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

项目摘要 实验诱导的疾病动物模型在疾病的开发、评估和治疗中起着关键作用。 优化人类疾病的治疗方法。随着基因工程的出现，这种模型系统 已经得到了实质性的改善;然而，对于大多数疾病实体，翻译失败率仍然很高。一 有前途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疾病，目的是改善 临床结果。除了其固有的生物学相关性之外，该模型的翻译优势包括 使用与人类过程平行的诊断和干预对个体患者进行纵向评估， 压缩的疾病时间表，允许快速评估治疗效果，以及研究的自由 独特的治疗组合代替标准护理。随着人类医学逐渐采用 旨在通过早期发现和干预来预防疾病进展，对宠物狗的研究 潜在贡献有价值的临床前信息。现在有几个资源支持这项工作，包括 NCI综合犬数据共享，SMART IACUC用于多中心研究，CTSA One Health Alliance和 显著改进的犬参考基因组（CanFam 4）和相关的关键组学工具。尽管有这些 宠物狗产生的数据与人类健康系统的有效协调和整合 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， 与观察性医学结局伙伴关系通用数据模型（OMOPv 5 + CDM）协调一致， 为核心研究基础设施生成的工具，包括TRANSLATOR（transanslational ANTRONIC Shared 观察研究）。在当前的应用程序中，我们将建立在我们以前的工作基础上， 利用宠物狗开发、验证和优化早期疾病检测工具，同时， 这些研究迭代地推进方法，以改善这种连接和应用， 人类健康过程的数据集。为了实现这一点，我们将1）证明液体活检检测早期 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 用于早期检测宠物犬心脏恶病质的集成超声/外泌体诊断并评估 制止浪费的新方法; 3）进一步提高我们的OMOPv 5 + CDM和相关信息学的实用性 实现宠物狗试验转化潜力的工具。一个优秀的团队融合人类和兽医 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 由人类健康专家组成的咨询小组支持的转化模型将促进成功的 完成规定的里程碑。重要的是，拟议的工作与父母整合并得到父母的支持 UM 1，确保早期疾病检测和干预的科学进步与 改进解释和未来科学效用所需的基本过程的方法。

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.