纳米医学
分布(数学)
药物输送
脾脏
癌症
肾
计算机科学
纳米颗粒
医学
数据库
材料科学
纳米技术
内科学
数学
数学分析
作者
Qiran Chen,Long Yuan,Wei-Chun Chou,Yi‐Hsien Cheng,Chunla He,Nancy A. Monteiro‐Riviere,Jim E. Riviere,Zhoumeng Lin
出处
期刊:ACS Nano
[American Chemical Society]
日期:2023-10-09
卷期号:17 (20): 19810-19831
被引量:11
标识
DOI:10.1021/acsnano.3c04037
摘要
Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of “Nano-Tumor Database”, which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded “Nano-Tumor Database” and several statistical models that may help nanomedicine design in the future.
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