High-throughput screening-based selection and scale-up of aqueous two-phase systems for pDNA purification

Authors

  • Matthias Wiendahl,

    1. Bioprocess & Chemistry Laboratories, Novo Nordisk A/S, Bagsværd, Denmark
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  • Stefan A. Oelmeier,

    1. Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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  • Florian Dismer,

    1. Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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  • Jürgen Hubbuch

    Corresponding author
    1. Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
    • Correspondence: Professor Jürgen Hubbuch, Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 1, 76131 Karlsruhe, Germany

      E-mail: juergen.hubbuch@kit.edu

      Fax: +49 721 608-46240

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Abstract

Plasmid DNA (pDNA) is among the promising gene delivery vehicles currently evaluated for gene therapy. The large-scale production of pDNA for pharmaceutical application necessitates purification steps with a high capacity and good separation of RNA from pDNA. Most commonly used process step in the production of biopharmaceutical, namely the divers modes of chromatography, fail as they offer too limited a capacity for the considerably larger pDNA molecules. Alternative separation steps might thus be beneficial. One such separation step, aqueous two-phase extraction (ATPE) has previously been shown to work well for the purification of pDNA. The application of such a process step is however hampered by the large amount of material and time that goes into its development. In this publication, we demonstrate the use of an automatic, miniaturized ATPE screening system to the separation of pDNA from RNA. Two optimization strategies are presented: response surface methodology and genetic algorithms. Using a fully automated optimization strategy, we derived promising conditions that were scale-up tenfold. The resulting purity and recovery surpassed previously published results demonstrating that a complex optimization task such as ATPE demands an appropriately complex optimization routine.

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