Predicting the emergence of drug-resistant bacteria
The ability to predict exactly where and when a future outbreak of antibiotic-resistant bacteria will emerge is of obvious utility for improving public health. But despite the fact that the public databases are already brimming with tens of thousands of cataloged DNA mutations that confer such resistance, those don’t reveal how other mutations may emerge, and forecasting outbreaks remains beyond the predictive power of modern science.
Barrett Deris (MIT) and colleagues believe that one solution to better forecasting is to understand how different mutations affect bacterial growth, and they have developed a simple quantitative model that predicts steady-state growth rates of bacteria in the presence of certain antibiotics. In Denver, they will describe how it predicts antibiotic-resistant bacterial growth of an assortment of drug-resistant strains based on each strain’s biophysical interactions with the drugs, which could help to explain why resistance may evolve faster for one drug versus another during treatment, Deris said.
Deris added that understanding a previously unappreciated feedback loop between drug resistance and bacterial growth reveals that small environmental or genetic changes can increase bacterial fitness dramatically in many cases. This knowledge should help to determine the likelihood that resistance will arise during treatment and should inform treatment strategies, suggesting the types of drugs and drug combinations for treatment that work most effectively while minimizing the risk of resistance emerging.
