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Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection, optimization, and preformulation screening
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DOI：10.1080/19420862.2025.2502127.png)
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Abstract
En 中文
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.
Keywords:
Antibody discovery
antibody screening
biologics
biophysical properties
CMC
data-driven decision making
developability
hierarchical-clustering analysis
lead selection
manufacturability
monoclonal antibodies
preformulation
protein engineering
statistical analysis
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