Application of a kNN-based similarity method to biopharmaceutical manufacturing.

Abstract

Machine learning-based similarity analysis is commonly found in many artificial intelligence applications like the one utilized in e-commerce and digital marketing. In this study, a kNN-based (k-nearest neighbors) similarity method is proposed for rapid biopharmaceutical process diagnosis and process performance monitoring. Our proposed application measures the spatial distance between batches, identifies the most similar historical batches, and ranks them in order of similarity. The proposed method considers the similarity in both multivariate and univariate feature spaces and measures batch deviations to a benchmarking batch. The feasibility and effectiveness of the proposed method are tested on a drug manufacturing process at Biogen.