Predicting pharmaceutical inkjet printing outcomes using machine learning DOI Creative Commons
Paola Carou‐Senra, Jun Jie Ong,

Brais Muñiz Castro

et al.

International Journal of Pharmaceutics X, Journal Year: 2023, Volume and Issue: 5, P. 100181 - 100181

Published: April 18, 2023

Inkjet printing has been extensively explored in recent years to produce personalised medicines due its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films complex polydrug implants. However, the multi-factorial nature of inkjet process makes formulation (e.g., composition, surface tension, viscosity) parameter optimization nozzle diameter, peak voltage, drop spacing) an empirical time-consuming endeavour. Instead, given wealth publicly available data on pharmaceutical printing, there is potential for a predictive model outcomes be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, support vector machine) predict printability drug dose were developed using dataset 687 formulations, consolidated in-house literature-mined inkjet-printed formulations. The optimized ML predicted formulations with accuracy 97.22%, quality prints 97.14%. This study demonstrates that can feasibly provide insights prior preparation, affording resource- time-savings.

Language: Английский

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation DOI Creative Commons
Shan Wang, Jinwei Di, Dan Wang

et al.

Pharmaceutics, Journal Year: 2022, Volume and Issue: 14(1), P. 183 - 183

Published: Jan. 13, 2022

During the development of a pharmaceutical formulation, powerful tool is needed to extract key points from complicated process parameters and material attributes. Artificial neural networks (ANNs), promising more flexible modeling technique, can address real intricate questions in high parallelism distributed pattern manner biological networks. The data mined analyzing based on ANNs have ability replace hundreds trial error experiments. been used for analysis by pharmaceutics researchers since 1990s it has now become research method science. This review focuses latest application progress prediction, characterization optimization formulation provide reference further interdisciplinary study ANNs.

Language: Английский

Citations

45

A Review of State-of-the-Art on Enabling Additive Manufacturing Processes for Precision Medicine DOI
Atheer Awad, Álvaro Goyanes, Abdul W. Basit

et al.

Journal of Manufacturing Science and Engineering, Journal Year: 2022, Volume and Issue: 145(1)

Published: Nov. 8, 2022

Abstract Precision medicine is an emerging healthcare delivery approach that considers variability between patients, such as genetic makeups, in contrast to the current one-size-fits-all designed treat average patient. The White House launched Medicine Initiative 2015, starting endeavor reshape delivery. To translate concept of precision from bench practice, advanced manufacturing will play integral part, including fabrication personalized drugs and drug devices screening platforms. These products are highly customized require robust yet flexible systems. field has rapidly evolved past five years. In this state-of-the-art review, manufactured for be introduced, followed by a brief review processing materials their characteristics. A on different processes applicable those aforementioned provided. status development regulatory submission quality control considerations also discussed. Finally, paper presents future outlook used medicine.

Language: Английский

Citations

40

Energy consumption and carbon footprint of 3D printing in pharmaceutical manufacture DOI Creative Commons
Moe Elbadawi, Abdul W. Basit, Simon Gaisford

et al.

International Journal of Pharmaceutics, Journal Year: 2023, Volume and Issue: 639, P. 122926 - 122926

Published: April 7, 2023

Achieving carbon neutrality is seen as an important goal in order to mitigate the effects of climate change, dioxide a major greenhouse gas that contributes global warming. Many countries, cities and organizations have set targets become neutral. The pharmaceutical sector no exception, being contributor emissions (emitting approximately 55% more than automotive for instance) hence need strategies reduce its environmental impact. Three-dimensional (3D) printing advanced fabrication technology has potential replace traditional manufacturing tools. Being new technology, impact 3D printed medicines not been investigated, which barrier uptake by industry. Here, energy consumption (and emission) printers considered, focusing on technologies successfully demonstrated produce solid dosage forms. 6 benchtop was measured during standby mode printing. On standby, ranged from 0.03 0.17 kWh. required producing 10 printlets 0.06 3.08 kWh, with using high temperatures consuming energy. Carbon between 11.60 112.16 g CO2 (eq) per printlets, comparable tableting. Further analyses revealed decreasing temperature found demand considerably, suggesting developing formulations are printable at lower can emissions. study delivers key initial insights into potentially transformative provides encouraging results demonstrating deliver quality without environmentally detrimental.

Language: Английский

Citations

40

3D printing technology: A new approach for the fabrication of personalized and customized pharmaceuticals DOI
Muneeb Ullah, Abdul Wahab, Shahid Ullah Khan

et al.

European Polymer Journal, Journal Year: 2023, Volume and Issue: 195, P. 112240 - 112240

Published: June 20, 2023

Language: Английский

Citations

40

Predicting pharmaceutical inkjet printing outcomes using machine learning DOI Creative Commons
Paola Carou‐Senra, Jun Jie Ong,

Brais Muñiz Castro

et al.

International Journal of Pharmaceutics X, Journal Year: 2023, Volume and Issue: 5, P. 100181 - 100181

Published: April 18, 2023

Inkjet printing has been extensively explored in recent years to produce personalised medicines due its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films complex polydrug implants. However, the multi-factorial nature of inkjet process makes formulation (e.g., composition, surface tension, viscosity) parameter optimization nozzle diameter, peak voltage, drop spacing) an empirical time-consuming endeavour. Instead, given wealth publicly available data on pharmaceutical printing, there is potential for a predictive model outcomes be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, support vector machine) predict printability drug dose were developed using dataset 687 formulations, consolidated in-house literature-mined inkjet-printed formulations. The optimized ML predicted formulations with accuracy 97.22%, quality prints 97.14%. This study demonstrates that can feasibly provide insights prior preparation, affording resource- time-savings.

Language: Английский

Citations

39