Title: Suppressing mutation-mediated resistance through antibiotic combination treatment
Funder: The Academy of Medical Sciences

Abstract: Antimicrobial resistance (AMR) is a looming threat to global health. To tackle AMR, we need novel strategies for preventing the emergence of resistance. Antimicrobial combination therapy (using multiple drugs as part of a single treatment) is a promising approach for countering resistance. However, we currently lack a rational framework for designing combination therapies that suppress resistance evolution. The aim of this research is to discover the evolutionary rules for designing effective combination therapies. I will focus on combinations that are unlikely to be overcome by acquiring a single multidrug resistance mechanism, and those that impose large penalties on bacterial growth. The motivation behind this strategy is that resistance against such combinations is both less likely to emerge during treatment and less likely to survive once treatment ends. I will use a proven experimental evolution approach to directly test whether combinations suppress resistance evolution in multidrug resistant clinical bacteria. This will be achieved through three objectives relating to the evolutionary causes and consequences of acquiring resistance to multiple antibiotics: 1. How do rates of resistance emergence vary among combination therapies? 2. How do fitness costs of resistance against combination therapies vary? 3. Are combination therapies effective over long-term evolution? This research will provide a framework for the rational design of antibiotic combination therapies guided by the evolutionary principles underlying resistance evolution. This will help preserve our existing arsenal of antibiotics, repurpose those that are underutilised due to resistance risks in monotherapy, and safeguard novel antibiotics currently in development.

Lay abstract: One of the biggest challenges faced by modern medicine is the increasing prevalence of antibiotic resistant bacterial infections. Bacteria become resistant through changes in their DNA that make them less susceptible to antibiotics. As discovering new antibiotics is slow and expensive, we need to discover new ways of preventing resistance by using antibiotics more cleverly. My research will investigate how to use antibiotics in ways that block the ability for bacteria to evolve resistance. One approach is to make it difficult for bacteria to gain the genetic changes required for resistance through treatments that combine multiple antibiotics together. Most treatments currently in use involve giving only one antibiotic at a time, but bacteria can usually become resistant to these through only one change in DNA. To resist combinations of antibiotics, bacteria generally need to acquire more than one resistance at the same time, which is extremely unlikely to occur. However, if resistance does happen to occur, having multiple DNA changes should also harm the ability for bacteria to grow when antibiotics are not being given. We therefore anticipate that antibiotic combination treatments will make resistance both less likely to occur, and less likely to persist if it does. Using laboratory experiments, I will discover what makes an antibiotic combination good at suppressing resistance evolution, and test it against resistant bacteria isolated from patients. This research will give us a new approach to using antibiotics that will safeguard our remaining effective antibiotics, and repurpose less effective ones into new treatments.

Research activity codes: 5.1 PHARMACEUTICALS
Health categories: INFECTION