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基于图像的肿瘤异质血管中药物输送的时空模型 - 计算方法。

Image-based spatio-temporal model of drug delivery in a heterogeneous vasculature of a solid tumor - Computational approach.

机构信息

Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Ontario, Canada; Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.

出版信息

Microvasc Res. 2019 May;123:111-124. doi: 10.1016/j.mvr.2019.01.005. Epub 2019 Jan 31.

Abstract

The solute transport distribution in a tumor is an important criterion in the evaluation of the cancer treatment efficacy. The fraction of killed cells after each treatment can quantify the therapeutic effect and plays as a helpful tool to evaluate the chemotherapy treatment schedules. In the present study, an image-based spatio-temporal computational model of a solid tumor is provided for calculation of interstitial fluid flow and solute transport. Current model incorporates heterogeneous microvasculature for angiogenesis instead of synthetic mathematical modeling. In this modeling process, a comprehensive model according to Convection-Diffusion-Reaction (CDR) equations is employed due to its high accuracy for simulating the binding and the uptake of the drug by tumor cells. Based on the velocity and the pressure distribution, transient distribution of the different drug concentrations (free, bound, and internalized) is calculated. Then, the fraction of killed cells is obtained according to the internalized concentration. Results indicate the dependence of the drug distribution on both time and space, as well as the microvasculature density. Free and bound drug concentration have the same trend over time, whereas, internalized and total drug concentration increases over time and reaches a constant value. The highest amount of concentration occurred in the tumor region due to the higher permeability of the blood vessels. Moreover, the fraction of killed cells is approximately 78.87% and 24.94% after treatment with doxorubicin for cancerous and normal tissues, respectively. In general, the presented methodology may be applied in the field of personalized medicine to optimize patient-specific treatments. Also, such image-based modeling of solid tumors can be used in laboratories that working on drug delivery and evaluating new drugs before using them for any in vivo or clinical studies.

摘要

肿瘤内溶质输送分布是评估癌症治疗效果的重要标准。每次治疗后杀死的细胞比例可以量化治疗效果,并作为评估化疗治疗方案的有用工具。在本研究中,提供了一种基于图像的实体瘤时空计算模型,用于计算间质液流动和溶质输送。当前模型结合了用于血管生成的异质微血管,而不是合成数学建模。在这个建模过程中,由于其对模拟药物与肿瘤细胞结合和摄取的高准确性,采用了基于对流-扩散-反应 (CDR) 方程的综合模型。根据速度和压力分布,计算不同药物浓度(游离、结合和内化)的瞬态分布。然后,根据内化浓度获得杀死的细胞比例。结果表明,药物分布取决于时间和空间以及微血管密度。游离药物和结合药物的浓度随时间呈相同趋势,而内化药物和总药物浓度随时间增加并达到恒定值。由于血管通透性较高,药物浓度在肿瘤区域最高。此外,用阿霉素治疗癌症组织和正常组织后,杀死的细胞比例分别约为 78.87%和 24.94%。总的来说,所提出的方法可以应用于个性化医学领域,以优化针对特定患者的治疗方案。此外,这种基于图像的实体瘤建模可用于药物输送实验室,在将其用于任何体内或临床研究之前评估新药物。

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