Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening

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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Main Authors: 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
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:mAbs
Subjects:
Antibody discovery
antibody screening
biologics
biophysical properties
CMC
data-driven decision making
Online Access:https://www.tandfonline.com/doi/10.1080/19420862.2025.2502127
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Summary: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.
ISSN:1942-0862
1942-0870

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