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Opioid response in paediatric cancer patients and the Val158Met polymorphism of the human catechol-O-methyltransferase (COMT) gene: an Italian study on 87 cancer children and a systematic review

  • 1,
  • 2,
  • 2,
  • 2,
  • 3,
  • 4,
  • 5,
  • 6,
  • 7,
  • 2,
  • 8,
  • 4, 9,
  • 3,
  • 10,
  • 2,
  • 2 and
  • 2, 11Email authorView ORCID ID profile
Contributed equally
BMC Cancer201919:113

https://doi.org/10.1186/s12885-019-5310-4

  • Received: 12 October 2018
  • Accepted: 21 January 2019
  • Published:
Open Peer Review reports

Abstract

Background

Genetic polymorphisms in genes involved in pain modulation have been reported to be associated to opioid efficacy and safety in different clinical settings.

Methods

The association between COMT Val158Met polymorphism (rs4680) and the inter-individual differences in the response to opioid analgesic therapy was investigated in a cohort of 87 Italian paediatric patients receiving opioids for cancer pain (STOP Pain study). Furthermore, a systematic review of the association between opioid response in cancer patients and the COMT polymorphism was performed in accordance with the Cochrane Handbook and the Prisma Statement.

Results

In the 87 paediatric patients, pain intensity (total time needed to reach the lowest possible level) was significantly higher for G/G than A/G and A/A carriers (p-value = 0.042). In the 60 patients treated only with morphine, the mean of total dose to reach the same pain intensity was significantly higher for G/G than A/G and A/A carriers (p-value = 0.010). Systematic review identified five studies on adults, reporting that opioid dose (mg after 24 h of treatment from the first pain measurement) was higher for G/G compared to A/G and A/A carriers.

Conclusions

Present research suggests that the A allele in COMT polymorphism could be a marker of opioid sensitivity in paediatric cancer patients (STOP Pain), as well as in adults (Systematic Review), indicating that the polymorphism impact could be not age-dependent in the cancer pain context.

Trial registration

Registration number: CRD42017057831.

Keywords

  • Cancer pain
  • Children
  • Opioid
  • Genetic polymorphisms
  • Systematic review

Background

Opioid analgesics are the treatment of choice for moderate-severe pain both in adults and in children [1, 2]. A significant variability both in efficacy and in safety has been observed in many studies aimed at describing inter-individual differences. In addition to demographic and disease-related factors, much research has focused on genetic variability [3]. Indeed, sequence variations in genes involved in opioid pharmacokinetics and pharmacodynamics, as well as in the modulation of pain pathways, have been reported to be associated with parameters of efficacy, such as the dose or the time to obtain analgesia, and of safety, such as the occurrence and severity of opioid side effects, in different clinical settings [3, 4]. Among genes associated with variability in the response to opioid analgesics, the gene encoding for catechol-o-methyltransferase (COMT) is known to be involved in pain modulation, presumably through dopamine-mediated change of enkephalins neuronal content [5], followed in turn by a compensatory regulation of μ-opioid receptors in various brain regions [5, 6].

In particular, the common (50% frequency in Caucasian population) 472G > A single nucleotide polymorphism (SNP) in exon 4 of the COMT gene, Val158Met (rs4680), leading to a three- to four-fold reduced activity of the enzyme [7, 8], has been shown to be associated to higher sensory and regional density of μ-opioid receptors and affect experimental pain ratings [9].

Malignancy- or chemotherapy-induced pain therapy is a challenge for paediatric oncologists. Available information on the impact of genetic variability in COMT on the effect of opioids in cancer pain remains limited, especially in children [10]. In Europe, the incidence rate of cancer in young subjects (0–14 years of age) is 13.9/100,000 [1, 11], with leukemia and nervous system cancer being the most represented (incidence 4.7 and 2.4, respectively). Between 3 and 5% of childhood cancers are bone tumors with osteosarcoma representing the most commonly diagnosed primary malignant cancer, with an incidence rate of 4.0 and 3.1 in young male and female subjects < 24 years, respectively [12].

While pharmacogenetic data on opioids in adult cancer patients are limited, those in paediatric subjects are completely missing. Thus, the aim of Suitable Treatment for Oncologic Pediatric Pain (STOP Pain) study was to investigate the association between the most studied COMT polymorphism, rs4680, and the inter-individual differences in response to opioid analgesic therapy in a cohort of paediatric cancer patients receiving opioids. The opioid dose employed and the modifications in pain intensity were evaluated as efficacy outcomes, and the number of central and gastrointestinal adverse effects as safety outcomes.

Furthermore, to evaluate whether the impact of this COMT polymorphism is age-dependent, data obtained in paediatric patients were compared to existing data in adult subjects. To this aim, a systematic review of published studies was conducted separately for three different outcomes: opioid dosing, pain intensity, and side effects.

Methods

STOP Pain study

Materials and methods of the present study have been already published in part by Lucenteforte et al. 2018 [13]. Briefly, STOP Pain is a prospective observational cohort pilot study enrolling hospitalized patients (0–17 years) between June 2011 and December 2014. Being a pilot study, we did not calculate the cohort sample size to test our hypothesis and all recruitable patients were enrolled. The study was approved by the institutional review board of Meyer Children’s Hospital. A psychologist, expert in paediatric pain management, administered to children parents structured questionnaires including demographic information, medical history, concomitant illnesses, and children lifestyle. Data about treatment responsiveness were collected to take in account any confounding variable and/or effect modifier. Patients were anonymized by unique Patient Code, and then matched with genotyping results obtained using the Taqman assay (ABI, Applied Biosystems, Foster City, CA).

