Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning

Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning

Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only limited to a few dozen data points for training,...

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Bibliographic Details
Main Authors: Lateefat A. Kalejaye, Jia-Min Chu, I-En Wu, Bismark Amofah, Amber Lee, Mark Hutchinson, Chacko Chakiath, Andrew Dippel, Gilad Kaplan, Melissa Damschroder, Valentin Stanev, Maryam Pouryahya, Mehdi Boroumand, Jenna Caldwell, Alison Hinton, Madison Kreitz, Mitali Shah, Austin Gallegos, Neil Mody, Pin-Kuang Lai
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:mAbs
Subjects:
Antibody viscosity
ensemble deep learning
high-concentration formulations
monoclonal antibodies
Online Access:https://www.tandfonline.com/doi/10.1080/19420862.2025.2483944
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https://www.tandfonline.com/doi/10.1080/19420862.2025.2483944

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