A hidden Markov model to assess drug-induced sleep fragmentation in the telemetered ratReport as inadecuate

A hidden Markov model to assess drug-induced sleep fragmentation in the telemetered rat - Download this document for free, or read online. Document in PDF available to download.

Journal of Pharmacokinetics and Pharmacodynamics

, Volume 38, Issue 6, pp 697–711

First Online: 10 September 2011Received: 27 December 2010Accepted: 12 August 2011


Drug-induced sleep fragmentation can cause sleep disturbances either via their intended pharmacological action or as a side effect. Examples of disturbances include excessive daytime sleepiness, insomnia and nightmares. Developing drugs without these side effects requires insight into the mechanisms leading to sleep disturbance. The characterization of the circadian sleep pattern by EEG following drug exposure has improved our understanding of these mechanisms and their translatability across species. The EEG shows frequent transitions between specific sleep states leading to multiple correlated sojourns in these states. We have developed a Markov model to consider the high correlation in the data and quantitatively compared sleep disturbance in telemetered rats induced by methylphenidate, which is known to disturb sleep, and of a new chemical entity NCE. It was assumed that these drugs could either accelerate or decelerate the transitions between the sleep states. The difference in sleep disturbance of methylphenidate and the NCE were quantitated and different mechanisms of action on rebound sleep were identified. The estimated effect showed that both compounds induce sleep fragmentation with methylphenidate being fivefold more potent compared to the NCE.

KeywordsHidden Markov model EEG Sleep Rats Methylphenidate NONMEM Electronic supplementary materialThe online version of this article doi:10.1007-s10928-011-9215-3 contains supplementary material, which is available to authorized users.

Download fulltext PDF

Author: C. Diack - O. Ackaert - B. A. Ploeger - P. H. van der Graaf - R. Gurrell - M. Ivarsson - D. Fairman

Source: https://link.springer.com/

Related documents