Need for a risk-based control strategy for managing glycosylation profile for biosimilar products

Machine learning tool as an enabler for rapid quantification of monoclonal antibodies N-glycans using fluorescence detector

Liquid chromatography-mass spectrometry (LC-MS) is widely used for identification and quantification of N-glycans of monoclonal antibodies (mAbs), owing to its high sensitivity and accuracy. However, its resource-intensive nature necessitates the development of rapid and cost-effective orthogonal analysis approaches. This study aims to develop an online method utilizing the Extreme Gradient Boosting (XGBoost) machine learning (ML) algorithm for real time quantification of InstantPC labelled N-glycans by Liquid Chromatography (LC) - fluorescence detector (FLD). The LC-FLD profile is pre-processed for baseline correction and noise reduction prior to fed to the machine learning (ML) algorithm. The algorithm has been successfully tested for commercial and inhouse developed mAbs and validated using LC-MS quantification as reference. The LC-FLD-ML model predicted values were at par with the LC-MS values with root mean square error of <0.5 and R2 of >0.95. The average errors using ML model (1.80 %) was reduced by a minimum of 28 % and 40 % for origin (1.5 %) and manual (1.07 %) based integration, respectively. The approach reduces the data analysis time per sample by ~70 % (from ~5 min to ~1.5 min), thereby offering a time and resource efficient orthogonality with LC-MS for quantification of N-glycans in mAbs.

Innovative Metrics for Reporting and Comparing the Glycan Structural Profile in Biotherapeutics

Glycosylation is a critical quality attribute in biotherapeutics, impacting properties such as protein stability, solubility, clearance rate, efficacy, immunogenicity, and safety. Due to the heterogenic and complex nature of protein glycosylation, comprehensive characterization is demanding. Moreover, the lack of standardized metrics for evaluating and comparing glycosylation profiles hinders comparability studies and the establishment of manufacturing control strategies. To address both challenges, we propose a standardized approach based on novel metrics for a comprehensive glycosylation fingerprint which greatly facilitates the reporting and objective comparison of glycosylation profiles. The analytical workflow is based on a liquid chromatography-mass spectrometry-based multi-attribute method. Based on the analytical data, a matrix of glycosylation-related quality attributes, both at site-specific and whole molecule level, are computed, which provide metrics for a comprehensive product glycosylation fingerprint. Two case studies illustrate the applicability of the proposed indices as a standardized and versatile approach for reporting all dimensions of the glycosylation profile. The proposed approach further facilitates the assessments of risks associated with changes in the glycosylation profile that may affect efficacy, clearance, and immunogenicity.

The Sincerest Form of Flattery? Biosimilars in Inflammatory Bowel Disease

Biosimilar medications have the potential to significantly reduce the cost of treatment in patients with inflammatory bowel disease. Observational studies have shown similar efficacy and safety of biosimilars to biologic reference products. Shared decision-making is crucial to the successful implementation of these agents.