Why do so many promising animal studies fail to translate to successful treatments for humans? Our lab uses biomedical informatics to identify large scale patterns of animal model use. This approach reveals systemic biases in the utilization of animal models that contribute to the translational gap. In particular, we are interested in developing text-mining programs and databases that can correlate interventional outcomes across human and animal species by intervention, drug class and choice of outcome measure. We focus on chronic and complex diseases, in particular neurodegenerative disease. Our text-mining approaches in Parkinson's disease (Menagerie) are being extended to accommodate Alzheimer's disease. This work, as well as neuropathologic assessment of aging macaque brains according to NIA-AA guidelines occurs in collaboration with the Alzheimer's Disease Research Center at Yale.
More recent COVID work employs principles of translational study design to determine when COVID will reach endemic status, and the other to assess the impact of concurrent influenza and SARS-CoV-2 infection in hamsters.
Specialized Terms: Biomedical informatics; Efficacy; Translational gap; Neurodegeneration; Neurobiology of movement disorders; Phenotypes of genetically engineered mice; Evolutionary biology of the eye; Veterinary Pathology; Mouse Phenotyping; Ocular pathology; Neuroanatomy of age-related gait disorders;
Animal Diseases; Neuroanatomy; Neurobiology; Ophthalmology; Pathologic Processes; Primates; Neurodegenerative Diseases; Informatics; Translational Research, Biomedical