Current status of in vivo bioanalysis of nano drug delivery systems

来源 :药物分析学报(英文) | 被引量 : 0次 | 上传用户:stat2009
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
The development of nano drug delivery systems (NDDSs) provides new approaches to fighting against diseases. The NDDSs are specially designed to serve as carriers for the delivery of active pharmaceutical ingredients (APIs) to their target sites, which would certainly extend the benefit of their unique physi-cochemical characteristics, such as prolonged circulation time, improved targeting and avoiding of drug-resistance. Despite the remarkable progress achieved over the last three decades, the understanding of the relationships between the in vivo pharmacokinetics of NDDSs and their safety profiles is insufficient. Analysis of NDDSs is far more complicated than the monitoring of small molecular drugs in terms of structure, composition and aggregation state, whereby almost all of the conventional techniques are inadequate for accurate profiling their pharmacokinetic behavior in vivo. Herein, the advanced bio-analysis for tracing the in vivo fate of NDDSs is summarized, including liquid chromatography tandem-mass spectrometry (LC-MS/MS), F(o)rster resonance energy transfer (FRET), aggregation-caused quench-ing (ACQ) fluorophore, aggregation-induced emission (AIE) fluorophores, enzyme-linked immunosor-bent assay (ELISA), magnetic resonance imaging (MRI), radiolabeling, fluorescence spectroscopy, laser ablation inductively coupled plasma MS (LA-ICP-MS), and size-exclusion chromatography (SEC). Based on these technologies, a comprehensive survey of monitoring the dynamic changes of NDDSs in struc-ture, composition and existing form in system (i.e. carrier polymers, released and encapsulated drug) with recent progress is provided. We hope that this review will be helpful in appropriate application methodology for investigating the pharmacokinetics and evaluating the efficacy and safety profiles of NDDSs.
其他文献
支持向量机(SVM)是机器学习中一种非常有效且流行的学习工具。由于它具有很好的泛化性能,已经被广泛的应用于各种应用领域。然而随着科技的不断发展,数据集的规模越来越大,SVM对
概率论是有着广泛应用的一门学科,是许多应用学科的理论基础.诸如信息论、数学风险论、保险精算理论等均是建立在概率论基础上的,其中对隐马尔可夫模型的研究是一个重要分支.
笔者在文章开篇先提几个问题,希望大家能带着问题来看文章.也许您看完了文章依然没有找到“标准答案”,但一定会在今后的工作中多一些思考,少一些“理所当然”.rn1.在临床实
期刊
二、结构特点(一)NYSE集团公司的构成纽约股票交易所和群岛交易所合并后,原纽约股票交易所的1366个交易席位拥有者每家获得30万美元现金和新集团80177股股票,共占集团公司的7
1957年,我正在华中师范大学就读中文系四年级,即本科毕业的最后一个学期,那时我是班长。同学们都在准备6月底的毕业考试,准备毕业以后,服从祖国的分配,到祖国最需要的地方去