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A Methodology for the Robust Evaluation of Pharmaceutical Processes under Uncertainty
Johnson, D. B. and Bogle, I. D. L. A Methodology for the Robust Evaluation of Pharmaceutical Processes under Uncertainty Chemical Papers, Vol.54, No. 6a, 2000, 398-405
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Document type:
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Článok z časopisu / Journal Article |
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Collection:
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Chemical papers
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| Attached Files |
| Name |
Description |
MIMEType |
Size |
Downloads |
n546aa398.pdf
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546aa398.pdf |
application/pdf |
235.96KB |
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| Author(s) |
Johnson, D. B.
Bogle, I. D. L.
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| Title |
A Methodology for the Robust Evaluation of Pharmaceutical Processes under Uncertainty
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| Journal name |
Chemical Papers
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| Publication date |
2000
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| Year available |
2000
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| Volume number |
54
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| Issue number |
6a
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| ISSN |
0366-6352
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| Start page |
398
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| End page |
405
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| Place of publication |
Poland
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| Publisher |
Versita
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| Collection year |
2000
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| Language |
english
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| Subject |
290000 Engineering and Technology
290600 Chemical Engineering
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| Abstract/Summary |
It is becoming widely recognized in the pharmaceutical industry that a more structured approach to process development would benefit the quality of the developed processes to ensure added value to the products in the long term. In this paper, a model-based methodology is proposed for the robust evaluation and development of pharmaceutical processes which realizes the pressures inherent to the industry and that critical decisions need to be made at all stages despite incomplete knowledge of the process. It aims to identify the most relevant process information subject to the availability and quality of the prevailing data at any given stage in the development, so that informed decisions can be made at short notice. Steps towards a multiscenario approach are proposed to resolve stochastic model parameter uncertainties in process sequences and evaluate robust performance indicators. Correlation analysis is used to provide an indication of the critical stage interactions under uncertainty so that potential causes of problems can be identified in a more rigorous manner. The potential benefits of the approach are demonstrated using a two-stage example under two parameter uncertainties. The directions required to complement a general methodology regarding practical application to real problems are indicated.
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