Department of Pharmaceutical Biomaterials, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 1417614411, Iran.
The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran.
Colloids Surf B Biointerfaces. 2019 Jul 1;179:505-516. doi: 10.1016/j.colsurfb.2019.04.003. Epub 2019 Apr 3.
Bio-nano interface investigation models are mainly based on the type of proteins present on corona, bio-nano interaction responses and the evaluation of final outcomes. Due to the extensive diversity in correlative models for investigation of nanoparticles biological responses, a comprehensive model considering different aspects of bio-nano interface from nanoparticles properties to protein corona fingerprints appeared to be essential and cannot be ignored. In order to minimize divergence in studies in the era of bio-nano interface and protein corona with following therapeutic implications, a useful investigation model on the basis of RADAR concept is suggested. The contents of RADAR concept consist of five modules: 1- Reshape of our strategy for synthesis of nanoparticles (NPs), 2- Application of NPs selected based on human fluid, 3- Delivery strategy of NPs selected based on target tissue, 4- Analysis of proteins present on corona using correct procedures and 5- Risk assessment and risk reduction upon the collection and analysis of results to increase drug delivery efficiency and drug efficacy. RADAR grouping strategy for revisiting protein corona phenomenon as a key of success will be discussed with respect to the current state of knowledge.
生物-纳米界面研究模型主要基于蛋白冠上存在的蛋白质类型、生物-纳米相互作用反应以及最终结果的评估。由于用于研究纳米颗粒生物反应的相关模型具有广泛的多样性,因此,从纳米颗粒特性到蛋白冠指纹图谱等不同方面综合考虑生物-纳米界面的全面模型显得至关重要,不容忽视。为了在具有治疗意义的生物-纳米界面和蛋白冠时代最小化研究中的分歧,我们建议基于 RADAR 概念建立一个有用的研究模型。RADAR 概念的内容包括五个模块:1- 重塑我们的纳米颗粒(NPs)合成策略,2- 根据人体流体选择 NPs 的应用,3- 根据目标组织选择 NPs 的传递策略,4- 使用正确的程序分析蛋白冠上的蛋白质,5- 在收集和分析结果时进行风险评估和降低风险,以提高药物输送效率和药物功效。我们将讨论 RADAR 分组策略,以回顾蛋白冠现象作为成功的关键,同时考虑到当前的知识状态。