posted on 2024-02-05, 20:11authored byJ. Joel Janke, Charles G. Starr, Jonathan S. Kingsbury, Norbert Furtmann, Christopher J. Roberts, Cesar Calero-Rubio
Monoclonal antibodies (mAbs) are
an important modality
of protein
therapeutics with broad applications for numerous diseases. However,
colloidal instabilities occurring at high protein concentrations can
limit the ability to develop stable, high-concentration liquid dosage
forms that are required for patient-centric, device-mediated products.
Therefore, it is advantageous to identify colloidally stable mAbs
early in the discovery process to ensure that they are selected for
development. Experimental screening for colloidal stability can be
time- and resource-consuming and is most feasible at the later stages
of drug development due to material requirements. Alternatively, computational
approaches have emerging potential to provide efficient screening
and focus developmental efforts on mAbs with the greatest developability
potential, while providing mechanistic relationships for colloidal
instability. In this work, coarse-grained, molecular-scale models
were fine-tuned to screen for colloidal stability at amino-acid resolution.
This model parameterization provides a framework to screen for mAb
self-interactions and extrapolate to bulk solution behavior. This
approach was applied to a wide array of mAbs under multiple buffer
conditions, demonstrating the utility of the presented computational
approach to augment early candidate screening and later formulation
strategies for protein therapeutics.