Leveraging multi-modal feature learning for predictions of antibody viscosity
The shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, a significant challenge associated with this transiti...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | mAbs |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2025.2490788 |
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| author | Krishna D. B. Anapindi Kai Liu Willie Wang Yao Yu Yan He Edward J. Hsieh Ying Huang Daniela Tomazela |
| author_facet | Krishna D. B. Anapindi Kai Liu Willie Wang Yao Yu Yan He Edward J. Hsieh Ying Huang Daniela Tomazela |
| author_sort | Krishna D. B. Anapindi |
| collection | DOAJ |
| description | The shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, a significant challenge associated with this transition is managing the viscosity of the administered solutions. High viscosity poses substantial development and manufacturability challenges, directly affecting patients by increasing injection time and causing pain at the injection site. Furthermore, high viscosity formulations can prolong residence time at the injection site, affecting absorption kinetics and potentially altering the intended pharmacological profile and therapeutic efficacy of the biologic candidate. Here, we report the application of a multimodal feature learning workflow for predicting the viscosity of antibodies in therapeutics discovery. It integrates multiple data sources including sequence, structural, physicochemical properties, as well as embeddings from a language model. This approach enables the model to learn from various underlying rules, such as physicochemical rules from molecular simulations and protein evolutionary patterns captured by large, pre-trained deep learning models. By comparing the effectiveness of this approach to other selected published viscosity prediction methods, this study provides insights into their intrinsic viscosity predictive potential and usability in early-stage therapeutics antibody development pipelines. |
| format | Article |
| id | doaj-art-e8eaaea3ed044aafa2f387a1bf031607 |
| institution | OA Journals |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-e8eaaea3ed044aafa2f387a1bf0316072025-08-20T02:16:39ZengTaylor & Francis GroupmAbs1942-08621942-08702025-12-0117110.1080/19420862.2025.2490788Leveraging multi-modal feature learning for predictions of antibody viscosityKrishna D. B. Anapindi0Kai Liu1Willie Wang2Yao Yu3Yan He4Edward J. Hsieh5Ying Huang6Daniela Tomazela7Protein Therapeutics, Gilead Sciences, Foster City, CA, USAResearch Data Sciences, Gilead Sciences, Foster City, CA, USAProtein Therapeutics, Gilead Sciences, Foster City, CA, USAProtein Therapeutics, Gilead Sciences, Foster City, CA, USAProtein Therapeutics, Gilead Sciences, Foster City, CA, USAProtein Therapeutics, Gilead Sciences, Foster City, CA, USAResearch Data Sciences, Gilead Sciences, Foster City, CA, USAProtein Therapeutics, Gilead Sciences, Foster City, CA, USAThe shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, a significant challenge associated with this transition is managing the viscosity of the administered solutions. High viscosity poses substantial development and manufacturability challenges, directly affecting patients by increasing injection time and causing pain at the injection site. Furthermore, high viscosity formulations can prolong residence time at the injection site, affecting absorption kinetics and potentially altering the intended pharmacological profile and therapeutic efficacy of the biologic candidate. Here, we report the application of a multimodal feature learning workflow for predicting the viscosity of antibodies in therapeutics discovery. It integrates multiple data sources including sequence, structural, physicochemical properties, as well as embeddings from a language model. This approach enables the model to learn from various underlying rules, such as physicochemical rules from molecular simulations and protein evolutionary patterns captured by large, pre-trained deep learning models. By comparing the effectiveness of this approach to other selected published viscosity prediction methods, this study provides insights into their intrinsic viscosity predictive potential and usability in early-stage therapeutics antibody development pipelines.https://www.tandfonline.com/doi/10.1080/19420862.2025.2490788Multimodal feature learningantibody viscosity predictionmachine learningbioinformatics |
| spellingShingle | Krishna D. B. Anapindi Kai Liu Willie Wang Yao Yu Yan He Edward J. Hsieh Ying Huang Daniela Tomazela Leveraging multi-modal feature learning for predictions of antibody viscosity mAbs Multimodal feature learning antibody viscosity prediction machine learning bioinformatics |
| title | Leveraging multi-modal feature learning for predictions of antibody viscosity |
| title_full | Leveraging multi-modal feature learning for predictions of antibody viscosity |
| title_fullStr | Leveraging multi-modal feature learning for predictions of antibody viscosity |
| title_full_unstemmed | Leveraging multi-modal feature learning for predictions of antibody viscosity |
| title_short | Leveraging multi-modal feature learning for predictions of antibody viscosity |
| title_sort | leveraging multi modal feature learning for predictions of antibody viscosity |
| topic | Multimodal feature learning antibody viscosity prediction machine learning bioinformatics |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2025.2490788 |
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