Novel use of FDA-approved drugs identified by cluster analysis of behavioral profiles

Combining supervised and unsupervised analyses to quantify behavioral phenotypes and validate therapeutic efficacy in a triple transgenic mouse model of Alzheimer's disease

Behavioral testing is an essential tool for evaluating cognitive function and dysfunction in preclinical research models. This is of special importance in the study of neurological disorders such as Alzheimer's disease. However, the reproducibility of classic behavioral assays is frequently compromised by interstudy variation, leading to ambiguous conclusions about the behavioral markers characterizing the disease. Here, we identify age- and genotype-driven differences between 3xTg-AD and non-transgenic control mice using a low-cost, highly customizable behavioral assay that requires little human intervention. Through behavioral phenotyping combining both supervised and unsupervised behavioral classification methods, we are able to validate the preventative effects of the immunosuppressant cyclosporine A in a rodent model of Alzheimer's disease, as well as the partially ameliorating effects of candidate drugs nebivolol and cabozantinib.

Behavioral effects of visual stimuli in adult zebrafish using a novel eight-tank imaging system

Introduction

Animals respond to various environmental cues. Animal behavior is complex, and behavior analysis can greatly help to understand brain function. Most of the available behavioral imaging setups are expensive, provide limited options for customization, and allow for behavioral imaging of one animal at a time.

Methods

The current study takes advantage of adult zebrafish as a model organism to study behavior in a novel behavioral setup allowing one to concurrently image 8 adult zebrafish.

Results

Our results indicate that adult zebrafish show a unique behavioral profile in response to visual stimuli such as moving lines. In the presence of moving lines, the fish spent more time exploring the tank and spent more time toward the edges of the tanks. In addition, the fish moved and oriented themselves against the direction of the moving lines, indicating a negative optomotor response (OMR). With repeated exposure to moving lines, we observed a reduced optomotor response in adult zebrafish.

Discussion

Our behavioral setup is relatively inexpensive, provides flexibility in the presentation of various animated visual stimuli, and offers improved throughput for analyzing behavior in adult zebrafish. This behavioral setup shows promising potential to quantify various behavioral measures and opens new avenues to understand complex behaviors.

Drug repurposing for neurodegenerative diseases using Zebrafish behavioral profiles

Drug repurposing can accelerate drug development while reducing the cost and risk of toxicity typically associated with de novo drug design. Several disorders lacking pharmacological solutions and exhibiting poor results in clinical trials - such as Alzheimer's disease (AD) - could benefit from a cost-effective approach to finding new therapeutics. We previously developed a neural network model, Z-LaP Tracker, capable of quantifying behaviors in zebrafish larvae relevant to cognitive function, including activity, reactivity, swimming patterns, and optomotor response in the presence of visual and acoustic stimuli. Using this model, we performed a high-throughput screening of FDA-approved drugs to identify compounds that affect zebrafish larval behavior in a manner consistent with the distinct behavior induced by calcineurin inhibitors. Cyclosporine (CsA) and other calcineurin inhibitors have garnered interest for their potential role in the prevention of AD. We generated behavioral profiles suitable for cluster analysis, through which we identified 64 candidate therapeutics for neurodegenerative disorders.

Finding Drug Repurposing Candidates for Neurodegenerative Diseases using Zebrafish Behavioral Profiles

Drug repurposing can accelerate drug development while reducing the cost and risk of toxicity typically associated with de novo drug design. Several disorders lacking pharmacological solutions and exhibiting poor results in clinical trials - such as Alzheimer's disease (AD) - could benefit from a cost-effective approach to finding new therapeutics. We previously developed a neural network model, Z-LaP Tracker, capable of quantifying behaviors in zebrafish larvae relevant to cognitive function, including activity, reactivity, swimming patterns, and optomotor response in the presence of visual and acoustic stimuli. Using this model, we performed a high-throughput screening of FDA-approved drugs to identify compounds that affect zebrafish larval behavior in a manner consistent with the distinct behavior induced by calcineurin inhibitors. Cyclosporine (CsA) and other calcineurin inhibitors have garnered interest for their potential role in the prevention of AD. We generated behavioral profiles suitable for cluster analysis, through which we identified 64 candidate therapeutics for neurodegenerative disorders.

