Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

• 主要な著者: Emily K. Makowski (著者), Hongwei Chen (著者), Matthew Lambert (著者), Eric M. Bennett (著者), Nicole S. Eschmann (著者), Yulei Zhang (著者), Jennifer M. Zupancic (著者), Alec A. Desai (著者), Matthew D. Smith (著者), Wenjia Lou (著者), Amendra Fernando (著者), Timothy Tully (著者), Christopher J. Gallo (著者), Laura Lin (著者), Peter M. Tessier (著者)
• フォーマット: 図書
• 出版事項: Taylor & Francis Group, 2022-12-01T00:00:00Z.
• 主題: article
• オンライン･アクセス: Connect to this object online.

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a h...