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一种用于模拟细胞毒性和纳米颗粒药物向实体瘤递送的定量平台。

A quantitative platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours.

作者信息

Wijeratne Peter A, Vavourakis Vasileios

机构信息

Centre for Medical Imaging Computing, Department of Computer Science, University College London, London, UK.

Department of Medical Physics and Biomedical Engineering, University College London, London, UK.

出版信息

Interface Focus. 2019 Jun 6;9(3):20180063. doi: 10.1098/rsfs.2018.0063. Epub 2019 Apr 19.

DOI:10.1098/rsfs.2018.0063
PMID:31065337
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6501342/
Abstract

The role of tumour-host mechano-biology and the mechanisms involved in the delivery of anti-cancer drugs have been extensively studied using and models. A complementary approach is offered by models, which can also potentially identify the main factors affecting the transport of tumour-targeting molecules. Here, we present a generalized three-dimensional modelling framework of dynamic solid tumour growth, angiogenesis and drug delivery. Crucially, the model allows for drug properties-such as size and binding affinity-to be explicitly defined, hence facilitating investigation into the interaction between the changing tumour-host microenvironment and cytotoxic and nanoparticle drugs. We use the model to qualitatively recapitulate experimental evidence of delivery efficacy of cytotoxic and nanoparticle drugs on matrix density (and hence porosity). Furthermore, we predict a highly heterogeneous distribution of nanoparticles after delivery; that nanoparticles require a high porosity extracellular matrix to cause tumour regression; and that post-injection transvascular fluid velocity depends on matrix porosity, and implicitly on the size of the drug used to treat the tumour. These results highlight the utility of predictive modelling in better understanding the factors governing efficient cytotoxic and nanoparticle drug delivery.

摘要

利用[具体模型1]和[具体模型2]模型,肿瘤-宿主机械生物学的作用以及抗癌药物递送所涉及的机制已得到广泛研究。[具体模型3]模型提供了一种补充方法,它还可能识别影响肿瘤靶向分子运输的主要因素。在此,我们提出了一个动态实体肿瘤生长、血管生成和药物递送的广义三维[具体模型名称]建模框架。至关重要的是,该模型允许明确界定药物特性,如大小和结合亲和力,从而便于研究不断变化的肿瘤-宿主微环境与细胞毒性药物和纳米颗粒药物之间的相互作用。我们使用该模型定性地概括了细胞毒性药物和纳米颗粒药物对基质密度(以及孔隙率)的递送效果的实验证据。此外,我们预测递送后纳米颗粒的分布高度不均一;纳米颗粒需要高孔隙率的细胞外基质才能导致肿瘤消退;注射后经血管流体速度取决于基质孔隙率,并隐含地取决于用于治疗肿瘤的药物大小。这些结果突出了预测性[具体模型名称]建模在更好地理解有效细胞毒性和纳米颗粒药物递送的控制因素方面的效用。

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本文引用的文献

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Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours.运用清除组织成像和体内灌注的计算流体动力学可预测肿瘤内的药物摄取和治疗反应。
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