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Clinical Pharmacokinetics and Pharmacodynamics of Dexmedetomidine
Dexmedetomidine is an α2-adrenoceptor agonist with sedative, anxiolytic, sympatholytic, and analgesic-sparing effects, and minimal depression of respiratory function. It is potent and highly…
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Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.
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Pharmacodynamic Interaction of Remifentanil and Dexmedetomidine on Depth of Sedation and Tolerance of Laryngoscopy.
TLDR
A three-phase crossover trial to study the pharmacodynamic interaction between remifentanil and dexmedetomidine in 30 age- and sex-stratified healthy volunteers found that, despite falling asleep, most subjects remained arousable by calling the subject's name, shaking the subject while shouting their name, or a trapezius squeeze, even after reaching supraclinical concentrations.
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Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach
TLDR
This work provides a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors and demonstrating that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep.
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Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learning
TLDR
It is shown that dexmedetomidine-induced deep sedation state mimics N3 sleep EEG patterns, using a large-scale EEG data-driven approach and machine learning framework.
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Dexmedetomidine Clearance Decreases with Increasing Drug Exposure: Implications for Current Dosing Regimens and Target-controlled Infusion Models Assuming Linear Pharmacokinetics
TLDR
A nonlinear three-compartment pharmacokinetic model that accurately described dexmedetomidine plasma concentrations is developed and may be safely administered up to target-controlled infusion targets under 2 ng · ml–1 using the Hannivoort model, which assumed linear pharmacokinetics.
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Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms
TLDR
Nonlinear machine-learning models using quantitative EEG features can accurately predict sedation levels and may provide a useful reference for developing next generation EEG based sedation level prediction systems using advanced machine learning algorithms.
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