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Chromatographic parameter determination for complex biological feedstocks.

Silvia M Pirrung1, Diogo Parruca da Cruz1, Alexander T Hanke1

  • 1Dept. of Biotechnology, Delft University of Technology, Van der Maasweg 9, Delft, 2629 HZ, the Netherlands.

Biotechnology Progress
|April 26, 2018
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Summary
This summary is machine-generated.

This study presents a robotic liquid handling system for determining chromatography model parameters from complex biological feedstocks, improving mechanistic model accuracy and efficiency for monoclonal antibody purification.

Keywords:
chromatographydownstream processing (DSP)high-throughput process development (HTPD)mechanistic modeling

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Area of Science:

  • Biotechnology
  • Chemical Engineering
  • Chromatography

Background:

  • Accurate model parameters are crucial for mechanistic chromatography models.
  • Complex feedstocks and limited sample material hinder parameter determination.
  • Current methods face obstacles in efficiently characterizing complex biological samples.

Purpose of the Study:

  • To implement and validate an automated approach for determining chromatography model parameters.
  • To utilize a robotic liquid handling system for direct analysis of complex feedstocks.
  • To enable accurate predictions for large-scale chromatography using small-scale experimental data.

Main Methods:

  • Utilized a robotic liquid handling system with RoboColumns (200 μL bed volume).
  • Determined isotherm parameters directly from a complex feedstock containing a monoclonal antibody.
  • Validated the approach by comparing experimental data with model predictions.

Main Results:

  • Achieved excellent agreement between model predictions and independent experimental data.
  • Demonstrated the effectiveness of RoboColumns for parameter determination.
  • Verified the accuracy of the developed approach for complex biological feedstocks.

Conclusions:

  • The robotic liquid handling approach successfully determines crucial model input parameters.
  • RoboColumns are suitable for obtaining isotherm parameters for large-scale chromatography predictions.
  • This method enhances the application of mechanistic models for complex biopharmaceutical processes.