By Catharine Paddock PhD

The rise of drug-resistant bacteria - such as MRSA - is making it increasingly difficult to control even common infections like pneumonia or urinary tract infections with standard antibiotics. After repeated exposure, the bugs mutate into strains that are immune to the drugs that once killed them.

There is clearly a desperate need for new drugs to fight these superbugs. But there is also another option - to extend the useful life of a drug. Now, researchers have developed a computer algorithm that can help in this area.

Imagine the war against a superbug as a chess game, with each move that your opponent makes being a mutation in the superbug that makes it more drug-resistant. 

To stand a good chance of winning, it helps to anticipate your opponent's most likely counter-moves.

Now, a team of researchers - including members from Duke University in Durham, NC - has developed a computer algorithm that stands a good chance of beating a superbug at its own game.

The software - called OSPREY - predicts the most likely mutations that a bug develops in response to a new drug before the drug is even given to patients.

Writing in the Proceedings of the National Academy of Sciences, the team describes how they tested OSPREY with the superbug MRSA (methicillin-resistant Staphylococcus aureus). 

The researchers programmed the algorithm to identify the genetic changes that MRSA would have to undergo in order to become resistant to a promising new class of experimental drug. And when they exposed MRSA to the new drugs, they found some of the genetic changes the software had predicted actually arose.

"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," says author Bruce Donald, a professor of computer science and biochemistry at Duke.

The team hopes the approach they are developing will give drug designers a head start in the race against superbugs, as co-author and Duke graduate student Pablo Gainza-Cirauqui explains:

"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."

Resistant forms of Staphylococcus aureus now kill 11,000 people in the US every year - more than HIV. In 1975, around 2% of infections caused by the bacterium were resistant to treatment - rising to 29% in 1991 - and now the proportion is 55%.

Depending on the drug, it can take up to 20 years for resistant strains to emerge. Sometimes it only takes 1 year.

Ability to anticipate new mutations beats searching 'libraries' of known mutations

The team believes approaches like OSPREY beat the current method where scientists have to look up "libraries" of previously observed resistance mutations - an approach that is not necessarily satisfactory for predicting future mutations. Prof. Donald explains:

"With a new drug, there is always the possibility that the organism will develop different mutations that had never been seen before. This is what really worries physicians."

OSPREY - which stands for Open Source Protein REdesign for You - is based on a protein design algorithm. It identifies changes to DNA sequences in the bacteria that would enable the resulting protein to block the drug while still being able to work normally.

The team tested OSPREY with a new class of drugs called propargyl-linked antifolates that attack a bacterial enzyme called dihydrofolate reductase (DHFR), used for building DNA and other tasks. The drugs - still to be tested in humans - are showing promise as a new treatment for MRSA infections.

Using OSPREY, the team came up with a ranked list of possible mutations. They picked out four - none of which had been seen before.

One predicted mutation reduced drug effectiveness by 58%

When they treated MRSA with the new drugs, they found more than half of the bacteria that survived carried the mutation they predicted would give the organism the greatest amount of resistance: a tiny change in the bacterial DNA that reduced the effectiveness of the new drugs by 58%.

"The fact that we actually found the new predicted mutations in bacteria is very exciting," Prof. Donald says, adding that the approach could be expanded to anticipate the bug's responses 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."

The team is now enhancing OSPREY to predict resistance mutations to drugs designed to treat E. coli and Enterococcus infections.

They believe OSPREY will be useful for predicting drug resistance in cancer, HIV, flu and other diseases where culturing resistant strains is harder than it is with bacteria.

Prof. Donald and colleagues are developing OSPREY in open source format so it is freely available for any researcher to use.

In September 2014, Medical News Today learned about a study that showed how an  old drug may lead to a potential new class of antibiotics . The study showed that lamotrigine - currently used as an anticonvulsant - can inhibit the assembly of ribosomes in bacteria.

Source: www.medicalnewstoday.com