Standard opioid conversion to intravenous (IV) morphine equivalents (ME) was performed [13, 14]. The dose of administered morphine was also considered separately by the other drugs. Regarding non-opioid analgesics, the conversion of these medications to IV ME was not performed.

Three indicators of dose were used for pain relief evaluation: cumulative dose (mg/kg) of IV ME administered during the first 24 h of treatment or titration phase (Dose24h); total dose (mg/kg) of IV ME from Day 1 to the last day of pain therapy (Dosetot); mean dose (mg/kg) required to achieve maximal total pain relief reported by the patient (DoseVAS = 0). Pain relief evaluation was also assessed using pain intensity scores. Children pain was measured using three different scales: visual analog scale (VAS) was compiled by 58 subjects over six years of age; Wong & Baker FACES Pain Rating Scale was administered to 5 children between the ages of four and six years of age; Face, Legs, Activity, Cry and Consolability (FLACC) scale was used by nurses to assess pain in 24 children (less than four years old) unable to communicate their pain. These measures were performed at time 0 (before treatment) and defined as PIto. Then, two parameters for pain intensity were considered: difference between the pain intensity after 24 h-treatment and PIto (∆ VAS); time to reach the lowest pain intensity reported by the patient (Time tot). The number of side effects was analyzed using three categorical (presence/absence) variables: onset of any adverse drug reactions (ADRs) and onset of gastrointestinal or CNS effects. Each patient could experience more than one ADR. ADRs were recorded every eight hours for each patient.

Statistical analyses

Continuous variables were checked for normality by using the Shapiro-Wilk W test. Differences of means for normally distributed variables were compared by one-way ANOVA; differences of medians for non-normally distributed variables were compared by Pearson chi-squared test. Differences of percentages of categorical variables were compared by chi-square test.

We also evaluated association between efficacy (high doses and high pain intensity, defined as values ≥medians, versus low doses and low pain intensity, defined as values <medians) and safety parameters (presence of side effects versus no side effects) and COMT rs4680 polymorphism by calculating odds ratios (ORs) and 95% confidence intervals (CIs) using logistic regression models adjusted for gender, age, body mass index (BMI), diagnosis, metastasis, pain location, and pain intensity at baseline. We considered a p-value < 0.05 as significant.

The software STATA 14.2 (StataCorp, 2011; Stata Statistical Software: Release 14. College Station, TX) was used for all analyses.

Systematic literature review

Cochrane Handbook and the Prisma Statement for Systematic Reviews [15] were used to perform the present review registered in PROSPERO with the number CRD42017057831.

Studies were systematically searched in PUBMED and EMBASE databases up to November 24, 2016 [13]. Full search strategy was reported in Additional file 1. Furthermore, we conducted an additional research for COMT gene to find all possible articles using as search key: COMT[tiab] AND cancer[tiab] AND opioid*[tiab].

Independent investigators (AP and GC) selected articles reviewing titles and abstracts. Two other independent reviewers (EL and VM) resolved through discussion and consensus any disagreements.

Then, AP and GC independently read the full texts selecting the original articles when patients were cancer patients, involved drugs were opioids, outcomes were related to opioid non-response and safety, and one or more genes were studied.

Mean age and gender of the sample, study type and size, location, year of publication, drugs used and genetic information (name of gene, investigated polymorphism and main results) were collected for each study. By study design, only results reported in papers investigating COMT gene as factor associated to therapy outcome were taken into consideration. “Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies” [16] was used to assess the quality of the included studies (see the criteria reported in Additional file 2).

Results

STOP Pain study

Patient population

One hundred thirty-three patients were evaluated from June 2011 to December 2014. Forty-six children were not included in the study for the following reasons: refusals of consent (n = 8), end of life patients (n = 12), early discharge (before to administer the questionnaires; n = 18), hospitalization in sterile room (n = 8). Table 1 showed the distribution of the main characteristics of the 87 Italian subjects included in the STOP Pain study. Of them, 56% were males. Most were older than 3 years of age and 43% older than 12. BMI was < 25th percentile in 42%, and between 25th and 75th in 26%. Main cancer diagnoses were leukemia or lymphoma (39%), followed by sarcoma (21%) and osteosarcoma (20%). Cancer metastases were present in 26% of the cases; pain in oral cavity in 49%, and skeletal pain in 16%. To achieve pain relief, patients were primarily treated with morphine (69%). A total of five patients (6%) were treated at least once with codeine-containing medication, in particular with a fixed dose combination of codeine and paracetamol. Of them, four patients were administered an oral preparation (codeine 30 mg + paracetamol 500 mg), and only one patient a rectal preparation (codeine 5 mg + paracetamol 200 mg).
Table 1

Characteristics of the 87 Italian subjects included in the STOP Pain Project: overall and stratified for COMT rs4680 polymorphism

 

Overall

G/Ga

A/Ga

A/Aa

p-value b

N (%)

N (%)

N (%)

N (%)

 

87

24 (30.38)

42 (53.16)

13 (16.46)

