Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa

Evolution is at the core of the impending antibiotic crisis. Sustainable therapy must thus account for the adaptive potential of pathogens. One option is to exploit evolutionary trade-offs, like collateral sensitivity, where evolved resistance to one antibiotic causes hypersensitivity to another one. To date, the evolutionary stability and thus clinical utility of this trade-off is unclear. We performed a critical experimental test on this key requirement, using evolution experiments with Pseudomonas aeruginosa, and identified three main outcomes: (i) bacteria commonly failed to counter hypersensitivity and went extinct; (ii) hypersensitivity sometimes converted into multidrug resistance; and (iii) resistance gains frequently caused re-sensitization to the previous drug, thereby maintaining the trade-off. Drug order affected the evolutionary outcome, most likely due to variation in the effect size of collateral sensitivity, epistasis among adaptive mutations, and fitness costs. Our finding of robust genetic trade-offs and drug-order effects can guide design of evolution-informed antibiotic therapy.

The impact of drug order complexity on prospective medication order review and verification time

Objective To assess if the amount of time a pharmacist spends verifying medication orders increases as medication orders become more complex. Materials and Methods The study was conducted by observing pharmacist verification of adult medication orders in an academic medical center. Drug order complexity was prospectively defined and validated using a classification system derived from 3 factors: the degree of order variability, ISMP high-alert classification, and a pharmacist perception survey. Screen capture software was used to measure pharmacist order review time for each classification. The annualized volume of low complexity drug orders was used to calculate the potential time savings if these were verified using an alternate system that did not require pharmacist review. Results The primary study hypothesis was not achieved. Regression results did not show statistical significance for moderate (n = 30, 23.7 seconds, sd = 23.3) or high complexity (n = 30, 18.6 seconds, sd = 23.1) drugs relative to the low complexity drugs (n = 30, 8.0 seconds, sd = 14.4) nor for moderate vs high complexity; (βmoderate vs low = 15.6, P = .113), (βhigh vs low = 10.3, P = .235), (βmoderate vs high = 5.3, P = .737). The sensitivity analysis showed statistical significance in the high vs low comparison (βhigh vs low = 13.8, P = .017). Discussion This study showed that verifying pharmacists spent less time than projected to verify medication orders of different complexities, but the time did not correlate with the classifications used in our complexity scale. Several mitigating factors, including operational aspects associated with timing antimicrobial orders, likely influenced order verification time. These factors should be evaluated in future studies which seek to define drug order complexity and optimize pharmacist time spent in medication order verification. Conclusion The findings suggest that there may be other factors involved in pharmacist decision-making that should be considered when categorizing drugs by perceived complexity.

A large-scale study reveals 24-h operational rhythms in hospital treatment

Hospitals operate 24 h a day, and it is assumed that important clinical decisions occur continuously around the clock. However, many aspects of hospital operation occur at specific times of day, including medical team rounding and shift changes. It is unclear whether this impacts patient care, as no studies have addressed this. We analyzed the daily distribution of ∼500,000 doses of 12 separate drugs in 1,546 inpatients at a major children’s hospital in the United States from 2010 to 2017. We tracked both order time (when a care provider places an electronic request for a drug) and dosing time (when the patient receives the drug). Order times were time-of-day−dependent, marked by distinct morning-time surges and overnight lulls. Nearly one-third of all 103,847 orders for treatment were placed between 8:00 AM and 12:00 PM. First doses from each order were also rhythmic but shifted by 2 h. These 24-h rhythms in orders and first doses were remarkably consistent across drugs, diagnosis, and hospital units. This rhythm in hospital medicine coincided with medical team rounding time, not necessarily immediate medical need. Lastly, we show that the clinical response to hydralazine, an acute antihypertensive, is dosing time-dependent and greatest at night, when the fewest doses were administered. The prevailing dogma is that hospital treatment is administered as needed regardless of time of day. Our findings challenge this notion and reveal a potential operational barrier to best clinical care.

Evolutionary stability of collateral sensitivity to antibiotics in the model pathogenPseudomonas aeruginosa

Evolution is at the core of the impending antibiotic crisis. Sustainable therapy must thus account for the adaptive potential of pathogens. One option is to exploit evolutionary trade-offs, like collateral sensitivity, where evolved resistance to one antibiotic causes hypersensitivity to another one. To date, the evolutionary stability and thus clinical utility of this trade-off is unclear. We performed a critical experimental test on this key requirement, using evolution experiments withPseudomonas aeruginosacombined with genomic and genetic analyses, and identified three main outcomes: (i) bacteria commonly failed to counter hypersensitivity and went extinct; (ii) hypersensitivity sometimes converted into multidrug resistance; and (iii) resistance gains occasionally caused re-sensitization to the previous drug, thereby maintaining the trade-off. Drug order affected the evolutionary outcome, most likely due to variation in fitness costs and epistasis among adaptive mutations. Our finding of robust genetic trade-offs and drug-order effects can guide design of evolution-informed antibiotic therapy.

DRUG-DRUG INTERACTION;

Drug-drug interaction (DDI) is a specific type of adverse event, which developsdue to multiple regimen therapy, and that may lead to significant hospitalization and death.Clinical and economic impact of drug interactions are increasingly accredited as a chiefconcern in critical care. Potentiating effects of DDIs in intensive care units are far more criticaldue to complex medications regimen, high risk severely ill population and associated metabolicand physiological disturbances which can impede drug effects. Pharmacist contribution isclassified as clarification of drug order, appropriate drug information provision, and advice forsubstitute treatment. A multidisciplinary approach is very necessary in developing a pharmacotherapeuticregimen designed to optimize patient outcome and minimize any potential dugdrug interactions. This review encompasses the prevalence, categorization, significance interm of patient safety and prescription efficacy, clinical and economic burdens, national andinternational data comparisons related to drug-drug interactions.