Monitoring Mammalian Cell Cultivations for Monoclonal Antibody Production Using Near-Infrared Spectroscopy

  • João G. Henriques
  • Stefan Buziol
  • Elena Stocker
  • Arthur Voogd
  • José C. Menezes
Chapter

Abstract

Near-infrared (NIR) spectroscopy as a process monitoring and process supervision technique is reviewed in the context of biomanufacturing.

An industrial pilot-plant mammalian cell cultivation process has been chosen to illustrate the use of on-line in-situ NIR monitoring by means of an immersion transflectance NIR probe.

NIR calibration development must be performed carefully and should incorporate a number of steps to obtain a properly validated model which exhibits long-term robustness and is independent of process scale. A description of such good modelling practises is given. In general, NIR can be as accurate as the reference methods employed and at least as precise provided that sufficient spectral selectivity and sensitivity exists.

NIR can also be used as a direct technique for very fast process monitoring and process supervision, thus enabling one to follow the trajectory of a process. This alternative to the indirect use of NIR through laborious calibration development with direct reference methods has been little explored. Since NIR is sensitive to both chemical and physical properties, the analysis of whole samples enables relevant process information to be captured and thus generates better process state estimates than by simply looking at defined process parameters one at a time.

Keywords

Process Analytical Technology Biomanufacturing Process Spectro scopy NIR mammalian cells cultivation 

Symbols and Abbreviations

HPLC

High performance liquid chromatography

LV

Latent variable

Mab

Monoclonal antibody

NIR

Near-infrared

PAT

Process analytical technologies

PC

Principal component

PCA

Principal component analysis

PLS

Partial least-squares

R2cv

Correlation coefficient for cross-validation predictions

R2p

Correlation coefficient for external validation predictions

RMSECV

Root mean square error of cross-validation

RMSEP

Root mean square error of prediction

SEL

Standard error of laboratory

SG

Savitzky-Golay

SNV

Standard normal variate

VIP

Variable importance plot

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • João G. Henriques
    • 1
    • 2
  • Stefan Buziol
    • 3
  • Elena Stocker
    • 3
  • Arthur Voogd
    • 4
    • 5
  • José C. Menezes
    • 6
  1. 1.4TUNE Engineering Ltd – Atrium SaldanhaLisbonPortugal
  2. 2.HOVIONE, Sete CasasLisbonPortugal
  3. 3.Roche Diagnostics GmbH – Pharmaceutical Biotech Production and DevelopmentPenzbergGermany
  4. 4.Yokogawa Europe BVAmersfoortThe Netherlands
  5. 5.LaboSer BVRotterdamThe Netherlands
  6. 6.IBB-Institute for Biotechnology and BioengineeringIST-Technical University of LisbonLisbonPortugal

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