SEP-363856, a Novel Psychotropic Agent with a Unique, Non-D2 Receptor Mechanism of Action.

For the past 50 years, the clinical efficacy of antipsychotic medications has relied on blockade of dopamine D2 receptors. Drug development of non-D2 compounds, seeking to avoid the limiting side effects of dopamine receptor blockade, has failed to date to yield new medicines for patients. In this work, we report the discovery of SEP-363856 (SEP-856), a novel psychotropic agent with a unique mechanism of action. SEP-856 was discovered in a medicinal chemistry effort utilizing a high throughput, high content, mouse-behavior phenotyping platform, in combination with in vitro screening, aimed at developing non-D2 (anti-target) compounds that could nevertheless retain efficacy across multiple animal models sensitive to D2-based pharmacological mechanisms. SEP-856 demonstrated broad efficacy in putative rodent models relating to aspects of schizophrenia, including phencyclidine (PCP)-induced hyperactivity, prepulse inhibition, and PCP-induced deficits in social interaction. In addition to its favorable pharmacokinetic properties, lack of D2 receptor occupancy, and the absence of catalepsy, SEP-856's broad profile was further highlighted by its robust suppression of rapid eye movement sleep in rats. Although the mechanism of action has not been fully elucidated, in vitro and in vivo pharmacology data as well as slice and in vivo electrophysiology recordings suggest that agonism at both trace amine-associated receptor 1 and 5-HT1A receptors is integral to its efficacy. Based on the preclinical data and its unique mechanism of action, SEP-856 is a promising new agent for the treatment of schizophrenia and represents a new pharmacological class expected to lack the side effects stemming from blockade of D2 signaling. SIGNIFICANCE STATEMENT: Since the discovery of chlorpromazine in the 1950s, the clinical efficacy of antipsychotic medications has relied on blockade of dopamine D2 receptors, which is associated with substantial side effects and little to no efficacy in treating the negative and cognitive symptoms of schizophrenia. In this study, we describe the discovery and pharmacology of SEP-363856, a novel psychotropic agent that does not exert its antipsychotic-like effects through direct interaction with D2 receptors. Although the mechanism of action has not been fully elucidated, our data suggest that agonism at both trace amine-associated receptor 1 and 5-HT1A receptors is integral to its efficacy. Based on its unique profile in preclinical species, SEP-363856 represents a promising candidate for the treatment of schizophrenia and potentially other neuropsychiatric disorders.

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.

Computational Analysis of Multidimensional Behavioral Alterations After Chronic Social Defeat Stress.

BACKGROUND: The study of depression in humans depends on animal models that attempt to mimic specific features of the human syndrome. Most studies focus on one or a few behavioral domains, with time and practical considerations prohibiting a comprehensive evaluation. Although machine learning has enabled unbiased analysis of behavior in animals, this has not yet been applied to animal models of psychiatric disease. METHODS: We performed chronic social defeat stress (CSDS) in mice and evaluated behavior with PsychoGenics' SmartCube, a high-throughput unbiased automated phenotyping platform that collects >2000 behavioral features based on machine learning. We evaluated group differences at several times post-CSDS and after administration of the antidepressant medication imipramine. RESULTS: SmartCube analysis after CSDS successfully separated control and defeated-susceptible mice, and defeated-resilient mice more resembled control mice. We observed a potentiation of CSDS effects over time. Treatment of susceptible mice with imipramine induced a 40.2% recovery of the defeated-susceptible phenotype as assessed by SmartCube. CONCLUSIONS: High-throughput analysis can simultaneously evaluate multiple behavioral alterations in an animal model for the study of depression, which provides a more unbiased and holistic approach to evaluating group differences after CSDS and perhaps can be applied to other mouse models of psychiatric disease.

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors.

We introduce and validate a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients. Compounds are prioritized on the basis of their ability to invert the concerted activity of master regulator proteins that mechanistically regulate tumor cell state, as assessed from systematic drug perturbation assays. We validated the approach on a cohort of 212 gastroenteropancreatic neuroendocrine tumors (GEP-NETs), a rare malignancy originating in the pancreas and gastrointestinal tract. The analysis identified several master regulator proteins, including key regulators of neuroendocrine lineage progenitor state and immunoevasion, whose role as critical tumor dependencies was experimentally confirmed. Transcriptome analysis of GEP-NET-derived cells, perturbed with a library of 107 compounds, identified the HDAC class I inhibitor entinostat as a potent inhibitor of master regulator activity for 42% of metastatic GEP-NET patients, abrogating tumor growth in vivo. This approach may thus complement current efforts in precision oncology.

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.

In need of high-throughput behavioral systems.

One of the current major bottlenecks in drug discovery is in vivo testing of candidate drugs in behavioral paradigms in normal or genetically altered mice. This testing is essential in discovering gene function and predicting potential efficacy of CNS drugs in humans. New efforts in the biotech community aim to alleviate this bottleneck by developing higher-throughput systems of behavioral, neurological and physiological analyses. Together with large pharmacological databases, equipped with state-of-the-art bioinformatic and/or data-mining algorithms, these systems will provide rapid and accurate indices of the therapeutic potential of novel drugs. By providing a substantial increase in the speed of behavioral testing, new high-throughput systems will facilitate current behavioral research with faster, more reliable approaches. Furthermore, screening whole drug-libraries and comparing the profiles of novel compounds to those of known compounds will facilitate the discovery of novel drugs. Target validation will also become more efficient with the fast characterization of novel mutant mice.

SAHA enhances synaptic function and plasticity in vitro but has limited brain availability in vivo and does not impact cognition.

Suberoylanilide hydroxamic acid (SAHA) is an inhibitor of histone deacetylases (HDACs) used for the treatment of cutaneous T cell lymphoma (CTCL) and under consideration for other indications. In vivo studies suggest reducing HDAC function can enhance synaptic function and memory, raising the possibility that SAHA treatment could have neurological benefits. We first examined the impacts of SAHA on synaptic function in vitro using rat organotypic hippocampal brain slices. Following several days of SAHA treatment, basal excitatory but not inhibitory synaptic function was enhanced. Presynaptic release probability and intrinsic neuronal excitability were unaffected suggesting SAHA treatment selectively enhanced postsynaptic excitatory function. In addition, long-term potentiation (LTP) of excitatory synapses was augmented, while long-term depression (LTD) was impaired in SAHA treated slices. Despite the in vitro synaptic enhancements, in vivo SAHA treatment did not rescue memory deficits in the Tg2576 mouse model of Alzheimer's disease (AD). Along with the lack of behavioral impact, pharmacokinetic analysis indicated poor brain availability of SAHA. Broader assessment of in vivo SAHA treatment using high-content phenotypic characterization of C57Bl6 mice failed to demonstrate significant behavioral effects of up to 150 mg/kg SAHA following either acute or chronic injections. Potentially explaining the low brain exposure and lack of behavioral impacts, SAHA was found to be a substrate of the blood brain barrier (BBB) efflux transporters Pgp and Bcrp1. Thus while our in vitro data show that HDAC inhibition can enhance excitatory synaptic strength and potentiation, our in vivo data suggests limited brain availability may contribute to the lack of behavioral impact of SAHA following peripheral delivery. These results do not predict CNS effects of SAHA during clinical use and also emphasize the importance of analyzing brain drug levels when interpreting preclinical behavioral pharmacology.