Influences on serum concentrations of morphine, M6G and M3G during routine clinical drug monitoring: A prospective survey in 300 adult cancer patients

Klepstad, Pål; Dale, Ola; Kaasa, Stein; Zahlsen, Kolbjørn; Aamo, Trond; Fayers, Peter; Borchgrevink, Petter C.

doi:10.1034/j.1399-6576.2003.00138.x

Cited by 45 publications

(32 citation statements)

References 19 publications

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“…Eighteen suitable datasets were obtained [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. Table 1 summarises their characteristics and the number of observations each contributed to various psychometric analyses.…”

Section: Resultsmentioning

confidence: 99%

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

et al. 2016

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Abstract:Purpose: To derive a health state classification system (HSCS) from the cancerspecific quality of life questionnaire, the EORTC QLQ-C30, as the basis for a multiattribute utility instrument.Methods: The conceptual model for the HSCS was based on the established domain structure of the QLQ-C30. Several criteria were considered to select a subset of dimensions and items for the HSCS. Expert opinion and patient input informed a priori Powered by Edit orial Manager® and ProduXion Manager® from Aries Syst em s Corporat ionselection of key dimensions. Psychometric criteria were assessed via secondary analysis of a pooled dataset comprising HRQOL and clinical data from 2616 patients from eight countries and a range of primary cancer sites, disease stages, and treatments. We used confirmatory factor analysis (CFA) to assess the conceptual model's robustness and generalisability. We assessed item floor effects (>75% observations at lowest score), disordered item response thresholds, coverage of the latent variable and differential item function (DIF) using Rasch analysis. We calculated effect sizes for known group comparisons based on disease stage and responsiveness to change. Seventy-nine cancer patients assessed the relative importance of items within dimensions.Results: CFA supported the conceptual model and its generalizability across primary cancer sites. After considering all criteria, 12 items were selected representing 10 dimensions: physical functioning (mobility), role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems.

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Section: Resultsmentioning

confidence: 99%

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

et al. 2016

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“…This variability was demonstrated in a study of 3,045 postsurgical patients who required anywhere from 1 to 20 boluses of morphine to obtain relief from postoperative pain [1]. Similarly, Klepstad et al [2] found considerable interindividual variation in effective serum concentrations of morphine and morphine-6-glucuronide in 300 patients with cancer. Numerous studies document that age, gender, race/ethnicity, mood states, and stress can influence the individual's pain experience.…”

Section: Introductionmentioning

confidence: 95%

Biological pathways and genetic variables involved in pain

et al. 2010

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Candidate gene association studies have been used to provide evidence for the genetic modulation of pain perception and response to analgesics. However, the nature and range of genetic modulation of pain is not well addressed due to the limited number of patients and the limited number of genes and genetic variants investigated in studies to date. Moreover, personalized analgesic treatments will require a more complete understanding of the effects of genetic variants and gene-gene interactions in response to analgesics.

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“…We then studied the effects of the three MOR agonists, morphine, DAMGO and fentanyl, used in a wide range of concentrations (1 nM-10 mM ), which includes concentrations similar to brain extracellular levels of endogenous opioids and clinically relevant concentrations of exogenous opioid agonists. Thus morphine, fentanyl and DAMGO were applied at concentrations (10 -9 to 10 -5 M) that included the nanomole regime covering the range of clinically relevant brain concentrations (Bouw et al, 2001), the opioid concentrations found in the cerebrospinal fluid (Bernards et al, 2003) or the plasma/serum (Veselis et al, 1994;Klepstad et al, 2003;Gunnar et al, 2004;Solassol et al, 2005) as well as the extracellular levels of endogenous brain opioids (~1 to 10 nM) (Lam et al, 2008).…”

Section: Figurementioning

confidence: 99%

Effects of μ‐opioid receptor modulation on the hippocampal network activity of sharp wave and ripples

Giannopoulos

Papatheodoropoulos

2013

British J Pharmacology

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BACKGROUND AND PURPOSEHippocampus-dependent memory involves the activity of sharp wave ripples (SWRs), which are thought to participate in the process of memory consolidation. The hippocampus contains high levels of endogenous opioids and of m-opioid receptors (MORs). Here, we have assessed the role of MOR agonists in the modulation of SWRs. EXPERIMENTAL APPROACHUsing recordings of extracellular potentials from the CA1 field of rat hippocampal slices, we examined the pharmacological actions of morphine, DAMGO and fentanyl on SWRs and on network excitability and paired-pulse inhibition. KEY RESULTSAll three MOR agonists (1 nM-10 mM) significantly increased the amplitude of sharp waves and the occurrence of SWR sequences, but reduced the initiation of episodes of SWRs. Fentanyl was most potent in producing these effects and morphine the least. Interestingly, although SWRs were reduced by relatively high concentrations (Ն100 nM) of all agonists, they were significantly enhanced by very low concentrations of morphine (5-10 nM). Morphine and DAMGO at moderate-to-high concentrations increased network excitability and reduced inhibition. Furthermore, DAMGO suppressed inhibition more readily than it increased excitation, whereas morphine suppressed inhibition only at high concentrations. These drug effects were reversed by the MOR antagonists naloxone and CTOP. CONCLUSIONS AND IMPLICATIONSWe found that the SWRs were significantly modulated by three MOR agonists and that the SWRs were very sensitive to subtle changes in the excitation/inhibition balance induced by MOR agonists. Such modulation might underlie the effects of these agonists on hippocampus-dependent memory. AbbreviationsICI, intra-cluster interval; IEI, inter-event interval; IEpI, inter-episode interval; MOR, m-opioid receptor; PPI, paired-pulse inhibition; PS, population spike; SWR, sharp wave ripple BJP British Journal of Pharmacology DOI:10.1111DOI:10. /j.1476DOI:10. -5381.2012 1146 British Journal of Pharmacology (2013) IntroductionEndogenous and clinically administered opioids act on many receptors (Waldhoer et al., 2004) and are involved in a wide variety of functions (Drolet et al., 2001) including hippocampus-dependent learning and memory (Wise, 1989;Meilandt et al., 2004;Bodnar, 2010;Kesner and Warthen, 2010;Dacher and Nugent, 2011). Nevertheless, the mechanisms by which opioids affect hippocampus-dependent memory appear to be highly complex and poorly understood.The critical involvement of the hippocampus in learning and memory (Eichenbaum, 2004;Squire et al., 2004) is underpinned by a number of different types of neuronal activity supporting distinct processes (Battaglia et al., 2011). For example, it is assumed that the encoding of new information that occurs during active behaviour is associated with q oscillation (Hasselmo, 2005) whereas memory consolidation takes place during inactivity (Alvarez and Squire, 1994;McGaugh, 2000). It is generally thought that memory consolidation entails the generation of irregular activity of sharp wave ripples...

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Influences on serum concentrations of morphine, M6G and M3G during routine clinical drug monitoring: A prospective survey in 300 adult cancer patients

Abstract: Patient characteristics predict only minor parts of the variability of morphine, M3G and M6G serum concentrations observed during routine clinical drug-monitoring in cancer patients.

Cited by 45 publications

References 19 publications

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

Biological pathways and genetic variables involved in pain

Effects of μ‐opioid receptor modulation on the hippocampal network activity of sharp wave and ripples

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