Artificial intelligence and machine learning applications in biopharmaceutical manufacturing

Artificial intelligence and machine learning (AI–ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI–ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurri...

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Veröffentlicht in:Trends in biotechnology (Regular ed.) Jg. 41; H. 4; S. 497 - 510
Hauptverfasser: Rathore, Anurag S., Nikita, Saxena, Thakur, Garima, Mishra, Somesh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 01.04.2023
Elsevier Limited
Schlagworte:
Algorithms
Artificial Intelligence
Artificial neural networks
Automation
Biological Products
biopharmaceutical manufacturing
Biopharmaceuticals
biotechnology
continuous processing
Data analysis
Industrial applications
industry
Internal Medicine
Learning algorithms
Machine Learning
Manufacturing
Multivariate analysis
Neural networks
Neural Networks, Computer
reinforcement learning
Software
United States > US
artificial neural networks
continuous processing
machine learning
biopharmaceutical manufacturing
artificial intelligence
reinforcement learning
ISSN:0167-7799, 1879-3096, 1879-3096
Online-Zugang:Volltext
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Zusammenfassung:Artificial intelligence and machine learning (AI–ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI–ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI–ML applications in biopharmaceutical manufacturing, with a focus on the most used AI–ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI–ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented. AI–ML have the potential to transform biopharmaceutical manufacturing by reducing the need for human supervision and enabling automated and continuous processing.Multivariate data analysis algorithms are the most widely used class of AI–ML approaches and have seen widespread implementation in biopharmaceutical industry.Artificial neural networks and reinforcement learning approaches are seeing increasing interest for monitoring and control of biopharmaceutical manufacturing.Key challenges include lack of regulatory guidance, incomplete data, difficulty of risk assessment, absence of biopharma-specific tools, and a shortage of trained talent.
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ISSN:0167-7799
1879-3096
1879-3096
DOI:10.1016/j.tibtech.2022.08.007