Objectives/Goals: The operation of a clinical trials unit involves multifaceted tasks and stakeholders. A competent information system is critical to daily operations while ensuring smooth conduct of clinical research. We share 15 years of experience in the design and implementation of such a system at Mayo Clinic to inform other institutions with similar interests. Methods/Study Population: The Informatics team collaborated closely with nurse leaders and elicited input from additional stakeholders including nurse unit coordinators, lab managers, schedulers, investigators, study coordinators, and regulatory specialists throughout the phases of system design, development and continuous enhancements, and expansion. The stakeholders offered insights on the corresponding requirements throughout the study life cycle, from engaging with the study sponsor, operational review for protocol execution, development of study budgets, human subject protection and risk mitigation, data management and integration, to outcome monitoring, and regulatory reporting. The activities were then translated into functional components and implemented as a seamless and effective solution. Results/Anticipated Results: Patient safety, scientific rigor, operation automation, efficiency, and regulatory requirements were all considered in developing an integrated system, or the clinical research trials unit (CRTU) Tools. Our institution has leveraged the system for essential tasks from the study start-up, visit scheduling and execution, specimen collection and tracking, to individual protocol metrics and billing. We adopted a measure-as-we-go methodology so that data such as visit census, resource usage, and protocol deviation are tracked and collected during routine use of the system. Specifically, an issues/concerns/exceptions (ICE) tool is used for quality control and patient safety. Moreover, data quality greatly benefits from a task dictionary, standardizing the study activities that can be ordered and executed. Discussion/Significance of Impact: The implementation of a well-rounded clinical trials unit information system not only improves the operation efficiency and team productivity but also ensures scientific rigor and contributes to patient safety. We believe the experience can be informative to other institutions. More details will be shared in the poster.

Objectives/Goals: Create opportunities for early-stage research apprentices to obtain real-world knowledge expand float pool to meet unmet research staffing needs, and decrease investigator burden Increase operational efficiencies, decrease start-up time, establish metrics, and ensure transparency responsible fiscal stewardship to approach cost neutrality Methods/Study Population: The Research Coordinator Support Service (RCSS) is a pool of research staff available for hire on an hourly basis by Johns Hopkins University (JHU) investigators. RCSS consists of Apprentices we train on the job as well as Coordinators and Senior staff who have completed the apprenticeship program. Started in 2012, RCSS was placed under new management in November 2020. An expansion proposal was submitted to senior leadership for additional financial and human resources. After approval new systems were implemented and additional hires were made. Several efficiencies were introduced in start-up, study assignment, transparency, invoicing, and overall operations to address the waitlist of 25 studies. Senior leadership now required extensive metric reporting to evaluate program success. Results/Anticipated Results: To address the waitlist, current staff was redirected from purely educational to study-related activities and several new hires were made. The waitlist reduced steadily over time and more research occurred. Average hours of research support per month more than doubled from under 500 to over 1,000. When our Administrator left, we implemented an automated hours-based reporting and invoicing tool resulting in substantial cost-savings over rehiring the position. Apprentices, now with rapid onboarding and early study assignments are reporting high satisfaction and many have been promoted to Coordinator positions. Detailed spreadsheets with relevant metrics were created which are accessible, and regularly reported, to senior leadership for decisions on promotions and additional hires. Discussion/Significance of Impact: Budget belt-tightening requires organizations to reduce expenses while continuing to provide the essential services investigators need. This focus has caused RCSS to examine our program and add efficiencies. We hope others looking to build or expand their float pools will benefit from our experiences and the specific efficiencies we implemented.

Objectives/Goals: To identify clinical trial teams that are at risk of not meeting their recruitment goals as early in the recruitment period as possible, this project aims to provide timely accrual information and projected forecasts for accruals by the end of the recruitment period across all trials at USC. Methods/Study Population: This project aggregates recruitment accrual data periodically from OnCore to create per-study accrual pages that contain an up-to-date accrual chart, metrics like expected and actual accrual per month, and projected recruitment based on an X-month moving average (3 months by default). Trials at risk are identified as early as possible by using these projections to classify risk. In this initial phase, we’ve classified trials as medium risk (80%–99% accrual) or high risk (less than 80% accrual). The dashboard is currently available for all clinical trials at USC and users are automatically restricted to the studies that they administer or work on depending on their role. Results/Anticipated Results: The dashboard will provide visibility across the institution for the current accrual for all clinical trials in a standard, user-friendly format and use the same metrics and definitions of risk for trial accruals not meeting their targets. This will allow the institution to identify trials that need intervention to get back on track using a single set of criteria across all research teams. Users in different roles, whether department heads, principal investigators, or study coordinators can view the current accrual for all the trials that they administer or work on in one central location. The dashboard will also help to identify quality issues in OnCore by performing data quality checks nightly. Discussion/Significance of Impact: By providing a central location for role-based access to timely clinical trial accrual for the institution, the dashboard helps to identify trials at risk of not meeting their recruitment targets as early as possible to provide corrective advice/measures.

Objectives/Goals: Collaborations between Academic Medical Centers (AMCs) and Historically Black Colleges & Universities (HBCUs) are critical to addressing health disparities and building research capacity. Herein, we examine the Duke-NCCU Collaborative Translational Research pilot funding program [2018–2023] to identify opportunities, challenges, and lessons learned from querying key stakeholders. Methods/Study Population: The Duke-NCCU collaborative pilot funding program was launched to support new inter-institutional collaborations that aim to accelerate research discoveries into testing in clinical or population settings. Eight one-year, $50,000 collaborative grants were awarded. Each funded team was assigned a CTSI Project Leader (PL) for project management support. To evaluate the program, we developed surveys targeting principal investigators (PI) and PLs. Questions covered collaboration motivation, goals, outcomes, operational processes, project management support, institutional differences, and challenges. Qualitative analysis will be employed to evaluate the responses and identify common themes. Results/Anticipated Results: The PI survey examines aspects of inter-institutional collaborations, focusing on common themes, such as authorship, definition of success, and institutional culture. The PL survey prompts feedback on managing inter-institutional teams, expectations, and challenges. Select questions were shared between both surveys to capture both perspectives. Surveys were reviewed by members of the Duke CTSI evaluation and team science teams. The PI survey will be disseminated to 16 investigators, while the PL survey will reach 5 project leaders. Built on Qualtrics, each survey takes 20–30 minutes to complete. To encourage participation, incentives will be offered as two $100 gift card drawings. Respondents can choose to complete the survey on Qualtrics or through a recorded and transcribed Zoom session. Discussion/Significance of Impact: AMC-HBCU inter-institutional collaborations drive innovation, workforce development, and equitable dissemination of outcomes. This study exemplifies collaboration, offering insights into translational research collaborations critical to advance equitable healthcare and improving population health.

Objectives/Goals: In January 2023, Mayo Clinic set a goal to have 10% of studies open for six months or more without accrual. At that time, Mayo Clinic Florida had 19% non-accruing studies and 18% non-accruing clinical trials. Research administration implemented strategies to improve accrual outcomes. Methods/Study Population: Two strategies were developed to address non-accruing trials: a clean-up approach and a proactive approach. The clean-up approach involves escalating studies that haven’t been enrolled in over 6 months, identifying barriers, and escalating communication with the principal investigator (PI) and research administration alongside a physician partner. The proactive approach targets studies at the 3-month mark to address issues before reaching 6 months without accrual. Both strategies aim to reduce the cost and effort of non-accruing studies by either creating an enrollment plan or closing the study. Results/Anticipated Results: Since implementation, Mayo Clinic Florida’s non-accruing study portfolio decreased by 10%, and its clinical trials non-accruing portfolio decreased by 7% as of October 2024. Research Administration tracks key metrics (reasons for no enrollment, justifications, and actions) to identify trends and mitigate future accrual risks. A REDCap electronic data capture tool hosted at Mayo Clinic (supported by CCaTS grant UL1TR002377)1 notifies principal investigators when their studies are non-accruing. Future plans include establishing an API with Mayo Clinic’s portfolio management system to streamline the process while maintaining awareness and collaboration. Discussion/Significance of Impact: Through increased monitoring, enhanced communication, and deeper collaboration, Mayo Clinic Florida effectively reduced non-accruing studies in its research portfolio. This approach minimizes effort and costs associated with under-enrolled studies while tracking key metrics to inform future study development.

Objectives/Goals: Historically, the Univ. of Missouri (MU) Sch. of Medicine (SOM) is known for its strong clinical and education programs. In 2020, MU recruited an Executive Vice Chancellor (EVC) for Health Affairs and subsequently Dean of the SOM who initiated programmatic steps to develop and establish a centralized clinical and translational research (CTR) infrastructure. Methods/Study Population: In order to develop and establish a CTR infrastructure, the EVC/Dean of the SOM created and recruited to the combined position of the Associate Dean (AD) for CTR and Associate Director (ADR) of clinical research (CR) for the Ellis Fischel Cancer Ctr. (EFCC) in 2021. The AD CTR was appointed the Chair of the Clinical and Translational Science Unit (CTSU) Steering Committee with the charge of establishing a 10,000 sq. ft. CTSU to be housed in the newly built $225M Roy Blunt NextGen Precision Health Bldg. and for re-building the Clinical Trials Support Office (CTSO), and the Clinical Trials Office (CTO) at the EFCC in his other role as the ADR CR. The AD CTR was also charged with implementing the OnCore clinical trial management system (CTMS) to centrally track and fiscally manage SOM clinical trials. Results/Anticipated Results: Between 2021 and 2023, a CTR infrastructure was developed and established at the MU SOM. A total of 25 new clinical research staff (CRS) and leadership were hired that included a Sr. Dir. of CR Operations, clinical research nurses (CRNs) and coordinators (CRCs), Regulatory/Data/Fiscal/Project/Compliance/Coverage Analysis Coordinators between the CTSU/CTSO and the CTO of the EFCC. The CTSU was built with 10 FTE CRS [CRNs  =  5, CRCs  =  2, administrative staff  =  2, Sr. Lab. Tech.  =  1, and a manager]; the CTSO was re-built with 9 FTE CRS [Fiscal (n = 3), Project (n = 2), Compliance (n = 2), Coverage Analysis (n = 1) and Recruitment (n = 1) coordinators]. The EFCC CTO was re-built with 8 FTE CRS [CRNs  =  4, Fiscal (n = 1), Data (n = 1) & Regulatory (n = 2) coordinators]. The implementation of the OnCore CTMS tracking function was also completed. Discussion/Significance of Impact: Overall, the development and establishment of the CTR infrastructure has led to a significant increase and enhancement (e.g., capacity) to conduct clinical trials at the MU SOM. For example, this has led to a significant increase in the average annual enrollment to interventional oncology clinical trials [n = 82 (2021–2023) vs. n = 42 (2016–2020), p = 0.004].

Objectives/Goals: Our goal for this project is to develop a metric that integrates the intersectional social and structural determinants of health and well-being into the existing policy development framework to impact the integration of such considerations on population mental health. Methods/Study Population: This project was developed from a case study module offered by the Translational Research Program at the University of Toronto. This course was designed to sharpen contextual inquiry skills and further develop a case through employing strategies, including outreach engagement with stakeholders, conducting informational interviews and formulating potential pathways forward based on the integration of insights from interdisciplinary perspectives. Results/Anticipated Results: The anticipated outcome would be improved mental health outcomes as measured by the Mental Health Commission of Canada’s Mental Health Indicators (Mental Health Commission of Canada, 2015) Discussion/Significance of Impact: Although there are established mental health indicators and policy development framework the two operate independently of each other. Our proposal bridge the gap between the sectors so that one may inform the other, and they can collectively formulate reflective and representative policies.

Objectives/goals: This project will focus on identifying the barriers that result in low adherence to quality care indicators that establish effective and efficient pediatric emergency care. The overall objective is to understand motivations behind adherence (or lack thereof) and find solutions to facilitate compliance. Methods/Study Population: This study will use a mixed-methods design to investigate the barriers. Quantitative data will be collected through a survey provided to healthcare providers involved in pediatric emergency care, including physicians, nurses, and administrative staff in both pediatric and general hospitals. Qualitative data will be collected through semi-structured interviews with a group of respondents to gain insight on their experience regarding compliance. Quantitative data will be analyzed using statistical analyses while qualitative data will undergo a thorough thematic analysis. Both sets of data will be reviewed to identify themes and differences in barriers across hospital types and healthcare roles. Results/Anticipated Results: We will have gathered insights and perspectives from key stakeholders that are relevant to our study to ensure a comprehensive understanding of any potential implications that may arise from our study. We anticipate that the specific results will highlight key differences in adherence between pediatric and general hospitals. The study is expected to identify specific barriers hindering compliance with established guidelines in both settings. The results may be used to increase adherence to critical quality indicators and improve patient care. Discussion/Significance of Impact: Pediatric injury care prioritizes the immediacy of care for children with acute illness and injury. With certain hospital protocols not being adhered to, there is a risk of wasting crucial time and resources that can affect patient care outcomes. The results would provide recommendations to improve and increase efficiency in pediatric injury care.

Objectives/Goals: To identify gaps in policy that influence enrollment trends of patients with multimorbidity in Phase III clinical trials. We aim to propose policy recommendations for increasing use of real-world evidence (RWE) that increases safety and efficacy information for patients with multimorbidities. Methods/Study Population: Conduct a systematic policy analysis on the current regulatory landscape of RWE, referencing the 2020 FDA Guidance “Enhancing the Diversity of Clinical Trial Populations.” Evaluate guidelines using the Department of Health and Human Services’ (HHS) 2016 Regulatory Impact Assessment (RIA) Framework. Utilize the Center for Drug Evaluation and Research’s New Molecular Entity Database to identify novel hypertensive drugs approved after 2006, and assess clinical studies’ alignment with the 2020 Guidance. Review additional policies, FDA guidelines, and ICH documents to establish baseline compliance. Two case studies will evaluate past policy impacts on drug development. Assess costs and benefits of increasing multimorbid patient enrollment to inform a policy framework. Results/Anticipated Results: Anticipated results include all components of the HHS’s RIA and a policy framework informed by the assessment. To identify problems, an analysis of clinical trial exclusion criteria in novel hypertensive drugs will be conducted to show diversity and enrollment gaps in regulatory policy, referencing the FDA’s 2020 Guidance. The RIA’s cost–benefit analysis will highlight costs faced for utilizing RWE and expanding enrollment criteria in Phase III studies. The cost–benefit analysis, RIA, and case studies will inform a policy framework that explains dynamics between stakeholders and outline policies that increase clinical trial representation in ways that are less burdensome to sponsors and patients. Discussion/Significance of Impact: By understanding the barriers to enrolling participants with multimorbid conditions, we can outline incentives to increase diverse trial populations, helping healthcare providers choose more treatments for complex conditions. This research supports policy recommendations to make drugs more representative of conditions the population faces.

Objectives/Goals: To present findings from a focus group study that evaluate clinical research professionals’ (CRPs) team science learning preferences. The study aims to better understand CRPs’ experiential perceptions of team science skills, training gaps, team cohesion, conflict, and contributions for their preferred team science training. Methods/Study Population: This study targeted CRPs across various roles in Academic Health Centers via focus groups. The focus groups will assess current skills, identify training gaps, and share experiences on team cohesion, team conflict, team contribution, and their thoughts and perceptions about clinical research professional team science training. The focus groups will be held via Zoom in the Autumn of 2024 with volunteer participants from an initial survey that was conducted earlier in 2024. We will report on combined data from multiple 90-minute focus groups, with approximately 6 participants per session. Results/Anticipated Results: The focus group facilitator’s guide includes questions informed by the CRP team science learning needs assessment results and other questions on team issues that would benefit from focused training. Focus group methods and demographic characteristics of the participants by role and experience level will also be presented. Qualitative analyses of recorded focus-group discussions will present key themes by demographic groups, and as a whole, these data will contribute to the development of CRP team science educational programs and toolkits. Discussion/Significance of Impact: CRPs are vital members of clinical translational science teams. Overlooking CRP team science training can negatively affect the efficiency and effectiveness of the clinical translational science enterprise. CRP team science skills will foster a more collaborative and productive research environment.

Objectives/Goals: This research aims to harness electronic health records (EHR) combined with machine learning (ML) to predict necrotizing enterocolitis (NEC) in preterm infants using data up to their first 14 days of life. We aim to provide interpretable results for clinical decisions that can reduce infant mortality rates and complications from NEC. Methods/Study Population: Through a retrospective cohort study using data from the University of Florida Integrated Data Repository and One Florida, we will develop machine learning models suitable for sequential data to predict NEC. Our inclusion criteria include very low birth weight (VLBW; < 1500g) infants born < 32 weeks gestation and EHR data availability from the first 14 days of life. We will include infants with NEC and infants without NEC to train our ML model. Exclusion criteria include infants diagnosed with spontaneous intestinal perforation and severe congenital anomalies/defects requiring surgery. Results/Anticipated Results: We anticipate that our model will provide an accurate and dynamic prediction for the risk of NEC in neonates using data up to the first 14 days of life. Our model will be interpretable to identify key risk factors and can integrate real-world clinical insights to increase early detection and improve patient outcomes. Discussion/Significance of Impact: The development of a model to predict NEC could be used in neonatal intensive care guidelines and protocols and could ultimately decrease mortality, reduce complications, improve the overall quality of care, and lower healthcare costs associated with NEC.

Objectives/Goals: The Standards for Reporting Implementation Studies (StaRI) are the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network 27-item checklist for Implementation Science. This study quantifies StaRI adherence among self-defined Implementation Science studies in published Learning Health Systems (LHS) research. Methods/Study Population: A medical librarian-designed a search strategy identified original Implementation Science research published in one of the top 20 Implementation Science journals between 2017 and 2021. Inclusion criteria included studies or protocols describing the implementation of any intervention in healthcare settings. Exclusion criteria included concept papers, non-implementation research, or editorials. Full-text documents were reviewed by two investigators to abstract and judge StaRI implementation and intervention adherence, partial adherence, or non-adherence. Results/Anticipated Results: A total of 330 documents were screened, 97 met inclusion criteria, and 47 were abstracted including 30 research studies and 17 protocols. Adherence to individual StaRI reporting items ranged from 13% to 100%. Most StaRI items were reported in >60% of manuscripts and protocols. The lowest adherence in research studies was noted around economic evaluation reporting for implementation (16%) or intervention (13%) strategies, harms (13%), contextual changes (30%), or fidelity of either the intervention (34%) or implementation (53%) approach. Subgroup analyses were infrequently contemplated or reported (43%). In protocols, the implications of the implementation strategy (41%) or intervention approach (47%) were not commonly reported. Discussion/Significance of Impact: When leveraging implementation science to report reproducible and sustainable practice change initiatives, LHS researchers will need to include assessments of economics, harms, context, and fidelity in order to attain higher levels of adherence to EQUATOR’s StaRI checklist.

Objectives/Goals: To design a flexible, comprehensive framework for Data Science Units to cultivate sustainable, long-term relationships with Clinical and Translational Science Research Units. Best practices for managing Data Science collaborations are presented to improve the quality and efficiency of research conducted throughout academic health centers. Methods/Study Population: Leaders of Data Science Units across six institutions formed a workgroup to develop guidance and best practices for Data Science Units to establish long-term, sustainable collaborations with Clinical and Translational Science Research Units. This guidance is based on tools and protocols developed and employed by the participating units, which range from larger groups with over 20 partnerships to a unit with three partnerships that is actively working to expand. Importantly, partnerships are highly variable, with some partnerships at one institution representing engagement with over 500 faculty, whereas some partnerships at another institution involve the lab of a single investigator. Results/Anticipated Results: We offer guidance in three domains: (1) Identifying the needs for a new partnership, including assessing required effort and data science expertise, setting partnership priorities, developing formal agreements, and identifying goals and metrics; (2) managing data science teams by implementing regular meetings, creating project intake and prioritization processes, and effort monitoring; and (3) evaluating the successes and failures/gaps of the collaboration by measuring the metrics mapped to the goals. For each domain, we provide specific suggestions on which parties should be involved and how frequently the processes should occur. This guidance is applicable both to larger collaborative partnerships and to smaller, single faculty or staff partnerships, whether they are new or well-established. Discussion/Significance of Impact: Effective collaboration between data scientists and clinical and translational investigators is key to advancing data-driven research. The guidance and resources are presented to support Data Science Units in successfully managing long-term collaborations through goal-development, evaluation, and adapting to evolving research needs.

Objectives/Goals: Develop a cervicovaginal mucus replacement to prevent bacterial vaginosis (BV) in pregnant women. Our therapeutic, specialized hydrogel in natural enhancement for vaginal health (SHINE-VH), is formulated through polymer chemistry, tested for efficacy and safety through microbiology, and translated through clinical and translational science. Methods/Study Population: We will develop SHINE-VH with optimized viscoelastic and mucoadhesive properties intended to mimic healthy vaginal mucus. SHINE-VH will be synthesized via robust photoiniferter methods and investigated through shear rheology, sugar binding, and permeability studies. To evaluate the biocompatibility and safety profile of SHINE-VH, we will utilize a series of in vitro models to test its impact on the viability and cytotoxicity of human vaginal epithelial cells. In addition, we will assess the capacity of SHINE-VH to fortify vaginal barrier integrity and modulate anti-inflammatory activities in a 2D epithelial barrier model exposed to BV-associated pathogens. Lastly, employing organ-on-a-chip technology, vaginal swabs from both healthy and suspected BV pregnant patients will be treated with SHINE-VH. Results/Anticipated Results: SHINE-VH will mimic the protective, hydrophilic gel network of natural mucins. The viscoelastic properties of our formulation determined by shear rheology will be tuned through concentration and polymer composition to mimic vaginal mucus. We will also show the facile movement of small molecule nutrients through the SHINE-VH network via sugar-binding and permeability tests. Additionally, we anticipate that the introduction of SHINE-VH, due to their xenobiotic nature as synthetic mucins, will modulate the microbiota by diminishing inflammation, thereby reinforcing the cervicovaginal mucus and cultivating a vaginal microbiome that is more resilient to the disruptive impacts of BV. Such modulation could lead to a marked difference between the SHINE-VH-treated and untreated groups. Discussion/Significance of Impact: BV affects ~30% of women globally and is associated with severe gynecologic and obstetric complications, representing a significant unmet need in women’s health. SHINE-VH offers a novel approach to BV management, aiming to strengthen vaginal mucosal integrity, potentially reducing BV prevalence, and improving women’s health outcomes.

Objectives/Goals: Delving into the intricate web of translational research collaborations, this study analyzed the evolving landscape of the Hispanic Alliance of Clinical and Translational Research from 2020 to 2024 using cutting-edge social network analysis (SNA). SNA is a powerful tool for visualizing, understanding, and harnessing the power of networks. Methods/Study Population: We conducted a systematic document review of all the Alliance IDeA-CTR Network Calls for Pilot Projects from 2020 to 2024 including key attributes of the investigators and collaborators (e.g., academic institution, highest degree, collaborator type). Scientific collaboration was defined as two or more researchers working together in a grant proposal for a pilot project application. Study data was recorded and tracked using an Excel spreadsheet. R-Statistical software was used to analyze and map the networks resulting from collaboration interactions comparing the 2020 Call and 2024 Call. Network statistics were performed including nodes, isolates, edges, components, density, diameter, average degree, and the size of the main component. Results/Anticipated Results: Within a vibrant network comprising over 150 investigators from local and national academic institutions, clinicians (49.3%), and basic researchers (25.4%) are predominant. Initial findings showcase a remarkable surge in interdisciplinary collaborations and affiliations over time. Preliminary findings demonstrated that the number of nodes/actors increased from 16 to 75 comparing 2020 to 2024 and the edges/relationships from 12 to 66. Notably, the number of translational research clusters surged from 4 to 18, with mentorship emerging as a critical conduit bridging diverse research clusters; 16 to 78 nodes in comparison from 2020 to 2024. More extensive collaborative clusters occurred across time with over 20 researchers collaborating. A mentor was the main actor connecting these research clusters. Discussion/Significance of Impact: This study unveils the intricacies and power of translational research dynamics, showing a palpable surge in collaboration diversity and depth. By harnessing data-driven insights, our approach catalyzes informed decision-making to amplify collaboration, diversity, and network efficacy, offering invaluable guidance for policy and practice.

Objectives/Goals: Osteosarcoma (OS) is the most common primary bone malignancy in humans and dogs. >40% of children and >90% of dogs succumb to metastatic disease. We hypothesize MYC overexpression in metastatic canine and human OS contributes to an immunosuppressive tumor environment by driving tumor-associated macrophage influx and T lymphocyte exclusion. Methods/Study Population: To characterize the role of oncogenic MYC signaling in the canine metastatic tumor immune microenvironment (TIME), 42 archived FFPE lung metastatic canine OS samples were evaluated for MYC copy number variation (CNV), mRNA, and protein expression via ddPCR, nanostring analysis, and immunohistochemistry (IHC). Seven samples also underwent GeoMX spatial profiling to more specifically evaluate T cell and macrophage transcriptional profiles based on MYC status. To determine the role of MYC target modulation as a potential therapeutic option, canine and human OS cell lines were treated with a novel MYC inhibitor (MYCi975) and assessed for effects on survival, proliferation, and cytokine profiles. Results/Anticipated Results: We demonstrate that copy number gains are not a key driver of MYC hyperactivity in canine metastatic OS. However, stratification based on MYC protein expression demonstrates that “MYC-high” tumors are associated with downregulation of cytotoxic effector T-cell associated transcripts and upregulation of tumor-associated macrophage (TAM) and extracellular matrix remodeling transcripts. We also report that MYCi975 treatment of canine and human OS cell lines results in significant inhibition of OS cell survival and proliferation at concentrations that are pharmacologically achievable in mice. Furthermore, we demonstrate MYC inhibition by MYCi975 is associated with reduced pro-inflammatory cytokine secretion in OS cell culture models. Discussion/Significance of Impact: While MYC overactivity in metastatic canine OS may not be genomically driven, other mechanisms that lead to increased MYC protein expression are associated with transcriptomic profiles supportive of local immunosuppression. Pharmacologic targeting of MYC may serve as a strategy to bolster immunotherapeutic options in metastatic OS treatment.

Objectives/Goals: High-performing translational teams (TTs) effectively draw knowledge from empirical data to develop health solutions. However, some TTs lack rigorous data approaches, resulting in inefficiency. The ICTR data science initiative integrates team-oriented data science for more innovative and reproducible translational research. Methods/Study Population: To help TTs better leverage data science, the Institute for Clinical and Translational Research (ICTR) at the University of Wisconsin-Madison orchestrated a strategic initiative involving four main actions. • Assess needs. Determine how TTs are using data science and identify essential tools for success. • Establish partnerships. Develop strategic relationships to centralize resources and engage data scientists. Provide team science training to ensure effective integration. • Develop educational pathways. Design and implement workshops to demystify novel data science tools and upskill translational scientists. • Facilitate culture change. Identify ways that all ICTR services can help identify needs, foster educational pathways, and encourage partnerships to help TTs better leverage data science. Results/Anticipated Results: Initial assessments indicated that fewer than 25% of TTs receiving pilot awards used data science tools, and only 10% had a data scientist on their team. Data from collaboration planning sessions indicated that few TTs used data science, but all were interested in learning more. To address this deficiency, ICTR partnered with the Data Science Institute and the Section of Applied Clinical Informatics. This expertise informed resource development (e.g., a data science primer, websites) and generated workshops. Educational opportunities include tailored workshops to help TTs better curate data and create more efficient workflows, graduate course modules to improve rigor and reproducibility, and seminars illustrating translational applications of AI, visualizations, and large data integration. Discussion/Significance of Impact: The ICTR Data Science Initiative was designed to empower TTs to more effectively integrate data to power translation. As data science approaches and expertise are embedded within teams, we anticipate continued increases in interest and usage of data science tools, collaborative publications, and data rich applications for extramural funding.

Objectives/Goals: The objective of the Clinical and Translational Science Awards (CTSA) Program Collaborative and Innovative Acceleration (CCIA) Award Initiative is to support synergistic collaborations to develop, demonstrate, and sustainably implement innovative solutions across and beyond the CTSA Consortium. Methods/Study Population: All CCIA awards between 2016 and 2022 were reviewed and analyzed by Fiscal year, activity code, and research area using NIH analytical tools and platforms. Subject matter experts categorized each award by research topics, study populations, stage of translational science, and innovation type. The number and type of collaborating organizations were noted and major accomplishments and expected outcomes were summarized. Results/Anticipated Results: Between FY2016 and FY2022, NCATS funded 37 U01 and 18 R21 CCIA awards including >90 different public and private partnering organizations. CCIA awards spanned all stages of translation including preclinical (26%), clinical (36%), implementation (31%), and public health research (7%). Of the 55 CCIA awards, 31% focused on urgent public health needs and 25% were designed to address health disparities. Broadly, types of innovations included: Data science-related projects (18%), clinical care innovations (15%), biomarker or clinical outcome assessments (13%), digital health solutions (11%), therapeutic development (11%), therapeutic discovery (9%), education and training (7%), diagnostic tools (5%), software tools (5%), or tools for clinical research (5%). In total, >735 publications cited CCIA awards. Discussion/Significance of Impact: For >8 years, the CCIAs have brought together researchers from diverse scientific disciplines across the nation to speed the development of new health solutions with broad impact. Advancements in genomic screening, for example, have led to policy changes while new delivery approaches have improved the quality of care for underserved populations.

Objectives/Goals: Community health workers (CHWs) are links between the community and healthcare. As primary care (PC) expands to address social drivers of health, CHWs are becoming part of PC teams, yet how the two integrate is not well understood. Using an input-mechanism-outcome (IMO) model, this research seeks to develop a model to expand CHW-PC integration efforts. Methods/Study Population: Participants were recruited from Roots Community Health Center (Roots), a CHC serving historically marginalized communities, that has successfully integrated a CHW role, Roots Health Navigators (RHNs), into PC services. The preliminary conceptual framework for this study was guided by an overarching IMO model and informed by social identity theory, team science, and the interprofessional care literature. A mixed methods study was conducted in three phases: 1) cross-sectional survey, 2) semi-structured interviews, and 3) model development. The survey identified team dynamics such as communication, trust, and shared understanding, and interviews explored how these collaborative teaming mechanisms take shape. Findings were merged into a final model of CHW-PC integration that was reviewed by Roots leaders. Results/Anticipated Results: Survey results (n  =  25) highlighted highly rated team dynamics including shared understanding and acting and feeling like a team. Qualitative findings (n  =  10) described how integration occurred through complex interactions that were community-responsive and collectively reduced burnout among the team. Joint findings noted the importance of RHNs to continuity of care, building trust, and enhancing PC team effectiveness. Findings informed the development of a model of CHW-PC integration. This expanded on the preliminary conceptual framework by highlighting the dynamic relationship between mechanisms, processes, and team emergent states, as well as providing evidence to support feedback loops between inputs, mechanisms, and outcomes with overarching influence from the contextual setting. Discussion/Significance of Impact: With a deeper understanding of the mechanisms of CHW-PC integration, findings informed the development of a model that can support other communities to replicate this approach to care and address critical patient needs. Teaming factors that sustain CHW-PC integration may be transferrable to other care teams integrating nontraditional roles.

Objectives/Goals: The World Trade Center (WTC) Health Program (Program) Data Group was formed to address the increasing volume and complexity of analytics requests and to improve the Program’s data management capacity. Over time, the Group’s role expanded to include comprehensive data leadership and providing data-based support for decision-making. Methods/Study Population: The Program provides medical monitoring and treatment for WTC-related conditions to those directly affected by the 9/11 attacks. These activities generate an abundance of administrative and surveillance data. The Data Group was formed to establish structures and processes that would be adaptable and efficient in leveraging these data. We created a unified workflow including a shared inbox, a standardized request form, and a request-managing tracker. We established communication channels to distribute requests efficiently. We designed a request form to balance the administrative burden on requestors with the need to gather useful information for analyses. We also developed a documentation system to extract key details from forms and incorporate other relevant data to support evaluation and record-keeping. Results/Anticipated Results: From November 2021 through the end of 2023, the Data Group processed and fulfilled 93 data requests. These requests covered a multitude of functional areas essential to the administration of a limited health benefits program. The following top five functional areas made up two-thirds of all requests: Contract Management (n = 30), Research and Quality (n = 15), Operations (n = 11), Medical Policy (n = 10), and Communications (n = 7). Leveraging data collected through our request tracker, the Group conducted annual evaluations and developed visualizations to analyze trends in these requests. The evaluations helped us identify knowledge gaps, highlight areas for improvement – across the Program and within our own processes, and continue to guide and support future Program priorities. Discussion/Significance of Impact: The creation of the Data Group and unified workflow fulfilled the Program’s increasing analytic needs, enhanced oversight of data quality and usage, and facilitated data-driven Program decision-making. Continual optimization of the group’s processes enables opportunities to identify gaps in and support a range of health care delivery initiatives.