Study of parasite genomes may predict malaria drug resistance

Analysing malaria parasite genomes may usher new and more effective treatments for the deadly mosquito-borne disease and also help predict drug resistance, according to a study.

Researchers at the University of California-San Diego analysed the genomes of hundreds of malaria parasites. The new approach helped them determine which genetic variants are most likely to confer drug resistance.

This will enable scientists to predict antimalarial drug resistance by using advanced technology like machine learning.

While previous drug resistance research can only look at one chemical agent at a time, the new study “creates a roadmap for understanding antimalarial drug resistance across more than a hundred different compounds”, said Elizabeth Winzeler, Professor at UC San Diego.

The approach, published in the journal Science, could also help predict treatment resistance in other infectious diseases and even cancer.

It is because “many of the resistant genes we studied are conserved across different species”, she added.

Malaria affects hundreds of millions of people worldwide and is a major public health threat in many tropical and subtropical regions.

Even though there has been significant progress toward controlling the disease, malaria continues to be a leading cause of morbidity and mortality.

One major reason is the spread of drug-resistant strains of Plasmodium falciparum, the parasite that causes malaria. It has repeatedly rendered first-line drugs inefficient.

For the study, the team analysed the genomes of 724 malaria parasites that evolved in the lab to resist one of 118 different antimalarial compounds. This included both established treatments and new experimental agents.

The team checked for patterns in the mutations that were associated with drug resistance. They found unique features of these genetic variants, such as their physical location within genes, that could be used to predict which variations are likely to contribute to drug resistance.