By Eliza Yeung, PhD, Associate Director of Process Characterization, R&D Services, Cytovance Biologics, and Philip Ramsey, PhD, Mathematics and Statistics, University of New Hampshire
Glycosylation is one of the most common post-translational modifications in mammalian-expressed biologics, and is considered to be a critical quality attribute of therapeutic glycoproteins. Due to its biological relevance, physiochemical assessment on the glycosylation profile is always important to the success of a drug development initiative. This article describes the combination of experimental design and machine learning techniques applied to characterize and optimize a conventional, non-derivatized glycoprofiling method on glycans derived from a human immunoglobulin using high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Two independent experimental designs, a 16-run definitive screening design (DSD) and a 28-run central composite design (CCD), were incorporated with a machine learning technique known as “self-validating ensemble modeling (SVEM)” and used to build predictive models for four chromatographic responses. We show that the predictive models created using SVEM on the DSD data reliably predicted the behavior of the chosen responses when applied to CCD validation data. This demonstrates that the DSD is an efficient alternative to the larger, traditional CCD in which the combination of experimental design and machine learning can effectively characterize and optimize analytical methods.