Biosimilarity Assessments of Model IgG1-Fc Glycoforms Using a Machine Learning Approach.

ABSTRACT

Biosimilarity assessments are performed to decide whether 2 preparations of complex biomolecules can be considered "highly similar." In this work, a machine learning approach is demonstrated as a mathematical tool for such assessments using a variety of analytical data sets. As proof-of-principle, physical stability data sets from 8 samples, 4 well-defined immunoglobulin G1-Fragment crystallizable glycoforms in 2 different formulations, were examined (see More et al., companion article in this issue). The data sets included triplicate measurements from 3 analytical methods across different pH and temperature conditions (2066 data features). Established machine learning techniques were used to determine whether the data sets contain sufficient discriminative power in this application. The support vector machine classifier identified the 8 distinct samples with high accuracy. For these data sets, there exists a minimum threshold in terms of information quality and volume to grant enough discriminative power. Generally, data from multiple analytical techniques, multiple pH conditions, and at least 200 representative features were required to achieve the highest discriminative accuracy. In addition to classification accuracy tests, various methods such as sample space visualization, similarity analysis based on Euclidean distance, and feature ranking by mutual information scores are demonstrated to display their effectiveness as modeling tools for biosimilarity assessments.