Molecular basis of mood and cognitive adverse events elucidated via a combination of pharmacovigilance data mining and functional enrichment analysis.


Journal

Archives of toxicology
ISSN: 1432-0738
Titre abrégé: Arch Toxicol
Pays: Germany
ID NLM: 0417615

Informations de publication

Date de publication:
08 2020
Historique:
received: 07 05 2020
accepted: 20 05 2020
pubmed: 7 6 2020
medline: 13 7 2021
entrez: 7 6 2020
Statut: ppublish

Résumé

Drug-induced Mood- and Cognition-related adverse events (MCAEs) are often only detected during the clinical trial phases of drug development, or even after marketing, thus posing a major safety concern and a challenge for both pharmaceutical companies and clinicians. To fill some gaps in the understanding and elucidate potential biological mechanisms of action frequently associated with MCAEs, we present a unique workflow linking observational population data with the available knowledge at molecular, cellular, and psychopharmacology levels. It is based on statistical analysis of pharmacovigilance reports and subsequent signaling pathway analyses, followed by evidence-based expert manual curation of the outcomes. Our analysis: (a) ranked pharmaceuticals with high occurrence of such adverse events (AEs), based on disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database, and (b) identified 120 associated genes and common pathway nodes possibly underlying MCAEs. Nearly two-thirds of the identified genes were related to immune modulation, which supports the critical involvement of immune cells and their responses in the regulation of the central nervous system function. This finding also means that pharmaceuticals with a negligible central nervous system exposure may induce MCAEs through dysregulation of the peripheral immune system. Knowledge gained through this workflow unravels putative hallmark biological targets and mediators of drug-induced mood and cognitive disorders that need to be further assessed and validated in experimental models. Thereafter, they can be used to substantially improve in silico/in vitro/in vivo tools for predicting these adversities at a preclinical stage.

Identifiants

pubmed: 32504122
doi: 10.1007/s00204-020-02788-1
pii: 10.1007/s00204-020-02788-1
pmc: PMC7395038
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2829-2845

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Auteurs

Christos Andronis (C)

Biovista, 34 Rodopoleos Street, 16777, Athens, Greece.

João Pedro Silva (JP)

UCIBIO, REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313, Porto, Portugal.

Eftychia Lekka (E)

Biovista, 34 Rodopoleos Street, 16777, Athens, Greece.

Vassilis Virvilis (V)

Biovista, 34 Rodopoleos Street, 16777, Athens, Greece.

Helena Carmo (H)

UCIBIO, REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313, Porto, Portugal.

Konstantina Bampali (K)

Department of Molecular Neurosciences, Medical University of Vienna, Spitalgasse 4, 1090, Vienna, Austria.

Margot Ernst (M)

Department of Molecular Neurosciences, Medical University of Vienna, Spitalgasse 4, 1090, Vienna, Austria.

Yang Hu (Y)

Translational PKPD Group, Department of Pharmaceutical Biosciences, Associate Member of SciLifeLab, Uppsala University, Uppsala, Sweden.

Irena Loryan (I)

Translational PKPD Group, Department of Pharmaceutical Biosciences, Associate Member of SciLifeLab, Uppsala University, Uppsala, Sweden.

Jacques Richard (J)

Sanofi R&D, 371 avenue Professeur Blayac, 34000, Montpellier, France.

Félix Carvalho (F)

UCIBIO, REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313, Porto, Portugal. felixdc@ff.up.pt.

Miroslav M Savić (MM)

Department of Pharmacology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000, Belgrade, Serbia. miroslav@pharmacy.bg.ac.rs.

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Classifications MeSH