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Episode: 48 - A Conversation with Peter Tessier: Classifying Antibodies to Assess Biologics Developability Features Early in the Discovery Process

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内容由Cambridge Healthtech Institute提供。所有播客内容(包括剧集、图形和播客描述)均由 Cambridge Healthtech Institute 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

In this month’s episode of the Chain, guest Peter Tessier, Albert M. Mattocks pharmaceutical sciences and chemical engineering professor at the University of Michigan, speaks with moderator Tariq Ghayur, scientific advisor and entrepreneur in residence at FairJourney Biologics, about expediting the developability of antibodies. He discusses the characteristics that best predict a molecule’s drug-like properties, the different assays used for various intended outcomes, and why every scientist must assess the “greatest potential impact” before embarking on a new experiment. Tessier also talks about the core traditions that help him lead students in the lab while fostering a learning environment of ownership, integrity, and self-motivation. Last, he shares his predictions on how computational data will advance antibody discovery and developability in the future.

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Manage episode 360371293 series 2967424
内容由Cambridge Healthtech Institute提供。所有播客内容(包括剧集、图形和播客描述)均由 Cambridge Healthtech Institute 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

In this month’s episode of the Chain, guest Peter Tessier, Albert M. Mattocks pharmaceutical sciences and chemical engineering professor at the University of Michigan, speaks with moderator Tariq Ghayur, scientific advisor and entrepreneur in residence at FairJourney Biologics, about expediting the developability of antibodies. He discusses the characteristics that best predict a molecule’s drug-like properties, the different assays used for various intended outcomes, and why every scientist must assess the “greatest potential impact” before embarking on a new experiment. Tessier also talks about the core traditions that help him lead students in the lab while fostering a learning environment of ownership, integrity, and self-motivation. Last, he shares his predictions on how computational data will advance antibody discovery and developability in the future.

  continue reading

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