Computational platform for predictive magnetohydrodynamic drug targeting

预测磁流体动力学药物靶向计算平台

• 批准号: 1706921
• 负责人: Andreas Linninger
• 金额: $ 29.8万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1706921&HistoricalAwards=false
• 关键词: Computational; platform; predictive; magnetohydrodynamic; drug

Special blood vessels, for example the blood brain barrier, in the head and spine keep most compounds away from the brain and central nervous system. Certain therapies for brain disorders may be ineffective, simply because the medication cannot reach the brain. To overcome this barrier, this project investigates new methods for the direct administration of therapeutic drugs into the brain. The main strategy is to direct magnetized drugs to specific locations using externally placed magnets, termed magnetic drug targeting to the brain. This research project also seeks to create a computer program for the design, optimization and testing of new therapeutics and delivery techniques. The ability to improve drug delivery methods using computational models potentially benefits all patients suffering from diseases related to the brain and helps pharmaceutical companies meet regulatory demands by enabling them to carefully evaluate potential side effects of new therapeutics. This research project also contributes to the education of a new generation of engineering students. A drug testing and simulation platform built on the University of Illinois at Chicago campus offers research opportunities for undergraduate and high school students and enables virtual experimentation with novel nanomedicine chemistry. In addition, a new graduate course on drug transport within the bioengineering and chemical engineering curricula is being created. Magnetic drug targeting for brain and central nervous system disorders is a promising delivery method for steering and pinpointing therapeutics to desired locations in the central nervous system. However, determining the parameters for optimal biodistribution inside a living organism poses a formidable engineering challenge. There is critical need to predict biodistribution under magnetic guidance. Currently, two main problems prevent computational fluid dynamics analysis from reliably predicting biodistribution in the central nervous system: (1) magnetohydrodynamic forces acting on nanoparticle suspensions have not been incorporated into existing computational fluid dynamics because they have not been characterized; and (2) existing algorithms require impractical simulation times exceeding central processing unit weeks or months because the mesh sizes necessary to represent central nervous system anatomy are massive, and transport equations exhibit both fast and slow dynamic modes. This research project addresses these obstacles in three parts. First, a magneto-hydrodynamic formulation based on concentrated solution theory is being developed to quantify field effects that steer nanoparticles suspended in pulsating cerebrospinal fluid flow. Then, parametric meshing is being implemented to generate smooth, body-fitted computational grids that represent the anatomical domain concisely at low central processing unit cost. Finally, problem decomposition is being performed that divides fluid flow, magnetic field effects and drug transport into three independently solvable sub-problems. The research results are being used to create a computational platform to optimize targeted nanoparticle-drug delivery to the central nervous system.

头部和脊柱的特殊血管，例如血脑屏障，使大多数化合物远离大脑和中枢神经系统。某些治疗脑部疾病的方法可能无效，仅仅是因为药物无法到达大脑。为了克服这一障碍，该项目研究了将治疗药物直接注入大脑的新方法。主要策略是使用外部放置的磁铁将磁化药物定向到特定位置，称为靶向大脑的磁性药物。该研究项目还寻求创建一个计算机程序，用于设计、优化和测试新的治疗方法和输送技术。使用计算模型改进药物输送方法的能力可能会使所有患有与大脑相关疾病的患者受益，并帮助制药公司通过使他们能够仔细评估新疗法的潜在副作用来满足监管要求。这个研究项目也有助于新一代工程学生的教育。伊利诺伊大学芝加哥校区建立了一个药物测试和模拟平台，为本科生和高中生提供了研究机会，并使新型纳米药物化学的虚拟实验成为可能。此外，正在生物工程和化学工程课程中开设一门新的关于药物运输的研究生课程。磁性药物靶向治疗脑和中枢神经系统疾病是一种很有前途的递送方法，用于指导和精确定位治疗在中枢神经系统中的所需位置。然而，确定生物体内最佳生物分布的参数是一项艰巨的工程挑战。目前迫切需要在磁引导下预测生物分布。目前，有两个主要问题阻碍了计算流体动力学分析可靠地预测中枢神经系统中的生物分布：(1)作用于纳米颗粒悬浮液的磁流体动力学力尚未纳入现有的计算流体动力学中，因为它们尚未被表征；(2)由于表示中枢神经系统解剖结构所需的网格尺寸非常大，并且传输方程呈现出快速和缓慢的动态模式，因此现有算法需要超出中央处理单元数周或数月的不切实际的模拟时间。本研究计划分三部分来解决这些障碍。首先，一种基于浓缩溶液理论的磁流体动力学公式正在开发中，用于量化悬浮在脉动脑脊液中的纳米颗粒的场效应。然后，实现参数网格划分，以低中央处理单元成本生成光滑的体拟合计算网格，简洁地表示解剖域。最后对问题进行分解，将流体流动、磁场效应和药物输运分解为三个独立可解的子问题。研究结果被用于创建一个计算平台，以优化靶向纳米颗粒药物向中枢神经系统的输送。

项目成果

An efficient full space-time discretization method for subject-specific hemodynamic simulations of cerebral arterial blood flow with distensible wall mechanics.

一种有效的全时空离散方法，用于利用可扩张壁力学对脑动脉血流进行特定受试者的血流动力学模拟。

• DOI: 10.1016/j.jbiomech.2019.02.014
• 发表时间: 2019
• 期刊: Journal of Biochemistry
• 影响因子: 2.7
• 作者: C Park, A Alaraj

In Vivo Intrathecal Tracer Dispersion in Cynomolgus Monkey Validates Wide Biodistribution Along Neuraxis

• DOI: 10.1109/tbme.2019.2930451
• 发表时间: 2020-04
• 期刊: IEEE Transactions on Biomedical Engineering
• 影响因子: 4.6
• 作者: K. Tangen;I. Nestorov;A. Verma;Jenna M. Sullivan;Robert W Holt;A. Linninger

Quantification of blood flow patterns in the cerebral arterial circulation of individual (human) subjects

• DOI: 10.1002/cnm.3288
• 发表时间: 2019-11
• 期刊: International Journal for Numerical Methods in Biomedical Engineering
• 影响因子: 2.1
• 作者: C. Park;Grant Hartung;A. Alaraj;X. Du;F. Charbel;A. Linninger

G Hartung, C Vesel, R Morley**, A Alaraj, J Sled, D Kleinfeld, A Linninger, Simulations of blood as a suspension predicts a depth dependent hematocrit in the circulation throughout the cerebral cortex, PLoS Computational Biology, 14(11), e1006549, 2018. (

G Hartung、C Vesel、R Morley**、A Alaraj、J Sled、D Kleinfeld、A Linninger，血液悬浮液模拟可预测整个大脑皮层循环中的深度依赖性血细胞比容，公共科学图书馆计算生物学，14(11)

• 发表时间: 2018
• 期刊: PLOS computational biology
• 影响因子: 4.3
• 作者: Grant Hartung, Claudia Vesel

Solvent fluctuations in the solvation shell determine the activation barrier for crystal growth rates

• DOI: 10.1073/pnas.1910691116
• 发表时间: 2019-11-26
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Dighe, Anish V.;Singh, Meenesh R.
• 通讯作者: Singh, Meenesh R.