RESUMO
Strongly attractive self-interaction of therapeutic protein candidates can impose challenges for manufacturing, filling, stability, and administration due to elevated viscosity or aggregation propensity. Suitable formulations can mitigate these issues to a certain extent. Understanding the self-interaction mechanism on a molecular basis and rational protein engineering provides a more fundamental approach, and it can save costs and efforts as well as alleviate risks at later stages of development. In this study, we used computational methods for the identification of aggregation-prone regions in a mAb and generated mutants based on these findings. We applied hydrogen-deuterium exchange mass spectrometry to identify distinct self-interaction hot spots. Ultimately, we generated mAb variants based on a combination of both approaches and identified mutants with low attractive self-interaction propensity, minimal off-target binding, and even improved target binding. Our data show that the introduction of arginine in spatial proximity to hydrophobic patches is highly beneficial on all these levels. For our mAb, variants that contain more than one aspartate residue flanking to the hydrophobic HCDR3 show decreased attractive self-interaction at unaffected off-target and target binding. The combined engineering strategy described here underlines the high potential of understanding self-interaction in the early stages of development to predict and reduce the risk of failure in subsequent development.
