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High-dose naloxone, an experimental tool uncovering latent sensitisation : pharmacokinetics in humans

Papathanasiou, Theodoros ; Springborg, Anders Deichmann ; Kongstad, Kenneth Thermann ; Staerk, Dan ; Møller, Kirsten ; Taylor, Bradley Kenneth ; Lund, Trine Meldgaard and Werner, Mads Utke LU (2019) In British Journal of Anaesthesia 123(2). p.204-214
Abstract

Background: Naloxone, an opioid receptor antagonist, is used as a pharmacological tool to detect tonic endogenous activation of opioid receptors in experimental pain models. We describe a pharmacokinetic model linking naloxone pharmacokinetics to its main metabolite after high-dose naloxone infusion. Methods: Eight healthy volunteers received a three-stage stepwise high-dose i.v. naloxone infusion (total dose 3.25 mg kg−1). Naloxone and naloxone-3-glucuronide (N3G) plasma concentrations were sampled from infusion onset to 334 min after infusion discontinuation. Pharmacokinetic analysis was performed using non-linear mixed effect models (NONMEM). The predictive performances of Dowling's and Yassen's models were evaluated, and... (More)

Background: Naloxone, an opioid receptor antagonist, is used as a pharmacological tool to detect tonic endogenous activation of opioid receptors in experimental pain models. We describe a pharmacokinetic model linking naloxone pharmacokinetics to its main metabolite after high-dose naloxone infusion. Methods: Eight healthy volunteers received a three-stage stepwise high-dose i.v. naloxone infusion (total dose 3.25 mg kg−1). Naloxone and naloxone-3-glucuronide (N3G) plasma concentrations were sampled from infusion onset to 334 min after infusion discontinuation. Pharmacokinetic analysis was performed using non-linear mixed effect models (NONMEM). The predictive performances of Dowling's and Yassen's models were evaluated, and target-controlled infusion simulations were performed. Results: Three- and two-compartment disposition models with linear elimination kinetics described the naloxone and N3G concentration time-courses, respectively. Two covariate models were developed: simple (weight proportional) and complex (with the shallow peripheral volume of distribution linearly increasing with body weight). The median prediction error (MDPE) and wobble for Dowling's model were –32.5% and 33.4%, respectively. For Yassen's model, the MDPE and wobble were 1.2% and 19.9%, respectively. Conclusions: A parent–metabolite pharmacokinetic model was developed for naloxone and N3G after high-dose naloxone infusion. No saturable pharmacokinetics were observed. Whereas Dowling's model was inaccurate and over-predicted naloxone concentrations, Yassen's model accurately predicted naloxone pharmacokinetics. The newly developed covariate models may be used for high-dose TCI-naloxone for experimental and clinical practice. Clinical trials registration: NCT01992146.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
central sensitisation, chronic pain, endogenous opioids, naloxone, opioid receptor antagonist, pharmacokinetics
in
British Journal of Anaesthesia
volume
123
issue
2
pages
204 - 214
publisher
Elsevier
external identifiers
  • scopus:85060082638
  • pmid:30915992
ISSN
0007-0912
DOI
10.1016/j.bja.2018.12.007
language
English
LU publication?
yes
id
73d7a618-7578-4b3b-8ebe-4e5f472b1d04
date added to LUP
2019-01-29 14:40:59
date last changed
2024-04-15 21:44:46
@article{73d7a618-7578-4b3b-8ebe-4e5f472b1d04,
  abstract     = {{<p>Background: Naloxone, an opioid receptor antagonist, is used as a pharmacological tool to detect tonic endogenous activation of opioid receptors in experimental pain models. We describe a pharmacokinetic model linking naloxone pharmacokinetics to its main metabolite after high-dose naloxone infusion. Methods: Eight healthy volunteers received a three-stage stepwise high-dose i.v. naloxone infusion (total dose 3.25 mg kg<sup>−1</sup>). Naloxone and naloxone-3-glucuronide (N3G) plasma concentrations were sampled from infusion onset to 334 min after infusion discontinuation. Pharmacokinetic analysis was performed using non-linear mixed effect models (NONMEM). The predictive performances of Dowling's and Yassen's models were evaluated, and target-controlled infusion simulations were performed. Results: Three- and two-compartment disposition models with linear elimination kinetics described the naloxone and N3G concentration time-courses, respectively. Two covariate models were developed: simple (weight proportional) and complex (with the shallow peripheral volume of distribution linearly increasing with body weight). The median prediction error (MDPE) and wobble for Dowling's model were –32.5% and 33.4%, respectively. For Yassen's model, the MDPE and wobble were 1.2% and 19.9%, respectively. Conclusions: A parent–metabolite pharmacokinetic model was developed for naloxone and N3G after high-dose naloxone infusion. No saturable pharmacokinetics were observed. Whereas Dowling's model was inaccurate and over-predicted naloxone concentrations, Yassen's model accurately predicted naloxone pharmacokinetics. The newly developed covariate models may be used for high-dose TCI-naloxone for experimental and clinical practice. Clinical trials registration: NCT01992146.</p>}},
  author       = {{Papathanasiou, Theodoros and Springborg, Anders Deichmann and Kongstad, Kenneth Thermann and Staerk, Dan and Møller, Kirsten and Taylor, Bradley Kenneth and Lund, Trine Meldgaard and Werner, Mads Utke}},
  issn         = {{0007-0912}},
  keywords     = {{central sensitisation; chronic pain; endogenous opioids; naloxone; opioid receptor antagonist; pharmacokinetics}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{2}},
  pages        = {{204--214}},
  publisher    = {{Elsevier}},
  series       = {{British Journal of Anaesthesia}},
  title        = {{High-dose naloxone, an experimental tool uncovering latent sensitisation : pharmacokinetics in humans}},
  url          = {{http://dx.doi.org/10.1016/j.bja.2018.12.007}},
  doi          = {{10.1016/j.bja.2018.12.007}},
  volume       = {{123}},
  year         = {{2019}},
}