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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：
10646439
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/10646439
关键词：
Affect Alveolar American Bayesian Analysis Biological Bleomycin Blood Vessels Blood capillaries 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 KDR gene Lesion Logic Lung Machine Learning Mediating Modeling Molecular Mus Myocardium 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 Skeletal Muscle Specific qualifier value Tamoxifen Terminal Disease Time Tissues Transforming Growth Factors Translating Validation Vascular Endothelial Growth Factor Receptor-1 Vascular Endothelial Growth Factors cell behavior experimental study human data idiopathic pulmonary fibrosis kinase inhibitor lung lesion models and simulation mortality mouse model new therapeutic target nintedanib novel novel therapeutic intervention novel therapeutics pre-clinical preclinical study predicting response predictive modeling prevent pulmonary agents pulmonary function pulmonary function decline response 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.

项目摘要在这个国家，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。和终末期疾病所造成的过度细胞外基质生产的肌成纤维细胞在分布整个肺部的病变或“纤维化灶”。尽管有两种FDA批准的药物作为标准治疗，IPF患者的死亡率在4年时超过30%，阻止疾病进展的药物，使IPF的诊断对超过50万美国人来说是死刑和这种疾病一起生活鉴定产生肌成纤维细胞的起源细胞对于发现可以停止或治愈IPF的治疗。基于我们的实验数据和计算模拟，研究小组，我们假设肌成纤维细胞来自微血管周细胞（正常情况下包绕毛细血管），此时异型周细胞-内皮相互作用被破坏。我们进一步重申，周细胞中激酶介导信号的策略性调节可阻止周细胞向肌成纤维细胞的转变并阻止IPF的进展。我们建议采用联合收割机计算建模与实验相结合的方法来研究周细胞向肌成纤维细胞的分化，并研究肺中微血管适应如何有助于在IPF。具体来说，我们将开发一种新的基于代理的模型（ABM），它将基于逻辑的细胞内信号网络来模拟细胞行为，并利用贝叶斯推理进行规则细化（目标1），验证ABM预测周细胞表型转变和纤维化灶出现的能力，使用IPF的鼠博来霉素模型对药物的反应（目的2），以及使用以下药物的桥接鼠实验：临床数据，以预测可药物化激酶驱动的信号通路如何通过以下途径影响IPF进展：调节周细胞和微血管（Aim 3）。据我们所知，我们提出的研究将是第一个将联合收割机计算建模与实验相结合，以研究微血管对IPF进展的贡献。在除了产生一个新的计算模型，该模型经验证可用于桥接临床前研究结果，临床结果，我们期望确定靶向微血管细胞的IPF新治疗方法，以前未被充分研究，但可能是这种致命疾病的关键因素。