Continuous Process Verification 4.0 application in upstream : adaptiveness implementation managed by AI in the hypoxic bioprocess of the Pichia pastoris cell factory
Gasset Franch, Arnau (Universitat Autònoma de Barcelona. Departament d'Enginyeria Química, Biològica i Ambiental)
Van Wijngaarden, Joeri (Aizon)
Mirabent, Ferran (Aizon)
Sales-Vallverdú, Albert (Universitat Autònoma de Barcelona. Departament d'Enginyeria Química, Biològica i Ambiental)
Garcia-Ortega, Xavier (Universitat Autònoma de Barcelona. Departament d'Enginyeria Química, Biològica i Ambiental)
Montesinos Seguí, José Luis (Universitat Autònoma de Barcelona. Departament d'Enginyeria Química, Biològica i Ambiental)
Manzano, Toni (Aizon)
Valero Barranco, Francisco (Universitat Autònoma de Barcelona. Departament d'Enginyeria Química, Biològica i Ambiental)

Data:	2024
Resum:	The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4. 0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient ( RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4. 0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.
Ajuts:	Agencia Estatal de Investigación PID2022-136936OB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00143 Agència de Gestió d'Ajuts Universitaris i de Recerca FI-DGR2019
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Artificial intelligence ; Industry 4.0 ; Continued process verification ; Digital twin ; Physiological control ; Respiratory quotient ; Pichia pastoris ; Recombinant protein production
Publicat a:	Frontiers in Bioengineering and Biotechnology, Vol. 12 (October 2024) , art. 1439638, ISSN 2296-4185

DOI: 10.3389/fbioe.2024.1439638
PMID: 39416276

18 p, 3.2 MB

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