Determination of robustness and optimal work conditions for a purification process of a therapeutic recombinant protein using response surface methodology

Authors

  • Ignacio Amadeo,

    1. Research and Development, Zelltek S.A., Paraje El Pozo, Ciudad Universitaria, S3000ZAA Santa Fe, Argentina
    2. Laboratorio de Cultivos Celulares, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, S3000ZAA Santa Fe, Argentina
    Search for more papers by this author
  • Laura V. Mauro,

    1. Research and Development, Zelltek S.A., Paraje El Pozo, Ciudad Universitaria, S3000ZAA Santa Fe, Argentina
    Search for more papers by this author
  • Eduardo Ortí,

    1. Research and Development, Zelltek S.A., Paraje El Pozo, Ciudad Universitaria, S3000ZAA Santa Fe, Argentina
    Search for more papers by this author
  • Guillermina Forno

    Corresponding author
    1. Research and Development, Zelltek S.A., Paraje El Pozo, Ciudad Universitaria, S3000ZAA Santa Fe, Argentina
    2. Laboratorio de Cultivos Celulares, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, S3000ZAA Santa Fe, Argentina
    • Research and Development, Zelltek S.A., Paraje El Pozo, Ciudad Universitaria, S3000ZAA Santa Fe, Argentina
    Search for more papers by this author

Abstract

A typical chromatographic purification step has numerous operating parameters that can impact its performance. As it is not feasible to evaluate the influence of each one, the current practice in biopharmaceutical industry is to apply risk analysis approach to identify process parameters that should be examined during process characterization. Once these parameters are identified, a response surface study can be run to help understand the relationship between critical inputs and outputs. We performed a study comprising optimization and robustness determination for a Blue-Sepharose purification step of rhEPO, a well-known therapeutic glycoprotein. Initially, risk analysis was fulfilled to identify key parameters. A small-scale model was created and qualified before its use in experimental studies, given by a Box–Behnken design with three factors. This method proved to be a very useful tool in bioprocess validation studies in which many input variables can affect product quality and safety. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2011

Ancillary