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Global sensitivity analysis for the determination of parameter importance in the chromatographic purification of polyclonal antibodies

✍ Scribed by Sunil Chhatre; Richard Francis; Anthony R Newcombe; Yuhong Zhou; Nigel Titchener-Hooker; Josh King; Eli Keshavarz-Moore


Publisher
Wiley (John Wiley & Sons)
Year
2008
Tongue
English
Weight
626 KB
Volume
83
Category
Article
ISSN
0268-2575

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✦ Synopsis


Abstract

BACKGROUND: The financial and technical challenges currently being faced by the pharmaceutical industry have prompted the development of computer models that rapidly evaluate manufacturing strategies for commercial‐scale drug production. The utility of a model is enhanced by the application of sensitivity analysis techniques (SA), which seek to quantify the relative importance of different input variables. Among several SA methods available, global sensitivity analysis (GSA) provides a powerful means of ranking inputs based on the contribution that each makes towards the variance of a model. To demonstrate the value of the approach, this paper describes the application of GSA to a mathematical model of the synthetic adsorbent‐based chromatographic capture of antibodies from ovine serum, a strategy being considered by Protherics UK Limited for production of an FDA‐approved rattlesnake anti‐venom.

RESULTS: Results indicated that the binding capacity of the adsorbent for the antibody molecules was the most significant factor influencing their recovery, followed closely by the flowrate at which the ovine serum was applied to the column and then finally the antibody concentration in the feedstock. Together, these accounted for almost 94% of the model variance and hence suggest that these would be the key variables to focus upon during process development.

CONCLUSION: Such data illustrate how GSA enables development studies to be concentrated upon those factors which exert the greatest influence on manufacturing performance. Ultimately, this reduces the size of the design space, making it easier to optimize a process and identify the most suitable operating conditions for large‐scale drug production. Copyright © 2007 Society of Chemical Industry


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