Stochastic Modeling of Tissue Injury, Edema and Targeted Drug Delivery

组织损伤、水肿和靶向药物输送的随机模型

• 批准号: 9901734
• 负责人: ARIF MASUD
• 金额: $ 19万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901734
• 关键词: Accounting; Acute; Adhesions; Arteries; Biocompatible Materials; Biological Availability; Biology; Blood Vessels; Blood flow; Calibration; Clinical; Collaborations; Coupled; Coupling; Data; Development; Diffuse; Dimensions; Dose; Drug Carriers; Drug Delivery Systems; Drug Experimentation; Drug Targeting; Drug Transport; Edema; Engineering; Ensure; Equation; Formulation; Geometry; Goals; Guidelines; Health; Image; In Vitro; Injury; Interphase; Ischemia; Kinetics; Laboratories; Lead; Limb structure; Mathematical Biology; Mathematics; Measurement; Medicine; Methods; Microfluidics; Modeling; Movement; Nature; Navier–Stokes equations; Outcomes Research; Particle Size; Patient Care; Pharmaceutical Preparations; Play; Principal Investigator; Property; Risk; Role; Shapes; Source; Statistical Data Interpretation; Stochastic Processes; System; Technology; Tissue Model; Tissues; Training Activity; Uncertainty; Validation; Variant; base; biological systems; computer code; design; drug efficacy; experimental study; improved; in vitro testing; in vivo; injured; innovation; mathematical model; migration; mouse model; nanosized; next generation; novel; particle; programs; simulation; spatiotemporal; stem; success; targeted delivery; viscoelasticity

Program Director/Principal Investigator (Last, First, Middle): Masud, Arif Targeted drug delivery using a nano-sized carrier is a multi-faceted problem that aims at achieving maximum efficacy with minimum dose of medicine. This problem gets compounded in biological systems due to their inherent uncertainty and spatiotemporal inhomogeneity. The sources of uncertainty are both aleatory and epistemic, stemming from natural variability, information uncertainty, and modeling approximations at multiple levels. Information uncertainty arises from sparse and imprecise data on hydrodynamic effects and drug transport via blood flow, propensity of the targeted tissue to absorb the drug, clinical measurement and imaging data .,processing errors, and qualitative information. Model uncertainty arises due to unknown model parameters, model form assumptions, and solution approximation errors. Unlike deterministic analysis typically employed in engineered systems, modeling and analysis methods for biological systems need to be grounded in stochastic methods and associated robust numerical formulations. These models can then be employed to carry out simulation-based statistical analysis of the effect of the various combinations of the modeled parameters thereby establishing risk informed decision guidelines. We hypothesize that size and shape of drug carriers play a significant role in increasing the number of drug carriers reaching targeted, injured tissue and, in turn, drugs available for treatments. A mathematical framework for stochastic models and associated computer code will be developed to simulate an ischemic vascular injury and subsequent edema in perivascular tissue and optimize geometry of drug carriers with minimal trials-and-error. We will examine the hypothesis by validating the developed mathematical model with drug carriers both in vitro and in vivo with a two-pronged approach. We will develop a variational framework for coupling stochastic PDEs for drug delivery and reduce dimensionality of the stochastic system via a novel fine-scale modeling concept. The mathematical framework will be validated via experimentation of drug carrier transport to targeted tissue using in vitro microfluidic and in vivo mouse models of the acute limb ischemia. The in vivo mouse model will generate data for the development of the mathematical model and for its calibration and validation. The new method and the computer codes will be applied to optimize adhesion and transendothelial migration of drug carriers to a target vascular wall, accounting for optimal particle size and shape. These studies will be of direct relevance to improving quality of patient care and health.

项目总监/首席研究员（最后、第一、中间）：Masud、Arif 使用纳米尺寸载体进行靶向药物输送是一个多方面的问题，旨在实现 以最小的药物剂量获得最大的疗效。这个问题在生物系统中变得更加复杂 由于其固有的不确定性和时空的不均匀性。不确定性的来源有两个 偶然性和认知性，源于自然可变性、信息不确定性和建模 多个级别的近似值。信息的不确定性源于稀疏且不精确的数据 流体动力学效应和通过血流的药物转运、目标组织吸收药物的倾向 药物、临床测量和成像数据、处理误差和定性信息。模型 由于未知的模型参数、模型形式假设和解近似而产生不确定性 错误。与工程系统中通常采用的确定性分析不同，建模和分析 生物系统方法需要以随机方法和相关的稳健方法为基础 数值公式。然后可以使用这些模型来进行基于模拟的统计 分析建模参数的各种组合的影响，从而确定风险 知情的决策指南。 我们假设药物载体的大小和形状在增加药物数量方面发挥着重要作用。 药物载体到达目标受损组织，进而提供可用于治疗的药物。一个数学 将开发随机模型框架和相关计算机代码来模拟缺血 血管损伤和随后的血管周围组织水肿，并优化药物载体的几何形状 最少的试验和错误。我们将通过验证开发的数学模型来检验假设 药物载体在体外和体内双管齐下。我们将开发一个变体 用于耦合随机偏微分方程以进行药物输送并降低随机变量维数的框架 通过新颖的精细建模概念来构建系统。数学框架将通过以下方式进行验证 使用体外微流体和体内小鼠进行药物载体转运至靶组织的实验 急性肢体缺血模型。体内小鼠模型将生成用于开发的数据 数学模型及其校准和验证。新方法和计算机代码将 用于优化药物载体对目标血管壁的粘附和跨内皮迁移， 考虑最佳颗粒尺寸和形状。这些研究将与提高质量直接相关 患者护理和健康。