Improving Methods for Analyzing Antimalarial Drug Efficacy Trials: Molecular Correction Based on Length-Polymorphic Markers msp-1 , msp-2 , and glurp

Abstract

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.

The duration of chemoprophylaxis against malaria after treatment with artesunate-amodiaquine and artemether-lumefantrine and the effects of pfmdr1 86Y and pfcrt 76T: a meta-analysis of individual patient data

“…We also did a sensitivity analysis to explore the possibility that some of the recrudescences identified by this PCR correction method could have been misclassified as reinfections. Recent work suggests that the percentage of patients experiencing recrudescence may be around 1-3% higher than estimated by standard PCR correction [64][65][66], with this error being relatively constant across transmission settings. We therefore also repeated our analysis after reclassifying some reinfections in each trial as recrudescences, sampling a number that would achieve a 3% higher recrudescence rate overall.…”

“…We weighted the sampling by timing of recurrent parasitemia in each patient as in Fig. 5 of [66], i.e., to allow for the fact that recrudescences are more likely to occur early during follow-up (see also Additional file 4:…”

“…Therefore, for parsimony, we did not include this factor in the final result. In a separate sensitivity analysis, carried out to allow for possible mistakes in PCR correction based on [65,66], we reclassified a proportion of reinfections as recrudescences so that the total failure rate (% patients with recrudescence) in each trial arm increased by 3%. This caused only a slight increase in the estimated median duration of protection, to 15.6 days (95% CI 13.0-18.9) after AS-AQ and 13.8 days (95% CI 11.3-17.1) after AL (see also Additional file 4: Figure S3 for details).…”

A Computer Modelling Approach To Evaluate the Accuracy of Microsatellite Markers for Classification of Recurrent Infections during Routine Monitoring of Antimalarial Drug Efficacy

“…It is likely to affect recrudescent and re-infecting clones equally such that we would not expect it to alter how recurrent infections are classified. We did not model parasite sequestration (see (31) for justification). The output of this PK/PD model was, for each patient, the exact number of parasites of each malaria clone (be that clone an initial infection or a reinfection) at each time-step of the model (days); see figure S1 [SI] for an example.…”

Genetic Diversity and Complexity of Plasmodium Falciparum Infections in the Microenvironment Among Siblings of the Same Household in North-central Nigeria

“…The genetic diversity and complexity of P. falciparum infections is to a very large extent an important indicator of malaria transmission intensity in a region and is a very useful marker for assessing naturallyacquired anti-malarial immunity as well as the impacts of intervention programmes [11,[33][34][35][36][37].…”