OBJECTIVES/SPECIFIC AIMS: Chemotherapy-related cognitive impairment (CRCI) affects 15-35% of breast cancer survivors and constitutes a significant challenge for survivor quality of life. Among older breast cancer survivors who received chemotherapy treatment, carriers of at least one ɛ4 allele of the APOE gene, which encodes apolipoprotein E, are at higher risk for developing CRCI than non-carriers. APOE4 is well characterized as the strongest genetic risk factor for Alzheimer’s disease, but how it contributes to CRCI is not yet understood, and no animal models of APOE genotype and CRCI have yet been established. To better understand how APOE4 acts as a risk factor for CRCI, we used APOE targeted replacement (TR) mice to develop a model of its effects on cognition following treatment with doxorubicin, a chemotherapy drug commonly used in breast cancer treatment. METHODS/STUDY POPULATION: Twelve-to-thirteen month old APOE3 and APOE4 targeted replacement mice expressing human APOE3 or human APOE4 under control of the endogenous murine promoter were treated with 10 mg/kg doxorubicin or equivolume saline given via two IP injections spaced one week apart. One week post-treatment, mice were tested using Open Field and Elevated Zero apparatuses to assess baseline locomotive activity and anxiety and exploratory behaviors. Five weeks post-treatment, mice were assessed using the Barnes Maze over four days of training trials and one 72 hour memory probe. RESULTS/ANTICIPATED RESULTS: We found no differences in Open Field and Elevated Zero behavior, indicating limited influence of doxorubicin treatment on locomotive and anxiety behaviors in both genotypes. During Barnes Maze training, APOE4 mice treated with doxorubicin showed increased latency compared to untreated APOE4 mice as well as treated and untreated APOE3 mice, indicating deficiencies in spatial learning. In APOE3 mice, no differences in performance were seen between doxorubicin-treated and untreated mice (n = 15-16/group, p <.0001). DISCUSSION/SIGNIFICANCE OF IMPACT: These results indicate that APOE4 targeted replacement mice have specific cognitive vulnerabilities to doxorubicin treatment that can be reliably detected using the Barnes Maze assessment. Future directions include experiments to determine how other chemotherapy drugs or drug combinations impact cognition of APOE4 mice. Ultimately this model may be used to assess preventive measures or therapies for CRCI in the vulnerable APOE4 carrier population with the ability to validate cognitive impacts of these interventions.

Single institutional review board (IRB) review of multisite research increased in frequency over a decade ago with a proliferation of master IRB reliance agreements supporting statewide and regional consortia and disease- and population-specific networks. Although successful, the increasing number of agreements presented significant challenges and illuminated potential benefits of a single, nationwide agreement. Anticipated changes in federal regulations highlighted the need to systematize and simplify IRB reliance. To address these challenges, the NIH National Center for Advancing Translational Sciences funded a project to establish a national IRB reliance network that would support national adoption of single IRB (sIRB) review. The Streamlined, Multisite, Accelerated Resources for Trials (SMART) IRB Platform launched in July 2016 to facilitate dissemination, adoption, and implementation of a collaboratively developed master IRB reliance agreement and supportive tools and resources. More than 580 institutions have joined SMART IRB’s Master Common Reciprocal Institutional Review Board Authorization Agreement and begun using the SMART IRB platform to support sIRB arrangements. Here, we describe the tenets of the agreement and operational benefits and challenges of its use. SMART IRB’s early success affirms the utility of collaborative, flexible, and centralized approaches to supporting sIRB review while highlighting the need for further national harmonization.

Formal mentoring programs are increasingly recognized as critical for faculty career development. We describe a mentoring academy (MA) developed for faculty across tracks (i.e., researchers, clinicians, educators) within a “school of health” encompassing schools of medicine and nursing. The program is anchored dually in a clinical and translational science center and a school of health. The structure includes the involvement of departmental and center mentoring directors to achieve widespread uptake and oversight. A fundamental resource provided by the MA includes providing workshops to enhance mentoring skills. Initiatives for junior faculty emphasize establishing and maintaining strong mentoring relationships and implementing individual development plans (IDPs) for career planning. We present self-report data on competency improvement from mentor workshops and data on resources and barriers identified by junior faculty (n = 222) in their IDPs. Mentors reported statistically significantly improved mentoring competency after workshop participation. Junior faculty most frequently identified mentors (61%) and collaborators (23%) as resources for goal attainment. Top barriers included insufficient time and time-management issues (57%), funding limitations (18%), work–life balance issues (18%), including inadequate time for self-care and career development activities. Our MA can serve as a model and roadmap for providing resources to faculty across traditional tracks within medical schools.

The mission of the National Center for Advancing Translational Science (NCATS) is to catalyze the generation of innovative methods and technologies that will enhance the development, testing, and implementation of diagnostics and therapeutics across a wide range of human diseases and conditions.1 NCATS funded a predoctoral TL1 training grant at our institution. We developed a novel team-based Translational Journal Club utilizing three-member teams to find a basic science paper and two clinical study papers that covered a single therapeutic, either a pivotal study or a dissemination and implementation study; one member of the team presented a paper on the above topics in successive weeks. In addition, the trainees attended lectures on: how to design a pivotal clinical trial, dissemination and implementation, and drug development from a basic science discovery through its approval. From these presentations, the trainees appreciated the T0 to T3/4 continuum and its challenges. They also attended sessions on how to present scientific concepts, making them better communicators. The trainees found the Translational Journal club to be very rewarding, illuminating, and providing a much better understanding of the translational research processes required to develop new therapies.

OBJECTIVES/SPECIFIC AIMS: Despite advancements in therapies, such as surgery, irradiation (IR) and chemotherapy, outcome for patients suffering from glioblastoma remains fatal; the median survival rate is only about 15 months. Even with novel therapeutic targets, networks and signaling pathways being discovered, monotherapy with such agents targeting such pathways has been disappointing in clinical trials. Poor prognosis for GBM can be attributed to several factors, including failure of drugs to cross the blood-brain-barrier (BBB), tumor heterogeneity, metastasis and angiogenesis. Development of tumor resistance, particularly to temozolomide (TMZ), creates a substantial clinical challenge.The primary focus of our work is to rationally develop novel combination therapies and dose regimens that mitigate resistance development. Specifically, our aim is to combine TMZ with small molecule inhibitors that are either currently in clinical trials or are approved drugs for other cancer types, and which target the disease at various resistance signaling pathways that are induced in response to TMZ monotherapy. METHODS/STUDY POPULATION: To accomplish this objective, an integrated PKPD modeling approach is used. The approach is largely based on the work of Cardilin, et al, 2018. A PK model for each drug is first defined. This is subsequently linked to a PD model description of tumor growth dynamics in the presence of a single drug or combinations of drugs. A key outcome of these combined PKPD models are tumor static concentration (TSC) curves of dual or triple combination drug regimens that identify combination drug exposures predicted to arrest tumor growth. This approach has been applied to TMZ in combination with abemaciclib (a dual CDK4/6 small molecule inhibitor) based on data from a published study evaluating abemaciclib efficacy in combination with TMZ in a glioblastoma xenograft model (Raub, et al, 2015). RESULTS/ANTICIPATED RESULTS: A PKPD model was developed to predict tumor growth kinetics for TMZ and abemaciclib monotherapy, as well as combination therapy. Population PK models in immune deficient NSG mice for temozolomide and abemaciclib were developed based on data obtained from original and published studies. Subsequently, the PK model was linked to tumor volume data obtained from U87-MG GBM subcutaneous xenografts, again using both original data as well as data from the Raub, et al, 2015 study. Model parameters quantifying tumor volume dynamics were precisely estimated (coefficient of variation < 30%). The developed PKPD model was used to calculate plasma concentrations of TMZ and abemaciclib that would arrest tumor growth, as well as combinations of concentrations of the two drugs that would accomplish the same endpoint. This so-called TSC curve for the TMZ and abemaciclib combination pair evidenced an additive effect of the two agents when administered together. These results will be presented. In addition, results from on-going PKPD studies of TMZ in combination with two other small molecule inhibitors, RG7388, an MDM2 inhibitor, and GDC0068, an AKT inhibitor, will also be presented. DISCUSSION/SIGNIFICANCE OF IMPACT: Our long-term goals are to further elucidate SOC-induced responses in GBM and establish combination treatment regimens that are safe and significantly improve therapeutic efficacy. Collectively, our studies will broadly influence chemotherapy of GBM by establishing a process to rationally design combination approaches that mitigate resistance development. These studies will ultimately provide opportunities to study other targeted agents tailored to individual molecular signatures of GBM, as well as other tumor types.

Project management expertise is employed across many professional sectors, including clinical research organizations, to ensure that efforts undertaken by the organization are completed on time and according to specifications and are capable of achieving the needed impact. Increasingly, project leaders (PLs) who possess this expertise are being employed in academic settings to support clinical and preclinical translational research team science. Duke University’s clinical and translational science enterprise has been an early adopter of project management to support clinical and preclinical programs. We review the history and evolution of project management and the PL role at Duke, examine case studies that illustrate their growing value to our academic research environment, and address challenges and solutions to employing project management in academia. Furthermore, we describe the critical role project leadership plays in accelerating and increasing the success of translational team science and team approaches frequently required for systems biology and “big data” scientific studies. Finally, we discuss perspectives from Duke project leadership professionals regarding the training needs and requirements for PLs working in academic clinical and translational science research settings.

Technological advancements in medical devices developed for adults far outpace the development of technologies designed for pediatric patients in the USA and other countries. This technology lag was previously reflected in a lack of pediatric-specific innovation within our academic institution. To address the institutional deficit of device innovation around pediatric patients, we formed unique partnerships both within our university and extending to the medical device industry, and developed novel programmatic approaches. The Pediatric Device Innovation Consortium (PDIC) bridges the medical device community and the University of Minnesota. Since 2014, the PDIC has supported 22 pediatric medical technology innovation projects, provided funds totaling more than $500,000, licensed two technologies, and advanced two technologies to patient use. Here, we describe the PDIC model and method, the PDIC approach to common challenges that arise in the development of small-market medical technologies at an academic institution, and iterations to our collaborative, multidisciplinary approach that have matured throughout our experience. The PDIC model continues to evolve to reflect the special needs of innovation for smaller markets and the unique role of clinician innovators. Our approach serves as a successful model for other institutions interested in creating support mechanisms for pediatric or small-market technology development.

Community advisory boards (CABs) are a valuable strategy for engaging and partnering with communities in research. Eighty-nine percent of Clinical and Translational Science Awardees (CTSA) responding to a 2011 survey reported having a CAB. CTSAs’ experiences with CABs are valuable for informing future practice. This study was conducted to describe common CAB implementation practices among CTSAs; document perceived benefits, challenges, and contributions; and examine their progress toward desirable outcomes. A cross-CTSA collaborative team collected survey data from respondents representing academic and/or community members affiliated with CTSAs with CABs. Data representing 44 CTSAs with CABs were analyzed using descriptive statistics. A majority of respondents reported practices reflecting respect for CAB members’ expertise and input such as compensation (75%), advisory purview beyond their CTSA’s Community Engagement program (88%), and influence over CAB operations. Three-quarters provide members with orientation and training on roles and responsibilities and 89% reported evaluating their CAB. Almost all respondents indicated their CTSA incorporates the feedback of their CABs to some degree; over half do so a lot or completely. This study profiles practices that inform CTSAs implementing a CAB and provide an evaluative benchmark for those with existing CABs.

Beyond medical schools’ historical focus on pillar missions including clinical care, education, and research, several medical schools now include community engagement (CE) as a mission. However, most academic health systems (AHSs) lack the tools to provide metrics, evaluation, and standardization for quantifying progress and contributions of the CE mission. Several nationwide initiatives, such as that driven by the Institute of Medicine recommending advances in CE metrics at institutions receiving Clinical and Translational Science Awards, have encouraged the research and development of systematic metrics for CE, but more progress is needed. The CE components practical model provides a foundation for analyzing and evaluating different types of CE activities at AHSs through five components: research, education, community outreach and community service, policy and advocacy, and clinical care. At the Medical College of Wisconsin (MCW), an annual survey administered to faculty and staff assessed the types and number of CE activities from the prior year. Survey results were combined to create a CE report for departments across the institution and inform MCW leadership. Insights gathered from the survey have contributed to next steps in CE tracking and evaluation, including the development of a CE dashboard to track CE activities in real time. The dashboard provides resources for how individuals can advance the CE mission through their work and guide CE at the institutional level.

Every research study that includes volunteer participants requires safety assurances in proportion to the risks of the study. Investigator-initiated clinical research can present unique regulatory challenges particularly for studies with a risk profile that warrants more oversight than minimal risk but less than for large, commercial, or high-risk research. The use of an independent safety officer (ISO) offers a middle way of right-sizing oversight to match the risk. ISOs are clinicians or researchers with relevant expertise who are independent of the investigator and the research study. Their relationship to the study is defined by a formal charter which is aligned with the protocol and Data and Safety Monitoring Plan to address the oversight process, responsibilities of the ISO, and clearly describe the variables to be monitored. The ISO responsibilities include reviewing safety data, adverse events, recruitment, demographics, study progress, data quality, protocol changes, and any new scientific information that pertains to the trial. Finally, the ISO reports in their review on any significant findings may propose modifications to the study or a need to stop the trial.

SPARCRequest© (Services, Pricing, & Application for Research Centers) is a web-based research management system that provides a modular and adaptable “electronic storefront” for research-related services. Developed by the South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina, it was released as open source (OS) code in 2014. The adoption of SPARCRequest© accelerated in 2016, when, to ensure responsiveness to the needs of partners, its governance also became open. This governance model enables OS partners to suggest and prioritize features for new releases. As a result, the software code has become more modularized and can be easily customized to meet the diverse needs of adopting hubs. This article describes innovative aspects of the OS governance model, including a multi-institutional committee structure to set strategic vision, make operational decisions, and develop technical solutions; a virtual roadmap that ensures transparency and aligns adopters with release-based goals; and a business process model that provides a robust voting mechanism for prioritizing new features while also enabling fast-paced bug fixes. OS software evolves best in open governance environments. OS governance has made SPARCRequest© more responsive to user needs, attracted more adopters, and increased the proportion of code contributed by adopters.

Accelerating innovation translation is a priority for improving healthcare and health. Although dissemination and implementation (D&I) research has made significant advances over the past decade, it has attended primarily to the implementation of long-standing, well-established practices and policies. We present a conceptual architecture for speeding translation of promising innovations as candidates for iterative testing in practice. Our framework to Design for Accelerated Translation (DART) aims to clarify whether, when, and how to act on evolving evidence to improve healthcare. We view translation of evidence to practice as a dynamic process and argue that much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation. The DART framework proposes that additional factors – demand, risk, and cost, in addition to the evolving evidence base – should influence the pace of translation over time. Attention to these underemphasized factors may lead to more dynamic decision-making about whether or not to adopt an emerging innovation or de-implement a suboptimal intervention. Finally, the DART framework outlines key actions that will speed movement from evidence to practice, including forming meaningful stakeholder partnerships, designing innovations for D&I, and engaging in a learning health system.

OBJECTIVES/SPECIFIC AIMS: To examine and compare the claims in Bzdok, Altman, and Brzywinski under a broader set of conditions by using unbiased methods of comparison. To explore how to accurately use various machine learning and traditional statistical methods in large-scale translational research by estimating their accuracy statistics. Then we will identify the methods with the best performance characteristics. METHODS/STUDY POPULATION: We conducted a simulation study with a microarray of gene expression data. We maintained the original structure proposed by Bzdok, Altman, and Brzywinski. The structure for gene expression data includes a total of 40 genes from 20 people, in which 10 people are phenotype positive and 10 are phenotype negative. In order to find a statistical difference 25% of the genes were set to be dysregulated across phenotype. This dysregulation forced the positive and negative phenotypes to have different mean population expressions. Additional variance was included to simulate genetic variation across the population. We also allowed for within person correlation across genes, which was not done in the original simulations. The following methods were used to determine the number of dysregulated genes in simulated data set: unadjusted p-values, Benjamini-Hochberg adjusted p-values, Bonferroni adjusted p-values, random forest importance levels, neural net prediction weights, and second-generation p-values. RESULTS/ANTICIPATED RESULTS: Results vary depending on whether a pre-specified significance level is used or the top 10 ranked values are taken. When all methods are given the same prior information of 10 dysregulated genes, the Benjamini-Hochberg adjusted p-values and the second-generation p-values generally outperform all other methods. We were not able to reproduce or validate the finding that random forest importance levels via a machine learning algorithm outperform classical methods. Almost uniformly, the machine learning methods did not yield improved accuracy statistics and they depend heavily on the a priori chosen number of dysregulated genes. DISCUSSION/SIGNIFICANCE OF IMPACT: In this context, machine learning methods do not outperform standard methods. Because of this and their additional complexity, machine learning approaches would not be preferable. Of all the approaches the second-generation p-value appears to offer significant benefit for the cost of a priori defining a region of trivially null effect sizes. The choice of an analysis method for large-scale translational data is critical to the success of any statistical investigation, and our simulations clearly highlight the various tradeoffs among the available methods.