fig. 1.

Diagram illustrating the model structure, its inputs, and its outputs. An initial population of parasites (Left) is distributed normally over age and moves to the right over time. The concentration of the drug over time (Center) is incorporated into the model by assuming that subsequent doses have the same concentration profile as the initial measured dose for each patient. The model then combines a dose–effect relationship with the drug concentration to determine how many parasites at each stage are killed by the drug at each time point. The total number of circulating parasites predicted by the model is fitted to the observed parasite density data (Center) and the resistance index for each stage is inferred from the dose–effect curve (Right).

Fig. 2.

The example results from fitting the model to the parasite clearance data in the form of predicted parasite load (log₁₀ scale) over time (red line) with observed parasite load over time (blue dots). The median of the rmsd of all 79 individual patient fits was 0.34 (range: 0.03–0.67) (on a log₁₀ scale). See Fig. S1 for the complete list of the results.

Fig. 3.

The Bland–Altman plot shows the parasite clearance rates from the observed parasite count data were in agreement with the modeled data. The range of agreement was defined as mean ±1.96 SD. Parasite clearance for each patient was expressed as the slope of the linear portion of the log parasitemia vs. time relationship.

Fig. 4.

The inferred in vivo concentration–effect curves of each asexual stage generated from the best fit parameters for each patient from (A) Pailin (western Cambodia) and (B) Wang Pha (northwestern Thailand).

Fig. 5.

The predicted average parasite clearance time with different artesunate dose regimens.