TY - JOUR
T1 - Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine
AU - Abdalla, Youssef
AU - Ferianc, Martin
AU - Awad, Atheer
AU - Kim, Jeesu
AU - Elbadawi, Moe
AU - Basit, Abdul W.
AU - Orlu, Mine
AU - Rodrigues, Miguel
N1 - © 2024 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/
PY - 2024/8/15
Y1 - 2024/8/15
N2 - 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.
AB - 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.
KW - Printed pharmaceuticals and oral drug delivery systems
KW - Additive manufacturing of drug products
KW - Artificial intelligence and machine learning
KW - Deep learning
KW - Uncertainty quantification
KW - Personalized medicines and digital healthcare
UR - http://www.scopus.com/inward/record.url?scp=85197744600&partnerID=8YFLogxK
U2 - 10.1016/j.ijpharm.2024.124440
DO - 10.1016/j.ijpharm.2024.124440
M3 - Article
SN - 0378-5173
VL - 661
SP - 1
EP - 10
JO - International Journal of Pharmaceutics
JF - International Journal of Pharmaceutics
M1 - 124440
ER -