ArticleInternational journal of pharmaceutics: X2022
Accelerating 3D printing of pharmaceutical products using machine learning.
Article in International journal of pharmaceutics: X, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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
16 citing papers in PubMed.
- AI Applied to Formulation Design, Process Optimization, and Quality Control in 3D Printed Drug Products: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
- In Silico Predictions Driving the Development of 3D-Printed Drug Delivery Systems.Pharmaceutics · 2025Article
- Revolutionizing Orthopedics through Integration of Artificial Intelligence and 3D Printing for Enhanced Patient Care.Indian journal of orthopaedics · 2025Review
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- Recent Advances in 4D Printing: A Review of Current Smart Materials, Technologies, and Drug Delivery Systems.Current pharmaceutical design · 2025Review
- Recent Developments and Applications of 3D-Printing Technology in Pharmaceutical Drug Delivery Systems: A New Research Direction and Future Trends.Current pharmaceutical design · 2025Review
- Review
- The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?Advanced healthcare materials · 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
- Predicting pharmaceutical inkjet printing outcomes using machine learning.International journal of pharmaceutics: X · 2023Article
- Article
- To infinity and beyond: Strategies for fabricating medicines in outer space.International journal of pharmaceutics: X · 2022Article
- Innovations in Chewable Formulations: The Novelty and Applications of 3D Printing in Drug Product Design.Pharmaceutics · 2022Review
- Three-Dimensional Printing Technologies for Drug Delivery Applications: Processes, Materials, and Effects.International journal of bioprinting · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional printing (3DP) has seen growing interest within the healthcare industry for its ability to fabricate personalized medicines and medical devices. However, it may be burdened by the lengthy empirical process of formulation development. Active research in pharmaceutical 3DP has led to a wealth of data that machine learning could utilize to provide predictions of formulation outcomes. A balanced dataset is critical for optimal predictive performance of machine learning (ML) models, but data available from published literature often only include positive results. In this study, in-house and literature-mined data on hot melt extrusion (HME) and fused deposition modeling (FDM) 3DP formulations were combined to give a more balanced dataset of 1594 formulations. The optimized ML models predicted the printability and filament mechanical characteristics with an accuracy of 84%, and predicted HME and FDM processing temperatures with a mean absolute error of 5.5 °C and 8.4 °C, respectively. The performance of these ML models was better than previous iterations with a smaller and a more imbalanced dataset, highlighting the importance of providing a structured and heterogeneous dataset for optimal ML performance. The optimized models were integrated in an updated web-application,
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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.