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Information Processing: Rate-Based Investigation of Cell Physiological Changes along Design Space Development.

Patrick Sagmeister1, Patrick Wechselberger, Christoph Herwig

  • 1Vienna University of Technology, Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna, Austria.

PDA Journal of Pharmaceutical Science and Technology
|November 28, 2012
PubMed
Summary

This study presents a novel method to extract mechanistic process knowledge from existing bioprocess data for quality by design (QbD) submissions. It enhances understanding of critical process parameters and quality attributes without new experiments.

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Area of Science:

  • Pharmaceutical Bioprocess Development
  • Quality by Design (QbD)
  • Process Analytical Technology (PAT)

Background:

  • Quality by Design (QbD) principles advocate for science- and risk-based pharmaceutical bioprocess development.
  • Current QbD approaches focus on statistical interactions between critical process parameters (CPPs) and critical quality attributes (CQAs), often neglecting mechanistic understanding.
  • Existing process data is underutilized for demonstrating deep process understanding.

Purpose of the Study:

  • To present a methodology for extracting mechanistic process knowledge from typical upstream bioprocess data.
  • To demonstrate how processed data can enhance design space development for QbD.
  • To achieve this without requiring additional experiments or analytical procedures.

Main Methods:

  • Data processing of online and offline measurements (e.g., off-gas, flow rates, cell density) into scale-independent specific rates and yield coefficients.
  • Information processing through regression of obtained data with investigated process parameters to identify mechanistic interactions.
  • Application to a multivariate study on recombinant product production in Escherichia coli K12.

Main Results:

  • Successfully extracted mechanistic knowledge regarding metabolic load, promoter regulation, and cell lysis.
  • Identified a time dependency of metabolic load.
  • Indicated potential promoter down-regulation at reduced temperatures and reduced cell lysis at higher specific feeding rates.

Conclusions:

  • The presented data and information processing methodology is complementary to QbD design space development.
  • This approach provides a basis for mechanistic modeling and enhances process understanding.
  • It enables the extraction of valuable mechanistic insights from existing bioprocess data.