Clinical and molecular surveillance of artemisinin resistant falciparum malaria in Myanmar (2009–2013)

Abstract Background Emergence of artemisinin-resistant malaria in Southeast Asian countries threatens the global control of malaria. Although K13 kelch propeller has been assessed for artemisinin resistance molecular marker, most of the mutations need to be validated. In this study, artemisinin resistance was assessed by clinical and molecular analysis, including k13 and recently reported markers, pfarps10, pffd and pfmdr2. Methods A prospective cohort study in 1160 uncomplicated falciparum patients was conducted after treatment with artemisinin-based combination therapy (ACT), in 6 sentinel sites in Myanmar from 2009 to 2013. Therapeutic efficacy of ACT was assessed by longitudinal follow ups. Molecular markers analysis was done on all available day 0 samples. Results True recrudescence treatment failures cases and day 3 parasite positivity were detected at only the southern Myanmar sites. Day 3 positive and k13 mutants with higher prevalence of underlying genetic foci predisposing to become k13 mutant were detected only in southern Myanmar since 2009 and comparatively fewer mutations of pfarps10, pffd, and pfmdr2 were observed in western Myanmar. K13 mutations, V127M of pfarps10, D193Y of pffd, and T448I of pfmdr2 were significantly associated with day 3 positivity (OR: 6.48, 3.88, 2.88, and 2.52, respectively). Conclusions Apart from k13, pfarps10, pffd and pfmdr2 are also useful for molecular surveillance of artemisinin resistance especially where k13 mutation has not been reported. Appropriate action to eliminate the resistant parasites and surveillance on artemisinin resistance should be strengthened in Myanmar. Trial registration This study was registered with ClinicalTrials.gov, identifier NCT02792816.

Abstract licence: CC BY-NC-ND

• Association of mutations in the Plasmodium falciparum Kelch13 gene (Pf3D7_1343700) with parasite clearance rates after artemisinin-based treatments—a WWARN individual patient data meta-analysis · 2019

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