A new tool to assess the risk of antibiotic evolution

Bacteria have dangerously evolved to destroy many of the drugs used to kill them
.
As a result, the growing antibiotic resistance crisis kills more than 700,000 people each year, making it one of the world's most pressing health problems

With the development of new antibiotics to treat infections stalled, many patients are now receiving multidrug-based treatments in the hope that their combined effects will prevent further resistance evolution
.
However, this polypharmacy has many risks and unknowns

Giving a drug to a patient often causes bacteria to evolve resistance to it
.
Fortunately, some of these drug-resistant mutants were more sensitive to a second drug, which allowed doctors to successfully treat the infection

Scientists at the University of California, San Diego have now developed a method that can help doctors calculate the odds of chance outcomes for different drug combinations, thereby improving the odds of a successful treatment
.
As described in the journal eLife, graduate student Sarah Ardell and assistant professor Sergey Kryazhimskiy have developed a mathematical model that calculates the risk of resistance evolution for various drugs

"The problem with treating bacteria with multiple drugs is that we don't know which mutations are available to bacteria," said Kryazhimskiy of the Division of Ecology, Behavior and Evolution in the Division of Biological Sciences
.
"In many cases, bacteria can mutate to make them resistant to both drugs, or they can mutate to make them resistant to the first drug but sensitive to the second

In developing this model, Ardell and Kryazhimskiy used a new concept called "jfe", which means "joint distribution of fitness effects (new mutations)"

After studying mutational data in E.
coli, the team of scientists identified many resistance mutations to various commonly used antibiotics that lead to indirect susceptibility (a beneficial outcome) or indirect resistance to other drugs (a detrimental result)
.
Their new model, they say, could help better predict the outcome of drug resistance, meaning a victory for infected patients, although it's not foolproof given the inevitable randomness of evolution

Adair said she was surprised to find that antibiotic resistance cannot be thought of as a simple deterministic process
.
The more she learned, the clearer it became that different bacterial populations evolved resistance in different ways, even under controlled laboratory conditions

The strain of bacteria, the concentration of the drug, and the nutrients in the microbial environment can all lead to a variety of outcomes
.

"But even if all these things are exactly the same, you can still get different results in two different iterations, because evolution is built up by random mutation," Adair said
.
"Two different populations may have randomly accumulated different mutations with different collateral effects, even if everything else is equal
.
There is a lot of variability and randomness in these processes, which is very important for patients
.
We The hope is to provide a combination of drugs that we have confidence in that produces as much indirect susceptibility as possible, not just a 50 percent chance
.
"

The scientists point out that much remains to be learned about the diversity of collateral effects of resistance mutations
.

Adair is now working on drugs to treat the same target, the ribosome, an important protein complex inside bacterial cells
.
She is building a model of cellular metabolism to understand jfe from a mechanistic perspective
.

"The key to our results is that we can predict the probability of developing collateral resistance," Kryazimskiy said
.
"It's not perfect, but it's better than not knowing what to expect at all
.
If we choose drug pairs carefully, we can minimize The probability of collateral resistance
.
We can't completely rule out an adverse outcome, but we can minimize the likelihood of it happening
.
Our work may ultimately help clinicians select drugs that minimize the evolution of multidrug resistance
.
"