A software predicting bacterial behavior when treated with certain drugs was developed by scientists at Duke University. As medicine evolves, so do drug-resistant bacterial strains. Nowadays, even simple infections such as pneumonia or urinary tract infections can prove to be more complicated than initially thought.
This leads to the necessity of designing newer more effective drugs. But before selling them on the market, researchers must first be certain that the new drug will have an appropriate lifespan.
In order to achieve these goals, scientists at Duke University developed an algorithm called OSPRSEY that predicts the behavior methicillin-resistant Staphylococcus aureus, or MRSA towards a newly designed drug, before it gets tested on patients.
Over the course of years, the rate of infections caused by Staphylococcus aureus resistant to drugs has gradually increased from 2 % in 1975 to 29% in 1991 reaching 55% nowadays – meaning more than 11,000 deaths only in the U.S. More people die from MRSA-related infections than due to HIV.
With the help of this software, scientists could identify what genetic mutations take place in order for the bacteria to become resistant to the new drug. Their research was published in Proceedings of the National Academy of Sciences.
After the team put the live strain of bacteria in a medium enriched with that certain substance, the bacteria developed two of the genetic mutations predicted by the software.
The program is based on ‘a protein design algorithm’ that predicts mutations by searching for sequences of modified DNA. These are the components that prevent the drug from binding, allowing the bacteria to continue its normal performance.
Through this computational approach scientists could learn how to intercept bacterial development and use the information while a new compound is still in the design phase.
As Duke graduate student Pablo Gainza-Cirauqui co-author of the paper explained:
“If we can somehow predict how bacteria might respond to a particular drug ahead of time, we can change the drug, or plan for the next one, or rule out therapies that are unlikely to remain effective for long.”
The researchers believe the software could be further used in predicting mutations not only in bacteria but also in cells or viruses, thus developing new, more effective treatments for influenza, HIV or cancer. This would be a huge advantage as it is more difficult to raise and study resistant cells or strains in the laboratory than it is with bacteria.
The program can be freely used by any researcher worldwide.
Image Source: University of Cambridge
