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

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 a...

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Bibliographic Details
Published inInternational journal of biological macromolecules p. 132694
Main Authors Shrivastava, Anuj, Nikita, Saxena, Rathore, Anurag S
Format Journal Article
LanguageEnglish
Published Netherlands 28.05.2024
Subjects
Glycan analysis
Process analytical technology
Machine learning
Monoclonal antibody
HPLC-FLD
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Summary: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 mAbs and validated using LC-MS quantification as reference. The model predicted values were at par with the LC-MS values with root mean square error of <0.5 and R 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.
ISSN:1879-0003