Abdalla, Y;
Ferianc, M;
Awad, A;
Kim, J;
Elbadawi, M;
Basit, AW;
Orlu, M;
(2024)
Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine.
International Journal of Pharmaceutics
, 661
, Article 124440. 10.1016/j.ijpharm.2024.124440.
Preview |
Text
1-s2.0-S0378517324006744-main.pdf - Published Version Download (2MB) | Preview |
Abstract
Medicines remain ineffective for over 50% of patients due to conventional mass production methods with fixed drug dosages. Three-dimensional (3D) printing, specifically selective laser sintering (SLS), offers a potential solution to this challenge, allowing the manufacturing of small, personalized batches of medication. Despite its simplicity and suitability for upscaling to large-scale production, SLS was not designed for pharmaceutical manufacturing and necessitates a time-consuming, trial-and-error adaptation process. In response, this study introduces a deep learning model trained on a variety of features to identify the best feature set to represent drugs and polymeric materials for the prediction of the printability of drug-loaded formulations using SLS. The proposed model demonstrates success by achieving 90% accuracy in predicting printability. Furthermore, explainability analysis unveils materials that facilitate SLS printability, offering invaluable insights for scientists to optimize SLS formulations, which can be expanded to other disciplines. This represents the first study in the field to develop an interpretable, uncertainty-optimized deep learning model for predicting the printability of drug-loaded formulations. This paves the way for accelerating formulation development, propelling us into a future of personalized medicine with unprecedented manufacturing precision.
| Type: | Article |
|---|---|
| Title: | Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ijpharm.2024.124440 |
| Publisher version: | https://doi.org/10.1016/j.ijpharm.2024.124440 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Printed pharmaceuticals and oral drug delivery systems, Additive manufacturing of drug products, Artificial intelligence and machine learning, Deep learning, Uncertainty quantification, Personalized medicines and digital healthcare |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS 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 > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng 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/10197693 |
Archive Staff Only
 |
View Item |
