Meta-Analysis of Material Properties Influencing Nanoparticle Plasma Pharmacokinetics.
Meta-Analysis of Material Properties Influencing Nanoparticle Plasma Pharmacokinetics.
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DOI：
10.1016/j.ijpharm.2023.122951
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发表时间：
2023-04
影响因子：
5.8
通讯作者：
Briana Macedo;Manthan Patel;Michael H. Zaleski;Parth Mody;Xiaonan Ma;Patrick Mei;J. Myerson;J. Brenner;P. Glassman
中科院分区：
文献类型：
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作者：
Briana Macedo;Manthan Patel;Michael H. Zaleski;Parth Mody;Xiaonan Ma;Patrick Mei;J. Myerson;J. Brenner;P. Glassman
血浆药代动力学（PK）的全面表征是新型治疗药物临床开发的关键步骤，通常用于小分子和生物制剂。然而，对于基于纳米颗粒的药物递送系统，甚至缺乏PK的基本表征。这导致了关于纳米颗粒特性如何控制PK的未经测试的概括。在这里，我们提出了一个荟萃分析的100纳米颗粒制剂静脉注射给药后，在小鼠中，以确定四个非房室分析（NCA）和纳米颗粒的四个基本属性（PEG化，zeta电位，大小和材料）的PK参数之间的任何相关性。按纳米颗粒性质分层的颗粒PK之间存在统计学显著差异。然而，这些性质与PK参数之间的单线性回归显示出较差的可预测性（对于所有分析，r2< 0.10），而多变量回归显示出改善的可预测性（r2> 0.38，t1/2除外）。这表明，没有一种单独的纳米颗粒性质甚至可以适度预测PK，而多种纳米颗粒特征的组合确实提供了适度的预测能力。改进的纳米颗粒特性报告将使纳米制剂之间的比较更加准确，并将提高我们预测体内行为和设计最佳纳米颗粒的能力。
Thorough characterization of the plasma pharmacokinetics (PK) is a critical step in clinical development of novel therapeutics and is routinely performed for small molecules and biologics. However, there is a paucity of even basic characterization of PK for nanoparticle-based drug delivery systems. This has led to untested generalizations about how nanoparticle properties govern PK. Here, we present a meta-analysis of 100 nanoparticle formulations following IV administration in mice to identify any correlations between four PK parameters derived by non-compartmental analysis (NCA) and four cardinal properties of nanoparticles: PEGylation, zeta potential, size, and material. There was a statistically significant difference between the PK of particles stratified by nanoparticle properties. However, single linear regression between these properties and PK parameters showed poor predictability (r2< 0.10 for all analyses), while multivariate regressions showed improved predictability (r2> 0.38, except for t1/2). This suggests that no single nanoparticle property alone is even moderately predictive of PK, while the combination of multiple nanoparticle features does provide moderate predictive power. Improved reporting of nanoparticle properties will enable more accurate comparison between nanoformulations and will enhance our ability to predictin vivobehavior and design optimal nanoparticles.