| Autor: |
Dave Pain, Ashley Westlake, Suzanne S. Farid, Nicholas Hutchinson, Adam Stonier, Nina F. Thornhill |
| Rok vydání: |
2011 |
| Předmět: |
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| Zdroj: |
ResearcherID |
| DOI: |
10.1016/b978-0-444-54298-4.50050-7 |
| Popis: |
This paper describes a decision-support tool that integrates Monte Carlo simulation data derived using a stochastic discrete-event simulation model to mimic process fluctuations with advanced multivariate statistical techniques to help pinpoint the potential root causes of sub-optimal facility fit issues. Principal component analysis combined with clustering algorithms was used to analyse the complex datasets from complete industrial batch processes for biopharmaceuticals. The challenge of visualising the multidimensional nature of the dataset was addressed using hierarchical and K-means clustering as well as parallel co-ordinate plots to help identify process fingerprints and characteristics of clusters leading to sub-optimal facility fit issues. Industrially-relevant case studies are presented that focus on technology transfer challenges for therapeutic antibodies moving from early phase to late phase clinical trials. The case study details how sub-optimal facility fit can be alleviated by allocating alternative product pool tanks. The impact of this operational change is then assessed by reviewing an updated process fingerprint. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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