Kristin Moksnes

Clinical pharmacology of methadone for pain.

Background: This topical review addresses methadones pharmacology, its application in malignant and non‐malignant pain conditions, practical issues related to methadone for the treatment of pain and its influence on QTc time.

European Palliative Care Research Collaborative pain guidelines: opioid switching to improve analgesia or reduce side effects. A systematic review.

According to a Cochrane review on opioid switching, sound evidence on the practice of substituting one strong opioid with another to improve pain control and reduce adverse effects was lacking in 2004. A systematic search strategy was developed to include studies after 2004, with adult cancer patients switching between strong opioids and reporting estimates of effect on pain and adverse effects. The search retrieved 288 publications (71 duplicates); 187 abstracts and 19 full papers were excluded. Eleven papers met the inclusion criteria; none were randomized controlled trials/meta-analyses. Studies comprised 280 patients (group size 10–32). A variety of opioids and switching strategies were studied. Pain intensity was significantly reduced in the majority of studies. Serious adverse effects were improved. Due to serious design limitations, the level of evidence was low (D). Randomized trials, with standardization of cohort classification, use of outcomes and analysis are warranted to establish the practice of opioid switching.

How to switch from morphine or oxycodone to methadone in cancer patients? a randomised clinical phase II trial.

AIM Opioid switching is a treatment strategy in cancer patients with unacceptable pain and/or adverse effects (AEs). We investigated whether patients switched to methadone by the stop and go (SAG) strategy have lower pain intensity (PI) than the patients switched over three days (3DS), and whether the SAG strategy is as safe as the 3DS strategy. METHODS In this prospective, open, parallel-group, multicentre study, 42 cancer patients on morphine or oxycodone were randomised to the SAG or 3DS switching-strategy to methadone. The methadone dose was calculated using a dose-dependent ratio. PI, AEs and serious adverse events (SAEs) were recorded daily for 14 days. Primary outcome was average PI day 3. Secondary outcomes were PI now and AEs day 3 and 14 and number of SAEs. RESULTS Twenty one patients were randomised to each group, 16 (SAG) and 19 (3DS) patients received methadone. The mean preswitch morphine doses were 900 mg/day in SAG and 1330 mg/day in 3DS. No differences between groups were found in mean average PI day 3 (mean difference 0.5 (CI -1.2-2.2); SAG 4.1 (CI 2.3-5.9) and 3DS 3.6 (CI 2.9-4.3) or in PI now. The SAG group had more dropouts and three SAEs (two deaths and one severe sedation). No SAEs were observed in the 3DS group. CONCLUSION The SAG patients reported a trend of more pain, had significantly more dropouts and three SAEs, which indicate that the SAG strategy should not replace the 3DS when switching from high doses of morphine or oxycodone to methadone.

A Double-Blind, Randomized, Crossover Comparison Between Single-Dose and Double-Dose Immediate-Release Oral Morphine at Bedtime in Cancer Patients

The European Association for Palliative Care guidelines for treatment of cancer pain recommend a double dose (DD) of immediate-release morphine at bedtime instead of single doses (SD) repeated every four hours throughout the night. A previous open controlled study reported more side effects after DD than after SD. The present study was a randomized, double-blind, crossover study comparison of DD and SD of immediate-release morphine during the night, followed by an open pharmacokinetic study. The primary outcome was average pain intensity during the night, as measured on an 11-point numerical rating scale. Secondary outcomes were morning pain, number of rescue medications, adverse effects (nausea, xerostomia, tiredness, sleep quality, and number of awaking episodes) and patient preference. Morphine and metabolites were quantified by a validated liquid chromatography-tandem mass spectrometry method. Nineteen patients completed the clinical study; 13 participated in the pharmacokinetic follow up. Average pain during the night for DD vs. SD was close to statistical significance (mean 0.8 and 1.4, respectively, P=0.058; mean [95% confidence interval] for the difference was 0.50 [0.02, 1.0]). A similar trend was observed for strongest night pain (P=0.069) and sleep quality (P=0.077). Only two patients required rescue morphine. Four patients had no treatment preference; nine and six favored DD and SD, respectively. DD patients displayed higher area under the curve for morphine and morphine-6-glucuronide during the first part of the night. Although DD tended to perform slightly better than SD, a difference in average pain during the night of 0.50 has little clinical significance, and the two procedures are, therefore, clinically equivalent. It is speculated whether the initial higher exposure to morphine-6-glucuronide may have clinical significance.

