Reduction of monoclonal antibody viscosity using interpretable machine learning
| Autor: | Emily K. Makowski, Hsin-Ting Chen, Tiexin Wang, Lina Wu, Jie Huang, Marissa Mock, Patrick Underhill, Emma Pelegri-O’Day, Erick Maglalang, Dwight Winters, Peter M. Tessier |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | mAbs, Vol 16, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 19420862 1942-0870 1942-0862 |
| DOI: | 10.1080/19420862.2024.2303781 |
| Popis: | ABSTRACTEarly identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|