RESEARCHERS have developed a mathematical model to help fight malaria by predicting genetic resistance to antimalarial drugs. Malaria is a preventable and curable disease; however, resistance to available antimalarial drugs causes a large amount of mortality and morbidity. Predicting resistance to drugs could help to manage one of the largest threats to global malaria control.
The research team used data from the Worldwide Antimalarial Resistance Network (WWARN) to track the prevalence of genetic markers indicating resistance. The model processes available data and fills in the gaps, making continuous predictions about the development of resistance, and considering both time and location.
“Health agencies can use this tool to understand when and where resistance to sulfadoxine-pyrimethamine (SP) is appropriate to use as part of preventative malaria treatments and where other antimalarial methods may need to be explored,” explained lead author Jennifer Flegg, University of Melbourne, Australia.
SP is an antimalaria drug that is frequently used in malaria prevention programmes across Africa. It is often implemented to create prophylaxis in infants and young children, and during pregnancy. However, developing resistance to SP is threatening prevention programmes.
“This study combines all of the available SP resistance data from the last two decades in a single model. It allows national malaria control programmes and researchers to get much-needed data on the degree of resistance in a given area in a given year. This allows us to understand better the impact of SP resistance on the effectiveness of these preventive interventions and determine if and when to consider alternative drugs for chemoprevention,” stated Feiko ter Kuile, Head of WWARN’s Malaria in Pregnancy Scientific Group.
The researchers hope that in the future data derived from this model can be used to guide health policies and reach the World Health Organization (WHO)’s target of eliminating malaria by 2030.