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Related Experiment Videos

Optimization-based framework for resin selection strategies in biopharmaceutical purification process development.

Songsong Liu1,2,3, Spyridon Gerontas1, David Gruber4

  • 1Centre for Process Systems Engineering, Dept. of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.

Biotechnology Progress
|April 11, 2017
PubMed
Summary

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This summary is machine-generated.

This study presents a new framework for selecting chromatography resins in biopharmaceutical manufacturing. It uses optimization to quickly identify the best resins for maximizing yield and purity in high-throughput screening.

Area of Science:

  • Biochemical Engineering
  • Process Development
  • Chromatography

Background:

  • Efficient resin selection is critical for biopharmaceutical purification process development.
  • High-throughput screening generates large datasets requiring robust analysis.
  • Optimizing yield and purity are key performance indicators in biomanufacturing.

Purpose of the Study:

  • To develop a decision support framework for rapid resin selection in integrated chromatographic separations.
  • To maximize key performance metrics like yield and purity during early-stage process development.
  • To apply the framework to an industrial case study for recombinant Fc Fusion protein purification.

Main Methods:

  • Development of a multiobjective mixed integer nonlinear programming (MINLP) model.
Keywords:
Dinkelbach's algorithmmultiobjective optimizationpurification process developmentresin selectionε-constraint method

Related Experiment Videos

  • Solution of the MINLP model using the ε-constraint method.
  • Application of Dinkelbach's algorithm to solve the resulting mixed integer linear fractional programming (MILFP) model.

    • Main Results:

    • The proposed framework successfully identified optimal resins for a two-step chromatographic purification process.
    • Demonstrated computational efficiency and flexibility in processing microscale experimental data.
    • Successfully applied to purify recombinant Fc Fusion protein from related impurities.

    Conclusions:

    • The optimization-based decision support framework significantly enhances resin selection efficiency.
    • The methodology provides a flexible and computationally advantageous approach for bioprocess development.
    • This framework is valuable for accelerating the purification process development of biopharmaceuticals.