Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach
Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI are important for rational selection of formulation development. In this study, we demonstrate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics that can be applied to protein therapeutics in general for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb are used as a model antibody proteins to examine the excipient-protein interaction, in silico. Metrics from SILCS include the distribution and predicted affinity of excipient and buffer interactions with the NIST Fab and the relation of the interactions to predicted PPI. Comparison with a range of experimental data shown a number of SILCS metrics to be of predictive utility. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which excipients binds that are involved in predicted protein-protein interactions correlate with the experimentally determined viscosity. In addition, a combination of number of binding sites and the predicted binding affinity are indicated to be predictive of relative protein stability. Comparison of arginine, trehalose and sucrose, all of which give the highest viscosity in combination with analysis of B22 and kD and the SILCS metrics indicate that higher viscosities are associated with a low number of predicted binding sites with the lower binding affinity of arginine leading to it's anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.
Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach, Molecular Pharmaceutics
(Accessed May 28, 2023)