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ABSTRACT
Solutions of monoclonal antibodies (mAbs) can show increased viscosity at high concentration, which can be a disadvantage during protein purification, filling, and administration. The viscosity is determined by protein-protein-interactions, which are influenced by the antibody’s sequence as well as solution conditions, like pH, buffer type, or the presence of salts and other excipients. To predict viscosity, experimental parameters, like the diffusion interaction parameter (kD), or computational tools harnessing information derived from primary sequence, are often used, but a reliable predictive tool is still missing. We present a modeling approach employing artificial neural networks (ANNs) using experimental factors combined with simulation-derived parameters plus viscosity data from 27 highly concentrated (180 mg/mL) mAbs. These ANNs can be used to predict if mAbs exhibit problematic viscosity at distinct concentrations or to model viscosity-concentration-curves.
Acknowledgments
The authors thank Lonza Basel employees Marigone Lenjani, Jill Werner, Sonja Rutz, Thomas Zech, Emie-kim Ngotan, and Katja In-Albon for support in experiments and data generation. The authors thank Lonza Slough employee Jean Aucamp for the purification and generous provision of the monoclonal antibodies. The support of Schrödinger for technical support with the software BioLuminate and its provision free of charge for an extended period is greatly acknowledged.
Disclosure statement
The authors report no conflict of interest.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2023.2169440