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Application of simplex-based experimental optimization to challenging bioprocess development problems: Case studies

Spyridon Konstantinidis1, John P Welsh2, David J Roush2

  • 1Dept. of Biochemical Engineering, The Advanced Centre for Biochemical Engineering, University College London, Bernard Katz Building, Gordon Street, London, WC1H 0AH, U.K.

Biotechnology Progress
|January 30, 2016
PubMed
Summary
This summary is machine-generated.

A novel Simplex algorithm variant outperforms traditional regression methods for optimizing bioprocess conditions. This approach efficiently identifies superior operating parameters, even with noisy data, reducing experimental needs in early-stage development.

Keywords:
DoEchromatographyhigh throughput bioprocess developmentprotein refoldingsimplex optimization

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Area of Science:

  • Biochemical Engineering
  • Process Development
  • Chromatography and Protein Refolding

Background:

  • High Throughput (HT) studies are crucial for identifying feasible operating conditions in early bioprocess development.
  • Conventional methods often rely on regression analysis, such as Design of Experiments.

Purpose of the Study:

  • To compare a Simplex algorithm variant with conventional regression-based methods for bioprocess optimization.
  • To evaluate the effectiveness in identifying superior operating conditions for polishing chromatography and protein refolding.

Main Methods:

  • A previously developed variant of the Simplex algorithm was employed.
  • The Simplex method was compared against regression-based methods using three experimental systems.
  • The study assessed performance with potentially noisy experimental data.

Main Results:

  • The Simplex algorithm variant proved more effective in identifying superior operating conditions.
  • The Simplex variant frequently reached the global optimum, unlike regression methods which often failed or found suboptimal conditions.
  • The Simplex-based method required fewer experiments and demonstrated robustness with noisy data.

Conclusions:

  • The Simplex-variant is highly effective for rapid optimization in early-phase bioprocess development.
  • This method is well-suited for integration with High Throughput analytical techniques, mitigating analytical bottlenecks.
  • The Simplex approach offers a more efficient and reliable alternative to traditional regression-based optimization techniques.