Biologics developability data analysis using hierarchical clustering accelerates candidate lead selection,...

Area of Science:

• Biotechnology
• Protein Engineering
• Drug Discovery

Background:

• Selecting optimal protein sequences is crucial for successful biologic drug development.
• High-throughput developability assessments rank potent molecules by biophysical properties.
• Manual analysis of extensive molecular data is time-consuming and prone to errors.

Purpose of the Study:

• To present hierarchical clustering analysis for data-driven lead selection of biologics.
• To apply this method for preformulation screening using high-throughput developability data.
• To simplify, streamline, and accelerate biologics discovery and early development.

Main Methods:

• Hierarchical clustering analysis was applied to high-throughput developability data.
• The method was used to prioritize antibody modalities, including bispecific antibodies and monoclonal antibodies.
• Applications included format and chain pairing, sequence optimization, and preformulation screening.

Main Results:

• Hierarchical clustering effectively ranked molecules based on developability and preformulation properties.
• The analysis facilitated prioritization across diverse antibody formats and optimization strategies.
• This approach demonstrated a substantial simplification and acceleration of the selection process.

Conclusions:

• Hierarchical clustering analysis offers a robust, data-driven method for biologic lead selection.
• The high-throughput approach enhances efficiency in ranking molecules for preclinical and clinical development.
• This methodology significantly streamlines the early stages of biologics discovery and development.