OBJECTIVES/GOALS: Glioblastoma (GBM) is the most malignant brain tumor in adults and remains incurable with an average survival of 15 months after diagnosis. There is great need for treatment options without side effects that are devastating to the quality of life for patients. GBM tumors can circumvent cellular damage by upregulating antioxidant production. METHODS/STUDY POPULATION: Highly aggressive tumors tend to exhibit increased oxidative metabolism, and thus rely on a mechanism to eliminate reactive oxygen species (ROS) in order for cells to evade autophagy and cell death. We propose that recurrent GBM cells achieve this is by promoting methionine metabolism, upregulating glutathione production and preventing ROS accumulation. We investigated the expression of AHCY and MAT2A, two key enzymes in the methionine pathway, at the gene and protein level in both GBM and non-GBM tissues. We probed for markers of cell death following pharmacological inhibition and siRNA knockdown, and performed metabolite-mediated rescue experiments. Finally, we evaluated changes in cellular respiration using the Seahorse XFe96 real-time mitochondrial stress test following inhibitor treatment. RESULTS/ANTICIPATED RESULTS: The selective AHCY inhibitor markedly reduced cell viability in different cancer cell types, but significantly reduced cell viability in recurrent GBM cells compared to newly diagnosed GBM (p=.009; MD: -0.828, 95% CI -1.350 to -0.306) and normal astrocytes (p=.073; MD: -0.609, 95% CI -1.305 to 0.085). AHCY and MAT2a protein expression appeared to be higher in GBM cells compared to normal astrocytes, medulloblastoma cells and other cancer cell lines. Genetic knockdown of AHCY and MAT2A demonstrated reduced cell viability, increased Caspase, SOD2, LC3-II and Transferrin receptor expression. Acute treatment with the AHCY inhibitor induced cell death, markedly reduced oxygen consumption rate and ablated spare respiratory capacity in recurrent GBM cells compared to newly diagnosed GBM cells. DISCUSSION/SIGNIFICANCE: Oxidative damage was induced following interference with key methionine pathway enzymes by pharmacological inhibition, while a similar concentration of drug largely preserved normal astrocyte viability. These results point to a novel targetable mechanism of disease progression and expand the realm of treatment options for recurrent GBM.

ABSTRACT IMPACT: The existing literature describing immune-related adverse events (irAE) has predominantly focused on clinical trial populations, which may not be representative of the broader population receiving immune checkpoint inhibitors (ICI), so we sought to perform a comprehensive evaluation of irAE in a real-world population of cancer patients being treated with ICI. OBJECTIVES/GOALS: With a cohort of patients with malignancy treated with ICI, we characterized incidence, severity, timecourse of ir-AE. We sought to inform providers who prescribe ICI to recognize the clinical burden of irAE in an effort to more effectively communicate the benefits and risks of ICI with patients. METHODS/STUDY POPULATION: After obtaining approval from the institutional review board, we used a pharmacy database to identify adult cancer patients treated with an ICI between January 2014 and October 2018. We used electronic medical records to obtain baseline variables. Each patient was followed at each clinic visit for 12 months for development of a physician-reported irAE.

For irAE, site and grade were recorded as documented by the provider. At diagnosis and each follow-up visit, we collected: 1) adjustments to ICI, immunosuppression, and hormone therapy.

Continuous variables were summarized using mean and standard deviation (SD) or median and interquartile range (IQR). Categorical variables were summarized using frequencies and percentages. Time to development and resolution of irAE were calculated using Kaplan-Meier curves. RESULTS/ANTICIPATED RESULTS: Among 131 patients, two-thirds were men, and 60% were Caucasian with a mean age was 65 years. Nearly 40% had an Eastern Cooperative Oncology Group performance score of 2 or higher. A small proportion (3.1%) had an autoimmune disorder. Nearly half had lung cancer (49.6%), and several had received radiation (33.6%). Over 70% were former or current smokers. In total, 57 patients (43.5%) developed an irAE, resultng in a total of 95 irAE, at a median of 208 days (126 days, not reached). The most common irAE included dermatitis, thyroiditis, pneumonitis, and hepatitis. Of 95 irAE, half were grade 1, 30% were grade 2, and nearly 20% were grade 3 or higher. Median time to resolution was 85 days (56-183 days). DISCUSSION/SIGNIFICANCE OF FINDINGS: This study demonstrates that irAE are clinically impactful in this relatively unfit, medically complex population, which may be a more accurate reflection of patients receiving ICI in everyday practice. This study showcases that this population is susceptible to irAE, although this needs to be examined in larger, prospective trials.

OBJECTIVES/SPECIFIC AIMS: National concerns about IRB-related research delays have led to re-assessment of IRB review processes at institutional levels. We sought to address whether a dedicated IRB Liaison Service at the Irving Institute’s central location could provide additional useful staff support to the investigator community for interactions with the IRB at various levels of protocol submission. METHODS/STUDY POPULATION: We evaluated the results of a user satisfaction survey and performed a focused in-depth analysis of Liaison Service impact. An online tracking and satisfaction survey was implemented for researchers to complete following each consultation. The goal was to gauge the uses, user types and usefulness of the Service, and to follow-up with researchers who might have additional questions. Data was gathered about users of the Service and their affiliations, and the topics and questions that were discussed. A terse summary was drafted to categorize each consultation that was conducted during office hour sessions. Additionally, surveys were emailed to researchers to gauge their experience with the Service and their overall satisfaction. Users completed the survey either in person at the end of the consultation, or by email request sent immediately following each in-person consultation. The impact of the IRB Liaison Service on IRB protocol approval times was analyzed for a random sub-sample of protocols for which consultations were provided. Consultations for studies with an associated IRB protocol number (i.e., at least initially submitted) from May 2015-June 2017 had been assigned a number in an Excel file. Using a randomization formula, a subset of 90 protocols was identified for further analysis. Protocols that did not result in an IRB submission and duplicate entries were removed. The final dataset consisted of 67 protocols. Those protocols were assessed by type of review process (expedited versus full board review), by status (new submission, first return, second return, etc.), and by which of the seven IRB committees completed the review. Consultations for each protocol included in this subset were reviewed using the notes about that consultation. IRB records in Columbia’s online research oversight system, Rascal, were also reviewed to assess the timing of and issues raised in subsequent IRB review. Factors examined included whether the protocol was approved at next submission and if not, whether questions raised in subsequent IRB returns were related to the topics discussed in the consultation. RESULTS/ANTICIPATED RESULTS: Since its inception in January 2015 through June 2017 (2.5 years), a total of 501 in-person consultations have been performed, usually 25-30 per month. Users were primarily study coordinators and investigators. Most requests concerned new protocol development, policy questions or assistance in addressing IRB comments from submitted protocols. Survey response rate was 43%. Results of 215 competed satisfaction surveys were 100% positive. Of 67 unique protocols analyzed for outcomes of the consultation, 73% were subsequently approved within 14 days. DISCUSSION/SIGNIFICANCE OF IMPACT: Overall, we have found the Liaison Service to be a popular addition to research support, and plan to continue the service. We will continue to evaluate its user satisfaction and usefulness. Additional focus will be placed on whether the Service can improve approval times for human subjects research for protocols using the Liaison Service.

At least 70% of premature adult deaths result from behaviors starting and reinforced in adolescence. The use of adolescent-centered outcomes and the necessity of creating space for the adolescent voice regarding research that directly impacts them is often overlooked. These omissions result in proposals and solutions that lack consideration of adolescent ingenuity, preferences, and needs. In 2021, Penn State PRO Wellness was awarded a Patient-Centered Outcomes Research Institute Engagement Award with the goal of addressing the gap in the inclusion of adolescents in research focused on teenage health. The resultant Adolescent Health Network (AHN) was developed in partnership with a stakeholder advisory board comprised of adolescents, parents, health researchers, and school staff. The AHN currently consists of 12 schools and 43 adolescents who have completed stakeholder training. For adolescents, the AHN simulates a school club or career enrichment activity with incoming freshmen replacing graduating seniors over time. For health researchers, the AHN provides rapid, easy access to a pool of adolescents with stakeholder training who are available to provide input on various aspects of a study from recruitment plans, to survey tools to dissemination strategies. This manuscript details the development, execution, and data from this novel program.

OBJECTIVES/GOALS: Irritability, a proneness to anger and frustration, is a transdiagnostic symptom associated with poor mental health outcomes. Levels of irritability vary across development and high-risk trajectories have been observed. This study aims to use machine learning to predict irritability trajectories across the transition to adolescence. METHODS/STUDY POPULATION: Data were from the Adolescent Brain Cognitive Development (ABCD) Study, which is a 10-year longitudinal study that tracks the brain development, cognitive skills, physical health, and psychosocial functioning of a large, national sample starting from preadolescence. The baseline sample consisted of 11,861 9-10-year-old preadolescent youth. Irritability was parent-rated at baseline, 1-year, 2-year, 3-year, and 4-year follow-ups on the Child Behavior Checklist (CBCL) irritability index. Latent class growth analysis (LCGA) was used to determine developmental trajectories of irritability. Two machine learning approaches were applied to develop predictive models of youth irritability developmental trajectories. We used baseline (preadolescent) variables that spanned a wide range of domains. RESULTS/ANTICIPATED RESULTS: Preliminary results fromthe LCGA indicated best support for a four-class model that differentiated growth trajectories in irritability across the transition to adolescence: 1) persistent low irritability (n = 8691, 73.27%), 2) moderate irritability and decreasing (n = 1257, 10.60%), 3) low to moderate irritability and increasing (n = 1295, 10.92%), and 4) chronic high irritability (n = 618, 5.21%). We expect the machine learning analyses to generate predictive models with acceptable accuracy. We hypothesize that the most important predictors in the models will originate from the youth mental health domain, including baseline youth irritability, externalizing symptoms, internalizing symptoms, and oppositional behaviors, and the parent psychopathology domain, particularly parent irritability. DISCUSSION/SIGNIFICANCE: The present study elucidates unique developmental trajectories of irritability and generates predictive models to classify high-risk irritability trajectories using machine learning approaches. Clinicians can use these predictive models to identify at-risk youth and provide early intervention to preadolescents at high risk.

OBJECTIVES/SPECIFIC AIMS: Because clinical trial results are instrumental in the approval of a new drug or changes to the practice of medicine, ensuring the accuracy and validity of collected data is critical in the clinical trial process. This function, routinely carried out by clinical trial monitors in industry-sponsored trials, is often lacking in investigator-initiated trials (IITs) conducted in academia. To address this challenge, we have developed a self-study module that can be used to cross-train academic researchers in essential concepts and practical approaches to monitoring. Furthermore, we are applying a framework drawn from implementation science in the development and launch of this initiative. This framework, as used in other educational programs, is employed here to close the gap between initiative and practice, thereby effectively disseminating this training would improve the quality of clinical trials in academia. METHODS/STUDY POPULATION: This research project applied exploration, installation and implementation stages of the implementation science process by 1) exploring the need for a new initiative, 2) disseminating results, 3) engaging stakeholders, 4) creating standard operating procedures (SOPs) for installation and implementation, 5) studying user satisfaction and effectiveness, 6) addressing feedback and 7) conducting implementation. RESULTS/ANTICIPATED RESULTS: From literature review and internet searches we determined that although numerous GCP training resources exist, most are too broad and lack the practical approaches to meet the complex requirements of monitoring. Moreover, most of the offerings identified are costly or inaccessible. With only about 65% of IITs reported as being monitored (Figures 1 and 2), it appears that there is a clear need for training tools that are easily available to a broader audience. And because monitoring skills are substantially different from those associated with research coordination, it is not surprising that research professionals believed that they would need additional training to become proficient. To address this need, we began developing a monitoring module. We engaged key stakeholders from academia and industry to gain insights into their needs. The results indicated that although our training module was effective, supplementary information on the fundamentals of clinical trials should be included for those new to the field. After incorporating suggested changes and completing the module, we conducted user testing to determine if our module is ready to be broadly disseminated (Figures 3 and 4). Following positive feedback from the group, we are currently in the process of disseminating our module and studying its impact. DISCUSSION/SIGNIFICANCE OF IMPACT: IITs are instrumental in translating academic research into product development. Deficiencies in the quality control of these trials can lead to inadequacies in data accuracy and validity that could lead to significant delays in bringing innovative therapies to patients. Recent NIH policies require data and safety monitoring for all of the trials it supports. The latest addendum to ICH GCP, E6(R2), discusses a need for quality management across the clinical trial lifecycle. As we continue to disseminate and share information during the development of our self-study monitoring module, we are engaging key stakeholders from academia, government, and private institutions to understand and address quality challenges in conducting clinical trials. Finally, this research informs dissemination and implementation research, specifically for creating training for academic research professionals.

OBJECTIVES/GOALS: Health care systems are complex, dynamic, and varied. Advances in artificial intelligence (AI) are enabling healthcare systems to use their own data to elicit patterns and design suitable interventions. To realize this potential, computer scientists and clinicians need an effective, practical, and replicable approach to collaboration METHODS/STUDY POPULATION: In this study, computer scientists partnered with clinicians to investigate predictors of avoidable emergency department use. The team sought an approach to computational medicine that could increase the relevance and impact of prediction to solve pressing problems in the health system. The team adopted an emergent architecture that engaged system leaders, computer scientists, data scientists, health services researchers, and practicing clinicians with deep ambulatory and inpatient knowledge to form the initial questions that shaped the prediction model; to understand nuances of coding and recording in source data and the implications for models; and to generate insights for promising points of intervention. The team recorded decisions and challenges as it progressed to analyze its function. RESULTS/ANTICIPATED RESULTS: Most avoidance models focus on a narrow time period around target events, or on high cost patients and events. This interdisciplinary team used their insights into the health system’s workflows and patient population to adopt a longitudinal approach to their prediction models. They used AI to build models of behavior in the system and consider prevention points across clinical units, time, and place. The holistic, systemwide focus enabled the team to generate insights that the system leaders and subsequently specific clinical units could apply to improve value and outcomes. A facilitated team process using learning system and cooperative network principles allowed a large and modular interdisciplinary team to build a transparent AI modeling process that yielded actionable insights into hypercomplex workflows. DISCUSSION/SIGNIFICANCE OF IMPACT: An architecture for involving diverse stakeholders in computational medicine projects can increase the relevance and impact of AI for solving care delivery problems in complex health systems. Translational science and computational medicine programs can foster this type of engagement and encourage a whole system perspective.

OBJECTIVES/GOALS: To identify factors enabling growing underage consumption of disposable Electronic Nicotine Delivery Systems (ENDS) by understanding young adults’perceptions and patterns of use of disposable ENDS through surveying college students. METHODS/STUDY POPULATION: Disposable ENDS are all-in-one devices with pre-filled nicotine liquid and a built-in battery. Recent data shows increased sales as users, including youth, are switching from pod-based to disposable ENDS. Gaps were identified via a literature review of current survey data revealing unknown information about disposable ENDS. Based on these data gaps, an anonymous survey was developed to gain insight into youth disposable ENDS use. The survey was distributed to college students via social media, university email chains, and flyers with QR codes. Responses were analyzed to identify trends and correlations in disposable ENDS use among college students. The survey was approved by USC IRB, Study ID: UP-22-00023. RESULTS/ANTICIPATED RESULTS: Between March 6 and October 28, 2022, 166 completed survey responses were collected; 158 were students. 80.4% (127/158) of surveys were eligible for analysis with the following criteria: 18-20 years old, under the US legal age to use ENDS. Of respondents aged 18-20, 57.5% (73/127) reported using ENDS at least once. 79.5% (58/73) of underage respondents used disposable ENDS and 72.9% (51/70) reported disposable ENDS as their usual device. 93.0% (53/57) of underage users reported using a flavored product, 56.1% (32/57) reported Flum®as their usual brand, 48.2% (27/56) reported convenience of use as the most attractive aspect of disposable ENDS, and 46.4% (26/56) obtained disposable ENDS from a convenience store. Of all disposable ENDS-using respondents, 98.7% (75/76) used for the first time while under the age of 21. DISCUSSION/SIGNIFICANCE: The survey continues to be open for data collection, with the goal of obtaining additional data. The goal with the additional data will be to create a comprehensive list of identified risk factors influencing underage disposable ENDS use and to suggest specific regulatory and policy reform to better address underage nicotine addiction.

ABSTRACT IMPACT: Quantification of neonatal THC exposure will allow for better insight into how THC exposure correlates with neurodevelopmental outcomes. OBJECTIVES/GOALS: Tetrahydrocannabinol (THC) use has become increasingly prevalent in recent years, including among pregnant women. However, few, if any, clinical studies have quantified precise in utero exposure levels of THC during pregnancy. Our study aims to fill this gap by using analytical methods to quantify THC in mother and baby following prenatal THC use. METHODS/STUDY POPULATION: Pregnant women were asked to give a self-report of all cannabis and cannabinoid use during pregnancy, including dose, frequency, and route of consumption. Upon arrival at the labor and delivery unit, maternal blood samples were collected. Immediately following birth and 24 hours after birth, umbilical cord and neonatal blood samples were collected, respectively. All blood samples were analyzed using tandem liquid chromatography-mass spectrometry (LC-MS) for the presence of THC, tetrahydrocannabinol carboxylic acid (THC-COOH), and hydroxy-tetrahydrocannabinol (THC-OH). Maternal THC and metabolite levels were compared to both cord and neonate samples. RESULTS/ANTICIPATED RESULTS: To date, we have collected 3 mother-infant sample dyads and 4 mother-infant control samples. We anticipate collecting a total of 20 mother-infant samples from each group. We will quantify levels of THC and its metabolites in maternal samples and compare these to cord and infant samples. We expect that THC/metabolite levels will vary as a function of dose and frequency of consumption. We also expect that THC/metabolites will be higher in umbilical cord blood relative to neonatal blood. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study is among the first to directly measure exposure in the neonate following prenatal cannabis use. Quantification of THC/metabolite concentrations will be supplemented with developmental evaluations of infants at 6 and 12 months of age in order to gain better insight into how THC exposure correlates with neurodevelopmental outcomes.

Objectives/Goals: This Weill Cornell Clinical and Translational Science Collaborative (CTSC) project evaluates whether large language models (LLMs) can generate accurate summaries of translational science benefits using the Translational Science Benefits Model (TSBM) framework, aiming to identify optimal LLMs and prompting strategies via expert review. Methods/Study Population: We are using prompt engineering to train multiple LLMs to generate one-page impact profiles based on the TSBM framework. LLMs will be selected via benchmarks, focusing on models excelling in information extraction. Leading LLMs (e.g., Llama 3.2, ChatGPT 4.0, Gemini 1.5 Pro, and Claude) and other high-performing models will be considered. Initial work has utilized Gemini 1.5 Pro. Models use data from CTSC-supported projects in WebCAMP, our local instantiation of a translational research activity tracking system used by >20 CTSA hubs, and manuscripts from the Overton database cited in policy documents. Human experts will evaluate the quality and accuracy of LLM-generated profiles. Results/Anticipated Results: Preliminary results using Gemini 1.5 Pro indicate that LLMs can generate coherent and informative impact profiles encompassing diverse areas within the TSBM. Face validity appears satisfactory, suggesting the outputs align with expectations. We anticipate that further exploration with other LLMs and expert validation will reveal strengths and weaknesses of the LLM approach, including the potential for naccuracies (“hallucinations”), informing further refinement of models and prompting strategies. Analysis of manuscripts cited in policy will provide valuable insights into communicating policy-relevant benefits effectively, and benchmark comparisons will identify optimal LLMs for this use case. Discussion/Significance of Impact: This project demonstrates LLMs’ potential for streamlining and enhancing impact reporting in translational science, enabling broader dissemination of research outcomes and promoting better understanding among stakeholders. Future work will integrate LLM-based reporting into research infrastructure.

Objectives/Goals: Our goal was to determine whether a consensus exists around 1) what the main barriers to innovation in Health artificial intelligence (AI) are 2) where there are gaps in education and training in Health AI and 3) where in their workflows organizations should implement AI to see the most immediate impact on productivity. Methods/Study Population: We employed a three-round Delphi method survey to stakeholders with health and/or engineering expertise. The first round was open-ended to generate responses to the three research questions. The second round asked participants to rank the responses and provide feedback as to their reasoning. The third round provided aggregated results and feedback and asked participants to re-rank the responses. Participants were attendees at a conference that brought people with health and/or engineering backgrounds together to discuss innovation in Health AI. 55 people in total participated across the three rounds. Results/Anticipated Results: Consensus emerged on all three questions: lack of trust was seen as the single greatest barrier to innovation, experience with implementation as the greatest gap in training, and automating health documentation as the point of most immediate impact. Consensus also emerged as to which of the 10–15 responses to each question were top priorities, which were somewhat significant, and which were not that important. Some of the rankings (such as implementation) seemed to reflect hot topics of discussion at the conference, but others (such as documentation) only emerged as significant in the surveys. Discussion/Significance of Impact: We successfully employed the Delphi method to discover what stakeholders think about three important questions in Health AI. Interestingly, although we polled experts from both health and engineering backgrounds, their answers converged on all three questions.

OBJECTIVES/GOALS: Transcriptional adaptation is a phenomenon in which a mutation in one gene leads to the genetic compensation of another homogenous gene. Understanding the mechanism of transcriptional adaptation may contribute to an explanation for variation in clinical manifestations of rare Amyotrophic lateral sclerosis patient phenotypes. METHODS/STUDY POPULATION: The presence of a premature termination codon triggers transcriptional activation. Therefore, we utilized CRISPR-Cas9 tool to generate a premature termination codon in CHCHD10 gene in multiple types of cells, including induced pluripotent stem cells derived from patient samples with known CHCHD10 mutations causative for Amyotrophic lateral sclerosis. CRISPR-Cas9 tool was delivered via ribonucleoprotein electroporation and transfect cell’s DNA was sequenced to validate gene editing. To confirm transcriptional adaption, changes in levels of protein and gene expression will be measured via immunoblot and quantification of CHCHD10 and CHCHCD2 from whole cells lysates of the edited cells. RESULTS/ANTICIPATED RESULTS: We anticipate that CHCHD2 transcriptional adaptation can functionally compensate for the locus loss of function of CHCHD10. This mechanism of transcriptional adaptation may contribute to an explanation for variation in clinical manifestations of patient phenotypes. DISCUSSION/SIGNIFICANCE: Our approach would advance discovery science towards by exploring transcriptional adaptation mechanism in humans, which can lead to novel therapies for rare Amyotrophic lateral sclerosis, such as CHCHD10.

OBJECTIVES/GOALS: As part of the Cleveland CTSA, “Collaborative Working Retreats” have been developed for the purpose of being a catalyst to move groups of interdisciplinary investigators and stakeholders to collaborative research teams with feasible and actionable translational research projects. METHODS/STUDY POPULATION: Groups of interdisciplinary investigators with engaged stakeholder(s) were invited to apply. Selected groups participated in a 4-hour, professionally facilitated retreat, tailored to the unique needs of each team. In addition to the facilitator, a graphic recorder was utilized to capture ideas and aid in decision making by creating a visual narrative linked to the team’s overall vision. Teams were charged with generating three translational research projects and writing a formal Team Action Plan (TAP) by two months post retreat. Retreat participants were asked to complete a survey to evaluate the retreat, and structured interviews were conducted with team leaders 4-6 months post retreat. RESULTS/ANTICIPATED RESULTS: Six groups were awarded retreats, comprised of 48 investigators (representing all schools in the university and 3 of 4 affiliated hospital systems) and 28 stakeholders for a total of 76 participants. 45% completed the followup survey. 77% said they would recommend the service to other teams or would use it again themselves and 97% stated their team benefited from having a facilitator. At 2 month follow up, one team had completed the TAP and subsequently applied for federal funding. However, 4 of the remaining 5 teams indicated that they had made significant progress, attributing progress to their retreat time. Each teams’ progress is being tracked for 2 years, using a newly developed metric. DISCUSSION/SIGNIFICANCE OF IMPACT: Facilitated retreats appear to serve as an important catalyst for progression of translational research projects, providing needed time and support for brainstorming and planning. Lessons learned, pre-retreat work, and tools for tailoring retreat content and tracking progress will be presented.

OBJECTIVES/GOALS: To understand the current landscape of clinical trials involving the homeless population by examining opioid or alcohol use disorders and the challenges in clinical trial recruitment. METHODS/STUDY POPULATION: Clinicaltrials.gov was searched with the keywords homeless or unhoused. The search was limited to studies conducted in the United States that were recruiting, not yet recruiting, active and not recruiting, completed, and enrolling. The search findings were further characterized and categorized by the definitions that were used for homelessness. Next, the trials were grouped based on the inclusion of alcohol or opioid use: (A) had no relevant mention, (B) included alcohol or opioid use as a secondary or other outcome measure, or (C) alcohol or opioid were the primary focus of the trial. Lastly, patterns and trends were identified for these trials. RESULTS/ANTICIPATED RESULTS: Out of 161 trials, 77 trials that met search criteria were identified then grouped based on how they classified homelessness: McKinney-Vento (n=5, 6%), DHHS (n=4, 5%), HUD (n=2, 3%), HEARTH Act (n=3, 4%), Custom (trials that specified parameters for homelessness, n=12, 16%), Not Specified (trials that provided no parameters for homelessness, n=26, 34%), and Other/Ambiguous (trials that used enrollment in an independent program as parameters or had unclear parameters, n=25, 32%). Of the 77 clinical trials that targeted homeless populations, 65% did not include alcohol use and 100% did not include opioid use in any outcome measure, 22% included alcohol use in a non-primary outcome measure, and 13% included alcohol use as the primary outcome measure of the study. DISCUSSION/SIGNIFICANCE: The number of clinical trials targeting homeless populations has increased over time, yet there is still no universal definition for classifying an individual as homeless. This lack of harmonization poses a challenge when coupled with the findings that there is a lack of clinical trials targeting opioid or alcohol use disorders.

OBJECTIVES/GOALS: We examined how individual characteristics and characteristics of the socioeconomic and built environment were associated with care coordination’s effect on cardiovascular disease (CVD) risks to identify geographic areas that may benefit from supplementary clinic-community linkages. METHODS/STUDY POPULATION: We analyzed data with geocoded residential addresses and data from electronic health records for 9946 adults from a Centers for Medicare & Medicaid Services funded innovation project from 7/1/2013 to 3/30/2015. Variables included patient-level demographics, Elixhauser comorbidity index, total time with a nurse care manager, and neighborhood factors such as poverty indicators, walkability, and social capital index. Outcomes were change in CVD risk factors, hemoglobin A1C, blood pressure (BP), and low-density lipoprotein (LDL). Generalized linear models were used to assess the effect of nurse care management program on outcomes after controlling for confounding factors. RESULTS/ANTICIPATED RESULTS: We report preliminary models that include patient demographics (age, sex, race), health care utilization, nurse care manager contact time, Elixhauser comorbidity index, neighborhood education status, percent of population below 200% federal poverty level, median home value, walkability score of the residential address, and social capital index. After adjusting for all mentioned variables, in adults with HbA1C more than 7.5% at baseline, females had worsening HbA1C by 0.53% over the study period. Additionally, LDL values in females worsened over the study period by 4.8 mg/dL after adjusting for all variables. No clinically significant changes were noted for BP. DISCUSSION/SIGNIFICANCE OF IMPACT: Women’s HbA1C and LDL worsened despite nurse care management and may benefit from additional community-based interventions or interventionists. In future analyses, we anticipate that CVD risk will worsen for patients with higher fast food proximity and with greater geographic distance from their PCP.

OBJECTIVES/GOALS: To understand the implications of minimal HIV generic drug availability on long-term HIV disease management, including anti-competitive agreements and patent strategies that hinder (ART) competition and ART medication safety and efficacy improvement. METHODS/STUDY POPULATION: Individuals living with Human Immunodeficiency Virus (HIV) require sustained ART treatment. Despite excessive ART costs, the treatments use active ingredients known to be toxic, causing bone and renal impairments. A review of pharmaceutical legal cases examined the role of strategic patenting–product hopping–and anti-competitive agreements, which prevent generic competition and contribute to minimal ART improvement. A literature review explored the long-term safety effects of ART medications on HIV patients and disease management. A cost analysis of HIV disease management assessed the implications of using treatments with known toxins and their contribution to comorbidities and their effect on the cost of overall HIV disease management. RESULTS/ANTICIPATED RESULTS: Ongoing lawsuits of major ART medication manufacturers demonstrate the intentional agreements made to hinder generic competition, which is an inherently anti-competitive strategy. Thus, prices for ART medications remained high, causing treatment costs to be a significant barrier to treatment access. Further, the entry of a new ART, Tenofovir Alafenamide Fumarate (TAF), was delayed despite knowing the key active ingredient in Tenofovir Disoproxil Fumarate (TDF) was toxic to patients long-term. As a result, patients were more likely to experience comorbidities, significantly increasing the cost of HIV care. The average cost of HIV care without comorbidities is $30,312. However, HIV care is $46,000 for two comorbidities and about $219,000 for people with 11 or more. DISCUSSION/SIGNIFICANCE: Abuse of patent rights prevents more effective HIV medication and generic option development. Thus, HIV disease management is inhibited. The only method of HIV disease management is patient adherence to ARTs. Patients taking less effective medications increase the development of comorbidities from toxic treatments, magnifying the cost of HIV care.