Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review.
Pharma 4.0
Process Analytical Technology
artificial neural network
machine learning
real-time release testing
Journal
The AAPS journal
ISSN: 1550-7416
Titre abrégé: AAPS J
Pays: United States
ID NLM: 101223209
Informations de publication
Date de publication:
14 06 2022
14 06 2022
Historique:
received:
25
02
2022
accepted:
06
04
2022
entrez:
13
6
2022
pubmed:
14
6
2022
medline:
16
6
2022
Statut:
epublish
Résumé
Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance.
Identifiants
pubmed: 35697951
doi: 10.1208/s12248-022-00706-0
pii: 10.1208/s12248-022-00706-0
doi:
Substances chimiques
Pharmaceutical Preparations
0
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
74Informations de copyright
© 2022. The Author(s).
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