Modeling to Design Treatments for Idiopathic Lung Fibrosis

特发性肺纤维化治疗设计的建模

基本信息

批准号：
10305193
负责人：
Thomas Harrison Barker
金额：
$ 54.82万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10305193
关键词：
Affect Alveolar American Bayesian Analysis Biological Bleomycin Blood capillaries Cardiac Cell Communication Cells Cessation of life Cicatrix Clinic Clinical Clinical Data Clinical Trials Computer Models Computer Simulation Country Data Deposition Development Diagnosis Disease Disease Progression Drug Combinations Drug Targeting Drug usage Endothelial Cells Endothelium Environment Extracellular Matrix FDA approved Fibroblasts Fibrosis Harvest Heterogeneity Histology Human Impairment Lesion Logic Lung Machine Learning Mediating Modeling Molecular Mus Myofibroblast Nature New Agents Outcome Output Patients Pericytes Pharmaceutical Preparations Phenotype Phosphotransferases Pirfenidone Platelet-Derived Growth Factor Platelet-Derived Growth Factor Receptor Process Production Progressive Disease Publishing Pulmonary Fibrosis Research Retina Signal Pathway Signal Transduction Tamoxifen Terminal Disease Time Tissues To specify Transforming Growth Factors Translating Validation Vascular Endothelial Growth Factor Receptor-1 Vascular Endothelial Growth Factors base cell behavior experimental study human data idiopathic pulmonary fibrosis kinase inhibitor models and simulation mortality mouse model new therapeutic target novel novel drug combination novel therapeutic intervention novel therapeutics pre-clinical preclinical study predicting response predictive modeling prevent pulmonary agents pulmonary function response skeletal standard of care therapy design transcriptome sequencing transcriptomics

项目摘要

PROJECT SUMMARY Every year in this country 40,000 patients are diagnosed with idiopathic pulmonary fibrosis (IPF), a progressive and terminal disease caused by excessive extracellular matrix production by myofibroblasts in distributed lesions, or “fibrotic foci”, throughout the lung. Despite the availability of two FDA-approved drugs that are considered standard of care, the mortality rate for IPF patients exceeds 30% at four years, and there are no drugs that halt disease progression, making diagnosis with IPF a death sentence for over 500,000 Americans living with this disease. Identifying the cells of origin that give rise to myofibroblasts is necessary for finding treatments that can halt or cure IPF. Based on experimental data and computational simulations from our research team, we hypothesize that myofibroblasts arise from microvascular pericytes (cells that normally enwrap capillaries) when heterotypic pericyte-endothelial interactions become disrupted. We further posit that strategic modulation of kinase-mediated signaling in pericytes can prevent pericyte-to-myofibroblast transitions and halt the progression of IPF. We propose to combine computational modeling with experiments to study pericyte-to-myofibroblast differentiation and to investigate how microvessel adaptations in the lung contribute to IPF. Specifically, we will develop a new agent-based model (ABM) that incorporates logic-based intracellular signaling networks to simulate cell behaviors and leverages Bayesian inference for rule refinement (Aim 1), validate the ABM's ability to predict pericyte phenotype transitions and the emergence of fibrotic foci in response to drugs using the murine bleomycin model of IPF (Aim 2), and bridge murine experiments with clinical data in order to predict how druggable kinase-driven signaling pathways affect IPF progression via modulation of pericytes and microvessels (Aim 3). To our knowledge, our proposed studies will be the first to combine computational modeling with experiments to study microvascular contributors to IPF progression. In addition to producing a new computational model that is validated for bridging pre-clinical study results to clinical outcomes, we expect to identify new therapeutic approaches for IPF that target microvascular cells, previously underexplored but potentially critical contributors to this deadly disease.

项目概要该国每年有 40,000 名患者被诊断患有特发性肺纤维化 (IPF)，这是一种进行性肺纤维化分布于各处的肌成纤维细胞产生过多的细胞外基质而引起的终末期疾病尽管有两种 FDA 批准的药物可用于治疗整个肺部的病变或“纤维化病灶”。考虑到护理标准，IPF 患者四年后的死亡率超过 30%，并且没有阻止疾病进展，使超过 500,000 美国人的 IPF 药物诊断等于死刑患有这种疾病的人必须识别产生肌成纤维细胞的起源细胞。可以阻止或治愈 IPF 的治疗方法基于我们的实验数据和计算模拟。研究小组，我们认为肌成纤维细胞起源于微血管周细胞（通常是当异型周细胞-内皮相互作用被破坏时，包裹毛细血管）。周细胞中激酶介导的信号传导的策略调节可以阻止周细胞向肌成纤维细胞的转变我们建议将计算模型与实验相结合来研究。周细胞向肌成纤维细胞的分化，并研究肺部微血管适应如何发挥作用具体来说，我们将开发一种新的基于代理的模型（ABM），其中结合了基于逻辑的细胞内模型。信号网络来模拟细胞行为并利用贝叶斯推理来细化规则（目标 1），验证 ABM 预测周细胞表型转变和纤维化病灶出现的能力使用 IPF 小鼠博来霉素模型对药物的反应（目标 2），并将小鼠实验与临床数据，以预测可药物激酶驱动的信号通路如何影响 IPF 进展据我们所知，我们提出的研究将是第一个对周细胞和微血管进行调节的研究。将计算模型与实验相结合，研究 IPF 进展的微血管因素。除了产生一个新的计算模型之外，该模型经过验证可以将临床前研究结果与临床结果，我们期望找到针对微血管细胞的 IPF 新治疗方法，以前尚未充分研究但可能是这种致命疾病的关键因素。