 

Gender

 Male

49 (56.32)

15 (62.50)

22 (52.38)

7 (53.85)

0.721

 Female

38 (43.68)

9 (37.50)

20 (47.62)

6 (46.15)

Age (months)

 0–36

18 (20.69)

4 (16.67)

8 (19.05)

4 (30.77)

0.466

  > 36–144

32 (36.78)

10 (41.67)

18 (42.86)

2 (15.38)

  > 144

37 (42.53)

10 (41.67)

16 (38.10)

7 (53.85)

BMI (percentile)

  < 25th

33 (42.31)

5 (25.00)

18 (46.15)

5 (38.46)

0.490

 25th- < 75th

20 (25.64)

8 (40.00)

8 (20.51)

4 (30.77)

  ≥ 75th

25 (32.05)

7 (35.00)

13 (33.33)

4 (30.77)

missing

9

    

Diagnosis

 Brain Tumour

6 (6.90)

2 (8.33)

2 (4.76)

0.728

 Leukaemia and Lymphoma

34 (39.08)

11 (45.83)

18 (42.86)

4 (30.77)

 Neuroblastoma

6 (6.90)

1 (4.17)

3 (7.14)

2 (15.38)

 Osteosarcoma

17 (19.54)

2 (8.33)

8 (19.05)

3 (23.08)

 Sarcoma

18 (20.69)

7 (29.17)

8 (19.05)

2 (15.38)

 Others

6 (6.90)

1 (4.17)

3 (7.14)

2 (15.38)

Metastasis

 No

64 (73.56)

19 (79.17)

26 (61.90)

12 (92.31)

0.067

 Yes

23 (26.44)

5 (20.83)

16 (38.10)

1 (7.69)

Pain location

 Abdominal

12 (13.79)

5 (20.83)

4 (9.52)

2 (15.38)

0.808

 Oral cavity

43 (49.43)

11 (45.83)

23 (54.76)

6 (46.15)

 Skeletal – Muscle

14 (16.09)

3 (12.50)

5 (11.90)

3 (23.08)

 Other

18 (20.69)

5 (20.83)

10 (23.81)

2 (15.38)

Pain Intensity (PIto), mean (95% CI)

4.34 (3.88–4.81)

4.08 (3.33–4.84)

4.57 (3.89–5.25)

4.38 (2.81–5.96)

0.674a

Drug

 morphine

60 (68.97)

16 (66.67)

32 (76.19)

8 (61.54)

 

 tramadol

19 (21.84)

5 (20.83)

9 (21.43)

3 (23.08)

0.632

 oxycodone

2 (2.30)

1 (4.17)

 codeine

2 (2.30)

1 (4.17)

1 (7.69)

 more than one

4 (4.60)

1 (4.17)

1 (2.38)

1 (7.69)

a Biological samples of 8 patients were not available for medical reasons. Genetic data were not available for technical reasons (failure of the genetic test)

b p-value from ANOVA

Biological samples of eight patients were not available for medical reasons, however main characteristics of these patients were comparable with those of genotyped patients (see Additional files 3 and 4).

Influence of COMT polymorphism on efficacy and safety parameters

None of the above characteristics was different among the three genotype groups (p-values ≥ 0.05). Genotype frequencies agreed with the Hardy–Weinberg equilibrium (p-value = 0.45) and consistent with 1000 Genome Project Data for European population (EUR) [17].

Table 2 shows mean and standard deviation values of opiate and morphine dose, pain intensity, and distribution of side effects in the three genotype groups. The mean of Timetot significantly differed across the three groups (p-value = 0.042). In particular, the mean for AG and AA groups was lower than GG (p-value = 0.0094 and 0.0026, respectively). No difference was observed for all the other considered variables (p-values ≥ 0.05).
Table 2

Efficacy and safety parameters according to COMT rs4680 polymorphism in the 87 subjects included in the STOP Pain Project

 

Overall

G/G

A/G

A/A

p-value

Opioids

 Dose (mg/kg)

  Dose24h

   mean (95% CI)

0.38 (0.33–0.42)

0.41 (0.30–0.52)

0.39 (0.33–0.45)

0.36 (0.23–0.48)

 

   median (interquartile range)

0.41 (0.19–0.50)

0.43 (0.16–0.57)

0.42 (0.24–0.49)

0.43 (0.19–0.46)

0.921*

  Dosetot

   mean (95% CI)

2.57 (2.17–2.96)

3.34 (2.45–4.23)

2.35 (1.83–2.87)

2.24 (1.11–3.37)

 

   median (interquartile range)

2.18 (1.20–3.50)

3.25 (1.73–5.23)

2.02 (1.26–2.97)

2.18 (1.06–3.07)

0.119*

  DoseVAS = 0

   mean (95% CI)

0.41 (0.27–0.55)

0.59 (0.18–1.01)

0.35 (0.22–0.48)

0.39 (0.03–0.75)

 

   median (interquartile range)

0.26 (0.10–0.49)

0.36 (0.07–0.74)

0.21 (0.11–0.47)

0.25 (0.10–0.41)

0.568*

Pain Intensity

 ∆ VAS, N (%)

   ≤ 2

46 (54.12)

15 (62.50)

20 (50.00)

6 (46.15)

0.533**

   ≥ 2

39 (45.88)

9 (37.50)

20 (50.00)

7 (53.85)

 Time tot (hours)

     

  mean (95% CI)

140.43 (126.81–154.05)

166.87 (143.42–190.33)

133.67 (111.87–155.46)

116.61 (87.64–145.59)

0.042***

  median (interquartile range)

133.00 (99.00–192.00)

190.50 (139.25–199.00)

130.00 (96.00–185.50)

120.00 (76.00–144.00)

 

Side effects, N (%)a

 Gastrointestinalb

23 (26.44)

8 (33.33)

9 (21.43)

3 (23.08)

0.553**

 CNSc

10 (11.49)

2 (8.33)

4 (9.52)

2 (15.38)

0.780**

 Totald

32 (36.78)

10 (41.67)

14 (33.33)

4 (30.77)

0.736**

Morphine

 Dose (mg/kg)

  Dose24h

   mean (95% CI)

0.49 (0.45–0.53)

0.55 (0.44–0.66)

0.47 (0.41–0.52)

0.49 (0.38–0.60)

 

   median (interquartile range)

0.46 (0.41–0.57)

0.50 (0.43–0.65)

0.46 (0.40–0.55)

0.46 (0.43–0.60)

0.170*

  Dosetot

   mean (95% CI)

3.19 (2.72–3.67)

4.39 (3.49–5.30)

2.75 (2.14–3.35)

3.04 (1.48–4.60)

 

   median (interquartile range)

2.93 (1.82–4.42)

4.52 (3.25)

2.23 (1.72–3.40)

2.98 (1.70–3.33)

0.050*

  DoseVAS = 0

   mean (95% CI)

0.54 (0.35–0.74)

0.88 (0.28–1.49)

0.41 (0.25–0.56)

0.56 (0.00–1.16)

 

   median (interquartile range)

0.36 (0.17–0.60)

0.54 (0.36–0.96)

0.26 (0.14–0.52)

0.37 (0.20–0.48)

0.257*

Pain Intensity

 ∆ VAS, N (%)

   ≤ 2

29 (49.15)

10 (62.50)

16 (51.61)

2 (25.00)

0.221**

   ≥ 2

30 (50.85)

6 (37.50)

15 (48.39)

6 (75.00)

 Time tot (hours)

  mean (95% CI)

147.96 (131.56–164.36)

174.62 (149.82–199.43)

139.53 (113.56–165.50)

134.00 (96.72–171.28)

0.154***

  median (interquartile range)

142.50 (101.50–193.50)

190.50 (150.00–199.00)

130.25 (100.5–188.75)

123.50 (99.50–173.25)

 

Side effects, N (%)a

 Gastrointestinalb

14 (23.33)

5 (31.25)

6 (18.75)

2 (25.00)

0.621**

 CNSc

6 (10.00)

1 (6.25)

2 (6.25)

1 (12.50)

0.817**

 Totald

19 (31.67)

6 (37.57)

9 (28.13)

2 (25.00)

0.752**

Opioids: morphine equivalents (patients that used more than one opioid)

Morphine: patients that used only morphine

Dose24h: total dose (mg/kg) of intravenous (IV) morphine equivalents (ME) administered during the titration phase; Dosetot: total dose (mg/kg) of IV ME; DoseVAS = 0: mean dose (mg/kg) required to achieve total pain relief

PIto: pain intensity before treatment, measured with FLACC (Face, Legs, Activity, Cry, Consolability) or VAS (Visual Analogic Scale) numeric scale or WONG & BAKER Pain Rating Scale (range: 0–10); ∆ VAS: difference between the pain intensity after 24 h of treatment and PIto; Time tot: time in hours to reach the lowest pain intensity possible

a N, number of patients who experienced that ADR; each patient could have experienced more than one ADRs

b Gastrointestinal effects included nausea/vomiting, diarrhea, and constipation

c CNS: Central Nervous System effects included agitation, drowsiness, headache, and sedation

d Total number of patients with gastrointestinal and/or central nervous system effects, and/or itching

* p-value from Pearson chi-squared test of the equality of the medians

** p-value form ANOVA

*** p-value from chi-squared test

Table 3 shows the association of efficacy and safety parameters with COMT rs4680 polymorphism. After the adjustment for gender, age, BMI, diagnosis, metastasis, pain location and pain intensity at baseline, we found a statistically significant association for the efficacy parameters Dosetot and Timetot. In particular, A/G subjects needed a lower total amount of drugs compared to G/G individuals (adjusted OR 0.27, 95% CI 0.08–0.87), and patients heterozygous or homozygous for the A allele reached the lowest pain intensity faster compared to G/G individuals (OR 0.18, 95% CI 0.05–0.63 and OR 0.11, 95% CI 0.02–0.56, respectively). No significance was found for the association of safety parameters and COMT genotype.
Table 3

Association of efficacy and safety parameters with COMT rs4680 polymorphism in the 87 subjects included in the STOP Pain Project

 

N (%)

ORa (95% CI)

G/G

A/G

A/A

Dose (mg/kg)

 High Dose24h (≥0.41 vs < 0.41)

12 (50.00) vs 12 (50.00)

21 (52.50) vs 19 (47.50)

7 (53.85) vs 6 (46.15)

1 (reference)

0.66 (0.19–2.25)

0.80 (0.16–4.01)

