Researchers have undertaken a comprehensive analysis of the genomes of numerous malaria parasites to identify genetic variants that may lead to drug resistance.
A team at the University of California San Diego examined the genomes of several hundred malaria parasites to find out which genetic variations are most likely to result in resistance to drugs. Their results, published in Science, have the potential to assist scientists in employing machine learning to foresee antimalarial drug resistance and to more effectively identify promising experimental treatments that require further development. This methodology might also be applicable for predicting treatment resistance in other infectious diseases and even in cancer.
“Much of the research into drug resistance focuses on one chemical agent at a time. However, we have developed a comprehensive framework for understanding antimalaria drug resistance across over a hundred different compounds,” remarked Elizabeth Winzeler, Ph.D., a professor at UC San Diego’s Skaggs School of Pharmacy and Pharmaceutical Sciences and the Department of Pediatrics at UC San Diego School of Medicine. “These findings will also have implications for other diseases since many of the resistance genes we analyzed are preserved across various species.”
Malaria, transmitted by mosquitoes, significantly impacts hundreds of millions of people around the globe and poses a serious public health challenge, especially in tropical and subtropical areas. Despite notable advancements in controlling malaria, it remains one of the foremost causes of illness and death, particularly in Africa, where the World Health Organization states that 95% of malaria fatalities occur. The proliferation of drug-resistant strains of Plasmodium falciparum, the causative agent of malaria, has led to repeated failures of first-line treatments.
“The demand for new and more effective treatments for malaria is critical, yet funding for malaria research and drug development is severely limited,” stated Winzeler, who also serves as the director of the Malaria Drug Accelerator funded by the Bill and Melinda Gates Foundation. “Nevertheless, the malaria research community is well-organized and highly collaborative, allowing our study to harness these strengths to create a valuable resource that will simplify the process of discovering and prioritizing new treatments for malaria.”
The research team examined the genomes of 724 laboratory-evolved malaria parasites that had developed resistance to one of 118 different antimalarial drugs, covering both established therapies and new experimental compounds. By analyzing patterns in the mutations linked to resistance, they were able to discover distinct characteristics of these genetic variants, such as their locations within genes, which could aid in predicting which variations might lead to drug resistance.
“Our primary objective is to utilize machine learning to better understand which compounds are most susceptible to resistance, enabling us to expedite the early stages of drug development and bring treatments to clinical trials more swiftly,” said Winzeler. “This study provides the essential data to train these innovative tools.”
“The research also reveals how gene networks interact to mediate resistance across various chemical classes and sets a guiding framework for our quest for compounds that are resistant to resistance,” adds David Fidock, Ph.D., a co-author and professor of microbiology and immunology at Columbia University Vagelos College of Physicians and Surgeons.
While these findings hold tremendous promise for the creation of new antimalarial medications, the researchers point out that their methodology could have relevance for other diseases as well. This is due to the genetic mechanisms that fuel drug resistance being similar across various pathogens and even within human cells. For instance, several mutations responsible for resistance in the study were sourced from a protein in P. falciparum known as PfMDR1, which facilitates the transport of substances within the cell, including the expulsion of drugs from their action sites. PfMDR1 has an identical counterpart in humans, and mutations in its human equivalent significantly contribute to treatment resistance in cancer.
“The potential ramifications of this study are vast and reach far beyond a single illness,” Winzeler concluded. “Investigating malaria provided us the chance to compile this resource, and we anticipate that these findings will transform how we approach drug resistance across the board, not limited to malaria alone.”
