Strategies for improving the performance of prediction models for response to immune checkpoint blockade therapy in cancer.

Immune checkpoint blockade (ICB) therapy holds promise for bringing long-lasting clinical gains for the treatment of cancer. However, studies show that only a fraction of patients respond to the treatment. In this regard, it is valuable to develop gene expression signatures based on RNA sequencing (RNAseq) data and machine learning methods to predict a patient's response to the ICB therapy, which contributes to more personalized treatment strategy and better management of cancer patients. However, due to the limited sample size of ICB trials with RNAseq data available and the vast number of candidate gene expression features, it is challenging to develop well-performed gene expression signatures. In this study, we used several published melanoma datasets and investigated approaches that can improve the construction of gene expression-based prediction models. We found that merging datasets from multiple studies and incorporating prior biological knowledge yielded prediction models with higher predictive accuracies. Our finding suggests that these two strategies are of high value to identify ICB response biomarkers in future studies.

Retirement risk factors, exercise management and muscle mass in US senior horses.

BACKGROUND: Information on the management and health of US senior horses (≥15 years of age) is currently limited. OBJECTIVES: Provide information on (1) primary use of US senior horses, (2) reasons and risk factors for horse retirement, (3) exercise management, (4) prevalence of low muscle mass and (5) risk factors for, and owner-perceived consequences of, low muscle mass. STUDY DESIGN: Online survey. METHODS: Survey responses from 2717 owners of U.S.-resident senior horses (≥15 years of age) were analysed descriptively and inferentially, using ordered and binomial logistic regression, ANOVA and the Kruskal-Wallis test. RESULTS: The most frequently reported primary uses were pleasure riding/driving (38.5%) and full retirement (39.8%). Most horses (61.5%) were retired between 15 and 24 years of age, with health problems being the main reason. Age, female sex, Thoroughbred breed and various medical conditions were identified as risk factors for retirement. In working horses (i.e., those not retired or semi-retired), exercise intensity was negatively associated with age. The owner-reported prevalence of low muscle mass in all horses was 17.2% (95%CI = 15.7-18.7). In those affected by low muscle mass, the ability to work and welfare-related aspects were commonly perceived to be impaired. Increasing age, sex (gelding), pituitary pars intermedia dysfunction, osteoarthritis, laminitis and primary use (retired and semi-retired vs. use for competition) were identified as risk factors for owner-reported low muscle mass. MAIN LIMITATIONS: Potential response, recall and sampling bias. Causal relationships cannot be established. CONCLUSIONS: Although structured exercise into old age may provide health benefits (as seen in elderly people), a large proportion of horses were fully retired in the current study. Senior horses were mainly retired for health problems and characterising these problems may aid in extending their work/active life. Low muscle mass was perceived to affect horses' welfare and ability to work, and identification of prevention and treatment strategies is therefore warranted.

A Murine Delayed-Healing Model Associates Immune Response with Functional Bone Regeneration after Trauma.

Nonunion and delayed-union fractures pose a significant clinical challenge, often leading to prolonged morbidity and impaired quality of life. Fracture-induced hematoma and acute inflammation are crucial for establishing the healing cascade. However, aberrant inflammatory phenotypes can suppress healing and cause bone resorption. Elucidating these mechanisms is necessary to develop potent immunomodulatory therapies and prevent nonunion. Here, we report a delayed fracture healing model enabling the modulation of interfragmentary strain that mimics the etiology of hypertrophic nonunions to elucidate the role of dysregulated immune response in poor healing outcomes. High interfragmentary strain (>15%) was associated with larger callus volumes but delayed bone healing, increased inflammation, and inferior healing outcomes, while lower strain levels (<5%) corresponded to normal bone healing. In addition, we found distinct differences in the ossification, chondrification, and fibrosis patterns between high and low-strain groups, underscoring the significant impact of strain on the healing process. A comprehensive analysis of the systemic immune response revealed dynamic alterations in immune cell populations and factors, particularly within the early hours and days post-fracture. Several immune factors exhibited significant correlations with various functional healing outcomes, indicating their potential as predictive markers for assessing fracture healing progression. Our results also highlighted the significance of timely resolution of proinflammatory signals and the elevation of pro-regenerative immune cell phenotypes in promoting bone regeneration. Multivariate analysis revealed that CD25+ T-regulatory cells were influential in predicting proper bone healing, followed by CD206+ macrophages, underscoring the pivotal role of immune cell populations in the bone healing process. In conclusion, our study provides valuable insights into the intricate interplay between interfragmentary strain, immune response, and the ultimate outcomes of fracture healing. By shedding light on the underlying mechanisms that drive hypertrophic nonunion pathogenesis, our research lays the foundation for enhanced surgical management of nonunions and offers a promising avenue for developing targeted therapeutic interventions and personalized treatment strategies for individuals suffering from fracture nonunion.

