Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening
Identification of an optimal single protein sequence at the discovery stage for preclinical and clinical development is critical to the rapid development and overall success of a biologic drug. High throughput developability assessments at the discovery stage are used to rank potent molecules by the...
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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/10.1080/19420862.2025.2502127 |
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| author | Kevin James Metcalf Galen Wo Jan Paulo Zaragoza Fahimeh Raoufi Jeanne Baker Daoyang Chen Mehabaw Derebe Jason Hogan Amy Hsu Esther Kofman David Leigh Mandy Li Dan Malashock Cate Mann Soha Motlagh Jihea Park Karthik Sathiyamoorthy Madhura Shidhore Yinyan Tang Kevin Teng Katharine Williams Andrew Waight Sultan Yilmaz Fan Zhang Huimin Zhong Laurence Fayadat-Dilman Marc Bailly |
| author_facet | Kevin James Metcalf Galen Wo Jan Paulo Zaragoza Fahimeh Raoufi Jeanne Baker Daoyang Chen Mehabaw Derebe Jason Hogan Amy Hsu Esther Kofman David Leigh Mandy Li Dan Malashock Cate Mann Soha Motlagh Jihea Park Karthik Sathiyamoorthy Madhura Shidhore Yinyan Tang Kevin Teng Katharine Williams Andrew Waight Sultan Yilmaz Fan Zhang Huimin Zhong Laurence Fayadat-Dilman Marc Bailly |
| author_sort | Kevin James Metcalf |
| collection | DOAJ |
| description | Identification of an optimal single protein sequence at the discovery stage for preclinical and clinical development is critical to the rapid development and overall success of a biologic drug. High throughput developability assessments at the discovery stage are used to rank potent molecules by their biophysical properties, deprioritize suboptimal molecules, or trigger additional rounds of protein engineering. Due to the amount of data acquired for these molecules, manual analysis methods to rank molecules are error prone and time-consuming. Here, we present applications of hierarchical clustering analysis for data-driven lead selection of biologics and preformulation screening using high throughput developability data. Hierarchical clustering analysis was applied here for prioritization of three different antibody modalities, including format and chain pairing of bispecific antibodies, sequence-optimized monoclonal antibodies from affinity maturation, preformulation screening of bispecific scFv-Fab fusion molecules, and monoclonal antibodies from an immunization campaign. This high-throughput method for ranking molecules by their developability characteristics and preformulation properties can substantially simplify, streamline, and accelerate biologics discovery and early development. |
| format | Article |
| id | doaj-art-8834102c765f4be1a9028841ceebd8ff |
| 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-8834102c765f4be1a9028841ceebd8ff2025-08-20T01:49:08ZengTaylor & Francis GroupmAbs1942-08621942-08702025-12-0117110.1080/19420862.2025.2502127Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screeningKevin James Metcalf0Galen Wo1Jan Paulo Zaragoza2Fahimeh Raoufi3Jeanne Baker4Daoyang Chen5Mehabaw Derebe6Jason Hogan7Amy Hsu8Esther Kofman9David Leigh10Mandy Li11Dan Malashock12Cate Mann13Soha Motlagh14Jihea Park15Karthik Sathiyamoorthy16Madhura Shidhore17Yinyan Tang18Kevin Teng19Katharine Williams20Andrew Waight21Sultan Yilmaz22Fan Zhang23Huimin Zhong24Laurence Fayadat-Dilman25Marc Bailly26Discovery Biologics, Merck & Co. Inc, Rahway, NJ, USAIT, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USAIT, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USAIT, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USAIT, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USADiscovery Biologics, Merck & Co. Inc, Rahway, NJ, USAIdentification of an optimal single protein sequence at the discovery stage for preclinical and clinical development is critical to the rapid development and overall success of a biologic drug. High throughput developability assessments at the discovery stage are used to rank potent molecules by their biophysical properties, deprioritize suboptimal molecules, or trigger additional rounds of protein engineering. Due to the amount of data acquired for these molecules, manual analysis methods to rank molecules are error prone and time-consuming. Here, we present applications of hierarchical clustering analysis for data-driven lead selection of biologics and preformulation screening using high throughput developability data. Hierarchical clustering analysis was applied here for prioritization of three different antibody modalities, including format and chain pairing of bispecific antibodies, sequence-optimized monoclonal antibodies from affinity maturation, preformulation screening of bispecific scFv-Fab fusion molecules, and monoclonal antibodies from an immunization campaign. This high-throughput method for ranking molecules by their developability characteristics and preformulation properties can substantially simplify, streamline, and accelerate biologics discovery and early development.https://www.tandfonline.com/doi/10.1080/19420862.2025.2502127Antibody discoveryantibody screeningbiologicsbiophysical propertiesCMCdata-driven decision making |
| spellingShingle | Kevin James Metcalf Galen Wo Jan Paulo Zaragoza Fahimeh Raoufi Jeanne Baker Daoyang Chen Mehabaw Derebe Jason Hogan Amy Hsu Esther Kofman David Leigh Mandy Li Dan Malashock Cate Mann Soha Motlagh Jihea Park Karthik Sathiyamoorthy Madhura Shidhore Yinyan Tang Kevin Teng Katharine Williams Andrew Waight Sultan Yilmaz Fan Zhang Huimin Zhong Laurence Fayadat-Dilman Marc Bailly Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening mAbs Antibody discovery antibody screening biologics biophysical properties CMC data-driven decision making |
| title | Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening |
| title_full | Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening |
| title_fullStr | Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening |
| title_full_unstemmed | Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening |
| title_short | Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening |
| title_sort | biologics developability data analysis using hierarchical clustering accelerates candidate lead selection optimization and preformulation screening |
| topic | Antibody discovery antibody screening biologics biophysical properties CMC data-driven decision making |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2025.2502127 |
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