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Antimalarial drugs have long half-lives, so clinical trials to monitor their efficacy require long periods of follow-up to capture drug failure that may become patent only weeks after treatment. Reinfections often occur during follow-up, so robust methods of distinguishing drug failures (recrudescence) from emerging new infections are needed to produce accurate failure rate estimates. Molecular correction aims to achieve this by comparing the genotype of a patient's pretreatment (initial) blood sample with that of any infection that occurs during follow-up, with matching genotypes indicating drug failure. We use an in silico approach to show that the widely used match-counting method of molecular correction with microsatellite markers is likely to be highly unreliable and may lead to gross under- or overestimates of the true failure rates, depending on the choice of matching criterion. A Bayesian algorithm for molecular correction was previously developed and utilized for analysis of in vivo efficacy trials. We validated this algorithm using in silico data and showed it had high specificity and generated accurate failure rate estimates. This conclusion was robust for multiple drugs, different levels of drug failure rates, different levels of transmission intensity in the study sites, and microsatellite genetic diversity. The Bayesian algorithm was inherently unable to accurately identify low-density recrudescence that occurred in a small number of patients, but this did not appear to compromise its utility as a highly effective molecular correction method for analyzing microsatellite genotypes. Strong consideration should be given to using Bayesian methodology to obtain accurate failure rate estimates during routine monitoring trials of antimalarial efficacy that use microsatellite markers.

Drug efficacy trials monitor the continued efficacy of front-line drugs against falciparum malaria. Overestimating efficacy results in a country retaining a failing drug as first-line treatment with associated increases in morbidity and mortality, while underestimating drug effectiveness leads to removal of an effective treatment with substantial practical and economic implications. Trials are challenging: they require long durations of follow-up to detect drug failures, and patients are frequently reinfected during that period. Molecular correction based on parasite genotypes distinguishes reinfections from drug failures to ensure the accuracy of failure rate estimates. Several molecular correction "algorithms" have been proposed, but which is most accurate and/or robust remains unknown. We used pharmacological modeling to simulate parasite dynamics and genetic signals that occur in patients enrolled in malaria drug clinical trials. We compared estimates of treatment failure obtained from a selection of proposed molecular correction algorithms against the known "true" failure rate in the model. Our findings are as follows. (i) Molecular correction is essential to avoid substantial overestimates of drug failure rates. (ii) The current WHO-recommended algorithm consistently underestimates the true failure rate. (iii) Newly proposed algorithms produce more accurate failure rate estimates; the most accurate algorithm depends on the choice of drug, trial follow-up length, and transmission intensity. (iv) Long durations of patient follow-up may be counterproductive; large numbers of new infections accumulate and may be misclassified, overestimating drug failure rate. (v) Our model was highly consistent with existing in vivo data. The current WHO-recommended method for molecular correction and analysis of clinical trials should be reevaluated and updated., (Copyright © 2019 Jones et al.)

Genotyping of allelic variants of Plasmodium falciparum merozoite surface proteins 1 and 2 ( msp-1 and msp-2 ), and the glutamate-rich protein is the gold standard for distinguishing reinfections from recrudescences in antimalarial drug trials. We compared performance of the recently developed 24-single-nucleotide polymorphism (SNP) Barcoding Assay against msp-1 and msp-2 genotyping in a cluster-randomized effectiveness trial of artemether-lumefantrine and dihydroartemisinin-piperaquine in Malawi. Rates of recrudescence and reinfection estimated by the two methods did not differ significantly (Fisher's exact test; P = 0.887 and P = 0.768, respectively). There was a strong agreement between the two methods in predicting treatment outcomes and resolving the genetic complexity of malaria infections in this setting. These results support the use of this SNP assay as an alternative method for correcting antimalarial efficacy/effectiveness data.

There is considerable concern that malaria parasites are starting to evolve resistance to the current generation of antimalarial drugs, the artemisinin-based combination therapies (ACTs). We use pharmacological modeling to investigate changes in ACT effectiveness likely to occur if current regimens are extended from 3 to 5 days or, alternatively, given twice daily over 3 days. We show that the pharmacology of artemisinins allows both regimen changes to substantially increase the artemisinin killing rate. Malaria patients rarely contain more than 10(12) parasites, while the standard dosing regimens allow approximately 1 in 10(10) parasites to survive artemisinin treatment. Parasite survival falls dramatically, to around 1 in 10(17) parasites if the dose is extended or split; theoretically, this increase in drug killing appears to be more than sufficient to restore failing ACT efficacy. One of the most widely used dosing regimens, artemether-lumefantrine, already successfully employs a twice-daily dosing regimen, and we argue that twice-daily dosing should be incorporated into all ACT regimen design considerations as a simple and effective way of ensuring the continued long-term effectiveness of ACTs., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)

Artemisinin-based combination therapies (ACTs) are currently the first-line drugs for treating uncomplicated falciparum malaria, the most deadly of the human malarias. Malaria parasite clearance rates estimated from patients' blood following ACT treatment have been widely adopted as a measure of drug effectiveness and as surveillance tools for detecting the presence of potential artemisinin resistance. This metric has not been investigated in detail, nor have its properties or potential shortcomings been identified. Herein, the pharmacology of drug treatment, parasite biology, and human immunity are combined to investigate the dynamics of parasite clearance following ACT. This approach parsimoniously recovers the principal clinical features and dynamics of clearance. Human immunity is the primary determinant of clearance rates, unless or until artemisinin killing has fallen to near-ineffective levels. Clearance rates are therefore highly insensitive metrics for surveillance that may lead to overconfidence, as even quite substantial reductions in drug sensitivity may not be detected as lower clearance rates. Equally serious is the use of clearance rates to quantify the impact of ACT regimen changes, as this strategy will plausibly miss even very substantial increases in drug effectiveness. In particular, the malaria community may be missing the opportunity to dramatically increase ACT effectiveness through regimen changes, particularly through a switch to twice-daily regimens and/or increases in artemisinin dosing levels. The malaria community therefore appears overreliant on a single metric of drug effectiveness, the parasite clearance rate, that has significant and serious shortcomings., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)

We describe here the results of an analysis of Plasmodium falciparum multidrug resistance protein 1 (pfmdr1) gene copy number from 440 field isolates from Papua New Guinea. No multiple copies of the gene were found, which corresponds to the lack of usage of mefloquine. These data extend regional knowledge about the distribution of multidrug-resistant P. falciparum.