Gut microbiome association with brain imaging markers, APOE genotype, calcium and vegetable intakes, and obesity in healthy aging adults.

Introduction: Advanced age is a significant factor in changes to brain physiology and cognitive functions. Recent research has highlighted the critical role of the gut microbiome in modulating brain functions during aging, which can be influenced by various factors such as apolipoprotein E (APOE) genetic variance, body mass index (BMI), diabetes, and dietary intake. However, the associations between the gut microbiome and these factors, as well as brain structural, vascular, and metabolic imaging markers, have not been well explored. Methods: We recruited 30 community dwelling older adults between age 55-85 in Kentucky. We collected the medical history from the electronic health record as well as the Dietary Screener Questionnaire. We performed APOE genotyping with an oral swab, gut microbiome analysis using metagenomics sequencing, and brain structural, vascular, and metabolic imaging using MRI. Results: Individuals with APOE e2 and APOE e4 genotypes had distinct microbiota composition, and higher level of pro-inflammatory microbiota were associated higher BMI and diabetes. In contrast, calcium- and vegetable-rich diets were associated with microbiota that produced short chain fatty acids leading to an anti-inflammatory state. We also found that important gut microbial butyrate producers were correlated with the volume of the thalamus and corpus callosum, which are regions of the brain responsible for relaying and processing information. Additionally, putative proinflammatory species were negatively correlated with GABA production, an inhibitory neurotransmitter. Furthermore, we observed that the relative abundance of bacteria from the family Eggerthellaceae, equol producers, was correlated with white matter integrity in tracts connecting the brain regions related to language, memory, and learning. Discussion: These findings highlight the importance of gut microbiome association with brain health in aging population and could have important implications aimed at optimizing healthy brain aging through precision prebiotic, probiotic or dietary interventions.

Weighted mean difference statistics for paired data in the presence of missing values.

Missing data is a common issue in many biomedical studies. Under a paired design, some subjects may have missing values in either one or both of the conditions due to loss of follow-up, insufficient biological samples, etc. Such partially paired data complicate statistical comparison of the distribution of the variable of interest between the two conditions. In this article, we propose a general class of test statistics based on the difference in weighted sample means without imposing any distributional or model assumption. An optimal weight is derived from this class of tests. Simulation studies show that our proposed test with the optimal weight performs well and outperforms existing methods in practical situations. Two cancer biomarker studies are provided for illustration.

Strategies for Improving the Performance of Prediction Models for Response to Immune Checkpoint Blockade Therapy in Cancer.

Immune checkpoint blockade (ICB) therapy holds promise for bringing long-lasting clinical gains for the treatment of cancer. However, studies show that only a fraction of patients respond to the treatment. In this regard, it is valuable to develop gene expression signatures based on RNA sequencing (RNAseq) data and machine learning methods to predict patients' response to the ICB therapy, which contributes to more personalized treatment strategy and better management of cancer patients. However, due to the limited sample size of ICB trials with RNAseq data available and the vast number of candidate gene expression features, it is challenging to develop well-performed gene expression signatures. In this study, we used several published melanoma datasets and investigated approaches that can improve the construction of gene expression-based prediction models. We found that merging datasets from multiple studies and incorporating prior biological knowledge yielded prediction models with higher predictive accuracies. Our finding suggests that these two strategies are of high value to identify ICB response biomarkers in future studies.

Genomic screening methodology not requiring barcoding: Single nucleotide polymorphism-based, mixed-cell screening (SMICS).

