Glycosylation Shapes the Efficacy and Safety of Diverse Protein, Gene and Cell Therapies

Rocamora, Frances; Peralta, Angelo Gabriel; Shin, Seunghyeon; Sorrentino, James V.; Wu, Mina; Toth, Eric A.; Fuerst, Thomas R.; Lewis, Nathan E.

doi:10.20944/preprints202303.0072.v1

Cited by 3 publications

(3 citation statements)

References 210 publications

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“…This post-translational modification impacts diverse processes, including pathogen binding, cell adhesion, signal transduction, and molecular trafficking 1,2 . Changes in glycan structures can influence many biological and physiological processes 3,4 . For example, changes in glycosylation can modulate inflammatory responses, facilitate viral immune escape, aid metastasis in cancer, orchestrate apoptosis, and participate in the pathophysiology of various genetic and infectious diseases 5,6 .…”

Section: Introductionmentioning

confidence: 99%

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Li,

Peralta,

Schoffelen

et al. 2024

Preprint

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Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.

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Section: Introductionmentioning

confidence: 99%

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Li,

Peralta,

Schoffelen

et al. 2024

Preprint

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show abstract

“…Improper glycosylation has been implicated in several disease states, and may be useful as an early indicator of cancer [1][2][3][4][5][6][7][8][9][10]. Proper glycosylation is vital to the efficacy of many protein therapeutics and vaccines [11][12][13]. The influence of glycans on protein function and organismal physiology highlights how imperative it is to identify flexible and affordable methods for sequencing glycans.…”

Section: Introductionmentioning

confidence: 99%

A Boltzmann model predicts glycan structures from lectin binding

Yom,

Chiang,

Lewis

2023

Preprint

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Glycans are complex polysaccharides involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we are able to predict the approximate structures of 90 +/- 5 % of N-glycans in our test set. We further show that our model generalizes well to the pharmaceutically relevant case of Chinese Hamster Ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

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“…Biotherapeutics include proteins and hormones, monoclonal antibodies, cytokines, cell and gene therapy products, vaccines, and stem cell therapies. During their development, manufacturing, and storage, they can suffer from structural modifications, known as posttranslational modifications (PTMs), which can ultimately alter their biological activity and safety (Das et al., 2020; Rocamora et al., 2023; Wen & Jawa, 2021). Thus, advancement in their production and their increased complexity force the need for cutting‐edge analytical approaches to deliver high‐quality products more efficiently.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Attribute Method (MAM) Analytical Workflow for Biotherapeutic Protein Characterization from Process Development to QC

Millán‐Martín,

Jakes,

Carillo

et al. 2023

Current Protocols

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The multi‐attribute method (MAM) has emerged significantly in recent years to support biotherapeutic protein characterization from process development to the QC environment. MAM is a liquid chromatography mass spectrometry (LC‐MS) based peptide mapping approach, which combines the benefits from liquid chromatography coupled to high resolution accurate mass mass spectrometry (LC‐HRAM MS), enabling direct assessment of protein sequence and product quality attributes with site specificity. These product quality attributes may impact efficacy, safety, stability, and process robustness. MAM is intended to replace conventional analytical approaches as it offers a more streamlined strategy for parallel monitoring of multiple attributes in a single analysis with high sensitivity and confidence, and ultimately supports more robust Quality by Design (QbD) approaches and faster decision cycles for biotherapeutic development. MAM consists of three main stages. The first stage is sample digestion, which typically entails proteolytic digestion of the protein. The second stage is reversed‐phase chromatographic separation of the generated peptides and detection by HRAM MS in two phases. During MAM Phase I (discovery phase), data‐dependent acquisition (DDA) MS/MS is performed to enable confident identification of peaks and development of a peptide workbook. During MAM Phase II (monitoring phase), full MS acquisition is only carried out for the monitoring of predefined product quality attributes (PQAs). The third stage is data processing, which entails analysis and reporting for each of the two phases including evaluation of sequence coverage, assessment of PQAs and peptide workbook creation during phase I, and targeted monitoring of predefined product attributes and new peak detection (NPD) during phase II. The latter is a comparative analysis that uses a base peak alignment algorithm to determine any non‐monitored differences between the LC‐MS chromatograms of a test sample and a reference standard. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: In‐solution sample digestionAlternate Protocol: Automated sample digestionBasic Protocol 2: Reversed‐phase chromatographic separation and detection by HRAM‐MS (RPLC‐HRAM MS)Basic Protocol 3: Data processing and reporting

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Glycosylation Shapes the Efficacy and Safety of Diverse Protein, Gene and Cell Therapies

Cited by 3 publications

References 210 publications

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

A Boltzmann model predicts glycan structures from lectin binding

Multi‐Attribute Method (MAM) Analytical Workflow for Biotherapeutic Protein Characterization from Process Development to QC

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