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Pharmaceutics
Special Issues
Machine Learning for Dosage Forms Developments
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Special Issue "Machine Learning for Dosage Forms Developments"

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Physical Pharmacy and Formulation".

Deadline for manuscript submissions: 30 September 2023 | Viewed by 7643

Special Issue Editor

Prof. Dr. Svetlana Ibrić

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Guest Editor
Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
Interests: controlled release dosage forms development; 3D printing; application of machine learning tools in product/process optimization and QbD implementation in development of dosage forms and processes

Special Issue Information

Dear Colleagues,

Over the last two decades, the pharmaceutical industry has faced a number of challenges. By implementing the quality by design concept in drug development, stakeholders are urged to become aware of the necessity of collecting more data on products and processes, and to analyze them accurately in order to gain useful knowledge. Along with the requirements in drug development, various machine learning techniques used in the pharmaceutical industry are being used more and more often and intensively.

This Special Issue “Machine Learning for Dosage Forms Developments” of Pharmaceutics addresses this multidisciplinary field and aims to discuss the application of machine learning tools in formulation and process dosage form development. Colleagues are invited to participate by proposing original and review papers that deal with the application of artificial intelligence in dosage form development, including different machine/deep learning tools, multivariate analysis, and computer-aided biopharmaceutical classification.

Prof. Dr. Svetlana Ibrić
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceutics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • artificial neural networks
  • dosage from development
  • process optimization
  • design space
  • quality by design

Published Papers (3 papers)

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Research

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Article
Application of Machine-Learning Algorithms for Better Understanding of Tableting Properties of Lactose Co-Processed with Lipid Excipients
by
Jelena Djuris
,
Slobodanka Cirin-Varadjan
,
Ivana Aleksic
,
Mihal Djuris
,
Sandra Cvijic
and
Svetlana Ibric
Pharmaceutics 2021, 13(5), 663; https://doi.org/10.3390/pharmaceutics13050663 - 05 May 2021
Cited by 12 | Viewed by 2288
Abstract
Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a [...] Read more.
Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients. Full article
(This article belongs to the Special Issue Machine Learning for Dosage Forms Developments)
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Review

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Review
Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media
by
Luca Pasquini
,
Antonio Napolitano
,
Matteo Pignatelli
,
Emanuela Tagliente
,
Chiara Parrillo
,
Francesco Nasta
,
Andrea Romano
,
Alessandro Bozzao
and
Alberto Di Napoli
Pharmaceutics 2022, 14(11), 2378; https://doi.org/10.3390/pharmaceutics14112378 - 04 Nov 2022
Cited by 3 | Viewed by 1756
Abstract
Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the [...] Read more.
Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of ‘virtual’ and ‘augmented’ contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media. Full article
(This article belongs to the Special Issue Machine Learning for Dosage Forms Developments)
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Review
Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions
by
Nimra Munir
,
Michael Nugent
,
Darren Whitaker
and
Marion McAfee
Pharmaceutics 2021, 13(9), 1432; https://doi.org/10.3390/pharmaceutics13091432 - 09 Sep 2021
Cited by 7 | Viewed by 2619
Abstract
In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by [...] Read more.
In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry. Full article
(This article belongs to the Special Issue Machine Learning for Dosage Forms Developments)
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