Although high-throughput, cancer cell-line screening is a time-honored, important tool for anti-cancer drug development, this process involves the testing of each, individual drug in each, individual cell-line. Despite the availability of robotic liquid handling systems, this process remains a time-consuming and costly investment. The Broad Institute developed a new method called Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) to screen a mixture of barcoded, tumor cell-lines. Although this methodology significantly improved the efficiency of screening large numbers of cell-lines, the barcoding process itself was tedious that requires gene transfection and subsequent selection of stable cell-lines. In this study, we developed a new, genomic approach for screening multiple cancer cell-lines using endogenous "tags" that did not require prior barcoding: single nucleotide polymorphism-based, mixed-cell screening (SMICS). The code for SMICS is available at https://github.com/MarkeyBBSRF/SMICS.

Body mass index percentiles versus body composition assessments: Challenges for disease risk classifications in children.

Background: Identifying at-risk children with optimal specificity and sensitivity to allow for the appropriate intervention strategies to be implemented is crucial to improving the health and well-being of children. We determined relationships of body mass indexes for age and sex percentile (BMI%) classifications to actual body composition using validated and convenient methodologies and compared fat and non-fat mass estimates to normative cut-off reference values to determine guideline reliability. We hypothesized that we would achieve an improved ability to identify at-risk children using simple, non-invasive body composition and index measures. Methods: Cross-sectional study of a volunteer convenience sample of 1,064 (537 boys) young children comparing Body Fat Percentage (BF%), Fat Mass Index (FMI), Fat-Free Mass Index (FFMI), determined via rapid bioimpedance methods vs. BMI% in children. Comparisons determined among weight classifications and boys vs. girls. Results: Amongst all subjects BMI% was generally correlated to body composition measures and indexes but nearly one quarter of children in the low-risk classifications (healthy weight or overweight BMI%) had higher BF% and/or lower FFMI than recommended standards. Substantial evidence of higher than expected fatness and or sarcopenia was found relative to risk status. Inaccuracies were more common in girls than boys and girls were found to have consistently higher BF% at any BMI%. Conclusions: The population studied raises concerns regarding actual risks for children of healthy or overweight categorized BMI% since many had higher than expected BF% and potential sarcopenia. When body composition and FMI and FFMI are used in conjunction with BMI% improved sensitivity, and accuracy of identifying children who may benefit from appropriate interventions results. These additional measures could help guide clinical decision making in settings of disease-risks stratifications and interventions.

Outpatient medications associated with protection from COVID-19 hospitalization.

The COVID-19 pandemic remains the pre-eminent global health problem, and yet after more than three years there is still no prophylactic agent against the disease aside from vaccines. The objective of this study was to evaluate whether pre-existing, outpatient medications approved by the US Food and Drug Administration (FDA) reduce the risk of hospitalization due to COVID-19. This was a retrospective cohort study of patients from across the United States infected with COVID-19 in the year 2020. The main outcome was adjusted odds of hospitalization for COVID-19 amongst those positive for the infection. Outcomes were adjusted for known risk factors for severe disease. 3,974,272 patients aged 18 or older with a diagnosis of COVID-19 in 2020 met our inclusion criteria and were included in the analysis. Mean age was 50.7 (SD 18). Of this group, 290,348 patients (7.3%) were hospitalized due to COVID-19, similar to the CDC's reported estimate (7.5%). Four drugs showed protective effects against COVID-19 hospitalization: rosuvastatin (aOR 0.91, p = 0.00000024), empagliflozin-metformin (aOR 0.69, p = 0.003), metformin (aOR 0.97, p = 0.017), and enoxaparin (aOR 0.88, p = 0.0048). Several pre-existing medications for outpatient use may reduce severity of disease and protect against COVID-19 hospitalization. Well-designed clinical trials are needed to assess the efficacy of these agents in a therapeutic or prophylactic setting.

Bayesian kinetic modeling for tracer-based metabolomic data.

BACKGROUND: Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. RESULTS: We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux . CONCLUSIONS: Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Effectiveness of telemedicine diabetic retinopathy screening in the USA: a protocol for systematic review and meta-analysis.

BACKGROUND: Diabetic retinopathy (DR) is the leading cause of vision loss among adults in the USA. Vision loss associated with diabetic retinopathy can be prevented with timely ophthalmologic care, and therefore, it is recommended that individuals with diabetes have annual retinal examinations. There is limited evidence on whether using telemedicine to screen for DR in primary care clinics in the USA effectively leads to increased DR screening rates. The objective of this systematic review is to collate evidence from existing studies to investigate the effectiveness of telemedicine DR screening (TDRS) in primary care clinics on DR screening rates. METHODS: Relevant studies will be identified through searching MEDLINE/PubMed interface, Scopus, and Web of Science from their inception until November 2021, as well as searching reference lists of included studies and previous related review articles or systematic reviews. There will be no restrictions on study design. Eligible studies will include subjects with either type 1 or type 2 diabetes, will evaluate telemedicine technology for screening of DR, will have been conducted in the USA, and will report DR screening rates or data necessary for calculating such rates. Two reviewers will screen search results independently. Risk-of-bias assessment and data extraction will be carried out by two reviewers. The version 2 of the Cochrane risk-of-bias tool (RoB 2) and the Newcastle-Ottawa scale (NOS) tool will be used to assess the quality and validity of individual studies. If feasible, we will conduct random-effects meta-analysis where appropriate. If possible, we will conduct subgroup analyses to explore potential heterogeneity sources (setting, socio-economic status, age, ethnicity, study design, outcomes). We will disseminate the findings through publications and relevant networks. DISCUSSION: This protocol outlines the methods for systematic review and synthesis of evidence of TDRS and its effect on DR screening rates. The results will be of interest to policy makers and program managers tasked with designing and implementing evidence-based services to prevent and manage diabetes and its complications in similar settings. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021231067.

Androgen aggravates aortic aneurysms via suppressing PD-1 in mice.

Androgen has long been recognized for its pivotal role in the sexual dimorphism of cardiovascular diseases, including aortic aneurysms, a devastating vascular disease with a higher prevalence and mortality rate in men than women. However, the molecular mechanism by which androgen mediates aortic aneurysms is largely unknown. Here, we report that male but not female mice develop aortic aneurysms in response to aldosterone and high salt (Aldo-salt). We demonstrate that both androgen and androgen receptors (AR) are crucial for the sexually dimorphic response to Aldo-salt. We identify T cells expressing programmed cell death protein 1 (PD-1), an immune checkpoint molecule important in immunity and cancer immunotherapy, as a key link between androgen and aortic aneurysms. We show that intraperitoneal injection of anti-PD-1 antibody reinstates Aldo-salt-induced aortic aneurysms in orchiectomized mice. Mechanistically, we demonstrate that AR binds to the PD-1 promoter to suppress its expression in the spleen. Hence, our study reveals an important but unexplored mechanism by which androgen contributes to aortic aneurysms by suppressing PD-1 expression in T cells. Our study also suggests that cancer patients predisposed to the risk factors of aortic aneurysms may be advised to screen for aortic aneurysms during immune checkpoint therapy.

Pattern of grade C molar-incisor pattern periodontitis in families.

BACKGROUND: The aim of this study was to determine the clinical and inflammatory response patterns for individual siblings diagnosed with grade C molar-incisor pattern periodontitis (C-MIP) and between the related siblings within families. METHODS: Sixty-nine siblings within 28 families with moderate-to-severe C-MIP were included. Clinical parameters were evaluated for symmetry regarding the affected type of teeth, side and/or arch, and bone loss pattern. The protein concentrations from in vitro whole blood cultures for 14 different lipopolysaccharide-stimulated inflammatory markers were correlated with the extent and severity of disease, within an individual sibling and among siblings within a family. RESULTS: A similar disease pattern was observed among all siblings and within families. The most common teeth affected were first molars and incisors or first molars only within the permanent dentition and only molars within the primary dentition (p < 0.001). Symmetry involving molars was higher than in incisors in siblings, regardless of arch or side affected (p = 0.020). Arc-shape/vertical bone defects were the most common (p = 0.006) and higher symmetry was found for these defects in the permanent dentition (p = 0.005). Positive correlations were found between age, clinical attachment loss, and percent affected sites with several inflammatory markers. The inflammatory responses for several inflammatory markers were correlated within and among families (p < 0.050). Specifically, the intraclass correlation coefficient within families was highest (>0.5) for interleukin (IL)-8, IL-6, and IL-10. CONCLUSIONS: Families with C-MIP presented similar patterns of disease. The level of an inflammatory response to bacteria seemed to play a role in the extent and severity of this disease, exemplified by the high degree of correlation in these families.

Balancing after randomization in orthopedic trials: Are we even or even paying attention?

