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Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence.
Mozafari, Negin; Mozafari, Niloofar; Dehshahri, Ali; Azadi, Amir.
Afiliação
  • Mozafari N; Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran.
  • Mozafari N; Design and System Operations Department, Regional Information Center for Science and Technology, 71946 94171 Shiraz, Iran.
  • Dehshahri A; Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran.
  • Azadi A; Pharmaceutical Sciences Research Centre, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran.
Mol Pharm ; 20(8): 3757-3778, 2023 08 07.
Article em En | MEDLINE | ID: mdl-37428824
Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado de Máquina Tipo de estudo: Prognostic_studies Idioma: En Revista: Mol Pharm Assunto da revista: BIOLOGIA MOLECULAR / FARMACIA / FARMACOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irã País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado de Máquina Tipo de estudo: Prognostic_studies Idioma: En Revista: Mol Pharm Assunto da revista: BIOLOGIA MOLECULAR / FARMACIA / FARMACOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irã País de publicação: Estados Unidos