Model can predict how drug interactions influence antibiotic resistance


Date:
July 27, 2021
Source:
eLife
Summary:
A model using simple changes in microbe growth curves could predict
how drug resistance evolves in response to different antibiotic
combinations, doses and sequences.



FULL STORY ========================================================================== Scientists have proposed a modelling framework which could predict how antibiotic resistance will evolve in response to different treatment combinations, according to a study published July 22 in eLife.


==========================================================================
The research could help doctors optimise the choice, timing, dose and
sequence of antibiotics used to treat common infections, helping to halt
the growing threat of antibiotic resistance to modern medicine.

"Drug combinations are a particularly promising approach for slowing resistance, but the evolutionary impacts of combination therapy
remain difficult to predict, especially in a clinical setting,"
explains first author Erida Gjini, Researcher at the Department
of Mathematics, Instituto Superior Tecnico, University of Lisbon,
Portugal. "Interactions between antibiotics can accelerate, reduce
or even reverse the evolution of resistance, and resistance to one
drug might also influence resistance to another. These interactions
involve genes, competing evolutionary pathways and external stressors,
making it a complex scenario to pick apart." In their study, Gjini
and co-author Kevin Wood of the University of Michigan, US, sought to
simplify things. They took a fundamental measurement of microbe fitness
-- their growth rate, measured by a simple growth curve over time - -
and linked this to resistance to two theoretical drugs. In the model,
they assumed that drug-resistant mutants respond to a high concentration
of drug in exactly the same way that drug-sensitive cells respond to
a low concentration of drug. This rescaling assumption means that the
growth behaviour of mutants can be inferred from the behaviour of the
ancestral (sensitive) cells, simply by measuring their growth over a
range of concentrations. The team then connected this assumption to a
famous statistical relationship, called the Price equation, to explain
how drug interactions and cross-resistance impact the way populations
evolve resistance quantitatively and adapt to drug combinations.

This rescaling model showed that the selection of resistance traits is determined by both the drug interaction and by cross-resistance (where
cells develop resistance to one of the drugs and become resistant to the
second drug at the same time). A mixture of two drugs in the model leads
to markedly different growth trajectories and rates of growth adaptation, depending on how the drugs interact. For example, growth adaptation
can be slowed by drugs that mutually weaken one another -- drugs that
interact 'antagonistically' -- but the effect can be tempered or even
reversed if resistance to one drug is highly correlated with resistance
to the other. The predictions of the model help explain counterintuitive behavior observed in past experiments, such as the slowed evolution seen
when combinations of tigecycline and ciprofloxacin -- two antibiotics
commonly used in clinical settings -- are applied simultaneously to the opportunistic pathogen Enterococcus faecalis.

Having established the basic model, the team then added in the effect
of mutations on drug resistance. They looked at two different routes to accumulating mutations: in the first, there was a uniform pathway between
the ancestral genetics and all possible mutation combinations. In
the second, they assumed that mutations must arise in a specific
sequence. They used a theoretical combination of two drugs, one at a
higher dose than the other, and found that the sequential pathway leads
to slower adaptation of growth, reflecting its evolution to the first
fittest mutant before adapting further.

In addition to being able to include mutations in the model, the team
also tested whether they could predict the effects of different timings
and sequences of antibiotic treatment. They studied two sequential
regimes, A and B, based on different dosage combinations of tigecycline
and ciprofloxacin.

They found that both the resistance levels to the two drugs and the growth
rate increases during treatment, as they anticipated. But the dynamics
of this increase depends on the relative duration of each treatment and
the total treatment length.

"We have built a model that incorporates drug interactions and
cross-resistance to predict how microbes will adapt over time in a
way that can then be experimentally measured," concludes co-author
Wood, who is an Associate Professor at U-M's Departments of Biophysics
and Physics. "In contrast to the classical genetics-based approaches
to studying drug resistance, we used simple scaling assumptions --
something commonly used in physics -- to dramatically reduce the
complexity of the problem. The approach helps us unravel a number of
competing evolutionary effects and may eventually offer a framework
for optimising time-dependent, multidrug treatments." This study has
been published as part of Evolutionary Medicine: A Special Issue from
eLife. To view the Special Issue, visit https://elifesciences.org/ collections/8d9426aa/evolutionary-medicine-a-special-issue.

The study was supported by Fundac,a~o Luso-Americana para o
Desenvolvimento (FLAD) grant 274/2016 and partly by Instituto Gulbenkian
de Cie^ncia (to Erida Gjini), and by the National Institutes of Health (1R35GM124875 to Kevin Wood) and the National Science Foundation (1553028,
also to Wood).

========================================================================== Story Source: Materials provided by eLife. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Erida Gjini, Kevin B Wood. Price equation captures the role of drug
interactions and collateral effects in the evolution of multidrug
resistance. eLife, 2021; 10 DOI: 10.7554/eLife.64851 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210727171544.htm

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