Evolved resistance to one antibiotic may be associated with "collateral" sensitivity to other drugs. Here, we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant–drug combinations. We find that collateral effects are pervasive but difficult to predict because independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. Although phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in a four-drug uniform cycle.

The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.

The growing threat of drug resistance has inspired a surge in evolution-based strategies for optimizing the efficacy of antibiotics. One promising approach involves harnessing collateral sensitivity-the increased susceptibility to one drug accompanying resistance to a different drug-to mitigate the spread of resistance. Unfortunately, because the mechanisms of collateral sensitivity are diverse and often poorly understood, the systematic design of multi-drug treatments based on these evolutionary trade-offs is extraordinarily difficult. In this work, we provide an extensive phenotypic characterization of collateral drug effects in E. faecalis, a gram-positive species among the leading causes of nosocomial infections. By combining parallel experimental evolution with high-throughput dose-response measurements, we provide quantitative profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict, as even mutants selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity. Overall, variability in collateral profiles is strongly correlated with the final level of resistance to the selecting drug. In addition, collateral effects to certain drugs (e.g. ceftriaxone) are considerably more variable than those to other drugs (e.g. fosfomycin), even for drugs from the same class. Remarkably, however, the sensitivity profiles cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the underlying statistical structure in the collateral profiles, we develop a simple mathematical framework based on a Markov decision process (MDP) to identify optimal antibiotic cycling policies that maximize expected collateral sensitivity. Importantly, these cycles can be tuned to optimize long-term treatment outcomes, leading to drug sequences that may produce long-term collateral sensitivity at the expense of short-term collateral resistance.

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and drug resistance profiles. For example, selection in a dual β-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and β-lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations are nearly growth-optimized along a contour connecting profiles selected by the component drugs. Our results highlight trade-offs between drug interactions and resistance profiles during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non–small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

DISCLAIMER: This article does not represent the official recommendation of the Cleveland Clinic or CaseWestern Reserve University School of Medicine, nor has it yet been peer reviewed. We are releasing it early, pre-peer review, to allow for quick dissemination/vetting by the scientific/clinical community given the necessity for rapid conservation of personal protective equipment (PPE) during this dire global situation. We welcome feedback from the community.Personal protective equipment (PPE), including face shields, surgical masks, and N95 respirators, is crucially important to the safety of both patients and medical personnel, particularly in the event of an infectious pandemic. As the incidence of Coronavirus Disease (COVID-19) increases exponentially in the United States and worldwide, healthcare provider demand for these necessities is currently outpacing supply. As such, strategies to extend the lifespan of the supply of medical equipment as safely as possible are critically important. In the midst of the current pandemic, there has been a concerted effort to identify viable ways to conserve PPE, including decontamination after use. Some hospitals have already begun using UV-C light to decontaminate N95 respirators and other PPE, but many lack the space or equipment to implement existing protocols. In this study, we outline a procedure by which PPE may be decontaminated using ultraviolet (UV) radiation in biosafety cabinets (BSCs), a common element of many academic, public health, and hospital laboratories, and discuss the dose ranges needed for effective decontamination of critical PPE. We further discuss obstacles to this approach including the possibility that the UV radiation levels vary within BSCs. Effective decontamination of N95 respirator masks or surgical masks requires UV-C doses of greater than 1 Jcm −2 , which would take a minimum of 4.3 hours per side when placing the N95 at the bottom of the BSCs tested in this study. Elevating the N95 mask by 48 cm (so that it lies 19 cm from the top of the BSC) would enable the delivery of germicidal doses of UV-C in 62 minutes per side. Effective decontamination of face shields likely requires a much lower UV-C dose, and may be achieved by placing the face shields at the bottom of the BSC for 20 minutes per side. Our results are intended to provide support to healthcare organizations looking for alternative methods to extend their reserves of PPE. We recognize that institutions will require robust quality control processes to guarantee the efficacy of any implemented decontamination protocol. We also recognize that in certain situations such institutional resources may not be available; while we subscribe to the general principle that some degree of decontamination is preferable to re-use without decontamination, we would strongly advise that in such cases at least some degree of on-site verification of UV dose delivery be performed.

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identi ed, much less is known about how di erent combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting di erent classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the speci c interplay between drug interaction and cross resistance. For example, selection in a dual -lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance pro les are nearly identical. On the other hand, populations evolved in an aminoglycoside and -lactam combination exhibit decreased growth adaptation and resistant pro les that depend on the speci c drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance pro les selected by drug combinations correspond to (nearly) optimized linear combinations of those selected by the component drugs. Our results highlight tradeo s between drug interactions and collateral e ects during the evolution of multi-drug resistance and emphasize evolutionary bene ts and disadvantages of particular drug pairs targeting enterococci.

Predicting the evolution of drug resistance in infectious diseases may enable us to make rational drug choices to avoid resistance and exploit known molecular mechanisms of resistance. Fitness landscapes are commonly used in computational studies to model the genotype-fitness mapping. Canonical fitness landscapes do not intrinsically model varying selection pressure. However, a challenge to predicting the emergence of drug resistance is that disease agents in a patient will never experience a constant environment - the selection pressure imposed by a drug will vary as a result of the drug pharmacokinetic profile, dosing schedule, and spatial concentration gradient. Furthermore, because of evolutionary costs of resistance and different levels of genotype-specific resistance, different drug concentrations may be optimal for the fitness of different genotypes. Explicit modeling of these trade-offs and the resulting rank-order changes in fitness may be important to accurately predict the evolution of a disease population within a patient or other heterogeneous environments. Fitness seascapes extend the fitness landscape model by allowing the mapping to vary according to an arbitrary environmental parameter (e.g., drug concentration), allowing us to model the evolution of resistance with realistic pharmacological considerations. Here, we explore the importance and utility of fitness seascapes in predicting the emergence of resistance. First, we show how modeling genotype-specific dose response curves is necessary to accurately predict evolutionary outcomes in changing environments. Then, using an empirical fitness seascape measured in engineered E. coli, we performed computational experiments observing the impact of the rate of change in drug concentration and simulated patient nonadherence on the probability of evolutionary escape. We found that a greater rate of change in drug concentration resulted in a lower rate of resistance, or a lower rate of evolutionary escape. In simulated patients, higher rates of drug regimen nonadherence were associated with greater rates of resistance. 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.