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Laboratory Scale Production and Purification of a Therapeutic Antibody
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Published on: January 24, 2017

Predicting solution aggregation rates for therapeutic proteins: approaches and challenges.

Christopher J Roberts1, Tapan K Das, Erinc Sahin

  • 1Department of Chemical Engineering and Center for Molecular and Engineering Thermodynamics, University of Delaware, Newark, DE 19716, United States. cjr@udel.edu

International Journal of Pharmaceutics
|April 19, 2011
PubMed
Summary

Predicting therapeutic protein aggregation rates is crucial for product development. Current methods face limitations, highlighting the need for further research to improve predictive capabilities for non-native aggregation.

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Area of Science:

  • Biopharmaceutical development
  • Protein aggregation kinetics
  • Formulation science

Background:

  • Non-native protein aggregation is a significant challenge in therapeutic protein product development, especially for liquid formulations.
  • Protein aggregates are often irreversible, necessitating control over aggregation rates under various solution conditions.
  • Accurate prediction of aggregation rates is vital for rational formulation design.

Purpose of the Study:

  • To review the principles of current rate-prediction approaches for non-native protein aggregation.
  • To discuss the strengths and limitations of different predictive methods.
  • To identify research areas for enhancing aggregation rate prediction.

Main Methods:

  • Analysis of principles underlying current aggregation rate prediction methods.
  • Evaluation of the strengths and limitations of various approaches.
  • Inclusion of illustrative examples from the authors' research.

Main Results:

  • Accurate prediction of protein aggregation rates remains a complex challenge.
  • Current methods have inherent limitations in predicting aggregation kinetics across diverse conditions.
  • Several factors contribute to the difficulty in precise rate prediction.

Conclusions:

  • Further research is essential to overcome the limitations in predicting non-native aggregation rates.
  • Improved predictive models will significantly benefit the rational design of therapeutic protein formulations.
  • Advancing predictive capabilities is key to ensuring the quality and efficacy of protein-based therapeutics.