Protein aggregation and high viscosity can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation and viscosity behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation of 21 and viscosity of 27 approved monospecific monoclonal antibodies at high concentration (150 mg/mL). For the aggregation model, the nearest neighbors and support vector regression models have correlation coefficients of 0.86 and 0.80, respectively. For the viscosity model, we found that the net charge in the mAbs and the amino acid composition in the Fv region are key features which govern the viscosity behavior. A predictive model based on the net charges of mAbs and a high viscosity index is presented as a fast screening tool for classifying low and high viscosity mAbs.

Machine learning applied to predict high concentration therapeutic antibody aggregation and viscosity

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.