ArticlePharmaceutical research2017
High Throughput Prediction Approach for Monoclonal Antibody Aggregation at High Concentration.
Article in Pharmaceutical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.Molecules (Basel, Switzerland) · 2026Review
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- The Osmolality and Hemolysis of High-Concentration Monoclonal Antibody Formulations.Pharmaceutical research · 2024Article
- Phase-Appropriate Application of Analytical Methods to Monitor Subvisible Particles Across the Biotherapeutic Drug Product Life Cycle.The AAPS journal · 2019Article
- Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeCharacterization 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.
methodsDifferential 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.
resultsHigh 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
conclusionsThis 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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Registered trials
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