Serum concentrations of opioids when comparing two switching strategies to methadone for cancer pain

PurposeOur aim was to compare pharmacological aspects of two switching strategies from morphine/oxycodone to methadone; the stop and go (SAG) strategy in which methadone is started directly after the initial opioid has been stopped, and the 3-days switch (3DS), in which morphine/oxycodone is gradually changed to methadone by cross-tapering over 3 days.MethodsForty-two cancer patients with pain and/or opioid side effects were assessed in this randomised trial. Trough serum concentrations of methadone, morphine, morphine-6-glucuronide (M6G), and oxycodone were measured on days 1, 2, 3, 4, 7, and 14. Primary outcome was number of patients with methadone concentrations in apparent CSS on day 4. Secondary outcomes were exposure to opioids during the first 3 days, interindividual variation of opioid concentrations, and correlation between methadone concentrations and pain intensity (PI) day 3.ResultsThirty-five patients received methadone (16 in the SAG group, 19 in the 3DS group). The median preswitch morphine equivalent doses were 620 (range 350–2000) mg/day in the SAG group and 800 (range 90–3600) mg/day in the 3DS group (p = 0.43);42% reached CSS for methadone in the SAG group on day 4 compared with 22% in the 3DS group (p = 0.42). The SAG group was significantly less exposed to morphine/M6G/oxycodone and significantly more exposed to methadone in the first 3 days. Methadone showed a low correlation with PI. More patients dropped out after intervention in the SAG group than in the 3DS group (38% vs. 5%; p = 0.032). One SAG patient suffered from respiratory depression on day 5.ConclusionThe SAG group was initially more exposed to methadone and less to the replaced opioids but without observed clinical benefit and with a higher dropout rate. Patients switched to methadone should be followed closely for the first 5 days, regardless of switching strategy.

Opioid switching to methadone: A pharmacoepidemiological study from a national prescription database

Background: Opioid switching to methadone is reported frequently to improve pain control in patients with an unacceptable balance between pain control and side effects during treatment with first line opioids, but carries a risk of drug accumulation and respiratory depression. To justify this risk it is required that less risky treatments are tried beforehand and that a sufficiently large proportion of patients experience a long-lasting improvement in pain control. Research questions: How large was the proportion of patients remaining on long term methadone treatment after a switch from a strong opioid to methadone? How long had the patients been treated with opioids before the switch to methadone? Methods: Longitudinal pharmacoepidemiological study from the complete national Norwegian Prescription Database. Results: One hundred and thirty (77%) cancer patients received more than one dispensed prescription of methadone. Forty-nine (40%) chronic non-malignant pain (CNMP) patients continued to have methadone prescriptions dispensed more than 6 months after the switch. Of 168 cancer patients, 48 (29%) had tried two strong opioids prior to the switch to methadone whereas 21 (12.5%) had tried three or more strong opioids. Similar numbers for 124 CNMP patients were 26 (21%) and 34 (27%), respectively. Interpretation: Opioid switching to methadone appears to provide a long lasting improvement in pain control in a significant proportion of patients. However, the study raises concerns that treatment options with less risk are not being exhausted prior to switching to methadone.

A physiologically-based recirculatory meta-model for nasal fentanyl in man

Pharmacokinetic (PK) and pharmacodynamic (PD) data were available from a study of a nasal delivery system for the opioid analgesic fentanyl, together with data on the kinetics of fentanyl in arterial blood in man, and in the lung and brain of sheep. Our aim was to reconcile these data using a physiologically-based population recirculatory PK–PD model, with emphasis on achieving a meta-model that could simultaneously account for the arterial and venous (arm) concentrations of fentanyl, could relate PD effects (pain scores) to the CNS concentrations of fentanyl, and could account for the effect of body size and age on fentanyl kinetics. Data on the concentration gradients of fentanyl across brain, lung and muscle were used to develop sub-models of fentanyl kinetics in these organs. The sub-models were incorporated into a “whole body” recirculatory model by adding additional sub-models for a venous mixing compartment, the liver and gut, the kidney and the “rest of the body” with blood flows and organ volumes based on values for a Standard Man. Inter-individual variability was achieved by allometric scaling of organ size and blood flows, evidence-based assumptions about the effect of weight and age on cardiac output, and inter-individual variability in the free fraction in plasma and hepatic extraction of fentanyl. Post-operative pain scores were found to be temporally related to the predicted brain concentrations of fentanyl. We conclude that a physiologically-based meta-modelling approach was able to describe clinical PK–PD studies of fentanyl whilst providing a mechanistic interpretation of key aspects of its disposition.