Database-Centric Method for Automated High-Throughput Deconvolution and Analysis of Kinetic Antibody Screening Data

Area of Science:

• Biophysics
• Drug Discovery
• Computational Chemistry

Background:

• Current industrial drug discovery relies on empirical screening of drug candidates against target molecules.
• High-throughput kinetic measurements offer advantages over equilibrium assessments by quantifying binding affinity components.
• Existing high-throughput analysis methods face bottlenecks in data processing, including handling poor biophysical quality data, user interface limitations, and lack of historical data integration.

Purpose of the Study:

• To develop a generally applicable method for automated analysis, storage, and retrieval of kinetic binding data.
• To overcome limitations in current drug discovery data processing, enabling on-the-fly deconvolution of poor quality data.
• To create a queryable format for historical kinetic data to enhance future analyses.

Main Methods:

• Development of a generally applicable method for automated kinetic binding data analysis.
• Implementation of on-the-fly deconvolution for poor quality kinetic data.
• Creation of a database-centric strategy for storing and retrieving historical kinetic data in a queryable format.

Main Results:

• The described method automates the analysis, storage, and retrieval of kinetic binding data.
• The system can deconvolve poor quality data in real-time.
• Historical kinetic data is stored and organized for efficient querying and future analysis.

Conclusions:

• The developed automated method streamlines kinetic binding data analysis in drug discovery.
• Database-centric strategies provide deeper insights into kinetic competition mechanisms.
• This approach facilitates rapid identification of allosteric effectors and presents kinetic competition data in absolute terms.