Fitness seascapes are necessary for realistic modeling of the evolutionary response to drug therapy

A persistent challenge in evolutionary medicine is predicting the evolution of drug resistance, which is complicated further when the drug concentration varies in time and space within a patient. Evolutionary trade-offs, or fitness costs of resistance, cause the evolutionary landscape to change dramatically as the drug selective pressure changes. In this work, we show how fitness seascapes, or collections of genotype-specific dose-response curves, more accurately describe dose-dependent evolution and the arrival of drug resistance. We measure a novel empirical fitness seascape in E. coli subject to cefotaxime, finding substantial growth rate penalties in exchange for drug resistance. In two computational experiments we show how the fitness seascape framework may be used to model evolution in changing environments. First, we show that the probability of evolutionary escape from extinction is dependent on the rate of environmental change, aligning with prior in vitro results. Then, we simulate patients undergoing a daily drug regimen for an infection with varying rates of nonadherence. We find that early drug regimen adherence is critical for successfully eliminating the infection, lending evidence to a "two strike" model of disease extinction. Our work integrates an empirical fitness seascape into an evolutionary model with realistic pharmacological considerations. Future work may leverage this platform to optimize dosing regimens or design adaptive therapies to avoid resistance.