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Development of bioreactor scale-down model using orthogonal projections to latent structures method and CO2
Jinxin Gao1, Laurie B Hazeltine2, Neal Stroud3
1Statistics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA.
Biotechnology Progress
|January 30, 2024
Summary
Qualifying scale-down models using orthogonal projections to latent structures (OPLS) analysis improves cell culture process understanding. This novel method identified critical process parameter differences, enhancing scale-up consistency and product quality.
Area of Science:
- Biopharmaceutical Process Development
- Chemical and Biochemical Engineering
- Statistical Modeling in Biotechnology
Background:
- Scale-down model qualification is crucial for robust large-scale cell culture process development and characterization.
- Traditional methods relying solely on harvest data may miss dynamic process variations between scales.
- Enhanced process understanding is vital for successful technology transfer and regulatory submissions.
Purpose of the Study:
- To introduce and demonstrate a novel statistical method, orthogonal projections to latent structures (OPLS) analysis, for comparing time-course cell culture data across scales.
- To identify discrepancies in bioreactor performance between small-scale (model) and large-scale (production) systems.
- To improve the qualification of scale-down models for biopharmaceutical manufacturing.
Main Methods:
- Utilized orthogonal projections to latent structures (OPLS) analysis to compare time-course cell culture data from small-scale and large-scale bioreactors.
- Analyzed dynamic parameters such as partial pressure of carbon dioxide (pCO2) and lactate profiles.
- Correlated observed process differences with product quality attributes, including fragments and galactosylation.
Main Results:
- OPLS analysis successfully identified significant differences in partial pressure of carbon dioxide (pCO2) and lactate profiles between scales.
- These process variations were linked to specific differences in product quality attributes (fragments and galactosylation).
- An improved small-scale model was developed, leading to enhanced consistency in process performance and product quality across scales.
Conclusions:
- Orthogonal projections to latent structures (OPLS) analysis provides valuable insights into process understanding and scale-up challenges.
- This statistical approach enables more rigorous scale-down model qualification by analyzing dynamic, time-course data.
- The improved model and methodology support regulatory submissions by demonstrating cross-scale consistency.
