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Protein-Protein Interactions of Highly Concentrated Monoclonal Antibody Solutions via Static Light Scattering and Influence on the Viscosity.

Jessica J HungBarton J DearCarl A KaroutaAmjad A ChowdhuryPaul Douglas GodfrinJonathan A BollingerMaria P NietoLogan R WilksTony Y ShayKishan RamachandranAyush SharmaJason K CheungThomas M TruskettKeith P Johnston
Published in: The journal of physical chemistry. B (2019)
The ability to design and formulate mAbs to minimize attractive interactions at high concentrations is important for protein processing, stability, and administration, particularly in subcutaneous delivery, where high viscosities are often challenging. The strength of protein-protein interactions (PPIs) of an IgG1 and IgG4 monoclonal antibody (mAb) from low to high concentration was determined by static light scattering (SLS) and used to understand viscosity data. The PPI were tuned using NaCl and five organic ionic co-solutes. The PPI strength was quantified by the normalized structure factor S(0)/ S(0)HS and Kirkwood-Buff integral G22/ G22,HS (HS = hard sphere) determined from the SLS data and also by fits with (1) a spherical Yukawa potential and (2) an interacting hard sphere (IHS) model, which describes attraction in terms of hypothetical oligomers. The IHS model was better able to capture the scattering behavior of the more strongly interacting systems (mAb and/or co-solute) than the spherical Yukawa potential. For each descriptor of PPI, linear correlations were obtained between the viscosity at high concentration (200 mg/mL) and the interaction strengths evaluated both at low (20 mg/mL) and high concentrations (200 mg/mL) for a given mAb. However, the only parameter that provided a correlation across both mAbs was the oligomer mass ratio ( moligomer/ mmonomer+dimer) from the IHS model, indicating the importance of self-association (in addition to the direct influence of the attractive PPI) on the viscosity.
Keyphrases
  • monoclonal antibody
  • protein protein
  • electronic health record
  • big data
  • machine learning
  • ionic liquid
  • artificial intelligence
  • monte carlo