| Autor: |
Faya P; a Allergan, Inc. , Parsippany , New Jersey , USA.; b Department of Statistical Science , Baylor University , Waco , Texas , USA., Seaman JW Jr; b Department of Statistical Science , Baylor University , Waco , Texas , USA., Stamey JD; b Department of Statistical Science , Baylor University , Waco , Texas , USA. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of biopharmaceutical statistics [J Biopharm Stat] 2017; Vol. 27 (1), pp. 159-174. Date of Electronic Publication: 2016 Feb 18. |
| DOI: |
10.1080/10543406.2016.1148717 |
| Abstrakt: |
Validation of pharmaceutical manufacturing processes is a regulatory requirement and plays a key role in the assurance of drug quality, safety, and efficacy. The FDA guidance on process validation recommends a life-cycle approach which involves process design, qualification, and verification. The European Medicines Agency makes similar recommendations. The main purpose of process validation is to establish scientific evidence that a process is capable of consistently delivering a quality product. A major challenge faced by manufacturers is the determination of the number of batches to be used for the qualification stage. In this article, we present a Bayesian assurance and sample size determination approach where prior process knowledge and data are used to determine the number of batches. An example is presented in which potency uniformity data is evaluated using a process capability metric. By using the posterior predictive distribution, we simulate qualification data and make a decision on the number of batches required for a desired level of assurance. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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