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Predicting pharmaceutical inkjet printing outcomes using machine learning

Carou-Senra, P; Ong, JJ; Castro, BM; Seoane-Viaño, I; Rodríguez-Pombo, L; Cabalar, P; Alvarez-Lorenzo, C; ... Goyanes, A; + view all (2023) Predicting pharmaceutical inkjet printing outcomes using machine learning. International Journal of Pharmaceutics: X , 5 , Article 100181. 10.1016/j.ijpx.2023.100181. (In press). Green open access

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Abstract

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.

Type: Article
Title: Predicting pharmaceutical inkjet printing outcomes using machine learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijpx.2023.100181
Publisher version: http://dx.doi.org/10.1016/j.ijpx.2023.100181
Language: English
Additional information: © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Additive manufacturing and personalized medications 2D and 3D printed drug products Artificial intelligence and digital health Desktop ink jet printing of pharmaceuticals and drug delivery systems Design and fabrication of medicinal products Rational formulation development
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics
URI: https://discovery.ucl.ac.uk/id/eprint/10169410
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