New tool assesses evolutionary risks of antibiotics
Researchers developed a mathematical model that can help doctors choose antibiotics that minimize drug resistance

Date:
January 19, 2022
Source:
University of California - San Diego
Summary:
Countering a rising antibiotic resistance crisis, doctors now
prescribe combinations of antibiotics. Yet many risks are involved
with such multi- drug combinations. Scientists have developed a
way to help doctors evaluate outcomes for different drug pairs
and boost the odds of successful treatment.



FULL STORY ========================================================================== Bacteria have dangerously evolved to thwart many of the medicines that
were designed to kill them. As a result, a growing antibiotic resistance
crisis is responsible for more than 700,000 deaths each year, emerging
as one of the world's most pressing health issues.


========================================================================== Since the development of new antibiotics to treat infections has stalled,
many patients now receive treatments based on multiple drugs in the
hopes that their joint therapeutic effects can forestall the evolution
of further resistance.

Yet there are plenty of risks and unknowns involved in such multi-drug treatments.

Giving one drug to a patient often causes bacteria to evolve resistance
against it. Fortunately, some of these resistant mutants become more susceptible to a second drug, which allows doctors to successfully treat
the infection. However, doctors can't always be sure when and if evolution
will take this fortunate course. Even worse, resistance against the
initial drug can backfire and cause an increase in resistance against the second drug, leaving the doctors without any further treatment options.

University of California San Diego scientists have now developed a way
that can help doctors calculate the odds of the fortuitous outcomes
for different drug pairs and thereby boost the odds of a successful
treatment. As described in the journal eLife, graduate student
Sarah Ardell and Assistant Professor Sergey Kryazhimskiy developed a mathematical model that can calculate the risk of resistance evolution
for various drug pairs.

"The problem with using multiple drugs to treat bacteria is that we just
don't know which mutations are available to bacteria," said Kryazhimskiy,
of the Division of Biological Sciences' Section of Ecology, Behavior
and Evolution.

"In many situations, bacteria can have access to mutations that make
them resistant to both drugs as well as to mutations that make them
resistant to the first drug but susceptible to the second one. In such situations, it's very difficult to predict which way the population will evolve. The model we developed allows us to make these predictions."
In developing the model, Ardell and Kryazhimskiy used a new concept
called "JDFE," which stands for "joint distribution of fitness effects
(of new mutations)." JDFE characterizes the various kinds of mutations available to bacteria and allows researchers to classify drug pairs into
those that facilitate or hinder multi-drug resistance.



========================================================================== Having looked at mutational data available for the bacterium Escherichia
coli, the researchers discovered many resistance mutations against
various commonly used antibiotics that lead to collateral sensitivity
(a beneficial outcome) or collateral resistance (a detrimental outcome)
with other drugs. They say their new model could help better predict
resistance outcomes, which signifies a win for infected patients, though
it is not foolproof given the unavoidable randomness of evolution.

Ardell said she was surprised to learn that antibiotic resistance cannot
be thought of as a simple deterministic process. The more she learned
the more it became clear that different bacterial populations evolve
resistance in different ways, even in controlled lab conditions. The same experiments carried out by different labs often produce contradictory
results, she found.

The strain of bacteria, concentration of drugs and the nutrients in the organism's environment can all lead to a mixed bag of results.

"But even if all of those things are exactly the same you could still
get different outcomes in two different iterations just because evolution builds up by random mutations," Ardell said. "Two different populations
could have randomly accumulated different mutations with different
collateral effects, even if everything else is equal. There is so much variability and randomness in these processes, which is an incredibly
important thing to think about for patients. We want to give drug pairs
that we are confident that will, as much as possible, produce collateral sensitivity -- and not just 50 percent of the time." The researchers
indicate there is still a lot to learn about the diversity of collateral effects ofresistance mutations.

Ardell is now studying pairs of drugs that treat the same target, the
ribosome, an important protein complex within bacterial cells. She
is building a metabolic model of the cell to understand JDFE from a
mechanistic perspective.

"The crux of our result is that we can predict the probability
of evolving collateral resistance," said Kryazhimskiy. "It's not
perfect but it's preferable to having no idea what will happen
at all. If we choose the drug pairs carefully, we can minimize
the probability of collateral resistance. We can't completely
preclude the adverse outcome, but we can minimize the chance
that it will happen. Our work may eventually help clinicians
choose drugs that minimize the evolution of multidrug resistance." ========================================================================== Story Source: Materials provided by
University_of_California_-_San_Diego. Original written by Mario
Aguilera. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Sarah M Ardell, Sergey Kryazhimskiy. The population genetics of
collateral resistance and sensitivity. eLife, 2021; 10 DOI:
10.7554/ eLife.73250 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220119090901.htm

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