Application of Deep Learning in Pharmaceutical Manufacturing

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Pharmaceutical Processes".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 1751

Special Issue Editors


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Guest Editor
Department of Applied Science and Technology, Politecnico di Torino, IT-10129 Torino, Italy
Interests: pharmaceutical manufacturing; formulation design; drug products; mathematical modeling; molecular simulations; process analytical technologies; process control; optimization; downstream processes; liquid chromatography; continuous manufacturing

E-Mail Website1 Website2
Guest Editor
Department of Applied Science and Technology, Politecnico di Torino, IT-10129 Torino, Italy
Interests: computational fluid dynamics; porous media; packed bed reactors; machine learning; deep learning; artificial intelligence; filtration; groundwater remediation

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) are attracting tremendous attention in the pharmaceutical industry as valuable tools for product and process development. Although the lack of large, standardized datasets can make their application challenging, there are many recent examples of successful applications wherein these approaches helped to improve drug manufacturing efficiency. Moreover, it is ever more clear how computer-generated data, by means of numerical simulations, can rapidly close the gap of data availability for the training of very sophisticated deep learning architectures. Thus, their applications include product design, process optimization, process monitoring and control, and many others. All these may involve both classical deep learning techniques (fully connected neural networks) and more recent architectures (convolutional and graph-convolutional neural networks).

This Special Issue on “Application of Deep Learning in Pharmaceutical Manufacturing” seeks high-quality papers focusing on the most recent advances in deep learning and their applications in drug product manufacturing. Topics include, but are not limited to:

  • Design of pharmaceutical formulation;
  • Prediction of physicochemical properties of drug products;
  • Design of innovative nanomedicine technologies and drug delivery systems;
  • Product quality control;
  • Process development and design of new manufacturing technologies;
  • Process monitoring and control;
  • Continuous manufacturing;
  • Use of innovative deep learning architectures for product design or process investigation.

 We hope you consider participating in this Special Issue.

Prof. Dr. Roberto Pisano
Dr. Gianluca Boccardo
Guest Editors

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. Processes 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 2400 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
  • artificial intelligence
  • pharmaceutical manufacturing
  • formulation
  • optimization
  • process control
  • process monitoring

Published Papers (1 paper)

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12 pages, 1344 KiB  
Technical Note
Automated Production at Scale of Induced Pluripotent Stem Cell-Derived Mesenchymal Stromal Cells, Chondrocytes and Extracellular Vehicles: Towards Real-Time Release
by Laura Herbst, Ferdinand Groten, Mary Murphy, Georgina Shaw, Bastian Nießing and Robert H. Schmitt
Processes 2023, 11(10), 2938; https://doi.org/10.3390/pr11102938 - 10 Oct 2023
Viewed by 1376
Abstract
Induced pluripotent stem cell (iPSC)-derived mesenchymal stem cells (iMSCs) are amenable for use in a clinical setting for treatment of osteoarthritis (OA), which remains one of the major illnesses worldwide. Aside from iPSC-derived iMSCs, chondrocytes (iCHO) and extracellular vesicles (EV) are also promising [...] Read more.
Induced pluripotent stem cell (iPSC)-derived mesenchymal stem cells (iMSCs) are amenable for use in a clinical setting for treatment of osteoarthritis (OA), which remains one of the major illnesses worldwide. Aside from iPSC-derived iMSCs, chondrocytes (iCHO) and extracellular vesicles (EV) are also promising candidates for treatment of OA. Manufacturing and quality control of iPSC-derived therapies is mainly manual and thus highly time consuming and susceptible to human error. A major challenge in translating iPSC-based treatments more widely is the lack of sufficiently scaled production technologies from seeding to fill-and-finish. Formerly, the Autostem platform was developed for the expansion of tissue-derived MSCs at scale in stirred tank bioreactors and subsequent fill-and-finish. Additionally, the StemCellDiscovery platform was developed to handle plate-based cultivation of adherent cells including their microscopic analysis. By combining the existing automation technology of both platforms, all required procedures can be integrated in the AutoCRAT system, designed to handle iPSC expansion, differentiation to iMSCs and iCHOs, pilot scale expansion, and formulation of iMSCs as well as extracellular vesicles and their purification. Furthermore, the platform is equipped with several in-line and at-line assays to determine product quality, purity, and safety. This paper highlights the need for adaptable and modular automation concepts. It also stresses the importance of ensuring safety of generated therapies by incorporating automated release testing and cleaning solutions in automated systems. The adapted platform concepts presented here will help translate these technologies for clinical production at the necessary scale. Full article
(This article belongs to the Special Issue Application of Deep Learning in Pharmaceutical Manufacturing)
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