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