Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats.
Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats.
复制标题
DOI：
10.1186/s12989-022-00489-4
复制
发表时间：
2022-07-08
影响因子：
10
通讯作者：
Lin, Zhoumeng
中科院分区：
文献类型：
--
作者：
Chou, Wei-Chun;Cheng, Yi-Hsien;Riviere, Jim E.;Monteiro-Riviere, Nancy A.;Kreyling, Wolfgang G.;Lin, Zhoumeng
关键词：
基于生理学的药代动力学 (PBPK) 模型是预测靶器官剂量测定和纳米颗粒 (NP) 风险评估的重要工具。构建 NP 多途径 PBPK 模型的方法尚未建立，也未进行系统评估。在本研究中，我们假设通常用于小分子的 PBPK 建模的传统路线到路线外推方法可能不适合 NP。为了检验这一假设，本研究的目的是开发一种多途径 PBPK 模型，用于成年大鼠中不同尺寸（1.4-200 nm）的金纳米颗粒（AuNP），采用不同的给药途径（即静脉注射（IV）、口服强饲、气管内滴注和气管内吸入），使用两种方法：传统的小分子途径到途径外推法和基于我们研究的特定途径数据的新方法。建议普遍应用于 NP。我们发现使用这种新方法的 PBPK 模型比传统方法具有更优越的性能。最终的 PBPK 模型使用贝叶斯分层方法和马尔可夫链蒙特卡罗模拟进行了严格优化，然后使用 R Shiny 转换为基于 Web 的界面。此外，建立了基于定量结构-活性关系（QSAR）的多元线性回归，以根据 AuNP 的理化特性（例如尺寸、表面积、剂量、Zeta 电位和 NP 数量）预测特定途径的关键生物分布参数（例如最大摄取率）。这些结果表明，无论给药途径如何，AuNPs 的大小和表面积是内吞/吞噬吸收率的主要决定因素，而 Zeta 电位是估计 IV 给药后胞吐释放率的重要参数。这项研究表明，小分子 PBPK 建模的传统路线到路线外推方法不适用于 NP。因此，应使用特定路线的数据来开发 NP 的多路线 PBPK 模型。这种基于 PBPK 的新型网络界面可作为外推至其他 NP 和人类的基础，以促进 NP 的生物分布估计、安全性和风险评估。在线版本包含可在 10.1186/s12989-022-00489-4 获取的补充材料。
Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4–200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs. We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure–activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration. This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs. The online version contains supplementary material available at 10.1186/s12989-022-00489-4.
登录
查看更多内容
DOI：
10.1016/j.nano.2014.06.005
发表时间：
2014-11-01
影响因子：
5.4
作者：
Fraga, Sonia;Brandao, Ana;Carmo, Helena
通讯作者：
Carmo, Helena
影响因子：
5
作者：
Bachler, Gerald;von Goetz, Natalie;Hungerbuhler, Konrad
通讯作者：
Hungerbuhler, Konrad
影响因子：
8
作者：
Dong D;Wang X;Wang H;Zhang X;Wang Y;Wu B
通讯作者：
Wu B
影响因子：
8
作者：
Carlander, Ulrika;Li, Dingsheng;Johanson, Gunnar
通讯作者：
Johanson, Gunnar
影响因子：
10
作者：
Geiser, Marianne;Quaile, Oliver;Kreyling, Wolfgang G.
通讯作者：
Kreyling, Wolfgang G.