Due to the rise of drug-resistant bacteria even common infections that were easily controlled for decades -- such as pneumonia or urinary tract infections -- are proving trickier to treat with standard antibiotics.
New drugs are desperately needed, but so are ways to maximize the effective lifespan of these drugs. Experiments are being conducted to predict superbugs' response to new drugs.
Researchers from 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.
In a study appearing in the journal Proceedings of the National Academy of Sciences, 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.
"This gives us a window into the future to see what bacteria will do to evade drugs that we design before a drug is deployed," Bruce Donald, co-author of the study, said in a statement.
Developing pre-emptive strategies while the drugs are still in the design phase will give scientists a head start on the next line of compounds that will be effective despite the germ's resistance mutations.
"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," said Duke graduate student Pablo Gainza-Cirauqui, who co-authored the paper.
Because bacteria reproduce so rapidly -- growing and dividing from one cell to two in less than an hour -- drug-resistant bacteria are constantly evolving, and researchers have to constantly develop new ways to kill them.
The model could also be expanded to anticipate a microbe's response more than one move ahead.
"We might even be able to coax a pathogen into developing mutations that enable it to evade one drug, but that then make it particularly susceptible to a second drug, like a one-two punch," Donald said.
Their computational approach could be especially useful for forecasting drug resistance mutations in other diseases, such as cancer, HIV and influenza, where raising resistant cells or strains in the lab is more difficult to do than with bacteria, according to the researchers.
The software they developed, called OSPREY, is open-source and freely available for any researcher to use.
