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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10151423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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