Machine learning predicts 3D printing performance of over 900 drug delivery systems
Use este enlace para citar
http://hdl.handle.net/2183/34766
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución-NoComercial-SinDerivadas 3.0 España
Coleccións
- GI-IRlab-Artigos [24]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Machine learning predicts 3D printing performance of over 900 drug delivery systemsAutor(es)
Data
2021-09Cita bibliográfica
B. Muñiz Castro, M. Elbadawi, J. J. Ong, T. Pollard, Z. Song, S. Gaisford, G. Pérez, A. W. Basit, P. Cabalar, and A. Goyanes, "Machine learning predicts 3D printing performance of over 900 drug delivery systems", Journal of Controlled Release, Vol. 337, pp. 530-545, 10 Sept. 2021, doi: 10.1016/j.jconrel.2021.07.046
É version de
https://doi.org/10.1016/j.jconrel.2021.07.046
Resumo
[Abstract]: Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow.
Palabras chave
Additive manufacturing and continuous manufacturing devices
Digital health and Industry 4.0
Personalized drug products and precision pharmaceuticals
Fused filament fabrication and fused deposition modeling
Material extrusion and drug delivery
Implant technology and tissue engineering
Digital health and Industry 4.0
Personalized drug products and precision pharmaceuticals
Fused filament fabrication and fused deposition modeling
Material extrusion and drug delivery
Implant technology and tissue engineering
Descrición
©2021 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Journal of Controlled Release. The Version of Record is available online at https://doi.org/10.1016/j.jconrel.2021.07.046 Versión aceptada de: B. Muñiz Castro, M. Elbadawi, J. J. Ong, T. Pollard, Z. Song, S. Gaisford, G. Pérez, A. W. Basit, P. Cabalar, and A. Goyanes, "Machine learning predicts 3D printing performance of over 900 drug delivery systems", Journal of Controlled Release, Vol. 337, pp. 530-545, 10 Sept. 2021, doi: 10.1016/j.jconrel.2021.07.046
Versión do editor
Dereitos
Atribución-NoComercial-SinDerivadas 3.0 España