 High Dosetot (≥2.18 vs < 2.18)

16 (66.67) vs 8 (33.33)

18 (42.86) vs 24 (57.14)

7 (53.85) vs 6 (46.15)

1 (reference)

0.27 (0.08–0.87)

0.42 (0.09–1.98)

 High DoseVAS = 0 (≥0.26 vs < 0.26)

14 (58.33) vs 10 (41.67)

26 (65.00) vs 14 (35.00)

9 (69.23) vs 4 (30.77)

1 (reference)

0.98 (0.30–3.24)

1.47 (0.29–7.40)

Pain Intensity

 High ∆VAS (≥2 vs < 2)

14 (58.33) vs 10 (41.67)

20 (47.62) vs 22 (52.38)

6 (46.15) vs 7 (53.85)

1 (reference)

0.51 (0.15–1.65)

0.57 (0.12–2.82)

 High Timetot (≥133 vs < 133)

19 (79.17) vs 5 (20.83)

19 (45.24) vs 23 (54.76)

4 (30.77) vs 9 (69.23)

1 (reference)

0.18 (0.05–0.63)

0.11 (0.02–0.56)

Side effects, N (%)

 Gastrointestinalb

8 (33.33) vs 16 (66.67)

9 (21.43) vs 33 (78.57)

3 (23.08) vs 10 (76.92)

1 (reference)

0.56 (0.16–1.91)

0.46 (0.09–2.44)

 CNSc

2 (8.33) vs 22 (91.67)

4 (9.52) vs 38 (90.48)

2 (15.38) vs 11 (84.62)

1 (reference)

1.39 (0.21–9.44)

1.62 (0.17–15.78)

 Totald

10 (58.33) vs 10 (41.67)

14 (33.33) vs 28 (66.67)

4 (30.77) vs 9 (69.23)

1 (reference)

0.86 (0.29–2.56)

0.58 (0.13–2.54)

Dose24h: total dose (mg/kg) of intravenous (IV) morphine equivalents (ME) administered during the titration phase; Dosetot: total dose (mg/kg) of IV ME; DoseVAS = 0: mean dose (mg/kg) required to achieve total pain relief

PIto: pain intensity before treatment, measured with FLACC (Face, Legs, Activity, Cry, Consolability) or VAS (Visual Analogic Scale) numeric scale or WONG & BAKER Pain Rating Scale (range: 0–10); ∆ VAS: difference between the pain intensity after 24 h of treatment and PIto; Time tot: time in hours to reach the lowest pain intensity possible

aOR and corresponding 95% confidence intervals from logistic regression models adjusted for gender, age, BMI, diagnosis, metastasis, pain location and pain intensity at baseline

bGastrointestinal side effects comprehend nausea, vomiting, diarrhea and constipation

cCNS, Central Nervous System side effects comprehend agitation, drowsiness, headache and sedation

dTotal side effects comprehend the occurrence of gastrointestinal and CNS side effects, and itching

Systematic literature review

Study selection

Systematic literature search produced 350 records. After an initial screening of titles and abstracts, 239 studies were excluded for the following reasons: not relevant (137); reviews (39); not human studies (36 studies); case reports (17); full text not available (10). We obtained the full text of 45 articles. Of them, 11 studies were defined not relevant because they did not focus on genotype effects on our clinical outcomes (pain relief and adverse events), and four focused on cancer patients’ postoperative pain and postoperative adverse events, thus they were excluded. Characteristics of studies excluded after full-text reading are shown in Additional file 5. Finally, we included a total of 31 studies (see the flowchart of study selection in Fig. 1): 30 from the above 45 studies and 1 additional study obtained with further search strategy (identifying 16 records).
Fig. 1
Fig. 1

PRISMA Flow diagram of the selection of studies searched with PUMBED and EMBASE and included in the systematic review

Study quality assessment

Of the 31 selected papers, nine investigated COMT and were analyzed in the present review [1826]; none of the studies was conducted on paediatric subjects. Assessment of methodological quality was performed for the considered studies using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies [16]. All studies met the quality criteria regarding the research question or objective (items 1), inclusion/exclusion criteria and participants’ recruitment (item 4), exposure of interest measurement prior to the outcome measurement (item 6), length of follow-up (item 7), independent and dependent variable description/definition (item 9 and 11), multiple assessment of the exposure (item 10), and lost at follow-up (item 13). Only 28 and 33% of studies received negative responses for the items 2 and 14, respectively. Participation rate of eligible patients (items 3) and sample size justification (item 5) were clearly described in 42 and 8% of studies, respectively. Categories of exposure (item 8) and blinding of assessors (item 12) were applicable only in 22 and 3% of studies, respectively.

Study description

Table 4 shows the characteristics of five studies (reported in nine papers) included in the systematic review. Three studies were conducted in Europe, one in Japan, and one in Tunisia. The EPOS study included the highest number of patients; drugs used were morphine in three studies, morphine, methadone, fentanyl, hydromorphone, buprenorphine, ketobemidone, and oxycodone in another and morphine, oxycodone, fentanyl, and methadone in the last study. Three studies reported data on opioid dose as well as on pain and side effects.
Table 4

Characteristics of five studies (nine papers) investigating the association between COMT gene and opioid response and/or side effects included in the systematic review

Study name, [ref]

Study design

Patients characteristics

Opioid administered

Data

M/F

Mean Age (years)

Opioid dose

Pain

Side effects

EPOS study

European observational study

Cancer pain patients

Morphine, methadone, fentanyl, hydromprphone, buprenorphine, ketobemidone, oxycodone

     

[18] (Klepstad, 2011)

 

2201 Caucasians

X

  

1154/1047

62.4

[19] (Barratt, 2014)a

 

667 subjects treated with transdermal fentanyl

   

334/342

Median: 64

[20] (Barratt, 2015)b

 

468 Caucasian subjects treated with transdermal fentanyl

 

X

X

218/250

Median: 64

[21] (Laugsand, 2011)

 

1579 subjects not receiving chemotherapy and with information on nausea and vomiting

  

X

850/729

61.9

[22] Matsuoka, 2012

Japanese observational study

48 Opioid-treatment-naïve cancer patients

Morphine

X

  

25/23

69.0

[23, 24] Rakvåg, 2005–2008

Norwegian observational study

207 Cancer pain Caucasian patients

Morphine

X

X

X

117/90

63.2

[28] Ross, 2008

United Kingdom case-control study

228 Cancer pain patients

Morphine, Oxycodone, fentanyl, methadone

 

X

X

106/122

57.2

[26] Chatti, 2016

Tunisian observational study

129 Cancer pain patients

Morphine

X

  

63/66

Number of patients for each age group:

17-25 yrs.: 12

26-45 yrs.: 50

46-65 yrs.: 67

aBarratt, 2014 [19] was a subgroup analysis of Klepstad, 2011 [18] (i.e. 676 subjects treated with transdermal fentanyl)

bBarratt, 2015 [20] was a subgroup analysis of Barratt, 2014 [19] (i.e. 468 Caucasian subjects treated only with transdermal fentanyl)

Tables 5, 6 and 7 report the evaluated association between opioid dose, pain intensity, and side effects with COMT rs4680 polymorphism. Two studies included in the systematic review agreed on the finding that opioid Dose24h was lower among subjects with the A (158 Met) allele [22, 23]. Only one study [26] reported an association between the Dosetot of oral morphine and dose escalation, without reaching statistical significance. Otherwise, no clear associations emerged with pain intensity (measured with Brief Pain Inventory) or with side effects, with the exception of the EPOS study in which the intensity of nausea and vomiting (EORTC score) was significantly lower among the subjects with A/G genotype (p-value = 0.002).
Table 5

Association between opioid dose and COMT rs4680 polymorphism in the studies included in the systematic review

Study name [ref]

Variable

Genotype frequency

(N, %)

Results (type of measure)

p-value

EPOS study [18] (Klepstad, 2011)

Dose in mg after 24 h

G/G (324, 22.18)

A/G (726, 49.69)

A/A (411, 28.13)

180 mg

180 mg

160 mg (median)

0.545

[22] Matsuoka, 2012

Dose in mg after 24 h

G/G (19, 46.34)

A/G (18, 43.90)

A/A (4, 9.76)

43.7 ± 21.4

28.9 ± 3.2

30.0 ± 0.0 (mean ± SD)

0.03

[23] Rakvåg, 2005

Dose in mg after 24 h

G/G (44, 21.25)

A/G (96, 46.38)

A/A (67, 32.37)

155 ± 160

117 ± 100

95 ± 99 (mean ± SD)

0.025

[26] Chatti, 2016

Total dose requirement

(continuous) AvsG

G/G (30, 23.3)

A/G (57, 44.2)

A/A (42, 32.6)

−2.10 (difference)

0.334

 

Need of escalation

(yes/no) AvsG

 

OR 0.76 (0.45; 1.27)

0.293

Table 6

Association between pain intensity and COMT rs4680 polymorphism in the studies included in the systematic review

Study name [ref]

Variable

Genotype frequency (N, %)

Results (type of measure)

p-value

EPOS study [20] (Barratt, 2015)

Brief Pain Inventory

G/G (109, 23.59)

A/G (243, 52.60)

A/A (110, 23.80)

Not reported

 

[23] Rakvåg, 2005

Brief Pain Inventory after 24 h

G/G (44, 21.25)

A/G (96, 46.38)

A/A (67, 32.37)

3.9 ± 2.2

3.7 ± 2.6

3.5 ± 2.3

(mean ± SD)

> 0.05

[28] Ross, 2008

Brief Pain Inventory after 24 h

G/G (46, 20.81)

A/G (119, 53.85)

A/A (56, 25.34)

Not reported

0.897

Table 7

Association between side effects and COMT rs4680 polymorphism in the studies included in the systematic review

Study name [ref]

Variable

Genotype frequency (N, %)

Results (type of measure)

p-value

EPOS study [20] (Barratt, 2015)

Tiredness, Depression, Cognitive Dysfunction, Constipation

G/G (109, 23.59)

A/G (243, 52.60)

A/A (110, 23.80)

Not reported

 

[21] (Laugsand, 2011)

Nausea and Vomiting EORTC Score

G/G (341, 21.93)

A/G (787, 50.61)

A/A (427, 27.46)

25.8 ± 30.5

21.5 ± 26.6

26.2 ± 29.2

(mean ± SD)

0.002

[23] Rakvåg, 2005

Fatigue EORTC Score

G/G (44, 21.25)

A/G (96, 46.38)

