Literature DB >> 29274822

Predicting Protein-Protein Interactions of Concentrated Antibody Solutions Using Dilute Solution Data and Coarse-Grained Molecular Models.

Cesar Calero-Rubio1, Ranendu Ghosh1, Atul Saluja2, Christopher J Roberts3.   

Abstract

Protein-protein interactions for solutions of an IgG1 molecule were quantified using static light scattering (SLS) measurements from low to high protein concentrations (c2). SLS was used to determine second osmotic virial coefficients (B22) at low c2, and excess Rayleigh profiles (Rex/K vs. c2) and zero-q structure factors (Sq=0) as a function of c2 at higher c2 for a series of conditions (pH, sucrose concentration, and total ionic strength [TIS]). Repulsive (attractive) interactions were observed at low TIS (high TIS) for pH 5 and 6.5, with increasing repulsions when 5% w/w sucrose was also present. Previously developed and refined coarse-grained antibody models were used to fit model parameters from B22 versus TIS data. The resulting parameters from low-c2 conditions were used as the sole input to multiprotein Monte Carlo simulations to predict high-c2Rex/K and Sq=0 behavior up to 150 g/L. Experimental results at high-c2 conditions were quantitatively predicted by the simulations for the coarse-grained models that treated antibody molecules as either 6 or 12 (sub) domains, which preserved the basic shape of a monoclonal antibody. Finally, preferential accumulation of sucrose around the protein surface was identified via high-precision density measurements, which self-consistently explained the simulation and experimental SLS results.
Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  biopharmaceuticals characterization; biophysical models; in silico modeling; light scattering (static); monoclonal antibody; protein formulation

Mesh:

Substances:

Year:  2017        PMID: 29274822      PMCID: PMC5916024          DOI: 10.1016/j.xphs.2017.12.015

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


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