ArticleInternational journal of pharmaceutics: X2023
Predicting pharmaceutical inkjet printing outcomes using machine learning.
Article in International journal of pharmaceutics: X, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Nail tattooing: a novel and minimally invasive technique for enhancing drug penetration through the nail.Drug delivery and translational research · 2026Article
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- Transforming Dental Care, Practice and Education with Additive Manufacturing and 3D Printing: Innovations in Materials, Technologies, and Future Pathways.Dentistry journal · 2025Review
- Behavior of Aqueous Medicated Inks on Porous Tablet Surfaces.Pharmaceutics · 2025Article
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- Nonlinear Waveform Optimization for Enhanced Ink Droplet Formation in Material Jetting.Micromachines · 2025Article
- Spiked Systems for Colonic Drug Delivery: Architectural Opportunities and Quality Assurance of Selective Laser Sintering.ACS biomaterials science & engineering · 2025Article
- Recent Developments and Applications of 3D-Printing Technology in Pharmaceutical Drug Delivery Systems: A New Research Direction and Future Trends.Current pharmaceutical design · 2025Review
- 3D-printed Laponite/Alginate hydrogel-based suppositories for versatile drug loading and release.Drug delivery and translational research · 2024Article
- 3D-printed Laponite/Alginate hydrogel-based suppositories for versatile drug loading and release.Drug delivery and translational research · 2024Article
- Rising role of 3D-printing in delivery of therapeutics for infectious disease.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
- 3D Printing of Dietary Products for the Management of Inborn Errors of Intermediary Metabolism in Pediatric Populations.Nutrients · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.