Browsing by Author "Lavieri, Robert R."

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    Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases
    (Frontiers Media, 2021-07-28) Challa, Anup P.; Zaleski, Nicole M.; Jerome, Rebecca N.; Lavieri, Robert R.; Shirey-Rice, Jana K.; Barnado, April; Lindsell, Christopher J.; Aronoff, David M.; Crofford, Leslie J.; Harris, Raymond C.; Ikizler, T. Alp; Mayer, Ingrid A.; Holroyd, Kenneth J.; Pulley, Jill M.; Medicine, School of Medicine
    Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.
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    Medication history-wide association studies for pharmacovigilance of pregnant patients
    (Springer Nature, 2022-09-16) Challa, Anup P.; Niu, Xinnan; Garrison, Etoi A.; Van Driest, Sara L.; Bastarache, Lisa M.; Lippmann, Ethan S.; Lavieri, Robert R.; Goldstein, Jeffery A.; Aronoff, David M.; Medicine, School of Medicine
    Background: Systematic exclusion of pregnant people from interventional clinical trials has created a public health emergency for millions of patients through a dearth of robust safety data for common drugs. Methods: We harnessed an enterprise collection of 2.8 M electronic health records (EHRs) from routine care, leveraging data linkages between mothers and their babies to detect drug safety signals in this population at full scale. Our mixed-methods signal detection approach stimulates new hypotheses for post-marketing surveillance agnostically of both drugs and diseases-by identifying 1,054 drugs historically prescribed to pregnant patients; developing a quantitative, medication history-wide association study; and integrating a qualitative evidence synthesis platform using expert clinician review for integration of biomedical specificity-to test the effects of maternal exposure to diverse drugs on the incidence of neurodevelopmental defects in their children. Results: We replicated known teratogenic risks and existing knowledge on drug structure-related teratogenicity; we also highlight 5 common drug classes for which we believe this work warrants updated assessment of their safety. Conclusion: Here, we present roots of an agile framework to guide enhanced medication regulations, as well as the ontological and analytical limitations that currently restrict the integration of real-world data into drug safety management during pregnancy. This research is not a replacement for inclusion of pregnant people in prospective clinical studies, but it presents a tractable team science approach to evaluating the utility of EHRs for new regulatory review programs-towards improving the delicate equipoise of accuracy and ethics in assessing drug safety in pregnancy.
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    Walk before you run: feasibility challenges and lessons learned from the PROCLAIM Study, a multicenter randomized controlled trial of misoprostol for prevention of recurrent C. difficile during COVID-19
    (Elsevier, 2023) Lavieri, Robert R.; Dubberke, Erik R.; McGill, Sarah K.; Bartelt, Luther; Smith, Stephanie A.; Pandur, Balint K.; Phillips, Sharon E.; Vermillion, Krista; Shirey-Rice, Jana; Pulley, Jill; Xu, Yaomin; Lindsell, Christopher J.; Zaleski, Nicole; Jerome, Rebecca; Doster, Ryan S.; Aronoff, David M.; Medicine, School of Medicine
    We analyzed our challenging experience with a randomized controlled trial of misoprostol for prevention of recurrent C. difficile. Despite careful prescreening and thoughtful protocol modifications to facilitate enrollment, we closed the study early after enrolling just 7 participants over 3 years. We share lessons learned, noting the importance of feasibility studies, inclusion of biomarker outcomes, and dissemination of such findings to inform future research design and implementation successes.