Washington: A new software has predicted MRSA's response to new drug before it is tested on patients.
Researchers at Duke University used software they developed to predict a constantly-evolving infectious bacterium's countermoves to one of these new drugs ahead of time, before the drug is even tested on patients.
Co-author Bruce Donald, a professor of computer science and biochemistry at Duke, said that this gives them a window into the future to see what bacteria will do to evade drugs that they design before a drug is deployed.
The team used their program to identify the genetic changes that will allow methicillin-resistant Staphylococcus aureus, or MRSA, to develop resistance to a class of new experimental drugs that show promise against the deadly bug.
When the researchers treated live bacteria with the new drug, two of the genetic changes actually arose, just as their algorithm predicted.
Duke graduate student Pablo Gainza-Cirauqui, who co-authored the paper, asserted that if they could somehow predict how bacteria might respond to a particular drug ahead of time, they could change the drug, or plan for the next one, or rule out therapies that were unlikely to remain effective for long.
A research team led by Donald at Duke and Amy Anderson at the University of Connecticut used a protein design algorithm they developed, called OSPREY, to identify DNA sequence changes in the bacteria that would enable the resulting protein to block the drug from binding, while still performing its normal work within the cell.
The team focused on a new class of experimental drugs that work by binding and inhibiting a bacterial enzyme called dihydrofolate reductase (DHFR), which plays an essential role in building DNA and other processes. The drugs, called propargyl-linked antifolates, show promise as a treatment for MRSA infections but have yet to be tested in humans.
When the scientists treated MRSA with the new drugs and sequenced the bacteria that survived, more than half of the surviving colonies carried the predicted mutation that conferred the greatest resistance -- a tiny change that reduced the drugs' effectiveness by 58-fold.
The study is published in the journal Proceedings of the National Academy of Sciences.