Review of machine learning algorithms' application in pharmaceutical technology

Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools prov...

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Autores principales:	Đuriš Jelena, Kurćubić Ivana, Ibrić Svetlana
Formato:	article
Lenguaje:	SR
Publicado:	Pharmaceutical Association of Serbia, Belgrade, Serbia 2021
Materias:	machine learning artificial neural networks quality by design pharmaceutical development process analytical technologies Pharmacy and materia medica RS1-441
Acceso en línea:	https://doaj.org/article/167c500868cb4e33abed0aee1b8c2816
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id	oai:doaj.org-article:167c500868cb4e33abed0aee1b8c2816
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spelling	oai:doaj.org-article:167c500868cb4e33abed0aee1b8c28162021-12-05T18:01:35ZReview of machine learning algorithms' application in pharmaceutical technology0004-19632217-876710.5937/arhfarm71-32499https://doaj.org/article/167c500868cb4e33abed0aee1b8c28162021-01-01T00:00:00Zhttps://scindeks-clanci.ceon.rs/data/pdf/0004-1963/2021/0004-19632104302Q.pdfhttps://doaj.org/toc/0004-1963https://doaj.org/toc/2217-8767Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.Đuriš JelenaKurćubić IvanaIbrić SvetlanaPharmaceutical Association of Serbia, Belgrade, Serbiaarticlemachine learningartificial neural networksquality by designpharmaceutical developmentprocess analytical technologiesPharmacy and materia medicaRS1-441SRArhiv za farmaciju, Vol 71, Iss 4, Pp 302-317 (2021)
institution	DOAJ
collection	DOAJ
language	SR
topic	machine learning artificial neural networks quality by design pharmaceutical development process analytical technologies Pharmacy and materia medica RS1-441
spellingShingle	machine learning artificial neural networks quality by design pharmaceutical development process analytical technologies Pharmacy and materia medica RS1-441 Đuriš Jelena Kurćubić Ivana Ibrić Svetlana Review of machine learning algorithms' application in pharmaceutical technology
description	Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.
format	article
author	Đuriš Jelena Kurćubić Ivana Ibrić Svetlana
author_facet	Đuriš Jelena Kurćubić Ivana Ibrić Svetlana
author_sort	Đuriš Jelena
title	Review of machine learning algorithms' application in pharmaceutical technology
title_short	Review of machine learning algorithms' application in pharmaceutical technology
title_full	Review of machine learning algorithms' application in pharmaceutical technology
title_fullStr	Review of machine learning algorithms' application in pharmaceutical technology
title_full_unstemmed	Review of machine learning algorithms' application in pharmaceutical technology
title_sort	review of machine learning algorithms' application in pharmaceutical technology
publisher	Pharmaceutical Association of Serbia, Belgrade, Serbia
publishDate	2021
url	https://doaj.org/article/167c500868cb4e33abed0aee1b8c2816
work_keys_str_mv	AT đurisjelena reviewofmachinelearningalgorithmsapplicationinpharmaceuticaltechnology AT kurcubicivana reviewofmachinelearningalgorithmsapplicationinpharmaceuticaltechnology AT ibricsvetlana reviewofmachinelearningalgorithmsapplicationinpharmaceuticaltechnology
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Review of machine learning algorithms' application in pharmaceutical technology

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