The team analysed 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.
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.
No comments:
Post a Comment