High Throughput Prediction Approach for Monoclonal Antibody Aggregation at High Concentration View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-09

AUTHORS

Mitja Zidar, Ana Šušterič, Miha Ravnik, Drago Kuzman

ABSTRACT

PURPOSE: Characterization of the monoclonal antibody aggregation process and identification of stability factors that could be used as indicators of aggregation propensity with an emphasis on a large number of samples and low protein material consumption. METHODS: Differential scanning calorimetry, dynamic light scattering and size exclusion chromatography were used as the main methodological approaches. Conformational stability, colloidal stability and aggregation kinetics were assessed for two different IgG monoclonal antibody (mAbs) subclasses. Aggregation was induced by exposing the mAbs to 55°C for 3 weeks. mAb samples were prepared in different formulations and concentrations from 1 mg/mL to 50 mg/mL. RESULTS: High temperature stress of mAb samples revealed that monoclonal antibodies followed first order aggregation kinetics, which suggests that the rate-limiting step of monomer loss was unimolecular. Conformational stability of mAbs was estimated with denaturation temperature measurements. Colloidal stability was assessed with dynamic interaction parameter k D . The correlation between aggregation kinetics and colloidal and conformational stability factors was evaluated and the dynamic interaction parameter was found to be a promising predictor of aggregation propensity of monoclonal antibodies. The meaning of using an intermolecular interaction parameter for prediction of what is essentially a unimolecular process is also discussed. CONCLUSIONS: This work estimates the significance of different predictors of aggregation propensity at high concentrations as a part of a high throughput, low resource screening method and is a contribution towards determining protein aggregation phenomena in actual systems used for the development and production of biopharmaceuticals. More... »

PAGES

1831-1839

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11095-017-2191-6

DOI

http://dx.doi.org/10.1007/s11095-017-2191-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085906500

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28593474


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    "description": "PURPOSE: Characterization of the monoclonal antibody aggregation process and identification of stability factors that could be used as indicators of aggregation propensity with an emphasis on a large number of samples and low protein material consumption.\nMETHODS: Differential scanning calorimetry, dynamic light scattering and size exclusion chromatography were used as the main methodological approaches. Conformational stability, colloidal stability and aggregation kinetics were assessed for two different IgG monoclonal antibody (mAbs) subclasses. Aggregation was induced by exposing the mAbs to 55\u00b0C for 3\u00a0weeks. mAb samples were prepared in different formulations and concentrations from 1\u00a0mg/mL to 50\u00a0mg/mL.\nRESULTS: High temperature stress of mAb samples revealed that monoclonal antibodies followed first order aggregation kinetics, which suggests that the rate-limiting step of monomer loss was unimolecular. Conformational stability of mAbs was estimated with denaturation temperature measurements. Colloidal stability was assessed with dynamic interaction parameter\u00a0k D . The correlation between aggregation kinetics and colloidal and conformational stability factors was evaluated and the dynamic interaction parameter was found to be a promising predictor of aggregation propensity of monoclonal antibodies. The meaning of using an intermolecular interaction parameter for prediction of what is essentially a unimolecular process is also discussed.\nCONCLUSIONS: This work estimates the significance of different predictors of aggregation propensity at high concentrations as a part of a high throughput, low resource screening method and is a contribution towards determining protein aggregation phenomena in actual systems used for the development and production of biopharmaceuticals.", 
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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11095-017-2191-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11095-017-2191-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11095-017-2191-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11095-017-2191-6'


 

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