A/A (67, 32.37)

73 ± 23

62 ± 24

66 ± 22

(mean ± SD)

> 0.05

 

Nausea and vomiting EORTC Score

 

30 ± 27

24 ± 26

29 ± 27

(mean ± SD)

> 0.05

 

Dyspnea EORTC Score

 

39 ± 35

38 ± 34

32 ± 33

(mean ± SD)

> 0.05

 

Sleep EORTC Score

 

39 ± 35

38 ± 34

32 ± 33

(mean ± SD)

> 0.05

 

Appetite EORTC Score

 

64 ± 36

49 ± 38

53 ± 38

(mean ± SD)

> 0.05

 

Constipation EORTC Score

 

56 ± 41

57 ± 37

54 ± 37

(mean ± SD)

> 0.05

[28] Ross, 2008

Central side effect

G/G (46, 20.81)

A/G (119, 53.85)

A/A (56, 25.34)

Not reported

0.956

EORTC European Organization for Research and Treatment of Cancer Core

Prisma checklist validation is reported in Additional file 6.

Comparison of STOP PAIN and systematic review results

Figure 2 allows a direct visualization via a forest plot of the comparison of the STOP PAIN data with those included in the systematic review, relative to the 24 h opioid cumulative dose (when reported as mean ± standard deviation (SD)).
Fig. 2
Fig. 2

Forest plot of the comparison of STOP Pain data with those included in the systematic review, relative to the 24-h opioid cumulative dose (when reported as mean ± SD)

Discussion

This is the first paediatric study addressing the role of COMT polymorphism rs4680 in opioid treatment of cancer patients as well as the inter-individual differences in the response to opioid analgesic therapy. Furthermore, we also compared the obtained evidences to existing data in adults retrieved by means of a systematic review of medical literature. The main result of the STOP Pain study was that paediatric cancer patients with the GG genotype needed a longer time to reach the lowest possible pain intensity compared with the A-containing genotypes. However, this association can be due to chance, given the many interaction tests carried out. The systematic review regarding adult cancer patients showed that the Dose24h was lower among subjects presenting the A allele [22, 23, 27], and no clear association was found between the polymorphism and either pain intensity or side effects.

Focusing on investigations into the role of COMT rs4680 polymorphism in cancer pain, the G/G genotype was shown to require higher morphine daily doses as compared to G/A and A/A ones [22, 23, 27]. This association was not confirmed in Caucasian adults by a large multicenter European (11 countries) study [18] and an investigation on Tunisian patients [26]. Genetic heterogeneity (and cosmopolitan areas) could contribute to this contradictory finding compared to the others homogenous populations [22, 23].

On the other hand, the first study showed an association of this polymorphism with opioid-induced nausea [21]. In another study, morphine central side effects were shown to be associated with a different COMT polymorphism [28]. The relevance of the selected candidate gene was due to the association between the catechol-O-methyltransferase and chronic pain: COMT affects dopamine concentration in the prefrontal cortex of the human brain, influencing pain regulation at different levels [29]. Several of the most frequent SNPs within COMT have been investigated in relation to mRNA expression, protein levels and enzyme activity. Principally, even if a more complex genetic basis could not be excluded, the nonsynonymous SNP rs4680 variation induced decreased enzyme thermostability and activity [29]. This lower activity of COMT enzyme, with a consequent increase of dopamine bioavailability [4], was reported to increase opioid receptor density and to enhance opioid analgesia and adverse effects in several types of cancer pain [18, 2123].

COMT polymorphisms associated with lower COMT activity have been studied in several painful conditions, such as postoperative surgery, cancer pain, neuropathic pain, and migraine or headache [30], not only in adult subjects but also in paediatric populations [3134]. In osteoarthritis, the low-activity allele of COMT [158Met or A] was associated with increased hip pain in patients with damaged hip [35]. Low COMT activity has been related to increased pain sensitivity in experimental tests, whereas high activity COMT haplotypes protect from the development of chronic muscle-skeletal painful conditions [36].

In our sample of paediatric patients, the association with rs4680 COMT polymorphism was found using the total dose requested for maximal possible pain reduction, whereas the 24-h dose that correlated in adult cancer patients only showed a slight tendency towards association. The reasons for this discrepancy may reside in differences between adults and children in both pharmacokinetics and pharmacotherapy regimens. In fact, while pharmacological therapy in adults is based on fixed-dose increments, treatment in children needs to be personalized based on age and weight (or body surface), considering incomplete organ maturation, differences in body composition and plasma proteins, in receptor density and type. For example, children younger than 11 years show faster clearance of morphine and morphine metabolites than older subjects [37]. Furthermore, there is wide variability in morphine clearance among paediatric subjects, particularly neonates and infants [38].

For these patients, analgesics are the drug class most commonly used in hospital [39], with morphine being one of the most used among the strong opioids [40], and some of these drugs (i.e., oxycodone, tramadol) are generally used in an off-label manner, with a potential increased risk of adverse effects. Moreover, experimental and clinical data support the presence of significant differences between opioid pharmacological characteristics [41]. Although all opioids interact with the mu (μ) opioid receptor as primary target, these compounds differ in chemical structures, in pharmacokinetics, efficacy, effectiveness, and toxicity [42]. Moreover, the use of equianalgesic tables raises several concerns: (1) calculating the median equivalence values based on them may be inaccurate, mainly for possible different variable equivalence ranges within or between different equianalgesic tables; (2) equivalence ratios derived from computation rather than from clinical data could be devoid of clinical context and might be grossly inaccurate; (3) formulations containing an opioid and other analgesics (i.e., nonsteroidal anti-inflammatory drugs) cannot be compared to a single opioid and calculation of equivalents may be difficult [43].

