Abdalla, Youssef;
Elbadawi, Moe;
Ji, Mengxuan;
Alkahtani, Manal;
Awad, Atheer;
Orlu, Mine;
Gaisford, Simon;
(2023)
Machine learning using Multi-Modal Data Predicts the Production of Selective Laser Sintered 3D Printed Drug Products.
International Journal of Pharmaceutics
, 633
, Article 122628. 10.1016/j.ijpharm.2023.122628.
Preview |
Text
1-s2.0-S0378517323000480-main.pdf - Published Version Download (4MB) | Preview |
Abstract
Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide range of pharmaceutical materials, including low-solubility compounds. However, the full potential of SLS for medicines is yet to be realized, requiring expertise and considerable time-consuming and resource-intensive trial-and-error research. Machine learning (ML), a subset of artificial intelligence, is an in silico tool that is accomplishing remarkable breakthroughs in several sectors for its ability to make highly accurate predictions. Therefore, the present study harnessed ML to predict the printability of SLS formulations. Using a dataset of 170 formulations from 78 materials, ML models were developed from inputs that included the formulation composition and characterization data retrieved from Fourier-transformed infrared spectroscopy (FT-IR), X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Multiple ML models were explored, including supervised and unsupervised approaches. The results revealed that ML can achieve high accuracies, by using the formulation composition leading to a maximum F1 score of 81.9%. Using the FT-IR, XRPD and DSC data as inputs resulted in an F1 score of 84.2%, 81.3%, and 80.1%, respectively. A subsequent ML pipeline was built to combine the predictions from FT-IR, XRPD and DSC into one consensus model, where the F1 score was found to further increase to 88.9%. Therefore, it was determined for the first time that ML predictions of 3D printability benefit from multi-modal data, combining numeric, spectral, thermogram and diffraction data. The study lays the groundwork for leveraging existing characterization data for developing high-performing computational models to accelerate developments.
| Type: | Article |
|---|---|
| Title: | Machine learning using Multi-Modal Data Predicts the Production of Selective Laser Sintered 3D Printed Drug Products |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ijpharm.2023.122628 |
| Publisher version: | https://doi.org/10.1016/j.ijpharm.2023.122628 |
| Language: | English |
| Additional information: | © 2023 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: | additive manufacturing of medicines, computational modelling, data fusion, digital manufacturing and internet of things, drug delivery and formulations, powder bed fusion |
| 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/10163921 |
Archive Staff Only
 |
View Item |
