Individual CTSA hubs are leading the national clinical and translational research efforts in developing new approaches to address the COVID-19 pandemic. This crucial role was natural. Long before the current crisis, CTSA hubs were committed to translation, building multidisciplinary teams of investigators and community partners, overcoming regulatory burdens, ensuring quality in clinical and human research, developing transformative informatics, and disruptive technologies for diagnostics and therapeutics. In this proposal, we build on our center?s active participation in meaningful clinical trials (e.g., the NIH Remdesivir RCT), the early creation of a biospecimen repository from COVID-19 patients, institutional commitment and fundraising that led to a $3.5 million pilot fund distribution, a robust and accessible clinical database repository, and the ongoing work of an NCATS-supported CTSA Collaboration Innovation Award (a coalition of the J. Craig Venter Institute, UCSD, UCI, and Stanford) focused on artificial intelligence approaches for the analysis of flow cytometry data. Using the emerging informatics framework of supervised generalized canonical correlation for integrative data analysis, we will link clinical data from COVID-19 patients enrolled in a variety of trials and at various stages of disease with innovative in vitro evaluation of innate and adaptive immunity, an area still poorly understood in SARS-CoV-2 pathology, obtained from patient biospecimens to obtain mechanistic insights of COVID-19 pathogenesis at a systems level. Innate and adaptive immunity are particularly relevant to COVID-19 disease pathogenesis because they play key, but distinct, roles at all phases of the illness (initial tissue-virus interaction; systemic responses; the cell-mediated cytokine storm leading to multi- organ failure and death, likely long after levels of viremia have fallen; and, ultimately, protective immunity). The current CCIA novel flow cytometry informatics research permits elucidation of dynamic cellular immune responses related to the COVID-19 pandemic that were heretofore unobservable. Using Hi-DAFi for mass cytometry analysis, validated informatics pipelines for single cell transcriptomics analysis, and cutting-edge statistical data integration and machine learning strategies tied back to the available clinical data we will be able to discover novel associations between cellular biomarkers and disease state, a particular therapy, and disease mediating factors such as age, health disparities, and the presence of other diseases or conditions like obesity. This information will aid in critical efforts to target new therapies and possibly identify idiosyncratic individual physiologic variables that render certain patients who seem to have no known comorbidities more vulnerable to severe COVID-19 disease. Finally, the robust connection between the UCI hub and both regional and national networks (e.g., BRAID, the coalition of the 5 UC CTSAs, and NCATS Trial Innovation Network) will provide an unprecedented opportunity to rapidly disseminate clinically relevant discoveries and engage the talent and insight of the many clinicians and scientists working tirelessly to end this pandemic.
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