Nanoparticle biodistribution coefficients: A quantitative approach for understanding the tissue distribution of nanoparticles

体内分布 药代动力学 纳米颗粒 基于生理学的药代动力学模型 体内 分布(数学) 脾脏 药理学 纳米技术 化学 材料科学 医学 生物 数学 生物化学 体外 内科学 数学分析 生物技术
作者
Mokshada Kumar,Priyanka Kulkarni,Shufang Liu,Nagendra Chemuturi,Dhaval K. Shah
出处
期刊:Advanced Drug Delivery Reviews [Elsevier]
卷期号:194: 114708-114708 被引量:56
标识
DOI:10.1016/j.addr.2023.114708
摘要

The objective of this manuscript is to provide quantitative insights into the tissue distribution of nanoparticles. Published pharmacokinetics of nanoparticles in plasma, tumor and 13 different tissues of mice were collected from literature. A total of 2018 datasets were analyzed and biodistribution of graphene oxide, lipid, polymeric, silica, iron oxide and gold nanoparticles in different tissues was quantitatively characterized using Nanoparticle Biodistribution Coefficients (NBC). It was observed that typically after intravenous administration most of the nanoparticles are accumulated in the liver (NBC = 17.56 %ID/g) and spleen (NBC = 12.1 %ID/g), while other tissues received less than 5 %ID/g. NBC values for kidney, lungs, heart, bones, brain, stomach, intestine, pancreas, skin, muscle and tumor were found to be 3.1 %ID/g, 2.8 %ID/g, 1.8 %ID/g, 0.9 %ID/g, 0.3 %ID/g, 1.2 %ID/g, 1.8 %ID/g, 1.2 %ID/g, 1.0 %ID/g, 0.6 %ID/g and 3.4 %ID/g, respectively. Significant variability in nanoparticle distribution was observed in certain organs such as liver, spleen and lungs. A large fraction of this variability could be explained by accounting for the differences in nanoparticle physicochemical properties such as size and material. A critical overview of published nanoparticle physiologically-based pharmacokinetic (PBPK) models is provided, and limitations in our current knowledge about in vitro and in vivo pharmacokinetics of nanoparticles that restrict the development of robust PBPK models is also discussed. It is hypothesized that robust quantitative assessment of whole-body pharmacokinetics of nanoparticles and development of mathematical models that can predict their disposition can improve the probability of successful clinical translation of these modalities.
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