Exceptions to the rule: When does resistance evolution not undermine antibiotic therapy in human bacterial infections?

The use of antibiotics to treat bacterial infections often imposes strong selection for antibiotic resistance. However, the prevalence of antibiotic resistance varies greatly across different combinations of pathogens and drugs. What underlies this variation? Systematic reviews, meta-analyses, and literature surveys capable of integrating data across many studies have tried to answer this question, but the vast majority of these studies have focused only on cases where resistance is common or problematic. Yet much could presumably be learned from the cases where resistance is infrequent or absent. Here we conducted a literature survey and a systematic review to study the evolution of antibiotic resistance across a wide range of pathogen-by-drug combinations (57 pathogens and 53 antibiotics from 15 drug classes). Using Akaike information criterion-based model selection and model-averaged parameter estimation we explored 14 different factors posited to be associated with resistance evolution. We find that the most robust predictors of high resistance are nosocomial transmission (i.e., hospital-acquired pathogens) and indirect transmission (e.g., vector-, water-, air-, or vehicle-borne pathogens). While the former was to be expected based on prior studies, the positive correlation between high resistance frequencies and indirect transmission is, to our knowledge, a novel insight. The most robust predictor of low resistance is zoonosis from wild animal reservoirs. We also found partial support that resistance was associated with pathogen type, horizontal gene transfer, commensalism, and human-to-human transmission. We did not find support for correlations between resistance and environmental reservoirs, mechanisms of drug action, and global drug use. This work explores the relative explanatory power of various pathogen and drug factors on resistance evolution, which is necessary to identify priority targets of stewardship efforts to slow the spread of drug-resistant pathogens. We are currently in the middle of an antibiotic resistance crisis. While basic evolutionary theory has provided insights on how to slow the spread of resistance, it has not yet been able to eliminate the crisis, and a common assumption is that antibiotic resistance is an inevitable consequence of antibiotic use. Yet there are numerous pathogen x drug combinations in which resistance has not yet become common. By identifying differences between the cases where resistance is common and the cases where it is not, it may be possible to develop new evolutionarily informed mitigation strategies to combat the antibiotic resistance crisis. Here, we have compiled published data on the frequency of antibiotic resistance for many pathogen x drug combinations, including those where resistance is common and those where it is absent or rare. By identifying correlations between the frequency of resistance and the features of these drugs and pathogens we have identified that high frequencies of resistance are associated with bacteria that exhibit hospital-associated transmission, that have an indirect mode of transmission, and that do not spread in wild animal populations. Our hope is that these conclusions will help identify novel strategies for controlling the spread of antibiotic resistance.