Prediction of Antibody Viscosity from Dilute Solution Measurements

Author:

Bhandari Kamal1ORCID,Wei Yangjie2ORCID,Amer Brendan R.2ORCID,Pelegri-O’Day Emma M.2,Huh Joon2,Schmit Jeremy D.1ORCID

Affiliation:

1. Department of Physics, Kansas State University, Manhattan, KS 66506, USA

2. Amgen Inc., Thousand Oaks, CA 91320, USA

Abstract

The high antibody doses required to achieve a therapeutic effect often necessitate high-concentration products that can lead to challenging viscosity issues in production and delivery. Predicting antibody viscosity in early development can play a pivotal role in reducing late-stage development costs. In recent years, numerous efforts have been made to predict antibody viscosity through dilute solution measurements. A key finding is that the entanglement of long, flexible complexes contributes to the sharp rise in antibody viscosity at the required dosing. This entanglement model establishes a connection between the two-body binding affinity and the many-body viscosity. Exploiting this insight, this study connects dilute solution measurements of self-association to high-concentration viscosity profiles to quantify the relationship between these regimes. The resulting model has exhibited success in predicting viscosity at high concentrations (around 150 mg/mL) from dilute solution measurements, with only a few outliers remaining. Our physics-based approach provides an understanding of fundamental physics, interpretable connections to experimental data, the potential to extrapolate beyond training conditions, and the capacity to effectively explain the physical mechanics behind these outliers. Conducting hypothesis-driven experiments that specifically target the viscosity and relaxation mechanisms of outlier molecules may allow us to unravel the intricacies of their behavior and, in turn, enhance the performance of our model.

Funder

Amgen preclinical research program

Publisher

MDPI AG

Subject

Drug Discovery,Immunology,Immunology and Allergy

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs;European Journal of Pharmaceutical Sciences;2024-03

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