Optimizing integration of veterinary clinical research findings with human health systems to improve strategies for early detection and intervention

项目总结 实验诱导的疾病动物模型在疾病的发展、评估和 优化人类疾病的治疗方法。随着基因工程的到来，这样的模型系统 有了很大的改善；然而，对于大多数疾病实体来说，翻译失败率仍然很高。一 有希望的方法包括使用患有自发性疾病的宠物狗来评估治疗策略 癌症、心力衰竭和神经退行性变等疾病，在人体试验之前，目标是改善 临床结果。除了它们固有的生物学相关性，这种模式的翻译优势包括 使用与人类过程平行的诊断和干预措施对个别患者进行纵向评估， 压缩的疾病时间表，允许快速评估治疗效果，并允许自由研究 独特的治疗组合，取代了护理标准。随着人类医学逐渐采用策略 旨在通过早期发现和干预来防止疾病进展，对宠物狗的研究有 有潜力贡献有价值的临床前信息。现在有几个资源支持这类工作，包括 NCI集成犬类数据共享中心、用于多站点研究的SMART IACUC、CTSA One Health Alliance和 显著改进的犬类参考基因组(CanFam4)和相关的关键组学工具。尽管如此 宠物狗产生的数据与人类健康系统的进展、有效匹配和整合 这仍然是一个巨大的挑战。为了开始解决这一差距，我们开发了一个兽医数据模型，该模型 与观察性医疗结果伙伴关系共同数据模型(OMOPv5+CDM)协调一致 为核心研究基础设施生成的工具，包括翻译器(翻译动物共享 合作观察研究)。在当前的应用程序中，我们将在以前工作的基础上， 使用宠物狗开发、验证和优化早期疾病检测工具，同时提供资源 这些研究旨在反复推进改善这种连接和应用的方法 人类健康过程的数据集。为了做到这一点，我们将早期进行液体活组织检查。 检测宠物狗的癌症复发并快速测试预防进展的创新策略；2)验证 超声/Exosome综合诊断早期诊断宠物犬心脏恶病质 制止浪费的新方法；以及3)进一步提高OMOPv5+CDM和相关信息学的效用 实现宠物狗试验翻译潜力的工具。一支融合了人类和兽医的优秀团队 医学、比较基因组学、生物医学工程、研究信息学基础设施和临床前 在一个由人类健康专家组成的咨询小组的支持下，翻译建模将促进成功 完成规定的里程碑。重要的是，拟议的工作与母公司整合并得到其支持 UM1，确保早期疾病检测和干预的科学进步与 改进解释和科学未来使用所必需的基本过程的方法学。

PROJECT SUMMARY Experimentally induced animal models of disease play a critical role in the development, evaluation and optimization of therapeutics for human disease. With the advent of genetic engineering, such model systems have substantially improved; however, translational failure rates remain high for most disease entities. One promising approach involves using pet dogs with spontaneous disease to evaluate treatment strategies for diseases such as cancer, heart failure and neurodegeneration prior to human trials, with the goal of improving clinical outcomes. Beyond their inherent biological relevance, translational advantages of this model include longitudinal assessment of individual patients using diagnostics and interventions that parallel human processes, compressed disease timelines that permit rapid evaluation of therapeutic impact, and the freedom to study unique treatment combinations in lieu of standards of care. As human medicine progressively adopts strategies designed to prevent disease progression through early detection and intervention, studies in pet dogs have the potential to contribute valuable preclinical information. Several resources now support such work including the NCI Integrated Canine Data Commons, SMART IACUC for multi-site studies, the CTSA One Health Alliance and a markedly improved canine reference genome (CanFam4) and associated key omics tools. Despite these advances, effective alignment and integration of data generated from pet dogs with human health systems remains a substantial challenge. To begin addressing this gap, we developed a veterinary data model that is harmonized with the Observational Medical Outcomes Partnership Common Data Model (OMOPv5+ CDM) and generated tools for core research infrastructure including, TRANSLATOR (TRanslational ANimal Shared ColLAboraTive Observational Research). In the current application, we will build upon our prior work and use pet dogs to develop, validate, and optimize tools for early disease detection, and in parallel, resource these studies to iteratively advance methodologies for improving connectivity and application of such data sets to human health processes. To accomplish this, we will 1) credential a liquid biopsy assay for early detection of cancer relapse in pet dogs and rapidly test innovative strategies to prevent progression; 2) validate an integrated ultrasound/exosome diagnostic for early detection of cardiac cachexia in pet dogs and assess novel approaches to halt wasting; and 3) further enhance the utility of our OMOPv5+ CDM and related informatics tools to realize the translational potential of pet dog trials. An outstanding team blending human and veterinary medicine, comparative genomics, biomedical engineering, research informatics infrastructure and preclinical translational modeling, supported by an advisory panel of human health experts, will facilitate successful completion of stated milestones. Importantly, the proposed work integrates with and is supported by the parent UM1, ensuring that scientific advancements for early disease detection and intervention co-evolve with methodologies that improve fundamental processes necessary for interpretation and future utility of the science.