Identifying artemisinin resistance from parasite clearance half-life data with a simple Shiny web application.

Sai Thein Than Tun,Sompob Saralamba,Lisa J White,Olivier Celhay,Xin Hui Chan,Tom Fieldman,Arjen M Dondorp,Yoel Lubell,Kyaw Myo Tun,Michelle Louise Gatton

doi:10.1371/journal.pone.0177840

Abstract

The emergence of artemisinin-resistant Plasmodium falciparum malaria is a major threat to malaria elimination. New tools for supporting the surveillance of artemisinin resistance are critical for current and future malaria control and elimination strategies. We have developed an open-access, user-friendly, web-based tool to analyse parasite clearance half-life data of P. falciparum infected patients after treatment with artemisinin derivatives, so that resistance to artemisinin can be identified. The tool can be accessed at bit.ly/id_artemisinin_resistance.

Highlights

The prospect of malaria elimination is under threat due to the emergence of parasites resistant to artemisinin derivatives, the most efficacious drugs against Plasmodium falciparum malaria [1]
The therapeutic efficacy of artemisinin-based combination therapy (ACT) has to be monitored every two years in order to respond to resistance [2]
White et al [3] developed a model to analyse data on parasite half-life as distributions of artemisinin-sensitive and artemisinin-resistant populations, which could help in the surveillance of artemisinin resistance

Summary

Introduction

The prospect of malaria elimination is under threat due to the emergence of parasites resistant to artemisinin derivatives, the most efficacious drugs against Plasmodium falciparum malaria [1]. White et al [3] developed a model to analyse data on parasite half-life as distributions of artemisinin-sensitive and artemisinin-resistant populations, which could help in the surveillance of artemisinin resistance. The source code of the model, developed in the R programming language[4] by White et al, is available on Github [5]. Interested parties have been using the model to analyse their own parasite half-life data. Until now, this has been limited to modellers proficient in the R programming language[4].

Methods and implementation

Results and discussion

Limitations