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Predicting pharmaceutical inkjet printing outcomes using machine learning

Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process make...

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Autores principales: Carou-Senra, Paola, Ong, Jun Jie, Castro, Brais Muñiz, Seoane-Viaño, Iria, Rodríguez-Pombo, Lucía, Cabalar, Pedro, Alvarez-Lorenzo, Carmen, Basit, Abdul W., Pérez, Gilberto, Goyanes, Alvaro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151423/
https://www.ncbi.nlm.nih.gov/pubmed/37143957
http://dx.doi.org/10.1016/j.ijpx.2023.100181
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author Carou-Senra, Paola
Ong, Jun Jie
Castro, Brais Muñiz
Seoane-Viaño, Iria
Rodríguez-Pombo, Lucía
Cabalar, Pedro
Alvarez-Lorenzo, Carmen
Basit, Abdul W.
Pérez, Gilberto
Goyanes, Alvaro
author_facet Carou-Senra, Paola
Ong, Jun Jie
Castro, Brais Muñiz
Seoane-Viaño, Iria
Rodríguez-Pombo, Lucía
Cabalar, Pedro
Alvarez-Lorenzo, Carmen
Basit, Abdul W.
Pérez, Gilberto
Goyanes, Alvaro
author_sort Carou-Senra, Paola
collection PubMed
description Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.g., composition, surface tension, and viscosity) and printing parameter optimization (e.g., nozzle diameter, peak voltage, and drop spacing) an empirical and time-consuming endeavour. Instead, given the wealth of publicly available data on pharmaceutical inkjet printing, there is potential for a predictive model for inkjet printing outcomes to be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose were developed using a dataset of 687 formulations, consolidated from in-house and literature-mined data on inkjet-printed formulations. The optimized ML models predicted the printability of formulations with an accuracy of 97.22%, and predicted the quality of the prints with an accuracy of 97.14%. This study demonstrates that ML models can feasibly provide predictive insights to inkjet printing outcomes prior to formulation preparation, affording resource- and time-savings.
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spelling pubmed-101514232023-05-03 Predicting pharmaceutical inkjet printing outcomes using machine learning Carou-Senra, Paola Ong, Jun Jie Castro, Brais Muñiz Seoane-Viaño, Iria Rodríguez-Pombo, Lucía Cabalar, Pedro Alvarez-Lorenzo, Carmen Basit, Abdul W. Pérez, Gilberto Goyanes, Alvaro Int J Pharm X Special Issue on Interreg project “Site-Specific Drug Delivery” Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.g., composition, surface tension, and viscosity) and printing parameter optimization (e.g., nozzle diameter, peak voltage, and drop spacing) an empirical and time-consuming endeavour. Instead, given the wealth of publicly available data on pharmaceutical inkjet printing, there is potential for a predictive model for inkjet printing outcomes to be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose were developed using a dataset of 687 formulations, consolidated from in-house and literature-mined data on inkjet-printed formulations. The optimized ML models predicted the printability of formulations with an accuracy of 97.22%, and predicted the quality of the prints with an accuracy of 97.14%. This study demonstrates that ML models can feasibly provide predictive insights to inkjet printing outcomes prior to formulation preparation, affording resource- and time-savings. Elsevier 2023-04-17 /pmc/articles/PMC10151423/ /pubmed/37143957 http://dx.doi.org/10.1016/j.ijpx.2023.100181 Text en © 2023 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Special Issue on Interreg project “Site-Specific Drug Delivery”
Carou-Senra, Paola
Ong, Jun Jie
Castro, Brais Muñiz
Seoane-Viaño, Iria
Rodríguez-Pombo, Lucía
Cabalar, Pedro
Alvarez-Lorenzo, Carmen
Basit, Abdul W.
Pérez, Gilberto
Goyanes, Alvaro
Predicting pharmaceutical inkjet printing outcomes using machine learning
title Predicting pharmaceutical inkjet printing outcomes using machine learning
title_full Predicting pharmaceutical inkjet printing outcomes using machine learning
title_fullStr Predicting pharmaceutical inkjet printing outcomes using machine learning
title_full_unstemmed Predicting pharmaceutical inkjet printing outcomes using machine learning
title_short Predicting pharmaceutical inkjet printing outcomes using machine learning
title_sort predicting pharmaceutical inkjet printing outcomes using machine learning
topic Special Issue on Interreg project “Site-Specific Drug Delivery”
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151423/
https://www.ncbi.nlm.nih.gov/pubmed/37143957
http://dx.doi.org/10.1016/j.ijpx.2023.100181
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