Researchers at the University of California San Diego have analyzed the genomes of hundreds of malaria parasites to pinpoint genetic variants likely linked to drug resistance. The study, published in science, could enable the use of machine learning to predict antimalarial drug resistance, helping scientists prioritize the most promising experimental treatments for development. This method may also be applied to predict resistance in other infectious diseases and even cancer.

Figure 1. Malaria Parasites

“We’ve created a roadmap for understanding antimalarial drug resistance across over a hundred different compounds,” said Elizabeth Winzeler, Ph.D., a professor at UC San Diego’s Skaggs School of Pharmacy and the Department of Pediatrics. “The findings will also be useful for other diseases, as many of the resistant genes we studied are shared across species.” Figure 1 shows Malaria Parasites.

Malaria, a mosquito-borne disease, affects hundreds of millions of people globally and remains a major health threat in tropical and subtropical areas. Despite progress in controlling it, malaria continues to be a leading cause of illness and death, particularly in Africa, where 95% of malaria-related deaths occur, according to the World Health Organization. Drug-resistant strains of Plasmodium falciparum, the malaria parasite, have caused first-line treatments to fail.

“There is an urgent need for new malaria treatments, but funding for research and drug development is limited,” said Winzeler, who also directs the Bill & Melinda Gates Foundation-funded Malaria Drug Accelerator. “However, the malaria research community is collaborative, and our study leverages these strengths to create a resource that will simplify identifying and prioritizing new treatments.”

The researchers studied the genomes of 724 malaria parasites, which were evolved in the lab to resist one of 118 different antimalarial compounds, including both established drugs and new experimental treatments. By analyzing patterns in mutations linked to resistance, they identified key genetic features, such as the physical locations of these mutations within genes, that can help predict which variants are most likely to contribute to resistance.

“Our goal is to use machine learning to identify compounds at high risk of resistance, which could streamline early drug development and accelerate treatments reaching clinical trials,” said Winzeler. “This study provides the data needed to develop these tools.”

The study also sheds light on how networks of genes work together to mediate resistance across different chemical classes of drugs, providing insights for finding resistance-resistant compounds, according to coauthor David Fidock, Ph.D., a microbiology and immunology professor at Columbia University.

While the findings are pivotal for antimalarial drug development, the researchers also emphasize that their approach is applicable to other diseases. This is because the genetic mechanisms behind drug resistance are similar across various pathogens and even human cells. For instance, many of the resistance-related mutations identified in P. falciparum were in a protein called PfMDR1, which transports substances, including drugs, away from their site of action. Humans have a counterpart to this protein, and mutations in it are a major cause of treatment resistance in cancer.

“The potential impact of this study is vast, extending beyond just malaria,” said Winzeler. “Studying malaria gave us the opportunity to compile this resource, and we hope it will transform the way we approach drug resistance in general, not just in malaria.”

Reference:

1. https://phys.org/news/2024-11-parasite-genome-analysis-approach-malaria.html
2. https://www.labmedica.com/molecular-diagnostics/articles/294803273new-approach-to-help-predict-drug-resistance-in-malaria-and-infectious-diseases.html
3. https://www.eurekalert.org/news-releases/1065641

Cite this article:

Priyadharshini S (2024), UC San Diego Study Identifies Key Genetic Variants Linked to Malaria Drug Resistance, with Implications for Broader Disease Research,Anathemas,pp. 270