Coverage for costs associated with reading of amyloid PET images from PARIS study

家长奖的项目总结 长期以来，人类阿尔茨海默病(AD)的研究一直受到以下事实的阻碍 AD的诊断只能通过尸检后的脑部病理评估来确定。最近， 已经有了可以直接成像AD(淀粉样斑块)两个特征的显像剂 和tau缠结)在活人身上。然而，这些显像剂昂贵且具有侵入性。 常规诊断。对基于血液的低成本诊断化验的需求从未像现在这样强烈。C2N 诊断公司一直在开发基于质谱学的分析方法，以研究 自2007年以来一直是生物流体。样品净化、β-淀粉样蛋白纯化及质谱学研究进展 仪器使可靠地定量人类血浆样本中的淀粉样β蛋白成为可能。 来自华盛顿大学兰德尔·贝特曼博士实验室的初步数据显示， 血浆中的两个淀粉样β多肽可以预测大脑中是否存在淀粉样斑块。这 当在阿尔茨海默氏症上展示时，技术可能会给AD诊断领域带来革命性的变化 在2017年7月的国际协会会议上，这一发现被宣布为“迄今为止最重大的新闻” 阿尔茨海默病临床研究的主要意见领袖之一。这次SBIR Fast的具体目标是- 跟踪应用是从分析和临床上验证C2N血浆检测作为诊断试验用于 预测大脑淀粉样变性。

PROJECT SUMMARY FOR PARENT AWARD For a long time, the study of Alzheimer’s disease (AD) in humans has been hampered by the fact that absolute AD diagnosis could only be ascertained by assessment of brain pathology upon autopsy. More recently, imaging agents have become available that allow direct imaging of the two hallmarks of AD (amyloid plaques and tau tangles) in living humans. However, these imaging agents are expensive and invasive for use in routine diagnostics. The need for low-cost blood based diagnostic assays has never been greater. C2N Diagnostics has been developing mass spectrometry-based assays that investigate amyloid beta peptides in biofluids since 2007. Recent advances in sample cleanup, purification of amyloid beta, and mass spectrometry instrumentation have made it possible to reliably quantify amyloid beta in human plasma samples. Preliminary data from Dr. Randall Bateman’s laboratory at Washington University shows that the ratio of two amyloid beta peptides in plasma can predict the presence of amyloid plaques in the brain. This technology could revolutionize the field of AD diagnostics, and when presented at the Alzheimer’s Association International Conference in July 2017, this discovery was pronounced as “by far the biggest news of the meeting” by one of the key opinion leaders in AD clinical research. The specific aims of this SBIR Fast- Track application are to analytically and clinically validate C2N’s plasma assay for use as a diagnostic test for predicting brain amyloidosis.