Repurposing FDA-approved drugs is an efficient and cost-effective approach in the development of therapeutics for a broad range of diseases. However, prediction of function can be challenging, especially in the brain. We screened a small-molecule library with FDA-approved drugs for effects on behavior. The studies were carried out using zebrafish larvae, imaged in a 384-well format. We found that various drugs affect activity, habituation, startle responses, excitability, and optomotor responses. The changes in behavior were organized in behavioral profiles, which were examined by hierarchical cluster analysis. One of the identified clusters includes the calcineurin inhibitors cyclosporine (CsA) and tacrolimus (FK506), which are immunosuppressants and potential therapeutics in the prevention of Alzheimer’s disease. The calcineurin inhibitors form a functional cluster with seemingly unrelated drugs, including bromocriptine, tetrabenazine, rosiglitazone, nebivolol, sorafenib, cabozantinib, tamoxifen, meclizine, and salmeterol. We propose that drugs with ‘CsA-type’ behavioral profiles are promising candidates for the prevention and treatment of Alzheimer’s disease.

Brain function studies greatly depend on quantification and analysis of behavior. While behavior can be imaged efficiently, the quantification of specific aspects of behavior is labor-intensive and may introduce individual biases. Recent advances in deep learning and artificial intelligence-based tools have made it possible to precisely track individual features of freely moving animals in diverse environments without any markers. In the current study, we developed Zebrafish Larvae Position Tracker (Z-LaP Tracker), a modification of the markerless position estimation software DeepLabCut, to quantify zebrafish larval behavior in a high-throughput 384-well setting. We utilized the high-contrast features of our model animal, zebrafish larvae, including the eyes and the yolk for our behavioral analysis. Using this experimental setup, we quantified relevant behaviors with similar accuracy to the analysis performed by humans. The changes in behavior were organized in behavioral profiles, which were examined by K-means and hierarchical cluster analysis. Calcineurin inhibitors exhibited a distinct behavioral profile characterized by increased activity, acoustic hyperexcitability, reduced visually guided behaviors, and reduced habituation to acoustic stimuli. The developed methodologies were used to identify ‘CsA-type’ drugs that might be promising candidates for the prevention and treatment of neurological disorders.

The analysis of mouse behavior is used in biomedical research to study brain function in health and disease. Well-established rapid assays allow for high-throughput analyses of behavior but have several drawbacks, including measurements of daytime behaviors in a nocturnal animal, effects of animal handling, and the lack of an acclimation period in the testing apparatus. We developed a novel 8-cage imaging system, with animated visual stimuli, for automated analyses of mouse behavior in 22-hour overnight recordings. Software for image analysis was developed in two open-source programs, ImageJ and DeepLabCut. The imaging system was used to measure multiple behaviors, including acclimation to the novel cage environment, day and nighttime activity, stretch-attend postures, location in various cage areas, and habituation to animated visual stimuli. These behaviors were summarized in behavioral profiles, which may be used in further studies to examine treatments for neural disorders.