Pharma 4.0-Artificially Intelligent Digital Twins for Solidified Nanosuspensions
Digital twins capacitate the industry 4.0 paradigm by predicting and optimizing the performance of physical assets of interest, mirroring a realistic in-silico representation of their functional behaviour. Although advanced digital twins set forth disrupting opportunities by delineating the in-servi...
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Published in | Pharmaceutics Vol. 14; no. 10; p. 2113 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.10.2022
MDPI |
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Abstract | Digital twins capacitate the industry 4.0 paradigm by predicting and optimizing the performance of physical assets of interest, mirroring a realistic in-silico representation of their functional behaviour. Although advanced digital twins set forth disrupting opportunities by delineating the in-service product and the related process dynamic performance, they have yet to be adopted by the pharma sector. The latter, currently struggles more than ever before to improve solubility of BCS II i.e., hard-to-dissolve active pharmaceutical ingredients by micronization and subsequent stabilization. Herein we construct and functionally validate the first artificially intelligent digital twin thread, capable of describing the course of manufacturing of such solidified nanosuspensions given a defined lifecycle starting point and predict and optimize the relevant process outcomes. To this end, we referenced experimental data as the sampling source, which we then augmented via pattern recognition utilizing neural network propagations. The zeta-dynamic potential metrics of the nanosuspensions were correlated to the interfacial Gibbs energy, while the density and heat capacity of the material system was calculated via the Saft-γ-Mie statistical fluid theory. The curated data was then fused to physical and empirical laws to choose the appropriate theory and numeric description, respectively, before being polished by tuning the critical parameters to achieve the best fit with reality. |
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AbstractList | Digital twins capacitate the industry 4.0 paradigm by predicting and optimizing the performance of physical assets of interest, mirroring a realistic in-silico representation of their functional behaviour. Although advanced digital twins set forth disrupting opportunities by delineating the in-service product and the related process dynamic performance, they have yet to be adopted by the pharma sector. The latter, currently struggles more than ever before to improve solubility of BCS II i.e., hard-to-dissolve active pharmaceutical ingredients by micronization and subsequent stabilization. Herein we construct and functionally validate the first artificially intelligent digital twin thread, capable of describing the course of manufacturing of such solidified nanosuspensions given a defined lifecycle starting point and predict and optimize the relevant process outcomes. To this end, we referenced experimental data as the sampling source, which we then augmented via pattern recognition utilizing neural network propagations. The zeta-dynamic potential metrics of the nanosuspensions were correlated to the interfacial Gibbs energy, while the density and heat capacity of the material system was calculated via the Saft-γ-Mie statistical fluid theory. The curated data was then fused to physical and empirical laws to choose the appropriate theory and numeric description, respectively, before being polished by tuning the critical parameters to achieve the best fit with reality. |
Audience | Academic |
Author | Ouranidis, Andreas Davidopoulou, Christina |
AuthorAffiliation | 2 Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece 1 Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece |
AuthorAffiliation_xml | – name: 2 Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece – name: 1 Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece |
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Cites_doi | 10.3354/meps139289 10.3390/jfb9010011 10.1081/DRT-120038734 10.3390/pr9060954 10.3390/pharmaceutics14010183 10.1016/B978-0-12-814427-5.00015-9 10.1016/j.cjche.2020.10.034 10.3390/pharmaceutics11030130 10.1016/j.addr.2015.12.010 10.1039/C7RA12099K 10.1016/0009-2509(72)85079-6 10.1088/0266-5611/30/11/114007 10.1021/i260057a032 10.1021/acs.molpharmaceut.8b00360 10.3390/ph14040365 10.1021/acs.jced.8b01163 10.1016/j.ifacol.2018.08.474 10.3390/pharmaceutics13091371 10.3390/pharmaceutics13111771 10.1109/SBEC.2013.54 10.1016/j.ultsonch.2011.04.005 10.1016/B978-0-12-801559-9.00006-5 10.1007/s43393-021-00024-0 |
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SubjectTerms | Analysis Artificial intelligence Composite materials digital twin Digital twins Energy Health aspects Manufacturing micronization milling nanosuspensions Neural networks Parameter estimation Particle size Pharma 4.0 Pharmaceutical industry spray drying Twins |
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Title | Pharma 4.0-Artificially Intelligent Digital Twins for Solidified Nanosuspensions |
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