{"id":105908,"date":"2025-02-04T11:19:59","date_gmt":"2025-02-04T17:19:59","guid":{"rendered":"https:\/\/engineering.wisc.edu\/?post_type=tribe_events&p=105908"},"modified":"2025-02-11T13:13:36","modified_gmt":"2025-02-11T19:13:36","slug":"bme-seminar-series-jacob-witten-phd","status":"publish","type":"tribe_events","link":"https:\/\/engineering.wisc.edu\/event\/bme-seminar-series-jacob-witten-phd\/","title":{"rendered":"BME Seminar Series: Jacob Witten, PhD"},"content":{"rendered":"
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Artificial intelligence for state-of-the-art gene therapy<\/h2>\n\n\n\n
\"Jacob<\/figure>\n\n\n\n

Jacob Witten, PhD
Postdoctoral Fellow
Anderson Lab
Massachusetts Institute of Technology<\/strong><\/p>\n\n\n\n

Abstract:
Lipid nanoparticles (LNPs) for RNA delivery have exploded onto the biomedical research scene with the success of mRNA vaccines for COVID-19. In addition to their promise as mRNA vaccines for infectious disease and cancer, LNPs have the potential to treat or cure patients with deadly lung diseases such as cystic fibrosis. However, gene therapy in the lung is a notoriously difficult challenge that has frustrated decades of researchers. Here, I take two approaches to identifying LNPs capable of addressing this challenge. First, I developed an in vitro primary cell platform to screen LNPs for lung mRNA delivery and identify two state-of-the-art LNPs for respiratory tract delivery to mice. Second, I developed Lipid Optimization using Neural networks (LiON), a deep learning strategy for LNP design. Using LiON I evaluated 1.6 million possible LNPs and identified two, FO-32 and FO-35, with mRNA delivery matching that of LNPs in ongoing clinical trials. Overall, this work shows the potential of deep learning to bring gene therapy to patients suffering from genetic disease.<\/p>\n\n\n\n

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