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. 2020 Nov 2;17(11):4323-4333.
doi: 10.1021/acs.molpharmaceut.0c00775. Epub 2020 Oct 6.

Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach

Sunhwan Jo  1 , Amy Xu  2 , Joseph E Curtis  2 , Sandeep Somani  3 , Alexander D MacKerell Jr  4
Affiliations

Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach

Sunhwan Jo et al. Mol Pharm. .

Abstract

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 is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is 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, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its 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.

Keywords: biologics; formulation; molecular dynamics; monoclonal antibody; protein-based drugs; protein−protein interactions.

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Conflict of interest statement

Conflict of Interest

ADM Jr is co-founder and Chief Scientific Officer of SilcsBio, LLC. SJ is an employee of SilcsBio LLC when the studies were performed.

Figures

Figure 1.
Figure 1.
Overview of SILCS-Biologics workflow.
Figure 2.
Figure 2.
NISTmAb Fab domain self-interaction preference surface. The interaction PPIP score is scaled to 0 (lower) to 1 (higher) and the color is assigned blue to red, respectively.
Figure 3.
Figure 3.
Antibody CNTO607 Fab domain self-interaction preference surface. The interaction PPIP score is scaled to 0 (lower) to 1 (higher) and the color is assigned blue to red, respectively.
Figure 4.
Figure 4.
Excipient-NISTmAb Fab domain interaction hotspots of Arg and Lys having LE < −0.25 kcal/mol. The surface of NISTmAb Fab domain is colored using the Fab self-interaction preference score.
Figure 5.
Figure 5.
Correlation between the number of binding sites with A) favorable binding affinity, B) with high PPI preference (PPIP), and C) both favorable binding affinity and high PPIP value. The solid line is the best-fit curve using linear regression. The excipient Arg data points are indicated as they are an outlier and the data for the plots are shown in Tables 1 and 2.
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