Journal Article

Sequence-Based Viscosity Prediction for Rapid Antibody Engineering

Whoriskey, John, Jain, Mani, Bennett, Brian, Estes, Bram, Hsu, Hailing, Jia, Lei

2024

Overview

Through machine learning, identifying correlations between amino acid sequences of antibodies and their observed characteristics, we developed an internal viscosity prediction model to empower the rapid engineering of therapeutic antibody candidates. For a highly viscous anti-IL-13 monoclonal antibody, we used a structure-based rational design strategy to generate a list of variants that were hypothesized to mitigate viscosity. Our viscosity prediction tool was then used as a screen to cull virtually engineered variants with a probability of high viscosity while advancing those with a probability of low viscosity to production and testing. By combining the rational design engineering strategy with the in silico viscosity prediction screening step, we were able to efficiently improve the highly viscous anti-IL-13 candidate, successfully decreasing the viscosity at 150 mg/mL from 34 cP to 13 cP in a panel of 16 variants.

• Publisher: MDPI AG,MDPI
• Subject: Amino Acid Sequence / Amino acids / Antibodies / Antibodies, Monoclonal - chemistry / Antibodies, Monoclonal - immunology / Biological products industry / Cellulose acetate / Engineering / Health aspects / Humans / Interleukin 13 / interleukin 13 (IL-13) / mAb / Machine Learning / Monoclonal antibodies / Observational learning / Plasmids / Prediction models / predictive model / Proprietary / Protein Engineering - methods / Proteins / Scientific equipment and supplies industry / therapeutic antibody / Viscosity