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Updated: Sep 15, 2025

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish
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Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Published on: April 16, 2019

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从物理化学描述器对纳米粒子生物分布的数据驱动预测

Jimeng Wu1,2, Peter Wick1, Bernd Nowack2

  • 1Empa, Swiss Federal Laboratories for Materials Science and Technology, Nanomaterials in Health Laboratory, Lerchenfeldstrasse 5, St. Gallen 9014, Switzerland.

ACS nano
|July 16, 2025
PubMed
概括

这项研究开发了一个新的非动物模型,使用物理化学特性来预测纳米粒子生物分布和药理动力学. 该框架准确地预测了纳米颗粒在体内如何表现,有助于更安全的设计.

关键词:
在 PBPK 建模中使用 PBPK 模型.在QSAR中使用QSAR.这是一个SSbD.生物分布预测预测纳米粒子的药理动力学

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Uptake of New Lipid-coated Nanoparticles Containing Falcarindiol by Human Mesenchymal Stem Cells09:34

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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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