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This study introduces a data logging and analysis strategy combining feature-based methods with principal component analysis and partial least square regression to accelerate bioprocess development. The approach enables comprehensive evaluation of extensive datasets, leading to a clearer understanding of bioreactor performance parameters.

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Area of Science:

  • Bioprocess Engineering
  • Data Science
  • Vaccine Development

Background:

  • Bioprocess development generates large, diverse datasets from various sources.
  • Efficient data evaluation is crucial for accelerating process development and optimization.
  • A clear data logging and analysis strategy is needed to manage complex bioprocess data.

Purpose of the Study:

  • To present a novel data logging and analysis strategy for bioprocess development.
  • To demonstrate the application of this strategy in an upstream processing case study for Sabin inactivated poliovirus vaccine (sIPV) production.
  • To accelerate and enhance bioprocess development through comprehensive data evaluation.

Main Methods:

  • A combined strategy using a feature-based approach, principal component analysis (PCA), and partial least square regression (PLSR).
  • Application to a case study involving 26 bioreactor runs for animal component-free upstream processing of sIPV.
  • Integration of design of experiments (DoE) parameters, bioreactor control data, and offline measurements for holistic analysis.

Main Results:

  • The strategy enabled the scrutiny of all data from 26 bioreactor runs in a single study.
  • Provided a more detailed, reliable, and exact view of parameters influencing bioreactor performance.
  • Successfully applied to analyze previously generated data from sIPV process development.

Conclusions:

  • The described data analysis strategy effectively integrates diverse datasets for comprehensive bioprocess evaluation.
  • This methodology enhances the understanding of critical process parameters, accelerating development.
  • Future work will focus on continuous process enhancement, intensified DoE, and integrated process modeling.