Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

ABSTRACT: Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 

SUBMITTER: Lai PK 

PROVIDER: S-EPMC8794240 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature