Multidrug Resistance

The increasing antibiotic resistance of pathogenic bacteria threatens to undermine modern healthcare and is particularly dangerous for people that are dependent on effective antibiotics for continuous treatment. Because of failures to develop new classes of antibiotics it is critical that existing antibiotics are used in optimal ways to reduce the risk of resistance without compromising treatment. Prophylactic or long-term treatment of chronic infections such as recurrent respiratory infections in patients with bronchiectasis, chronic obstructive pulmonary disease (COPD) or cystic fibrosis, lead to evolution of multidrug resistant bacteria that are extremely difficult to treat successfully leading to increased morbidity and mortality.

Pseudomonas aeruginosa is a major cause of respiratory infections, especially in people with cystic fibrosis or weakened immune systems, that are difficult to treat because of its high intrinsic resistance to several commonly used antibiotics and ability to form biofilms. During long-term treatment, typically with several antibiotics, P. aeruginosa evolves multidrug resistance within each patient by chromosomal mutations, rather than by acquisition of new genes by horizontal gene transfer. Resistance is achieved mainly by target mutations and by mutation in regulators of efflux pumps that can pump out several different antibiotics from the cell resulting in multidrug resistance.

If the evolution of P. aeruginosa populations in response to different antibiotics could be predicted and ultimately controlled this would not only provide us with a model system for predicting evolution but could also be useful in a clinical setting. However, predicting evolutionary trajectories are difficult as the effects of mutations on fitness or resistance are rarely simply additive due to epistasis. Mechanistic mathematical models of the molecular interactions underpinning the genotype-to-phenotype map combined with experimental data could be used to predict growth rates, collateral sensitivity, and cross-resistance phenotypes. This could allow prediction of evolutionary trajectories for laboratory populations in response to different treatments with sequential treatment or combinations of antibiotics. Ultimately, the development of refined forecasting models could also be used to guide populations into evolutionary dead ends where they can no longer evolve further resistance or do so only at a high cost in terms of a reduced growth rate.