Automation of Assay Endpoints for Brain Slice Models of Neurodegenerative Disease

Duke University, Durham NC

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Abstract

DESCRIPTION (provided by applicant): For new drug and drug target discovery in neurological and neuropsychiatric disorders, transitioning from efficacy in cell-based assays to benefit in whole-animal models has always been difficult and uncertain. Ideally, drug discovery studies would be conducted, as much as possible, in whole-animal models of disease, but in vivo animal experiments are tremendously costly and time- consuming. Conversely, while cell line and primary culture-based assays are rapid and inexpensive, they are compromised by phenotypic changes when neurons are cultured and/or immortalized, and, importantly, by the normal 3-dimensional milieu and local inter-cellular interactions of brain tissue architecture being unavoidably lost. To help bridge this gap between cell-based and whole-animal efficacy studies, we have developed a series of intact brain tissue-based models for CNS disorders including stroke, Huntington's disease (HD), and Alzheimer's disease (AD). In our published studies, we have shown the utility of such assays in advancing a range of gene target identification and drug development programs. To support the use of these brain slice explant models in the context of larger-scale discovery efforts, we have developed numerous technological and process innovations over the last decade, including high-throughput brain slicing and biolistic gene gun devices for transfection of brain slices with disease-relevant genes and assay reporter constructs. The overarching goal of the present proposal is to solve the final rate-limiting barrier to full scalability of this approach, namely, the automation of brain slice-based assay endpoints. To date, all of the brain slice disease models we have developed have been analyzed using laborious manual endpoint assays; nevertheless, assay throughput has been sufficient to support the screening of hundreds to thousands of compounds or gene targets per year even with a modest-sized scientific team. Implementation of turnkey unbiased, automated microscopy and high-content analysis (HCA) platforms would increase the throughput of these assays by 10-fold or more, and enable full support of large-scale systems biology, bioinformatics, and drug discovery programs. Such a bridging stage of high-throughput biology screening between cell-based and whole-animal models should significantly increase the likelihood of success of drug and drug target discovery and development programs, by providing a preview of both efficacy as well as potential adverse off-target effects in intact neurl tissue assays before substantial time and financial commitments are made to full in vivo efficacy studies.