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电化学驱动的生物电子学中可编程药物输送的建模。

Modeling programmable drug delivery in bioelectronics with electrochemical actuation.

机构信息

Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208.

Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208.

出版信息

Proc Natl Acad Sci U S A. 2021 Mar 16;118(11). doi: 10.1073/pnas.2026405118.

Abstract

Drug delivery systems featuring electrochemical actuation represent an emerging class of biomedical technology with programmable volume/flowrate capabilities for localized delivery. Recent work establishes applications in neuroscience experiments involving small animals in the context of pharmacological response. However, for programmable delivery, the available flowrate control and delivery time models fail to consider key variables of the drug delivery system--microfluidic resistance and membrane stiffness. Here we establish an analytical model that accounts for the missing variables and provides a scalable understanding of each variable influence in the physics of delivery process (i.e., maximum flowrate, delivery time). This analytical model accounts for the key parameters--initial environmental pressure, initial volume, microfluidic resistance, flexible membrane, current, and temperature--to control the delivery and bypasses numerical simulations allowing faster system optimization for different in vivo experiments. We show that the delivery process is controlled by three nondimensional parameters, and the volume/flowrate results from the proposed analytical model agree with the numerical results and experiments. These results have relevance to the many emerging applications of programmable delivery in clinical studies within the neuroscience and broader biomedical communities.

摘要

电化学驱动的药物输送系统是一类新兴的生物医学技术,具有可编程的体积/流量能力,可用于局部输送。最近的工作在涉及小动物的神经科学实验中建立了药理学反应方面的应用。然而,对于可编程输送,现有的流量控制和输送时间模型未能考虑药物输送系统的关键变量——微流阻力和膜硬度。在这里,我们建立了一个分析模型,该模型考虑了缺失的变量,并提供了对输送过程物理中每个变量影响的可扩展理解(即最大流量、输送时间)。该分析模型考虑了关键参数——初始环境压力、初始体积、微流阻力、柔性膜、电流和温度——以控制输送并绕过数值模拟,从而为不同的体内实验更快地优化系统。我们表明,输送过程由三个无量纲参数控制,并且所提出的分析模型的体积/流量结果与数值结果和实验结果一致。这些结果与神经科学和更广泛的生物医学领域中许多新兴的可编程输送临床应用相关。

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