An 8-cage imaging system for automated analyses of mouse behavior

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 nocturnal animals, 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-h overnight recordings. Software for image analysis was developed in two open-source programs, ImageJ and DeepLabCut. The imaging system was tested using 4-5 month-old female wild-type mice and 3xTg-AD mice, a widely-used model to study Alzheimer's disease (AD). The overnight recordings provided measurements of 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. The behavioral profiles were different in wild-type and 3xTg-AD mice. AD-model mice displayed reduced acclimation to the novel cage environment, were hyperactive during the first hour of darkness, and spent less time at home in comparison to wild-type mice. We propose that the imaging system may be used to study various neurological and neurodegenerative disorders, including Alzheimer's disease.

Zebrafish Larvae Position Tracker (Z-LaP Tracker): a high-throughput deep-learning behavioral approach for the identification of calcineurin pathway-modulating drugs using zebrafish larvae

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.

Distinguishing Curable from Progressive Dementias for Defining Cancer Care Options

The likelihood of a diagnosis of dementia increases with a person's age, as is also the case for many cancers, including melanoma and multiple myeloma, where the median age of diagnosis is above 60 years. However, patients diagnosed with dementia are less likely to be offered invasive curative therapies for cancer. Together with analysis of diet and medication history, advanced imaging methods and genetic profiling can now indicate more about syndromes causing the neurological symptoms. Cachexia, malnutrition, dehydration, alcohol consumption, and even loneliness can all accentuate or cause the "3Ds" of dementia, delirium and depression. Many common drugs, especially in the context of polypharmacy, can cause cognitive difficulties resembling neurodegenerative disease. These syndromes may be reversed by diet, social and caregiver changes, and stopping potentially inappropriate medications (PIMs). More insidious are immune reactions to many different autoantigens, some of which are related to cancers and tumors. These can induce movement and cognitive difficulties that mimic Alzheimer's and Parkinson's diseases and other ataxias associated with aging. Paraneoplastic neurological syndromes may be reversed by directed immunotherapies if detected in their early stages but are best treated by removal of the causative tumor. A full genetic workup should be done for all individuals as soon as possible after diagnosis, to guide less invasive treatments suitable for frail individuals. While surgical interventions may be contraindicated, genetic profile guided immunotherapies, oral treatments, and radiation may be equally curative in a significant number of cancers.

FDA-Approved Kinase Inhibitors in Preclinical and Clinical Trials for Neurological Disorders

Cancers and neurological disorders are two major types of diseases. We previously developed a new concept termed "Aberrant Cell Cycle Diseases" (ACCD), revealing that these two diseases share a common mechanism of aberrant cell cycle re-entry. The aberrant cell cycle re-entry is manifested as kinase/oncogene activation and tumor suppressor inactivation, which are hallmarks of both tumor growth in cancers and neuronal death in neurological disorders. Therefore, some cancer therapies (e.g., kinase inhibition, tumor suppressor elevation) can be leveraged for neurological treatments. The United States Food and Drug Administration (US FDA) has so far approved 74 kinase inhibitors, with numerous other kinase inhibitors in clinical trials, mostly for the treatment of cancers. In contrast, there are dire unmet needs of FDA-approved drugs for neurological treatments, such as Alzheimer's disease (AD), intracerebral hemorrhage (ICH), ischemic stroke (IS), traumatic brain injury (TBI), and others. In this review, we list these 74 FDA-approved kinase-targeted drugs and identify those that have been reported in preclinical and/or clinical trials for neurological disorders, with a purpose of discussing the feasibility and applicability of leveraging these cancer drugs (FDA-approved kinase inhibitors) for neurological treatments.

A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism

With the development of research on the complex aetiology of many diseases, computational drug repositioning methodology has proven to be a shortcut to costly and inefficient traditional methods. Therefore, developing more promising computational methods is indispensable for finding new candidate diseases to treat with existing drugs. In this paper, a model integrating a new variant of message passing neural network and a novel-gated fusion mechanism called GLGMPNN is proposed for drug-disease association prediction. First, a light-gated message passing neural network (LGMPNN), including message passing, aggregation and updating, is proposed to separately extract multiple pieces of information from the similarity networks and the association network. Then, a gated fusion mechanism consisting of a forget gate and an output gate is applied to integrate the multiple pieces of information to extent. The forget gate calculated by the multiple embeddings is built to integrate the association information into the similarity information. Furthermore, the final node representations are controlled by the output gate, which fuses the topology information of the networks and the initial similarity information. Finally, a bilinear decoder is adopted to reconstruct an adjacency matrix for drug-disease associations. Evaluated by 10-fold cross-validations, GLGMPNN achieves excellent performance compared with the current models. The following studies show that our model can effectively discover novel drug-disease associations.