Precision Mass Spec Imaging Based Structure-Function Signatures of Diabetic Glomerulopathy

基于精密质谱成像的糖尿病肾小球病的结构功能特征

• 批准号: 10596057
• 负责人: Leila Hejazi
• 金额: $ 0.65万
• 依托单位: SYGNAMAP, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-08 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10596057
• 关键词: Address; Adopted; Biological Markers; Biopsy; Biotechnology; Coupled; Data; Diabetic Nephropathy; Dialysis procedure; Disease; End stage renal failure; Image; Kidney; Kidney Diseases; Life; Link; Medicare; Pathology; Pathway interactions; Patients; Phase; Pre-Clinical Model; Precision therapeutics; Process; Renal glomerular disease; Reproducibility; Small Business Innovation Research Grant; Structure; System; Technology; Therapeutic; Tissues; United States; base; computational platform; cost; diabetic; drug development; insight; kidney biopsy; novel therapeutics; programs

Project Summary/Abstract There are currently >450,000 patients on dialysis and ~120,000 patients will have to begin dialysis each year in the United States. Along with a marked reduction in quality of patient life, the cost to the US Medicare system is in excess of $114B per year. Therefore, there is an urgent need for developing new platforms that will enhance the drug development process for new therapies to reduce the rate of progression to end-stage kidney disease. Non-invasive biomarkers may be useful but do not directly identify the pathways linked to pathology in the kidney. An approach that integrates tissue structure with functional readouts in a kidney biopsy would be a major advance. We are developing a computational platform that leverages mass spec imaging data coupled with computational pathology for kidney tissue as a technology that will address this unmet need. With our computational platform we can identify signatures linked to normal and abnormal pathology on the same tissue section. This is a powerful approach to understand kidney pathology and will be of great value for drug development for kidney disease. Parameters related to reproducibility across pre-clinical models of diabetic kidney disease interpretation will be optimized during Phase I. Upon completion of the SBIR Phase I project we will have rigorous data to determine consistency of diseased glomerular signatures in diabetic nephropathy. This proof of concept data will make it attractive to pharma and biotech to adopt this platform for application for their therapeutic programs for diabetic kidney disease.

项目摘要/摘要 目前有450,000名透析患者，约有120,000名患者必须开始透析 每年在美国。随着患者生活质量的明显降低，成本 美国医疗保险系统每年超过$ 114B。因此，迫切需要 开发新平台，以增强新疗法的药物开发过程 降低进展到末期肾脏疾病的速度。非侵入性生物标志物可能是 有用但不要直接识别与肾脏病理学相关的途径。一种方法 将组织结构与肾脏活检中的功能读数相结合将是一个重大进展。 我们正在开发一个计算平台，该平台利用质量成像数据和 肾脏组织的计算病理学是一种解决这种未满足需求的技术。和 我们的计算平台我们可以确定与正常和异常病理相关的签名 在同一组织部分。这是一种了解肾脏病理学的强大方法，并将 对于肾脏疾病的药物开发非常有价值。与可重复性有关的参数 在糖尿病肾脏疾病的临床前模型中，将在阶段进行优化 I. SBIR I期项目完成后，我们将拥有严格的数据来确定 糖尿病肾病中患病的肾小球特征的一致性。此概念数据证明 将使Pharma和Biotech具有吸引力，以采用此平台以申请其 糖尿病肾脏疾病的治疗程序。