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首页> 美国卫生研究院文献>Frontiers in Neurology >Improving the Predictions of Computational Models of Convection-Enhanced Drug Delivery by Accounting for Diffusion Non-gaussianity
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Improving the Predictions of Computational Models of Convection-Enhanced Drug Delivery by Accounting for Diffusion Non-gaussianity

机译：通过考虑扩散非高斯性来改善对流增强药物传递的计算模型的预测

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Convection-enhanced delivery (CED) is an innovative method of drug delivery to the human brain, that bypasses the blood-brain barrier by injecting the drug directly into the brain. CED aims to target pathological tissue for central nervous system conditions such as Parkinson's and Huntington's disease, epilepsy, brain tumors, and ischemic stroke. Computational fluid dynamics models have been constructed to predict the drug distribution in CED, allowing clinicians advance planning of the procedure. These models require patient-specific information about the microstructure of the brain tissue, which can be collected non-invasively using magnetic resonance imaging (MRI) pre-infusion. Existing models employ the diffusion tensor, which represents Gaussian diffusion in brain tissue, to provide predictions for the drug concentration. However, those predictions are not always in agreement with experimental observations. In this work we present a novel computational fluid dynamics model for CED that does not use the diffusion tensor, but rather the diffusion probability that is experimentally measured through diffusion MRI, at an individual-participant level. Our model takes into account effects of the brain microstructure on the motion of drug molecules not taken into account in previous approaches, namely the restriction and hindrance that those molecules experience when moving in the brain tissue, and can improve the drug concentration predictions. The duration of the associated MRI protocol is 19 min, and therefore feasible for clinical populations. We first prove theoretically that the two models predict different drug distributions. Then, using in vivo high-resolution diffusion MRI data from a healthy participant, we derive and compare predictions using both models, in order to identify the impact of including the effects of restriction and hindrance. Including those effects results in different drug distributions, and the observed differences exhibit statistically significant correlations with measures of diffusion non-Gaussianity in brain tissue. The differences are more pronounced for infusion in white-matter areas of the brain. Using experimental results from the literature along with our simulation results, we show that the inclusion of the effects of diffusion non-Gaussianity in models of CED is necessary, if reliable predictions that can be used in the clinic are to be generated by CED models.
机译：对流增强输送（CED）是一种创新的向人脑输送药物的方法，它通过将药物直接注入大脑来绕过血脑屏障。 CED的目标是针对帕金森氏病和亨廷顿氏病，癫痫，脑瘤和缺血性中风等中枢神经系统疾病的病理组织。已经建立了计算流体动力学模型来预测CED中的药物分布，从而使临床医生可以预先计划该程序。这些模型需要有关脑组织微结构的患者特定信息，可以使用磁共振成像（MRI）预输注以非侵入方式收集这些信息。现有模型采用扩散张量来表示药物浓度，该张量表示脑组织中的高斯扩散。但是，这些预测并不总是与实验观察一致。在这项工作中，我们提出了一种针对CED的新型计算流体动力学模型，该模型不使用扩散张量，而是使用通过扩散MRI实验性地在个体参与者水平上测量的扩散概率。我们的模型考虑到了大脑微观结构对药物分子运动的影响，而先前方法并未考虑这些分子，即这些分子在脑组织中运动时受到的限制和阻碍，并且可以改善药物浓度的预测。相关MRI协议的持续时间为19分钟，因此对于临床人群是可行的。我们首先从理论上证明这两个模型可预测不同的药物分布。然后，使用来自健康参与者的体内高分辨率扩散MRI数据，我们导出并比较了使用这两种模型的预测，以便确定包括限制和阻碍作用在内的影响。包括这些影响会导致药物分布不同，并且观察到的差异与脑组织中扩散非高斯性的度量具有统计学上的显着相关性。对于脑白质区域的输注，差异更为明显。利用文献中的实验结果以及我们的模拟结果，我们表明，如果要通过CED模型生成可用于临床的可靠预测，则必须在CED模型中包括扩散非高斯效应。

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