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 behavi...

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Main Authors: Alberto Ambesi-Impiombato, Kimberly Cox, Sylvie Ramboz, Daniela Brunner, Mukesh Bansal, Emer Leahy
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-07-01
Series:Frontiers in Pharmacology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fphar.2023.1128562/full
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author Alberto Ambesi-Impiombato
Kimberly Cox
Sylvie Ramboz
Daniela Brunner
Mukesh Bansal
Emer Leahy
author_facet Alberto Ambesi-Impiombato
Kimberly Cox
Sylvie Ramboz
Daniela Brunner
Mukesh Bansal
Emer Leahy
author_sort Alberto Ambesi-Impiombato
collection DOAJ
description 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.
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spelling doaj.art-78fbc2a580bc4b54a731f043a32686752023-07-25T07:44:59ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122023-07-011410.3389/fphar.2023.11285621128562Enrichment analysis of phenotypic data for drug repurposing in rare diseasesAlberto Ambesi-ImpiombatoKimberly CoxSylvie RambozDaniela BrunnerMukesh BansalEmer LeahyDrug-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.https://www.frontiersin.org/articles/10.3389/fphar.2023.1128562/fulldrug discoveryphenotypic screeningtianeptinehuntington (disease)animal modelssmartcube
spellingShingle Alberto Ambesi-Impiombato
Kimberly Cox
Sylvie Ramboz
Daniela Brunner
Mukesh Bansal
Emer Leahy
Enrichment analysis of phenotypic data for drug repurposing in rare diseases
Frontiers in Pharmacology
drug discovery
phenotypic screening
tianeptine
huntington (disease)
animal models
smartcube
title Enrichment analysis of phenotypic data for drug repurposing in rare diseases
title_full Enrichment analysis of phenotypic data for drug repurposing in rare diseases
title_fullStr Enrichment analysis of phenotypic data for drug repurposing in rare diseases
title_full_unstemmed Enrichment analysis of phenotypic data for drug repurposing in rare diseases
title_short Enrichment analysis of phenotypic data for drug repurposing in rare diseases
title_sort enrichment analysis of phenotypic data for drug repurposing in rare diseases
topic drug discovery
phenotypic screening
tianeptine
huntington (disease)
animal models
smartcube
url https://www.frontiersin.org/articles/10.3389/fphar.2023.1128562/full
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AT danielabrunner enrichmentanalysisofphenotypicdatafordrugrepurposinginrarediseases
AT mukeshbansal enrichmentanalysisofphenotypicdatafordrugrepurposinginrarediseases
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