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.

PROJECT NARRATIVE Over the past decade, pet dogs with spontaneous disease have been progressively integrated into the preclinical drug and device testing paradigm with the goal of reducing high rates of translational failure that hamper therapeutic advancement. However, precise application and incorporation of data generated from this model system into human health practices remains a significant barrier. Studies outlined in this proposal will leverage two disease settings in pet dogs (cancer and cachexia from heart failure) to develop, optimize, and credential approaches for early detection and intervention, while simultaneously improving and expanding methodologies for effectively connecting information across both species.