Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems
A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems.
The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of “on demand” treatments.
In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.
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Aikaterini Dedeloudi, Edward Weaver, Dimitrios A. Lamprou, Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems, International Journal of Pharmaceutics, Volume 636, 2023, 122818, ISSN 0378-5173, https://doi.org/10.1016/j.ijpharm.2023.122818.