Elbadawi, M;
Muñiz Castro, B;
Gavins, FKH;
Jie Ong, J;
Gaisford, S;
Pérez, G;
Basit, AW;
... Goyanes, Á; + view all
(2020)
M3DISEEN: A Novel Machine Learning Approach for Predicting the 3D Printability of Medicines.
International Journal of Pharmaceutics
, 590
, Article 119837. 10.1016/j.ijpharm.2020.119837.
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Abstract
Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) 3-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing, which includes producing filaments by hot melt extrusion (HME), using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (http://m3diseen.com/predictions/).
| Type: | Article |
|---|---|
| Title: | M3DISEEN: A Novel Machine Learning Approach for Predicting the 3D Printability of Medicines |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ijpharm.2020.119837 |
| Publisher version: | https://doi.org/10.1016/j.ijpharm.2020.119837 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | 3D printed drug products, additive manufacturing, feature engineering, fused filament fabrication, gastrointestinal drug delivery, material extrusion, personalized pharmaceuticals |
| 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/10110944 |
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