Computational Screening for mAb Colloidal Stability with Coarse-Grained, Molecular-Scale Simulations.

Area of Science:

• Protein therapeutics
• Biopharmaceutical development
• Computational biophysics

Background:

• Monoclonal antibodies (mAbs) are vital protein therapeutics, but their development is often hindered by colloidal instabilities at high concentrations.
• Developing stable, high-concentration liquid formulations is essential for patient-centric, device-mediated delivery of protein therapeutics.
• Current experimental methods for assessing colloidal stability are time-consuming and resource-intensive, typically performed late in development.

Purpose of the Study:

• To develop and validate an efficient computational approach for early screening of colloidal stability in monoclonal antibodies (mAbs).
• To provide a framework for predicting mAb self-interactions and bulk solution behavior, aiding in the selection of developable candidates.
• To support both early-stage candidate screening and later-stage formulation strategies for protein therapeutics.

Main Methods:

• Fine-tuning of coarse-grained, molecular-scale models for screening colloidal stability at amino-acid resolution.
• Development of a computational framework to analyze mAb self-interactions and predict bulk solution behavior.
• Application of the computational model to a diverse set of mAbs across various buffer conditions.

Main Results:

• The computational model successfully screened for colloidal stability of monoclonal antibodies (mAbs) at amino-acid resolution.
• The model's parameterization provides a robust framework for assessing mAb self-interactions and predicting solution behavior.
• The approach demonstrated utility in augmenting early candidate screening and informing formulation strategies for protein therapeutics.

Conclusions:

• Computational screening of colloidal stability offers an efficient alternative to experimental methods for early-stage drug development.
• The developed molecular-scale modeling approach can identify colloidally stable mAbs, facilitating the selection of promising therapeutic candidates.
• This computational strategy enhances the development of stable, high-concentration liquid formulations for protein therapeutics.