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生成基于细胞的药物输送系统中的知识空白及其与人工智能的可能融合。

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence.

机构信息

Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran.

Design and System Operations Department, Regional Information Center for Science and Technology, 71946 94171 Shiraz, Iran.

出版信息

Mol Pharm. 2023 Aug 7;20(8):3757-3778. doi: 10.1021/acs.molpharmaceut.3c00162. Epub 2023 Jul 10.

Abstract

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.

摘要

基于细胞的药物传递系统是靶向传递的新策略,其中细胞或细胞膜衍生系统被用作载体,并以受控的方式释放其货物。最近,人们对细胞作为载体系统治疗多种疾病给予了极大关注。基于细胞的药物传递系统的发展存在各种挑战。预测这些平台的特性是其开发的前提步骤,以减少不良影响。将纳米技术和人工智能集成在一起会产生更具创新性的技术。人工智能可以快速挖掘数据,并更快速、更准确地做出决策。机器学习作为更广泛的人工智能的一个子集,已被用于纳米医学设计更安全的纳米材料。在这里,描述了如何使用人工智能和机器学习的潜在预测模型来解决基于细胞的药物传递系统的发展挑战。描述了最著名的基于细胞的药物传递系统及其挑战。最后但同样重要的是,强调了人工智能及其在纳米医学中使用的大多数类型。本综述表明了开发细胞或其衍生物作为载体的挑战,以及如何将其与人工智能和机器学习的潜在预测模型一起使用。

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