Capturing dynamic and inter-dependent biointerfaces in nanotechnology designs

描述（由申请人提供）：纳米颗粒系统（NP）可用于提供诊断和治疗，包括小分子和大分子，基因载体和生物传感器。由于NP是通用的，可以由不同类型的材料制成，并且可以具有不同的尺寸，表面电荷和表面修饰，因此有可能根据其预期功能定制NP的设计。这些目标可以通过预测NP递送到靶点和生物界面（例如，NP处置和与靶点的相互作用）的定量模型大大促进。一般来说，肿瘤特性，本质上是生物学的，是动态的，并被各种变量改变，可以对NP处置产生不同的，有时是意想不到的影响。这些情况反过来又对目标地点的NP命运产生了不确定性，因此对NP设计提出了问题。例如，如何设计NP来预测肿瘤不同部位的传输机制（扩散vs对流）的肿瘤内异质性，或治疗引起的肿瘤脉管系统或性质的变化？如果NP设计/选择没有考虑到肿瘤的多样性/动态特性，误差范围是多少？同样，一些NP属性的设计也会产生不确定或相反的结果。例如，NP经常被靶向配体表面修饰，但配体与细胞表面受体的结合限制了NP的运输。在肿瘤选择性和肿瘤穿透性之间产生最佳平衡的结合特性是什么？聚乙二醇化增加循环时间，但也减少NP的内吞作用。聚乙二醇化的范围是多少才能实现最佳的肿瘤靶向？我们建议，上述和类似的问题可以通过开发计算模型来解决，这些模型使用相对较少的体外和体内实验数据来描述NP在实体肿瘤中的外渗、间质沉积和运输，以及作为NP/肿瘤特性和生物界面以及治疗计划（剂量强度和频率）的功能。我们将采用平衡的经验理论方法，利用我们在药代动力学，药物/NP递送，建模，模拟，肿瘤异质性以及体外和体内实验方面的综合专业知识。模型参数要么是实验室生成的，从文献中获得，使用众所周知的方程计算，或者，在无法测量的参数的情况下，通过将数据拟合到方程中。通过进行实验并将实验室生成的数据与模型预测的数据进行比较来评估模型的性能。我们已经开发了第一代模型，成功地使用了药物/NP-细胞-蛋白质相互作用在二维单层中的体外数据来预测小分子药物和NP到肿瘤的体内运输/递送。我们进一步使用这些模型，连同体内血管密度和直径的测量，来模拟化疗的效果，以及肿瘤内异质性的影响。该项目有望为NP设计原则做出贡献，并加速癌症纳米技术的发展。

DESCRIPTION (provided by applicant): Nanoparticle systems (NP) can be used to deliver diagnostics and therapeutics including small and large molecules, gene vectors, and biosensor. As NP is versatile and can be made of different types of materials, and can have different sizes, surface charges, and surface modifications, there is the potential to tailor the design of NP for its intended function. Such goals can be greatly facilitated by quantitative models that predict the NP delivery to target sites and the biointerfaces (e.g., NP disposition and interactions with targets). In general, tumor properties, biological in nature, are dynamic and altered by a variety of variables and can produce diverse and at times unexpected effects on NP disposition. These situations in turn create uncertainties on the fate of NP at target sites and hence questions on the NP design. For example, how should one design NP in anticipation of intratumoral heterogeneity in the transport mechanisms (diffusion vs convection) in different parts of a tumor, or treatment-induced changes in tumor vasculature or properties? What are the margins of error if the NP design/selection does not take into account the diverse/dynamic tumor properties? Similarly, some NP properties by design will produce uncertain or opposite outcomes. For example, NP is frequently surface-modified with targeting ligands, but binding of ligands to cell surface receptors limits NP transport. What are the binding characteristics that would yield an optimal balance between tumor selectivity and tumor penetration? Pegylation increases circulation times but also decreases the endocytosis of NP. What is the range of % pegylation to enable optimal tumor targeting? We propose that the above and similar questions can be addressed by developing computation models that use relatively few in vitro and in vivo experimental data to describe the extravasation, interstitial deposition and transport, and internalization of NP in solid tumors as functions of NP/tumor properties and biointerfaces, and treatment schedules (dose intensity and frequency). We will take a balanced empirical-theoretical approach that uses our combined expertise in pharmacokinetics, drug/NP delivery, modeling, simulations, tumor heterogeneity, and in vitro and in vivo experimentations. The model parameters are either lab-generated, obtained from the literature, calculated using well-known equations, or, in the case of parameters that cannot be measured, by fitting the data to equations. Model performance is evaluated by conducting experiments and comparing the lab-generated data to the model-predicted data. We have developed first-generation models that successfully used in vitro data of drug/NP-cell-protein interactions in 2-D monolayers to predict the in vivo transport/delivery of a small molecule drug and NP to tumors. We further used these models, together with in vivo measurements of vessel density and diameter, to simulate the effect of chemotherapy, as well as the effects of intra-tumoral heterogeneity. This project is expected to contribute to NP design principles and accelerate the development of cancer nanotechnology.