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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| 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/10.1080/19420862.2025.2483944 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Lateefat A. Kalejaye |
| collection | DOAJ |
| description | 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, a bottleneck for generalizability. In this study, we measured the viscosity of a panel of 229 monoclonal antibodies (mAbs) to develop predictive models for high concentration mAb screening. We developed DeepViscosity, consisting of 102 ensemble artificial neural network models to classify low-viscosity (≤20 cP) and high-viscosity (>20 cP) mAbs at 150 mg/mL, using 30 features from a sequence-based DeepSP model. Two independent test sets, comprising 16 and 38 mAbs with known experimental viscosity, were used to assess DeepViscosity’s generalizability. The model exhibited an accuracy of 87.5% and 89.5% on both test sets, respectively, surpassing other predictive methods. DeepViscosity will facilitate early-stage antibody development to select low-viscosity antibodies for improved manufacturability and formulation properties, critical for subcutaneous drug delivery. The webserver-based application can be freely accessed via https://devpred.onrender.com/DeepViscosity. |
| format | Article |
| id | doaj-art-e7dcd89959a34066856e99ebdd9e026a |
| institution | DOAJ |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-e7dcd89959a34066856e99ebdd9e026a2025-08-20T02:57:53ZengTaylor & Francis GroupmAbs1942-08621942-08702025-12-0117110.1080/19420862.2025.2483944Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learningLateefat A. Kalejaye0Jia-Min Chu1I-En Wu2Bismark Amofah3Amber Lee4Mark Hutchinson5Chacko Chakiath6Andrew Dippel7Gilad Kaplan8Melissa Damschroder9Valentin Stanev10Maryam Pouryahya11Mehdi Boroumand12Jenna Caldwell13Alison Hinton14Madison Kreitz15Mitali Shah16Austin Gallegos17Neil Mody18Pin-Kuang Lai19Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ, USADepartment of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ, USADepartment of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USAData Science and Modelling, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USAData Science and Modelling, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USAData Science and Modelling, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USADosage Form Design and Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USADosage Form Design and Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USADosage Form Design and Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USADosage Form Design and Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USADosage Form Design and Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USADosage Form Design and Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USADepartment of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ, USAHighly 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, a bottleneck for generalizability. In this study, we measured the viscosity of a panel of 229 monoclonal antibodies (mAbs) to develop predictive models for high concentration mAb screening. We developed DeepViscosity, consisting of 102 ensemble artificial neural network models to classify low-viscosity (≤20 cP) and high-viscosity (>20 cP) mAbs at 150 mg/mL, using 30 features from a sequence-based DeepSP model. Two independent test sets, comprising 16 and 38 mAbs with known experimental viscosity, were used to assess DeepViscosity’s generalizability. The model exhibited an accuracy of 87.5% and 89.5% on both test sets, respectively, surpassing other predictive methods. DeepViscosity will facilitate early-stage antibody development to select low-viscosity antibodies for improved manufacturability and formulation properties, critical for subcutaneous drug delivery. The webserver-based application can be freely accessed via https://devpred.onrender.com/DeepViscosity.https://www.tandfonline.com/doi/10.1080/19420862.2025.2483944Antibody viscosityensemble deep learninghigh-concentration formulationsmonoclonal antibodies |
| spellingShingle | 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 Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning mAbs Antibody viscosity ensemble deep learning high-concentration formulations monoclonal antibodies |
| title | Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning |
| title_full | Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning |
| title_fullStr | Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning |
| title_full_unstemmed | Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning |
| title_short | Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning |
| title_sort | accelerating high concentration monoclonal antibody development with large scale viscosity data and ensemble deep learning |
| topic | Antibody viscosity ensemble deep learning high-concentration formulations monoclonal antibodies |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2025.2483944 |
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