Optimizing properties of antireceptor antibodies using kinetic computational models and experiments

Area of Science:

• Biochemistry
• Immunology
• Computational Biology

Background:

• Monoclonal antibodies (mAbs) are crucial anticancer therapeutics targeting tumor-associated proteins.
• Understanding mAb binding kinetics and avidity is key to optimizing therapeutic potency.
• Kinetic computational models offer insights into mAb-target interactions.

Purpose of the Study:

• To develop and validate a method for assessing the relative importance of monovalent affinity and bivalent avidity in mAb drug potency.
• To introduce and characterize the avidity factor (χ) within a kinetic computational model.
• To demonstrate the predictive power of computational models for mAb binding and target inhibition.

Main Methods:

• Developed the virtual flow cytometry (VFC) method, integrating experimental binding kinetics and affinity data into a kinetic computational model.
• Introduced the avidity factor (χ) to quantify antibody cross-linking ability.
• Validated the model's ability to predict mAb binding curves across various experimental conditions.

Main Results:

• The VFC method successfully describes and predicts mAb binding curves, incorporating the avidity factor (χ).
• Computationally demonstrated that antibodies with high antigen cross-linking ability exhibit significant potency advantages.
• Identified the avidity factor (χ) as a physical, epitope-dependent property of mAbs.

Conclusions:

• Antibody cross-linking, quantified by the avidity factor (χ), is a critical design parameter for therapeutic antibody performance, alongside monovalent affinity.
• The VFC method provides a robust framework for evaluating mAb avidity and cross-linking potential.
• Incorporating avidity assessment into mAb screening can enhance the development of potent anticancer therapeutics.