Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin.

The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challenging concern for manufacturers. A noble strategy for mapping best and suitable storage conditions for recombinant human serum albumin (rHSA) in laboratory mixture was optimized using chromatographic data as per principal component analysis (PCA), and similarity was defined using hierarchical cluster analysis. In contrast, separability was defined using linear discriminant analysis (LDA) models.