An efficient formulation design for high-concentration protein formulations von Miko Schleinitz | ISBN 9783843946513
Buchcover ISBN 9783843946513

An efficient formulation design for high-concentration protein formulations

von Miko Schleinitz
Over the last decades, the interest in biopharmaceuticals has increased due to the demand for high-specific monoclonal antibodies to cure autoimmune diseases and cancer. In order to get insight into molecular interaction behavior, especially protein-protein interactions, and improve formulation conditions, the mxDLVO model was extended to model second osmotic virial coefficients B22 (protein-protein interactions) of immunoglobulin G (IgG) in the presence of different excipient types (salts, sugars, amino acids, surfactants). The advantage of this model is its capability to model the single-excipient influence on B22 of IgG with minimal experimental effort and in addition predict B22 of IgG in the presence of excipient-mixtures without any additional experiments.
Building up on this approach, optimal formulation windows were identified by combining the information of water activity coefficients (water-water/water-excipient interactions) using the ePC-SAFT equation-of-state with B22 predictions using the mxDLVO model in a hybrid modeling approach. In this regard, optimal formulations were not solely dependent on favorable protein-protein interactions but also on favorable water-water interactions.
Delivering an all-in-one modeling solution that considers all molecular interactions in protein formulations, the ePC-SAFT equation-of-state was applied using parameters derived from static light scattering data to predict B22 and cross virial coefficients B23 (protein-excipient interactions) of IgG in the presence of excipients. This approach not only reduces the experimental effort to a minimum, but also gives insight into molecular interactions of protein, excipient and water molecules. It also allows predicting protein properties (e.g. solution densities) using just one thermodynamic model.
The results of this work offer a great contribution to the identification of optimal protein formulation conditions using thermodynamic models in early formulation development.


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