OBJECTIVES/GOALS: The Monte Carlo dose calculation method is often considered the “gold standard” for patient dose calculations and can be as radiation dose measurements. Our study aims to develop a true Monte Carlo model that can be implemented in our clinic as part of our routine patient-specific quality assurance. METHODS/STUDY POPULATION: We have configured and validated a model of one of our linear accelerators used for radiation therapy treatments using the EGSnrc Monte Carlo simulation software. Measured dosimetric data was obtained from the linear accelerator and was used as the standard to compare the doses calculated with our model in EGSnrc. We will compare dose calculations between commercial treatment planning systems, the EGSnrc Monte Carlo model, and patient-specific measurements. We will implement the Monte Carlo model in our clinic for routine second-checks of patient plans, and to recalculate plans delivered to patients using machine log files. RESULTS/ANTICIPATED RESULTS: Our Monte Carlo model is within 1% agreement with our measured dosimetric data, and is an accurate representation of our linear accelerators used for patient treatments. With this high level of accuracy, we have begun simulating more complex patient treatment geometries, and expect the level of accuracy to be within 1% of measured data. We believe the Monte Carlo calculation based on machine log files will correlate with patient-specific QA analysis and results. The Monte Carlo model will be a useful tool in improving our patient-specific quality assurance protocol and can be utilized in further research. DISCUSSION/SIGNIFICANCE OF IMPACT: This work can be implemented directly in clinical practice to ensure patient doses are calculated as accurately as possible. These methods can be used by clinics who do not have access to more advanced dose calculation software, ensuring accuracy for all patients undergoing radiotherapy treatments.

OBJECTIVES/GOALS: Our purpose is to promote traffic toward the NJ ACTS Regulatory Cores recently launched website and increase investigator engagement through marketing strategies on LinkedIn. Landscaping to characterize the profiles of researchers on LinkedIn was also completed to estimate the feasibility of engaging with a target population on LinkedIn. METHODS/STUDY POPULATION: Insight gathering was performed to analyze what percentage of researchers possessed a LinkedIn profile and actively used their accounts. A sample population consisting of 284 NJ ACTS members were analyzed to summarize the type of researchers on LinkedIn, and their likelihood of responding to LinkedIn marketing campaigns. Efforts to launch a company LinkedIn page and collect followers were completed. Different methods of promotion were evaluated, including direct vs. mass email outreach to over 600+ researchers at Rutgers. Effectiveness of our platform was measured by comparing/overlaying Regulatory website traffic with LinkedIn traffic, as well as tracking the metrics of LinkedIn posts. RESULTS/ANTICIPATED RESULTS: Among 284 NJ ACTS members, 76% (n=215) possess a LinkedIn profile, but only 21% (n=59) are actively interacting with material on LinkedIn, such as creating, commenting, or sharing posts. Among the NJ ACTS LinkedIn users, 27% of individuals (n=57/215) responded to a direct outreach. Retention of the created organizational page was strong, as most users who visited the Regulatory Core page were likely to become followers. Massive email outreach to 600+ researchers within RBHS did not yield a strong LinkedIn following, however it did result in strong signals of website traffic during the days after the promotion was sent. Engagement with posts on LinkedIn can also be amplified and messaging proliferated when colleagues reshare Regulatory posts on their personal feeds. DISCUSSION/SIGNIFICANCE: 3/4 of academic researchers are likely to be on LinkedIn but may not be active users of the platform. The most effective outreach is through direct messaging as opposed to broader, less individualized tactics (including mass email outreach). Evidence suggests potential to utilize LinkedIn to proactively engage in regulatory-related activities.

OBJECTIVES/GOALS: Compare metabolic health of type 2 diabetics on GLP-1 to those on traditional therapy METHODS/STUDY POPULATION: Outcomes of interest of this study include analyzing GLP-1 agonists on overall metabolic health, focusing primarily on weight loss and ability to wane off insulin without rebounding metabolic health. The data will be collected in a retrospective chart review from medical records of type II diabetics from Children’s of Alabama and will follow patients over two years. The charts have been narrowed to those patients prescribed GLP-1 agonists who have been on the medication for at least one year with consistent visits to the endocrinology clinic.The following data will be collected from the charts:race/ethnicity, date of visit, BMI/weight, A1C, insulin therapy, lipids, LFTs (AST/ALT), and insurance coverage RESULTS/ANTICIPATED RESULTS: With these results, we hope to study the metabolic health effects of GLP-1 agonists on type II diabetes in the pediatric population. It is known that obesity is a risk factor for type II diabetes, and that GLP-1 agonists aid in weight-loss in adults. Further research is needed to see the real world health effects, and with these results we hope to assess if GLP-1 agonist have an affect on metabolic health within the pediatric population by collecting data on values aforementioned. We also hope to compare and contrast the different GLP-1 agonists being used based on adherence, insurance coverage, adverse effects, and patient preference. Currently, only Liraglutide and Exetanide are approved for pediatric type II diabetics. DISCUSSION/SIGNIFICANCE: Insulin use can lead to weight gain; metformin does not aidin weight reductions. If GLP-1 agonists aid in weight loss, it could potentially help slow complications of diabetes in the pediatric population. B-cell function in children declines more rapidly; and, as a result, insulin resistance occurs more rapidly.

ABSTRACT IMPACT: Alterations in mitochondrial metabolism affect any tissue, especially those with the highest demand for energy. As the symptoms and clinical manifestations are heterogenous, disease diagnosis is challenging. The implementation of genetic-first approach in the diagnosis of mitochondrial diseases will expedite confirmation, treatment, management, and counseling of affected Puerto Rican individuals. OBJECTIVES/GOALS: Mitochondrial diseases are rare, and diagnosis is complex due to the heterogeneity of clinical manifestations. We aim to develop and implement a testing algorithm using a genetics-first approach, facilitating the identification of variants that contribute to mitochondrial disease’s etiology and influence onset and progression in Puerto Ricans. METHODS/STUDY POPULATION: This is a cross-sectional study for characterizing clinical laboratory results from profiles used to evaluate metabolic diseases in individuals with suspected mitochondrial disorders from 2018 to 2021. A subset of 25 individuals from biochemical profile will be recruited to analyze their medical and family history, metabolic biomarkers in blood and urine, hearing test, imaging and chromosomal microarray. The implementation of a genetic testing algorithm using whole exome and mitochondrial DNA sequencing will be performed in a subset of 11 randomized individuals. Descriptive analysis will be reported, including a catalog of all variants. Multivariate analysis will be performed to estimate the statistical association between variants and phenotypes reported and adjusting for potential confounders. RESULTS/ANTICIPATED RESULTS: The biochemical profile of pediatric Puerto Rican individuals suspected of having mitochondrial diseases will be altered and can be used to differentiate among other metabolic causes. We expect to find altered levels of lactate, pyruvate and carnitines in serum, as well as altered organic acids in urine. The implementation of a testing algorithm using both, mitochondrial DNA and whole exome sequencing as first approach will be enabling the identification of disease-causing variants, thus enhancing and confirming the diagnosis of mitochondrial disease in Puerto Ricans. We will be able to identify rare/novel variants specific to our Hispanic population, for both nuclear and mitochondrial DNA. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study will help to characterize the metabolic profile of pediatric Puerto Ricans. No previous study has been reported that describes testing algorithms for genetic diagnosis of mitochondrial disease in our population. Variants found will contribute to a deep understanding of the genetic contribution to phenotypes and disease susceptibility.

Objectives/Goals: A clinical decision support tool (CDS) with an individualized prescribing approach was piloted to determine impact on opioid overprescribing rates for surgical patients at discharge. As a follow-up, clinicians were interviewed to determine current challenges to opioid prescribing and required components of an effective CDS for discharge opioids. Methods/Study Population: In the EHR-based randomized control trial, patients admitted to a qualifying surgical service for 24+ hours postoperatively were randomized to the individualized CDS tool with a taper based on inpatient opioid use or the standard discharge order set. Performance of the CDS tool was assessed by rate of opioid mismatch, that is, the difference between median inpatient and daily discharge dose in morphine milligram equivalents (MMEs). Pilot results were further investigated using a sequential explanatory design. Semi-structured interviews were held with discharging clinicians. A purposive sampling strategy with snowball sampling was used to identify providers. Data saturation was used to determine sample sufficiency. Interview transcripts were coded and thematically analyzed, with 40% group coded. Results/Anticipated Results: Among 61 randomized patients, median discharge MME (30 [IQR 22.5, 45] remained four times the median inpatient MME (7.5 [IQR 0, 30]) after the pilot period. Nine nurse practitioners and six general surgery residents were interviewed (n=15). Perceived challenges to opioid prescribing centered on three themes: limited protocolization for standard cases; lack of workflow efficiency; and systemic barriers (patient insurance and pharmacy). Clinicians identified at least one useful component of the tool for their current practice. Crucial CDS tool components identified by prescribing clinicians included seamless integration with the electronic health record, adequate clinician training, effective communication of treatment plan with the patient, and clinician buy-in to overcome current prescribing culture. Discussion/Significance of Impact: Multiple patient- and system-related factors impede standardization of opioid prescribing for postoperative patients. CDS tools in this area are a well-received idea, and the tool is now being redesigned to promote effective integration into clinician workflow with multiple stages of intervention, prior to resuming the randomized control trial.

OBJECTIVES/GOALS: This study will collect multimodal and longitudinal data in adults with obsessive-compulsive disorder and healthy controls. A mixed effects random forest machine learning approach will be taken to develop a model that can predict individualized longitudinal OCD symptom burden. METHODS/STUDY POPULATION: Baseline resting state functional MRI (rsfMRI) and measures of symptom burden will be collected in adults with OCD and healthy controls. Longitudinal measures of behavior and physiology–such as heart rate, activity, and sleep metrics - will be collected using Fitbit Charge 5 tracker. Daily assessments of symptom burden and functional status will be collected through a smartphone app. Individuals with OCD will start pharmacotherapy during the study period and all participants will be followed for a total of 10 weeks. Repeat rsfMRI imaging will occur at study conclusion. Data will be analyzed using a mixed effects random forest machine learning algorithm with assessment of model performance. RESULTS/ANTICIPATED RESULTS: Prior studies of symptom severity in psychiatric illness and affect in non-clinical populations have found longitudinal features - such as lexical and acoustic measures, participant context, heart rate, and sleep metrics–that were predictive of these states over time. It is anticipated that the present study will extend these results to individuals with OCD and identify physiologic and behavioral features that track personalized symptom burden longitudinally in this patient population. A model able to predict when symptoms are elevated could allow for provision of additional treatment or interventions targeted to times of high symptom burden. DISCUSSION/SIGNIFICANCE: This study will be the first to collect and analyze longitudinal measures of behavior, symptoms, and physiology in patients with OCD with a goal of predicting symptom burden. Identification of elevated symptom burden would allow for implementation of just-in-time treatment, during these periods.

Objectives/Goals: A key barrier to clinical trial (CT) access is knowing which trials are enrolling. Frontiers has developed a Trial Finder mobile app that displays streamlined, up-to-date trial availability that can be integrated to other institutions. We’re gathering usability feedback and exploring interest from other institutions in adopting the tool. Methods/Study Population: The Frontiers Trial Finder app is a unique tool that extracts trial data and status directly from a Clinical Trial Management System (CTMS). It includes helpful links to ClinicalTrials.gov and provides ways to connect with the study team. The app updates automatically every night, storing the extracted data in a separate stream that can be accessed through the app or online. Its interface is custom-designed in-house and fully customizable, allowing the integration of additional resources such as FAQs, links to partner affiliates, and more. During onboarding, we identified two key efforts: first, organizing the trial directory by defining parent groups and subgroups; and second, encouraging research personnel to keep CTMS trial information current, as it now serves both operational and marketing purposes. Results/Anticipated Results: To organize the trial directory, we used the institution’s divisions as primary categories and disease types as subcategories. Since the CTMS previously didn’t capture ”disease type,” we added this field to the CT activation process. For studies already underway, we generated a report of currently enrolling trials. CTMS and CTSA staff collaborated to manually assign a disease type to each study. Going forward, adding a disease type to the directory will be a standard part of CT activation. Another challenge was that study teams were not accustomed to updating the CTMS when a study remained active but was no longer enrolling – largely because the CTMS had not been used for marketing. To support this shift, we launched a communication campaign to ensure all study teams understood and adopted the new expectations. Discussion/Significance of Impact: Depending on the state of an institution’s CTMS, onboarding this tool may require institutional messaging effort. However, the long-term advantages of automated data exchange significantly improve the efficiency of connecting volunteers to trials. Clinicians and volunteers alike can benefit from reliable, up-to-date trial availability.

OBJECTIVES/GOALS: Renal vascular resistance (RVR) is the opposition to blood flow by renal arteries. At the population level, dietary salt increases RVR and blood pressure (BP), which are associated with cardiovascular disease. Recent data indicate exogenous ketones may offset adverse cardiorenal effects of salt. METHODS/STUDY POPULATION: Our registered clinical trial (NCT05545501) is a double-blinded, placebo-controlled, crossover study. Participants are being randomized to three 10-day conditions: A) control; B) high salt; C) high salt and ketone supplementation. Ten participants are enrolled (target 30 participants). Renal blood velocity (RBV) in the renal and segmental arteries will be measured in the decubitus position using Doppler ultrasound during a 3-minute baseline and 3-minute cold pressor test. We will measure brachial BP with an automated oscillometric BP monitor. RVR will be calculated as mean BP divided by RBV. Statistical analyses will include ANOVA and correlations with α set at ≤ 0.05. RESULTS/ANTICIPATED RESULTS: We anticipate attenuated RBV and increased BP during the cold pressor test, particularly following high salt loading, leading to greater RVR. We hypothesize ketone supplementation will attenuate the high salt induced increase in RVR during the cold pressor test. In addition to RVR we will examine renal vascular conductance which is the ease with which blood flows through arteries, calculated as RBV divided by mean BP. Additional hemodynamics such as heart rate and systolic and diastolic BP will be reported and correlated with primary outcomes. DISCUSSION/SIGNIFICANCE: Dietary salt plays a role in hypertension, cardiovascular disease, and chronic kidney disease, which are leading causes of death. Ketone supplementation may be a promising approach to counteract the detrimental effects of high dietary salt and improve cardiovascular health in adults.

OBJECTIVES/GOALS: The LHS concept has been promoted as a means for health systems to improve quality, safety, efficiency and equity. NAMs definition has been widely adopted, but is broad and has led to variation in how LHS is operationalized. Drawing on a taxonomy developed through a review of literature, we developed a tool that shows how LHSs are implemented in practice. METHODS/STUDY POPULATION: The LHS Implementation Assessment Tool (LHS-IAT) will indicate which forms of work are being carried out by a health system that purports to operate a LHS. LHS-IAT is based on the LHS Consolidated Framework (LHS-CF); which was developed through a qualitative analysis of LHS literature. LHS-CF contains 38 primary elements’ and 56 secondary elements’ that have been associated with the LHS construct. These elements are organized into 5 bodies of work• (e.g.; translating evidence into practice) and 4 enabling conditions• (e.g.; supportive culture). LHS-IAT assesses whether a health system operating as an LHS is implementing each of the key elements in LHS-CF. The usefulness of LHS-IAT will be demonstrated by applying the tool to 5 LHSs that have been described in the literature. RESULTS/ANTICIPATED RESULTS: LHS-IAT produces a quantitative profile for any given health system operating as a LHS; each LHS element is assessed as either emphasized; otherwise present; or absent. With this information, we create profiles for each implementation of LHS, using spider graphs. Systems that emphasize different elements will have different shapes for their spider graphs. Based on our initial coding of publications, we expect at least 4 distinct profiles within our sample, reflecting differences in emphasis on factors such as: continuous improvement practices, adoption of internally and externally tested interventions, research conducted to address patient care issues prioritized by institutional leaders, investigator-initiated research, clinician-engaged research, and engagement of patients and families. DISCUSSION/SIGNIFICANCE: The LHS-IAT will show differences in how health systems are translating the LHS concept into practice. This will allow for a shared language for those studying and/or implementing LHS. With the ability to map out an approach, health system leaders will have a tool to clarify intent and gain consensus as to which LHS model they want to implement and invest in.

OBJECTIVES/GOALS: Clinical tissue specimens are primarily destined for formalin fixed, paraffin embedded processing to create a basis for diagnosis by microscopic examination. Innovations in specimen processing are required to expand its availability for inclusion as the substrate in assays that can contribute to the further development of Precision Medicine. METHODS/STUDY POPULATION: Transurethral resection of bladder tumors were selected for testing based on availability and tissue composition. A wash step was used to generate daughter aliquots composed of dislodged cells and a solution with prior contact to the parent tissue. This wash step served two purposes: 1) reduce the amount of contaminating material from spreading to other cases, a problem known to be associated with this type of specimen; and 2) create aliquots from which additional informative data could be generated. These daughter aliquots were then examined to determine their value as a source for exosome profiling, metabolomic studies, molecular characterization and organoid development. The parent tissue was not compromised, was able to undergo conventional processing and yielded results equivalent to unwashed specimens. RESULTS/ANTICIPATED RESULTS: Exosomes secreted by the tumor cells were identified to be present in the daughter aliquots by a combination of their isolation using CD31 and detection of miR-21 expression. These exosomes were confirmed to be not related to fragmented cells from testing for beta-tubulin. A global/discovery-based approach using mass spectrometry provided insights into early characterization of metabolomic profiles present in these tumor cells. Ample amounts of high quality DNA (226 ng/ul concentrations; 11.3 ug total) were recovered from the dislodged, excess cells in the wash for molecular studies. Finally, from viable cells recovered in one of the daughter wash aliquots, the ability to grow organoids was proven to be possible and reproducible. DISCUSSION/SIGNIFICANCE: Based on these results, the value of the clinical specimen can be markedly expanded for utilization in research and possible clinical use without detracting from the parent tissue. This non-destructive, easy to adopt wash procedure can potentially lead to an influx of data that may ultimately prove useful in improving patient care.

OBJECTIVES/GOALS: Our goal is to determine if a precision medicine approach to guide anti-platelet therapy for patients with ACS, post PCI, is feasible for a diverse urban population. Also, we will evaluate if guided therapy reduces major adverse cardiovascular events (MACE) while remaining economically sustainable. METHODS/STUDY POPULATION: This prospective, pragmatic study will enroll two-hundred patients with ACS undergoing PCI, receiving DAPT. Patients will receive point-of-care CYP2C19 genotyping. Patients with at least one loss-of-function (LoF) allele will be recommended prasugrel. Those without LoF alleles, will be recommended to take prasugrel for 7 days then clopidogrel for 7 days, followed with platelet reactivity phenotyping. Patients with HPR >208 P2Y12 reaction units will take prasugrel; the remainder will take clopidogrel. We will review electronic health records and contact patients at baseline, then at 1, 3, 6, and 12 months to collect data for cardiovascular and health-related quality of life (HRQoL) outcomes. RESULTS/ANTICIPATED RESULTS: Feasibility and clinical utility will be measured by the proportion of patients with a genotype or phenotype leading to a clinical recommendation of alternative therapy and whether or not recommendations were accepted by clinicians. Effectiveness will be measured by combined MACE (composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke), stent thrombosis, and major and minor bleeding over the study period. Cost of testing, 30-day hospital readmission, and HRQoL questionnaires will be included for pharmacoeconomic analysis from an institutional perspective. DISCUSSION/SIGNIFICANCE OF IMPACT: There are no studies investigating the clinical utility of implementing guided anti-platelet selection, combining CYP2C19 genotyping and HPR phenotyping. We anticipate incorporating this precision medicine approach to guide P2Y12 inhibitor selection will be feasible while improving patient outcomes.

ABSTRACT IMPACT: It provides insight in the relationship between pediatrics and clinical research and how pediatric participation in CT translates to clinical significance in form of drug labels, which inform clinicians on how to prescribe pediatric medications. OBJECTIVES/GOALS: Assessing the extent that the Best Pharmaceuticals for Children Act (BPCA) advances pediatric inclusion in clinical trials (CTs) and the availability of pediatric-specific drug information METHODS/STUDY POPULATION: The BPCA provides the U.S. Food and Drug Administration (FDA) authority to solicit sponsors whose drugs may benefit pediatric populations. Participation is voluntary and provides additional market exclusivity and pediatric information. CTs that received marketing exclusivity from 2016-2018 under BPCA were reviewed using Clinicaltrials.gov to access the legislation’s impact. CTs were categorized according to eligibility: (1) pediatric and adult groups, (2) pediatrics, and (3) pediatric sub-groups. Studies were excluded for ambiguous age data. Studies open to both groups were evaluated for pediatric participation. Each drug was searched in DailyMed.com for published pediatric indications. RESULTS/ANTICIPATED RESULTS: Between 2016 - 2018, 22 drugs received marketing exclusivity under BPCA. Of the 196 CTs conducted for these drugs, 135 were available to adults and pediatrics, 10 were available to the entire pediatric population, and 51 were available to specific pediatric sub-populations. Exclusion criteria permitted only 118 of the CTs for assessment where eligibility included both pediatric and adult populations, of which 65 of these had less than 1% pediatric representation. Of the 22 drugs, 20 have pediatric indications. Over this three-year period, the number of CTs where adults and pediatrics were eligible were greater than CTs for pediatric only or pediatric subpopulations. DISCUSSION/SIGNIFICANCE OF FINDINGS: It is prevalent for BPCA compliant CTs to include both; 65% of drugs (13/20) with pediatric indications had more studies involving both groups than only pediatrics. Adequate pediatric CT representation is necessary for developing pediatric drug labeling with meaningful data for clinical indications.

Interprofessional healthcare team function is critical to the effective delivery of patient care. Team members must possess teamwork competencies, as team function impacts patient, staff, team, and healthcare organizational outcomes. There is evidence that team training is beneficial; however, consensus on the optimal training content, methods, and evaluation is lacking. This manuscript will focus on training content. Team science and training research indicates that an effective team training program must be founded upon teamwork competencies. The Team FIRST framework asserts there are 10 teamwork competencies essential for healthcare providers: recognizing criticality of teamwork, creating a psychologically safe environment, structured communication, closed-loop communication, asking clarifying questions, sharing unique information, optimizing team mental models, mutual trust, mutual performance monitoring, and reflection/debriefing. The Team FIRST framework was conceptualized to instill these evidence-based teamwork competencies in healthcare professionals to improve interprofessional collaboration. This framework is founded in validated team science research and serves future efforts to develop and pilot educational strategies that educate healthcare workers on these competencies.

Objectives/Goals: There is growing interest in using physiological data produced from medical devices such as 12-lead electrocardiographs within the Observational Health Data Sciences and Informatic community. We have introduced new waveform extensions to support this need and illustrate its implementation using 12-lead ECG. Methods/Study Population: We identified 8,822 12-lead ECGs from Tufts Medical Center between January 1, 2018 and April 31, 2024. 6,702 of these ECGs were linked to patients in our Observational Medical Outcomes Partnership (OMOP) Research Data Warehouse (TRDW). The cohort of patients with linked ECGs were extracted into a study OMOP database in which the new Waveform Extension (WFExt) were implemented. A data extract/transform/load import process was developed that complied with the WFExt specifications and formed a new custom vocabulary and concepts to support waveform data that is not presently supported by standard vocabularies such as SNOMED and LOINC. Results/Anticipated Results: Using the OMOP Waveform Extensions allowed us to capture the full range of high-resolution physiological data contained in 12-lead electrocardiograms. The OMOP common data model is designed to capture low-resolution data contained in electronic health record systems. In emergency medicine, critical care, and neonatal care, medical devices play an important role in driving treatment decisions. Research therefore needs access to both clinical and medical device data in developing clinical decision support tools. In this project, we demonstrate the ability to connect clinical data such as outcomes in an OMOP database with device measurements, computerized interpretations, metadata, and provide linkage to the source device record to support signal processing and new algorithm development using the WFExt. Discussion/Significance of Impact: The OMOP WFExt mirrors work to support imaging data, and both extend the utility and research capacities among the many Clinical Translational Science Institutes that have already implemented an OMOP-based research warehouse. The WFExt is a standards-based and open science approach to support research and backed by a worldwide OMOP community.

Objectives/Goals: Rare disease patients often face lengthy delays in receiving accurate diagnoses or experience misdiagnoses due to a lack of available information. The NCATS Rare Disease Alert System (RDAS) is a public, comprehensive rare disease resource to collect and share accurate, up-to-date, and standardized data on rare diseases. Methods/Study Population: RDAS is composed of a frontend UI, Application Programming Interfaces, and backend Neo4j graph database. Each component of data collection, data annotation, data standardization, and data representation as steps were implemented during the process of each graph database creation. The UI allows users to search, browse, and subscribe to RDAS to receive the latest information and findings about their rare disease(s) of interest. The back-end data include four knowledge graphs built by integrating information from the NCATS Genetic and Rare Disease program, PubMed articles, clinical trials, and NIH grant funding. Ultimately, the integrative information pertinent to rare diseases from RDAS would advance rare diseases research. Results/Anticipated Results: Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting among mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. Discussion/Significance of Impact: We developed an integrative knowledge graph with rare disease data and demonstrated its use as a source to identify and generate scientific evidence to support rare disease research. With the success of this study, we plan to implement advanced computation to analyze more funding related data and link to other types of data to perform further research.