Process Development Senior Scientist Amgen, California
Assessing Critical Quality Attributes (CQAs) typically involves analyzing protein sequences for hot spots and confirming them experimentally. Sequence alignment offers limited information, whereas 3D structural modeling provides detailed insights into solvent accessibility and protein-protein interactions, significantly improving CQA predictions and reducing laboratory work. Our study examined protein therapeutics in late-stage clinical development, using crystal structures and AlphaFold-generated models. We utilized extensive characterization data, including modification levels from forced degradation studies and chromatography fractionation, to assess impacts on biological activities and effector functions. Evaluated molecules included monoclonal antibodies and other proteins, focusing on post-translational modifications like deamidation, isomerization, glycation, tryptophan and methionine oxidation. Results showed molecular modeling predictions were consistent with observed data, correlating solvent accessibility with modification degrees. Structural modeling enhances CQA predictions, expediting drug development and improving success rates.
Learning Objectives:
Upon completion, participants will be familiar with advanced 3D structural modeling skills to better predict Critical Quality Attributes (CQAs) with enhanced precision.
Upon completion, participants will understand how various parameters contribute to the prediction and prioritization of CQAs during early and late-stage drug development.
Upon completion, participants will be able to evaluate the impact of post-translational modifications on protein stability and efficacy using molecular modeling techniques.
