Literature DB >> 34238624

Disrupting 3D printing of medicines with machine learning.

Moe Elbadawi1, Laura E McCoubrey1, Francesca K H Gavins1, Jun J Ong1, Alvaro Goyanes2, Simon Gaisford3, Abdul W Basit4.   

Abstract

3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare.
Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  3D Printed drug products and formulations; Industry 4.0 and digital health; additive manufacturing; biomedical engineering and pharmaceutical sciences; personalized oral drug delivery systems and medical devices; translational pharmaceutics

Year:  2021        PMID: 34238624     DOI: 10.1016/j.tips.2021.06.002

Source DB:  PubMed          Journal:  Trends Pharmacol Sci        ISSN: 0165-6147            Impact factor:   14.819


  5 in total

1.  Accelerating 3D printing of pharmaceutical products using machine learning.

Authors:  Jun Jie Ong; Brais Muñiz Castro; Simon Gaisford; Pedro Cabalar; Abdul W Basit; Gilberto Pérez; Alvaro Goyanes
Journal:  Int J Pharm X       Date:  2022-06-09

2.  Scalable Biofabrication: A Perspective on the Current State and Future Potentials of Process Automation in 3D-Bioprinting Applications.

Authors:  Nils Lindner; Andreas Blaeser
Journal:  Front Bioeng Biotechnol       Date:  2022-05-20

3.  Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota.

Authors:  Laura E McCoubrey; Stavriani Thomaidou; Moe Elbadawi; Simon Gaisford; Mine Orlu; Abdul W Basit
Journal:  Pharmaceutics       Date:  2021-11-25       Impact factor: 6.321

4.  Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development.

Authors:  Colm S O'Reilly; Moe Elbadawi; Neel Desai; Simon Gaisford; Abdul W Basit; Mine Orlu
Journal:  Pharmaceutics       Date:  2021-12-17       Impact factor: 6.321

Review 5.  State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation.

Authors:  Shan Wang; Jinwei Di; Dan Wang; Xudong Dai; Yabing Hua; Xiang Gao; Aiping Zheng; Jing Gao
Journal:  Pharmaceutics       Date:  2022-01-13       Impact factor: 6.321

  5 in total

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