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

• Main Authors: Emily K. Makowski (Author), Hongwei Chen (Author), Matthew Lambert (Author), Eric M. Bennett (Author), Nicole S. Eschmann (Author), Yulei Zhang (Author), Jennifer M. Zupancic (Author), Alec A. Desai (Author), Matthew D. Smith (Author), Wenjia Lou (Author), Amendra Fernando (Author), Timothy Tully (Author), Christopher J. Gallo (Author), Laura Lin (Author), Peter M. Tessier (Author)
• Format: Book
• Published: Taylor & Francis Group, 2022-12-01T00:00:00Z.
• Subjects: article
• Online Access: 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...