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

摘要/摘要 药物开发和临床试验一直是发病率和死亡率呈指数级下降的核心 与新冠肺炎相关。虽然在中国发现了不成比例的新冠肺炎死亡率 在代表不足的少数族裔社区，登记参加新冠肺炎临床试验一直是具有挑战性的。 此类挑战与患者、系统、提供者和社区障碍相关，包括 不信任，健康的社会决定因素，未能达到资格标准，以及对研究缺乏认识。至 为了解决这些问题，德克萨斯农工大学(TAMU)与美国学院协会建立了合作伙伴关系 药房(AACP)。该项目的主要目标是使用人工智能(AI)和机器 学习(ML)，建立一个将采用创新战略的国家网络，并进行培训和教育 研究人员将在城镇居民管理社区推进新冠肺炎健康公平性研究。TAMU-药学院 (COP)建立了几个外展方案活动，旨在教育和登记正在进行的URM 临床试验。美国国立卫生研究院最近资助TAMU-COP进行了一项现代新冠肺炎疫苗临床试验。塔木岛 招募了25%的参与者，约35%被归类为URM参与者，主要是西班牙裔。 长期目标是(I)采用创新的方法、培训和教育方法来增加URM 临床试验招生。(2)改进城市规划管理的沟通策略和知情信息 人口。中心假设是URM参与临床试验的犹豫不决源于长期的... 长期的不信任，缺乏符合文化能力的信息，缺乏获得护理的机会，以及对 真正的健康冠军。此外，以社区为基础的药剂师和药学教员具有独特的作用。 作为社区临床研究人员，招募URM人群作为新冠肺炎临床试验的参与者。这个 中心假设将通过以下两个目标进行检验：目标1：利用人工智能和 机器学习支持目标社区的环境扫描，以优化URM参与 在新冠肺炎的临床试验中。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对临床试验的参与 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。眼前的预期结果将是更好地了解COVID的关键要素-- 19项临床试验中的犹豫不决和解决犹豫不决的有效干预措施。据预计， 建议的干预措施将对临床试验的登记产生积极影响。

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.