Computational modeling of microfluidic data provides high-throughput affinity estimates for monoclonal antibodies

Area of Science:

• Biophysical Chemistry
• Analytical Chemistry
• Microfluidics

Background:

• Accurate affinity measurement is crucial for monoclonal antibody (mAb) discovery and vaccine antigen development.
• Existing affinity assays often suffer from low throughput and limited dynamic range.
• There is a need for innovative, high-throughput, and broad-range affinity measurement methodologies.

Purpose of the Study:

• To develop a high-throughput, broad-range methodology for measuring antibody affinity by combining microfluidics and quantum-mechanical scattering theory.
• To establish a computational method for affinity quantification based on antibody-antigen capture profiles.
• To validate the method's accuracy, precision, and dynamic range for various antibody-antigen interactions.

Main Methods:

• Utilized microfluidic technology with immobilized antigens in micro-columns.
• Generated fluorescence intensity profiles of labeled mAbs and antigen-binding fragments under out-of-equilibrium conditions.
• Employed computational data analysis based on the Landau probability distribution for affinity quantification.

Main Results:

• Successfully described 841 antibody-antigen capture profiles using the Landau distribution.
• Identified a scale parameter 'W' independent of antibody concentration, correlating significantly with the equilibrium dissociation constant (K_D).
• Achieved high precision (median CV: 5%) and a broad dynamic range (K_D: ~10^-7 to ~10^-11 M), with a turnaround time reduced from 2 days to 2 hours.

Conclusions:

• The developed method offers a fast, reproducible, and high-throughput approach for accurate antibody affinity measurement.
• This computational modeling of antibody capture profiles significantly enhances the efficiency of antibody discovery and vaccine development.
• The technique requires minimal sample volumes and provides a wide dynamic range, addressing limitations of current assays.