Abstract：
:In this proof-of-concept study, a methodology is proposed to systematically analyze large data historians of secondary pharmaceutical manufacturing systems using data mining techniques. The objective is to develop an approach enabling to automatically retrieve operation-relevant information that can assist the management in the periodic review of a manufactory system. The proposed methodology allows one to automatically perform three tasks: the identification of single batches within the entire data-sequence of the historical dataset, the identification of distinct operating phases within each batch, and the characterization of a batch with respect to an assigned multivariate set of operating characteristics. The approach is tested on a six-month dataset of a commercial-scale granulation/drying system, where several millions of data entries are recorded. The quality of results and the generality of the approach indicate that there is a strong potential for extending the method to even larger historical datasets and to different operations, thus making it an advanced PAT tool that can assist the implementation of continual improvement paradigms within a quality-by-design framework.
journal_name
Int J Pharmjournal_title
International journal of pharmaceuticsauthors
Meneghetti N,Facco P,Bezzo F,Himawan C,Zomer S,Barolo Mdoi
10.1016/j.ijpharm.2016.03.035subject
Has Abstractpub_date
2016-05-30 00:00:00pages
394-408issue
1-2eissn
0378-5173issn
1873-3476pii
S0378-5173(16)30235-6journal_volume
505pub_type
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