Dataset Information

Comprehensive Multidimensional Liquid Chromatography-Mass Spectrometry for the Characterization of Charge Variants of a Bispecific Antibody.

ABSTRACT: Identification and further characterization of antibody charge variants is a crucial step during biopharmaceutical drug development, particularly with regard to the increasing complexity of novel antibody formats. As a standard analytical approach, manual offline fractionation of charge variants by cation-exchange chromatography followed by comprehensive analytical testing is applied. These conventional workflows are time-consuming and labor-intensive and overall reach their limits in terms of chromatographic separation of enhanced structural heterogeneities raised from new antibody formats. For these reasons, we aimed to develop an alternative online characterization strategy for charge variant characterization of a therapeutic bispecific antibody by online mD-LC-MS at middle-up (2D-LC-MS) and bottom-up (4D-LC-MS) level. Using the implemented online mD-LC-MS approach, all medium- and even low-abundant product variants previously identified by offline fraction experiments and liquid chromatography mass spectrometry could be monitored. The herein reported automated online mD-LC-MS methodology therefore represents a complementary and in part alternative approach for analytical method validation including multiattribute monitoring (MAM) strategies by mass spectrometry, offering various benefits including increased throughput and reduced sample handling and combined protein information at intact protein and peptide level.

SUBMITTER: Grunert I

PROVIDER: S-EPMC9732868 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comprehensive Multidimensional Liquid Chromatography-Mass Spectrometry for the Characterization of Charge Variants of a Bispecific Antibody.

Grunert Ingrid I Heinrich Katrin K Hingar Michael M Ernst Juliane J Winter Martin M Bomans Katrin K Wagner Katharina K Fevre Arnaud A Reusch Dietmar D Wuhrer Manfred M Bulau Patrick P

Journal of the American Society for Mass Spectrometry 20221128 12

Identification and further characterization of antibody charge variants is a crucial step during biopharmaceutical drug development, particularly with regard to the increasing complexity of novel antibody formats. As a standard analytical approach, manual offline fractionation of charge variants by cation-exchange chromatography followed by comprehensive analytical testing is applied. These conventional workflows are time-consuming and labor-intensive and overall reach their limits in terms of c ...[more]

PMID: 36442848

Similar Datasets

Project description:BackgroundParkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.MethodsWe presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.ResultsA list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.ConclusionsThis study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.

Dataset Information

Comprehensive Multidimensional Liquid Chromatography-Mass Spectrometry for the Characterization of Charge Variants of a Bispecific Antibody.

Publications

Comprehensive Multidimensional Liquid Chromatography-Mass Spectrometry for the Characterization of Charge Variants of a Bispecific Antibody.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets