We recently wrote about a new microcalorimetry technique that allows doctors to avoid antibiotic resistance by prescribing the proper combination of antibiotics on the first try, but the other piece of the equation is figuring out scenarios in which antibiotic resistance is being developed so it can be avoided. Researchers from the University of Michigan and the University of Lisbon have come up with a modeling framework that predicts the establishment of antibiotic resistance.
To develop the tool, the researchers looked at the fundamental measurement of microbe fitness and calculated their resistance to two theoretical drugs. This experiment allowed them to assess how the growth behavior of mutants can be modified by the behavior of the ancestral (sensitive) cells at a range of different drug concentrations.
Factoring in the Price equation, which calculates how drug interactions and cross-resistance affect drug resistance, they found that the choice of drug combinations changes the level of resistance to each drug. In other words, the resistance outcome of a drug can be vastly different based on the others it is combined with.
The model can now be used to predict resistance development with different drug combinations and see how the timing of those drugs affects resistance as well. “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,” said Kevin Wood, a University of Michigan biophysicist.
Source study: eLife – Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance
