Utilizing the Pharmacy Advances Clinical Trials (PACT) Network to Achieve Diversity in COVID Clinical Trials: A Strategic Framework

摘要/总结 药物开发和临床试验一直是发病率和死亡率指数下降的核心 与COVID-19相关。尽管在以下国家观察到不成比例的COVID-19死亡率， 在少数族裔（URM）社区中，COVID-19临床试验的招募一直具有挑战性。 这些挑战与患者、系统、提供者和社区障碍有关，包括 不信任、健康的社会决定因素、不符合资格标准以及缺乏学习意识。到 为了解决这些问题，德克萨斯A&M大学（TAMU）与美国学院协会合作， 药房（AACP）该项目的主要目标是采用人工智能（AI）和机器 学习（ML），发展一个国家网络，将从事创新战略，并培训和教育 研究人员在URM社区推进COVID-19健康公平研究。TAMU -药学院 (COP)已经建立了几个外展计划活动，旨在教育和招收URM， 临床试验NIH最近资助TAMU-COP进行Moderna COVID-19疫苗临床试验。塔木 招募了25%的参与者，大约35%被归类为URM参与者，大多数是西班牙裔。 长期目标是：㈠采用创新办法、培训和教育方法， 临床试验入组。(ii)改进统一资源管理的沟通战略和知情信息 人口。核心假设是，URM参与临床试验的犹豫源于长期的- 长期的不信任，缺乏文化上合适的信息，缺乏获得护理的机会， 真正的健康冠军此外，以社区为基础的药剂师和药学系具有独特的作用 作为社区临床研究人员招募URM人群作为COVID-19临床试验的参与者。的 中心假设将通过以下两个目标进行测试：目标1：利用人工智能， 机器学习可在目标社区进行环境扫描，以优化URM参与 在COVID-19临床试验中。目标2：建立和培训社区调查员网络， 药剂师和其他合作伙伴直接增加URM的临床试验参与 人口。这些目标将通过创新的AI/ML分析、培训、教育和社区来实现。 网络直接的预期结果将是更好地了解COVID的关键因素- 19项临床试验在URM中的犹豫以及解决犹豫的有效干预措施。预计， 建议的干预措施将对临床试验的入组产生积极影响。

ABSTRACT/SUMMARY Drug development and clinical trials have been central to the exponential decline in morbidity and mortality associated with COVID-19. While disproportionate COVID-19 mortalities have been observed in underrepresented minority (URM) communities, enrollment into COVID-19 clinical trials has been challenging. Such challenges have been associated with patient, system, provider, and community barriers, including mistrust, social determinants of health, failure to meet eligibility criteria, and lack of awareness of studies. To address these issues, Texas A&M University (TAMU) has partnered with the American Association of Colleges of Pharmacy (AACP). The primary goals of this project are to employ Artificial Intelligence (AI) and Machine Learning (ML), develop a national network that would engage innovative strategies, and to train and educate researchers to advance COVID-19 health equity research in URM communities. TAMU – College of Pharmacy (COP) has established several outreach program activities designed to educate and enroll URMs in ongoing clinical trials. The NIH recently funded TAMU-COP to conduct a Moderna COVID-19 Vaccine clinical trial. TAMU recruited 25% of participants, approximately 35% are classified as URM participants, majority Hispanics. The long-term goal is to (i) Employ innovative approaches, training, and education methods to increase URM clinical trial enrollment. (ii) Improve communication strategies and informed messages for URM populations. The central hypothesis is that the hesitancy of URM participation in clinical trials stems from long- standing mistrust, lack of culturally competent messaging, lack of access to care, and underrepresentation of authentic health champions. In addition, community-based pharmacists and pharmacy faculty have a unique role as community clinical researchers in recruiting URM populations as participants in COVID-19 clinical trials. The central hypothesis will be tested via the following two aims: Aim 1: Leverage Artificial Intelligence and Machine Learning to enable environmental scans in target communities to optimize URM participation in COVID-19 clinical trials. Aim 2: Create and train a network of community-based investigators, pharmacists, and other partners to directly increase clinical trials participation among URM populations. These aims will be pursued via innovative AI/ML analytics, training, education, and community network. The immediate expected outcomes will be a better understanding of the critical elements of COVID- 19 clinical trials hesitancy in URMs and effective interventions for addressing hesitancy. It is anticipated the proposed interventions will positively impact enrollment in clinical trials.