Researchers have created a mathematical model to predict genetic resistance to antimalarial drugs in Africa to administer considered one of the largest threats to global malarial control.

Malaria is a life-threatening disease brought on by parasites and spread to humans through infected mosquitos. It’s preventable and curable, yet resistance to current antimalarial drugs is causing avoidable lack of life. The World Health Organisation estimated there have been 241 million cases of malaria worldwide in 2020, with greater than 600,000 deaths.

In research published today in PLOS Computational Biology, a world research team used data from the WorldWide Antimalarial Resistance Network (WWARN), a world, scientifically independent collaboration, to map the prevalence of genetic markers that indicate resistance to Plasmodium falciparum – the parasite that causes malaria.

Lead writer Associate Professor Jennifer Flegg from the University of Melbourne said malaria has devastating impacts on lower-income countries and effective treatment is essential to elimination.

“The antimalarial drug sulfadoxine-pyrimethamine (SP) is often utilized in various preventative malaria treatment programs in Africa, particularly for infants, young children and while pregnant. But we all know its efficacy as a treatment is threatened in areas where resistance to SP is high,” Associate Professor Flegg said.

“The statistical mapping tool we now have developed is critical for health organisations to grasp the spread of antimalarial resistance. The model takes in the info that is accessible and fills within the gaps by making continuous predictions in space and time.

“Health agencies can use this tool to grasp when and where SP is suitable to make use of as part preventive malaria treatments and where other antimalarial methods may should be explored.”

Professor Karen Barnes, Head of WWARN Pharmacology and Elimination, said there may be a rapidly increasing need for malaria chemoprevention (drugs that prevent malaria infections), but there are limited treatment options available.

This timely evidence of the extent of SP resistance across Africa will help to tell where SP preventive treatment, alone or together with other antimalarials, could be almost definitely to have the best impact.”

Professor Karen Barnes, Head of WWARN Pharmacology and Elimination

Professor Feiko ter Kuile, Head of WWARN’s Malaria in Pregnancy Scientific Group, said the updated model of SP resistance in Africa was long overdue.

“Numerous the resistance mapping has understandably focused on the emerging resistance to the artemisinin-based antimalarials used for treating malaria. Increasing resistance of the malaria parasite to sulfadoxine-pyrimethamine in Africa has been a priority for several many years. Nevertheless, easily accessible resistance data was lacking,” Professor ter Kuile said.

“This study combines all of the available SP resistance data from the last twenty years 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 12 months. This permits us to grasp higher the impact of sulfadoxine-pyrimethamine resistance on the effectiveness of those preventive interventions and determine if and when to think about alternative drugs for chemoprevention.”

Associate Professor Flegg said, “This research tool should help guide health policies that may bring the World Health Organisation’s ambitious goal of eliminating malaria by 2030 one step closer.”

The team included researchers from the University of Melbourne, the University of Oxford, Johnson C. Smith University, the University of Cape Town and the University of Witwatersrand.

The research received funding from the Bill & Melinda Gates Foundation, the Smith Institute for Applied Research, and the Australian Research Council.

Source:

Journal reference:

Flegg, J.A., et al. (2022) Spatiotemporal spread of Plasmodium falciparum mutations for resistance to sulfadoxine-pyrimethamine across Africa, 1990 – 2020. PLOS Computational Biology. doi.org/10.1371/journal.pcbi.1010317.