A cornerstone of evidence-based medicine is the randomized controlled trial (RCT). While randomization seeks to balance study groups on potential confounders, this is not always achieved. Especially in orthopedic research where RCTs are often of modest size, imbalances can exist and may be a significant issue. We sought to describe whether orthopedic RCTs assess balancing between study groups, use statistical hypothesis testing to compare baseline characteristics between groups, and have balanced baseline characteristics between groups. All RCTs from four leading orthopedic journals published between July 2019 and June 2020 were identified and those reporting original trial results reviewed for discussion of balancing, use of statistical significance testing to compare baseline characteristics, and patient reported outcome measures (PROMs) at baseline. Standardized mean differences of baseline PROMs were calculated to assess balancing. Of 86 orthopedic RCTs reviewed, 59 (69%) assessed balancing and 50 (58%) used statistical significance testing to compare baseline characteristics. Of 74 articles specifying a primary outcome, 33 (45%) used a PROM with 23 (70%) reporting baseline PROM values. Of these articles, 17 (74%) had a difference of less than 0.25 standard deviations (SDs) between groups, 4 (17%) had a difference of between 0.25 and 0.50 SDs, and 3 (13%) had a difference greater than 0.5 SDs. Orthopedic RCTs usually assess balancing after randomization though there is room for improvement with over half of articles using hypothesis testing to assess baseline differences as opposed to a measure of the magnitude of the difference.

Mechanisms of γδ T cell accumulation in visceral adipose tissue with aging.

γδ T cells are resident in visceral adipose tissue (VAT) where they show an age-associated increase in numbers and contribute to local and systemic chronic inflammation. However, regulation of this population and mechanisms for the age-dependent accumulation are not known. In this study, we identified a progressive trend of γδ T cell accumulation in VAT over the lifespan in mice and explored physiological mechanisms contributing to accumulation. Using isochronic parabiotic pairs of wild-type (WT) and T cell receptor delta knockout (TCRδ KO) mice at young and old age, we confirmed that VAT γδ T cells are predominately a tissue-resident population which is sustained in aging. Migration of peripheral γδ T cells into VAT was observed at less than 10%, with a decreasing trend by aging, suggesting a minor contribution of recruitment to γδ T cell accumulation with aging. Since tissue-resident T cell numbers are tightly regulated by a balance between proliferation and programmed cell death, we further explored these processes. Using in vivo EdU incorporation and the proliferation marker Ki67, we found that the absolute number of proliferating γδ T cells in VAT is significantly higher in the aged compared to young and middle-aged mice, despite a decline in the proportion of proliferating to non-proliferating cells by age. Analysis of apoptosis via caspase 3/7 activation revealed that VAT γδ T cells show reduced apoptosis starting at middle age and continuing into old age. Further, induction of apoptosis using pharmacological inhibitors of Bcl2 family proteins revealed that VAT γδ T cells at middle age are uniquely protected from apoptosis via a mechanism independent of traditional anti-apoptotic Bcl2-family proteins. Collectively, these data indicate that protection from apoptosis at middle age increases survival of tissue-resident γδ T cells resulting in an increased number of proliferative cells from middle age onward, and leading to the age-associated accumulation of γδ T cells in VAT. These findings are important to better understand how adipose tissue dysfunction and related changes in the immune profile contribute to inflammaging among the elderly.

Does prophylactic local tobramycin injection lower open fracture infection rates?

Objective: To determine whether local aqueous tobramycin injection in combination with systemic perioperative IV antibiotic prophylaxis will reduce the rate of fracture-related infection (FRI) after open fracture fixation. Other Outcomes of Interest: (1) To compare fracture nonunion rates and report differences between treatment and control groups and (2) compare bacterial speciation and antibiotic sensitivity among groups that develop FRI. Design: Phase 3 prospective, randomized clinical trial. Setting: Two level 1 trauma centers. Participants: Six hundred subjects (300 in study/tobramycin group and 300 in control/standard practice group) will be enrolled and assigned to the study group or control group using a randomization table. Patients with open extremity fractures that receive definitive internal surgical fixation will be considered. Intervention: Aqueous local tobramycin will be injected into the wound cavity (down to bone) after debridement, irrigation, and fixation, following closure. Main Outcome Measurements: Outcomes will look at the presence or absence of FRI, the rate of fracture nonunion, and determine speciation of gram-negative and Staph bacteria in each group with a FRI. Results: Not applicable. Conclusion: The proposed work will determine whether local tobramycin delivery plus perioperative standard antibiotic synergism will minimize the occurrence of open extremity FRI. Level of Evidence: Level 1.

