Robust Algorithm for Systematic Classification of Malaria Late Treatment Failures as Recrudescence or Reinfection Using Microsatellite Genotyping

Abstract

Routine therapeutic efficacy monitoring to measure the response to antimalarial treatment is a cornerstone of malaria control. To correctly measure drug efficacy, therapeutic efficacy studies require genotyping parasites from late treatment failures to differentiate between recrudescent infections and reinfections. However, there is a lack of statistical methods to systematically classify late treatment failures from genotyping data. A Bayesian algorithm was developed to estimate the posterior probability of late treatment failure being the result of a recrudescent infection from microsatellite genotyping data. The algorithm was implemented using a Monte Carlo Markov chain approach and was used to classify late treatment failures using published microsatellite data from therapeutic efficacy studies in Ethiopia and Angola. The algorithm classified 85% of the Ethiopian and 95% of the Angolan late treatment failures as either likely reinfection or likely recrudescence, defined as a posterior probability of recrudescence of <0.1 or >0.9, respectively. The adjusted efficacies calculated using the new algorithm differed from efficacies estimated using commonly used methods for differentiating recrudescence from reinfection. In a high-transmission setting such as Angola, as few as 15 samples needed to be genotyped in order to have enough power to correctly classify treatment failures. Analysis of microsatellite genotyping data for differentiating between recrudescence and reinfection benefits from an approach that both systematically classifies late treatment failures and estimates the uncertainty of these classifications. Researchers analyzing genotyping data from antimalarial therapeutic efficacy monitoring are urged to publish their raw genetic data and to estimate the uncertainty around their classification. P rompt and effective treatment for malaria is one of the pillars of malaria control, the global effort to reduce the burden of this parasitic disease responsible for 627,000 deaths annually (1). However, the malaria parasite has historically shown a tendency to develop resistance to antimalarial drugs. The emergence of resistance to virtually all antimalarial drugs, including quinine, chloroquine, sulfadoxine-pyrimethamine, mefloquine, and recently, artemisinins, has been documented (2-4). As a result, the World Health Organization (WHO) recommends that all countries in which malaria is endemic routinely retest the efficacy of first-and second-line antimalarials to monitor for early signs of resistance and to aid in the development of revised treatment guidelines (5).The main tool for surveillance of antimalarial resistance is the biennial in vivo therapeutic efficacy study that follows a standard WHO protocol (5). These are typically small clinical outcome studies that administer directly observed therapy of an antimalarial agent to patients presenting to health facilities with uncomplicated malaria. Patients not adequately clearing the infection within 4 days are classified as early treatment failures. If cl...

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

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Ensemble modeling highlights importance of understanding parasite-host behavior in preclinical antimalarial drug development

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Efficacy and safety of artesunate-amodiaquine and artemether-lumefantrine and prevalence of molecular markers associated with resistance, Guinea, 2016

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