Objectives: Cognitive impairment models are used in clinical studies aimed at proving pharmacology of drugs being developed for Alzheimer's disease and other cognitive disorders. Due to rising interest in nicotinic agonists, we aimed to establish a method to monitor neurophysiological effects of modulating the nicotinic cholinergic system.
Methods: In a four-way cross-over study, eyes-closed rest EEG was recorded in 28 healthy subjects receiving mecamylamine-a nicotinic acetylcholine receptor (nAChR) antagonist, which induces temporary cognitive dysfunction in healthy subjects-with co-administration of placebo, nicotine or galantamine.
Results: Using machine learning to optimally contrast the effects of 30 mg of mecamylamine and placebo on the brain, we developed a nAChR index that consists of 10 EEG biomarkers and shows high classification accuracy (∼95% non-cross-validated, ∼70% cross-validated). Importantly, using the nAChR index, we demonstrate reversal of mecamylamine-induced neurophysiological effects due to 16 mg of galantamine as well as administering 21 mg of nicotine transdermally.
Conclusions: Our findings indicate that the mecamylamine challenge model jointly with the nAChR index-a measure of the nicotinic EEG profile-could aid future proof-of-pharmacology studies to demonstrate effects of nicotinic cholinergic compounds.
Significance: This novel measure for quantifying nicotinic cholinergic effects on the EEG could serve as a useful tool in drug development of pro-cognitive compounds.
Keywords: Biomarkers; Clinical trials; Electroencephalography; Machine learning; Neuronal oscillations; Neuropharmacology; Predictive analysis.
Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.