Functional recovery outcomes following acute stroke is associated with abundance of gut microbiota related to inflammation, butyrate and secondary bile acid.

Accumulating evidence suggests that gut microbes modulate brain plasticity via the bidirectional gut-brain axis and play a role in stroke rehabilitation. However, the microbial species alterations associated with stroke and their correlation with functional outcome measures following acute stroke remain unknown. Here we measure post-stroke gut dysbiosis and how it correlates with gut permeability and cognitive functions in 12 stroke participants, 18 controls with risk factors for stroke, and 12 controls without risk factors. Stool samples were used to measure the microbiome with whole genome shotgun sequencing and leaky gut markers. We genotyped APOE status and measured diet composition and motor, cognitive, and emotional status using NIH Toolbox. We used linear regression methods to identify gut microbial associations with cognitive and emotional assessments. We did not find significance differences between the two control groups. In contrast, the bacteria populations of the Stroke group were statistically dissimilar from the control groups. Relative abundance analysis revealed notable decreases in butyrate-producing microbial taxa, secondary bile acid-producing taxa, and equol-producing taxa. The Stroke group had higher levels of the leaky gut marker alpha-1-antitrypsin in the stool than either of the groups and several taxa including Roseburia species (a butyrate producer) were negatively correlated with alpha-1-antitrypsin. Stroke participants scored lower on memory testing than those in the two control groups. Stroke participants with more Roseburia performed better on the picture vocabulary task; more Bacteroides uniformis (a butyrate producer) and less Escherichia coli (a pro-inflammatory species) reported higher levels of self-efficacy. Intakes of fiber, fruit and vegetable were lower, but sweetened beverages were higher, in the Stroke group compared with controls. Vegetable consumption was correlated with many bacterial changes among the participants, but only the species Clostridium bolteae, a pro-inflammatory species, was significantly associated with stroke. Our findings indicate that stroke is associated with a higher abundance of proinflammatory species and a lower abundance of butyrate producers and secondary bile acid producers. These altered microbial communities are associated with poorer functional performances. Future studies targeting the gut microbiome should be developed to elucidate whether its manipulation could optimize rehabilitation and boost recovery.

Validated Preclinical Mouse Model for Therapeutic Testing against Multidrug-Resistant Pseudomonas aeruginosa Strains.

The rise in infections caused by antibiotic-resistant bacteria is outpacing the development of new antibiotics. The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are a group of clinically important bacteria that have developed resistance to multiple antibiotics and are commonly referred to as multidrug resistant (MDR). The medical and research communities have recognized that, without new antimicrobials, infections by MDR bacteria will soon become a leading cause of morbidity and death. Therefore, there is an ever-growing need to expedite the development of novel antimicrobials to combat these infections. Toward this end, we set out to refine an existing mouse model of pulmonary Pseudomonas aeruginosa infection to generate a robust preclinical tool that can be used to rapidly and accurately predict novel antimicrobial efficacy. This refinement was achieved by characterizing the virulence of a panel of genetically diverse MDR P. aeruginosa strains in this model, by both 50% lethal dose (LD50) analysis and natural history studies. Further, we defined two antibiotic regimens (aztreonam and amikacin) that can be used as comparators during the future evaluation of novel antimicrobials, and we confirmed that the model can effectively differentiate between successful and unsuccessful treatments, as predicted by in vitro inhibitory data. This validated model represents an important tool in our arsenal to develop new therapies to combat MDR P. aeruginosa strains, with the ability to provide rapid preclinical evaluation of novel antimicrobials and support data from clinical studies during the investigational drug development process. IMPORTANCE The prevalence of antibiotic resistance among bacterial pathogens is a growing problem that necessitates the development of new antibiotics. Preclinical animal models are important tools to facilitate and speed the development of novel antimicrobials. Successful outcomes in animal models not only justify progression of new drugs into human clinical trials but also can support FDA decisions if clinical trial sizes are small due to a small population of infections with specific drug-resistant strains. However, in both cases the preclinical animal model needs to be well characterized and provide robust and reproducible data. Toward this goal, we have refined an existing mouse model to better predict the efficacy of novel antibiotics. This improved model provides an important tool to better predict the clinical success of new antibiotics.