Literature DB >> 28946123

Characterization of 30 therapeutic antibodies and related products by size exclusion chromatography: Feasibility assessment for future mass spectrometry hyphenation.

Alexandre Goyon1, Valentina D'Atri1, Olivier Colas2, Szabolcs Fekete1, Alain Beck2, Davy Guillarme3.   

Abstract

Despite the popularity of targeted and immune therapies, the number of studies dealing with the quantitation of aggregates for Food and Drug Administration (FDA) and European Medicines Agency (EMA) approved mAb and related products are still very scarce in literature. In this work, 30 therapeutic proteins including monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), Fc-fusion proteins and a bi-specific antibody (bsAb) were investigated using size exclusion chromatography (SEC). Their levels of high molecular weight species (HMWS) were experimentally estimated between 0.1% and 13.1%. Except for blinatumomab, etanercept and pembrolizumab, the HMWS amount for the other antibodies was well below the limit of 5% usually set a specification for therapeutic mAbs in the biopharmaceutical industry. The main chromatographic peak shape of 24 therapeutic antibodies and the NIST mAb [1] was found suitable (0.8<As<1.5) with a generic SEC method involving potassium-based salts mobile phase. Conversely, only acidic therapeutic proteins (pI<7) could be successfully analyzed with a mass spectrometry (MS) compatible mobile phase containing 100mM ammonium acetate. This study aimed to provide HMWS data for 30 therapeutic proteins covering a wide range of physico-chemical properties with molecular weights between 54 and 153kDa, pI values comprised between 6.1 and 9.4 and hydrophobic interaction chromatography (HIC) retention factors ranging from 1.2 to 6.0 for the mAbs.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Aggregates; Antibody-drug conjugates; Bi-specific antibodies; Fc-fusion proteins; Monoclonal antibodies; SEC-MS; Size exclusion chromatography

Mesh:

Substances:

Year:  2017        PMID: 28946123     DOI: 10.1016/j.jchromb.2017.09.027

Source DB:  PubMed          Journal:  J Chromatogr B Analyt Technol Biomed Life Sci        ISSN: 1570-0232            Impact factor:   3.205


  8 in total

Review 1.  Structure, heterogeneity and developability assessment of therapeutic antibodies.

Authors:  Yingda Xu; Dongdong Wang; Bruce Mason; Tony Rossomando; Ning Li; Dingjiang Liu; Jason K Cheung; Wei Xu; Smita Raghava; Amit Katiyar; Christine Nowak; Tao Xiang; Diane D Dong; Joanne Sun; Alain Beck; Hongcheng Liu
Journal:  MAbs       Date:  2018-12-17       Impact factor: 5.857

2.  Rapid LC-MS Method for Accurate Molecular Weight Determination of Membrane and Hydrophobic Proteins.

Authors:  Jennifer L Lippens; Pascal F Egea; Chris Spahr; Amit Vaish; James E Keener; Michael T Marty; Joseph A Loo; Iain D G Campuzano
Journal:  Anal Chem       Date:  2018-10-31       Impact factor: 6.986

Review 3.  Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Authors:  Rahul Khetan; Robin Curtis; Charlotte M Deane; Johannes Thorling Hadsund; Uddipan Kar; Konrad Krawczyk; Daisuke Kuroda; Sarah A Robinson; Pietro Sormanni; Kouhei Tsumoto; Jim Warwicker; Andrew C R Martin
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

4.  Probing Protein Denaturation during Size-Exclusion Chromatography Using Native Mass Spectrometry.

Authors:  Iro K Ventouri; Daniel B A Malheiro; Robert L C Voeten; Sander Kok; Maarten Honing; Govert W Somsen; Rob Haselberg
Journal:  Anal Chem       Date:  2020-03-06       Impact factor: 6.986

5.  Discovery of compounds with viscosity-reducing effects on biopharmaceutical formulations with monoclonal antibodies.

Authors:  Matic Proj; Mitja Zidar; Blaž Lebar; Nika Strašek; Goran Miličić; Aleš Žula; Stanislav Gobec
Journal:  Comput Struct Biotechnol J       Date:  2022-09-26       Impact factor: 6.155

6.  Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies.

Authors:  Max Hebditch; Jim Warwicker
Journal:  PeerJ       Date:  2019-12-18       Impact factor: 2.984

7.  Conformational Ensembles of Antibodies Determine Their Hydrophobicity.

Authors:  Franz Waibl; Monica L Fernández-Quintero; Anna S Kamenik; Johannes Kraml; Florian Hofer; Hubert Kettenberger; Guy Georges; Klaus R Liedl
Journal:  Biophys J       Date:  2020-11-18       Impact factor: 4.033

8.  Direct coupling of size exclusion chromatography and mass spectrometry for the characterization of complex monoclonal antibody products.

Authors:  Amarande Murisier; Marie Andrie; Szabolcs Fekete; Matthew Lauber; Valentina D'Atri; Katharina Iwan; Davy Guillarme
Journal:  J Sep Sci       Date:  2022-03-28       Impact factor: 3.614

  8 in total

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