Enrichment analysis of phenotypic data for drug repurposing in rare diseases.

Drug-induced Behavioral Signature Analysis (DBSA), is a machine learning (ML) method for in silico screening of compounds, inspired by analytical methods quantifying gene enrichment in genomic analyses. When applied to behavioral data it can identify drugs that can potentially reverse in vivo behavioral symptoms in animal models of human disease and suggest new hypotheses for drug discovery and repurposing. We present a proof-of-concept study aiming to assess Drug-induced Behavioral Signature Analysis (DBSA) as a systematic approach for drug discovery for rare disorders. We applied Drug-induced Behavioral Signature Analysis to high-content behavioral data obtained with SmartCube®, an automated in vivo phenotyping platform. The therapeutic potential of several dozen approved drugs was assessed for phenotypic reversal of the behavioral profile of a Huntington's Disease (HD) murine model, the Q175 heterozygous knock-in mice. The in silico Drug-induced Behavioral Signature Analysis predictions were enriched for drugs known to be effective in the symptomatic treatment of Huntington's Disease, including bupropion, modafinil, methylphenidate, and several SSRIs, as well as the atypical antidepressant tianeptine. To validate the method, we tested acute and chronic effects of tianeptine (20 mg/kg, i. p.) in vivo, using Q175 mice and wild type controls. In both experiments, tianeptine significantly rescued the behavioral phenotype assessed with the SmartCube® platform. Our target-agnostic method thus showed promise for identification of symptomatic relief treatments for rare disorders, providing an alternative method for hypothesis generation and drug discovery for disorders with huge disease burden and unmet medical needs.

Neurobehavioral effects of selected tobacco constituents in rodents following subchronic administration.

Bidirectional correlations between cigarette smoking and affective disorders, such as depression, anxiety, and schizophrenia, are well documented. These findings have led to substantial investigations into the effects of the major tobacco alkaloid, nicotine, and to a lesser extent, of other tobacco constituents, on the central nervous system (CNS). However, systematic profiling of the neuropharmacological effects of tobacco constituents is limited. To elucidate the effects of selected tobacco constituents on the CNS, we used the SmartCube® system, which captures and classifies behavioral features of compound-treated mice, to profile the psychiatric drugs-like properties of previously reported neuroactive tobacco compounds in mice. Daily intraperitoneal injection of nicotine (0.5 and 1 mg/kg/day) and anatabine (5 mg/kg/day) for 7 days produced antidepressant-like behavioral SmartCube® signatures in mice, and these results were supported by the improved active coping responses in the forced swim tests. Conversely, ferulic acid did not show any identifiable class signatures in the SmartCube® tests, but rather displayed subclass signatures associated with acetylcholinesterase inhibitors. In novel object recognition memory test in rats, ferulic acid improved memory after 7 days of subcutaneous injection at 0.3 or 3 mg/kg/day. These results support previous findings showing the antidepressant drug-like effects of nicotine and the nootropic effects of ferulic acid. This is also the first report on the antidepressant drug-like effects of anatabine in rodents. This study provides a systemic behavioral evaluation of tobacco alkaloids and further insights into the association between affective disorders and smoking incidence.

High-throughput analysis of behavior for drug discovery.

Drug testing with traditional behavioral assays constitutes a major bottleneck in the development of novel therapies. PsychoGenics developed three comprehensive high-throughput systems, SmartCube(®), NeuroCube(®) and PhenoCube(®) systems, to increase the efficiency of the drug screening and phenotyping in rodents. These three systems capture different domains of behavior, namely, cognitive, motor, circadian, social, anxiety-like, gait and others, using custom-built computer vision software and machine learning algorithms for analysis. This review exemplifies the use of the three systems and explains how they can advance drug screening with their applications to phenotyping of disease models, drug screening, selection of lead candidates, behavior-driven lead optimization, and drug repurposing.

Reprint of: Highthroughtput analysis of behavior for drug discovery.

Drug testing with traditional behavioral assays constitutes a major bottleneck in the development of novel therapies. PsychoGenics developed three comprehensive highthroughtput systems, SmartCube(®), NeuroCube(®) and PhenoCube(®) systems, to increase the efficiency of the drug screening and phenotyping in rodents. These three systems capture different domains of behavior, namely, cognitive, motor, circadian, social, anxiety-like, gait and others, using custom-built computer vision software and machine learning algorithms for analysis. This review exemplifies the use of the three systems and explains how they can advance drug screening with their applications to phenotyping of disease models, drug screening, selection of lead candidates, behavior-driven lead optimization, and drug repurposing.