GABAA receptor-mediated seizure liabilities: a mixed-methods screening approach

GABAA 受体介导的癫痫发作倾向:一种混合方法筛查方法

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作者:Konstantina Bampali, Filip Koniuszewski, Florian D Vogel, Jure Fabjan, Christos Andronis, Eftychia Lekka, Vassilis Virvillis, Thomas Seidel, Annie Delaunois, Leandro Royer, Michael G Rolf, Chiara Giuliano, Martin Traebert, Gautier Roussignol, Magali Fric-Bordat, Ludmilla Mazelin-Winum, Sharon D Brya

Abstract

GABAA receptors, members of the pentameric ligand-gated ion channel superfamily, are widely expressed in the central nervous system and mediate a broad range of pharmaco-toxicological effects including bidirectional changes to seizure threshold. Thus, detection of GABAA receptor-mediated seizure liabilities is a big, partly unmet need in early preclinical drug development. This is in part due to the plethora of allosteric binding sites that are present on different subtypes of GABAA receptors and the critical lack of screening methods that detect interactions with any of these sites. To improve in silico screening methods, we assembled an inventory of allosteric binding sites based on structural data. Pharmacophore models representing several of the binding sites were constructed. These models from the NeuroDeRisk IL Profiler were used for in silico screening of a compiled collection of drugs with known GABAA receptor interactions to generate testable hypotheses. Amoxapine was one of the hits identified and subjected to an array of in vitro assays to examine molecular and cellular effects on neuronal excitability and in vivo locomotor pattern changes in zebrafish larvae. An additional level of analysis for our compound collection is provided by pharmacovigilance alerts using FAERS data. Inspired by the Adverse Outcome Pathway framework, we postulate several candidate pathways leading from specific binding sites to acute seizure induction. The whole workflow can be utilized for any compound collection and should inform about GABAA receptor-mediated seizure risks more comprehensively compared to standard displacement screens, as it rests chiefly on functional data.

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