Overall, results obtained for adults and children in both the total amount of opioids administered and in the dose administered during the first 24 h, could be primarily related to the factors mentioned above: developmental pharmacokinetic, medications’ differences, and the use of conversion tables. These variables could have influenced the relationship between COMT polymorphism and the opioids doses.

In light of these considerations, the secondary aim of the present study (whether the impact of rs4680 polymorphism is age-dependent) is not completely pointed out. Our results suggest that, independently from age, rs4680 could actively influence the efficacy of pain therapy also in children. Future studies, with large number of paediatric patients, are needed to fully ascertain the trend towards association shown also by our observational data.

Current guidelines encourage accurate monitoring of pain in children and its treatment [2] in order to reduce as much as possible, the psychological and behavioral effects of the pain sensation, whose impact is greater in subjects during growth [44]. However, pain perception and ability to report it is not homogeneous within the paediatric population making it necessary to use different pain evaluation scales, which for younger subjects rely only on signs: this can be another factor concurring to increase the inter-individual variation in the requested opioid dose, particularly during the initial titration phase. Furthermore, the inclusion of different pain rating scales raises the problem of consistency between the scales, although FLACC scores were reported to be comparable to those generated using 0-to-10 number rating scales [45], and pain severity ratings with the Wong & Baker scale resulted highly correlated with those of VAS [46].

The number of enrolled patients in the STOP Pain study was relatively small. However, the uniform treatment regimen, with 69% of patients receiving titration of morphine by continuous i.v. infusion, reduces the effect of this shortcoming. Future studies should prospect a multi-center collaborative setting to address this point. Moreover, regarding pharmacological treatment, we could not establish whether any non-opioid analgesics used in our sample may have affected pain ratings and side effects. This information would be very important, since some agents may synergize with opioids and/or have similar genetic influences on metabolism and/or drug sensitivity.

Another limitation is represented by the mixed nature of pain in cancer patients. In fact, paediatric cancer pain can vary from acute, procedure-related pain to progressive chronic and breakthrough pain, associated to disease progression and/or treatment. Moreover, due to the relatively small sample size when considering the wide variability of cancer diagnoses, it was difficult to state whether the type and location of the primary cancer or metastases influenced the pain measurements independent of genetic influences on opioid metabolism and receptor activity. Thus, whether cancer-associated pain may represent an additional bias, it may only be determined through a large study including comparison of patients with pain of different origins. Finally, an additional obvious limitation is that the present results in children can only be compared with those reported in studies conducted on adults, simply due to the lack of similar studies in paediatric subjects. On the other hand, comparison of paediatric data with those obtained in adults, collected in a systematic review, has shown differences in the relevant parameters (24 h dose vs. total dose) providing useful information for future studies in children.

Conclusions

The results of this research show that paediatric cancer patients with the GG genotype, compared with the A-containing genotypes, received a higher mean dose of morphine equivalents and needed a longer time to reach the lowest possible pain intensity. These results suggest that the presence of A allele in COMT rs4680 SNP could represent an evaluable marker of opioid sensitivity in paediatric cancer patients, as well as in adults. Although further studies are needed to confirm these findings, to date these evidences are still not sufficient to support a previous expensive evaluation of COMT SNPs before starting an opioid therapy in children suffering for cancer pain.

Notes

Abbreviations

ADRs: 

Adverse drug reaction

BMI: 

Body mass index

CIs: 

Confidence intervals

COMT: 

Catechol-O-methyltransferase

FLACC: 

Face, Legs, Activity, Cry and Consolability

IV: 

Intravenous

ME: 

Morphine equivalents

ORs: 

Odds ratios

SD: 

Standard deviation

SNP: 

Single nucleotide polymorphism

STOP PAIN: 

Suitable Treatment for Oncologic Pediatric Pain

VAS: 

Visual analog scale

Declarations

Acknowledgements

STOP Pain was initiated by the Tuscany Region and was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC): their support is greatly appreciated. We also wish to thank the Meyer Children Hospital’s staff, all the patients, and their parents.

Funding

This work was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC) grant number [10465]. The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

All data generated or analyzed during the present review are included in this article and its Additional files. The datasets generated and analyzed during the STOP Pain project are available from the authors upon reasonable request.

Authors’ contributions

VM: PI of the STOP Pain study; VM, EL, RB: conception and design of the work; MA, AMesseri: patient recruitment; LV, GC, AP, RB, NL: data collection; SG, LG, VC, MLC: genetic analyses; EL, VM, AV: data analysis and interpretation; AV, EL, LG, VM: drafting the article; EL, VM, AV, Amugelli, RB: critical revision of the article. All authors discussed the results and implications of the manuscript and approved the final version to be published.

Ethics approval and consent to participate

The STOP Pain study obtained ethics approval from the institutional review board at Meyer Children’s Hospital (Protocol letter 9116/2010, December 14, 2010). Written informed consent to participate in the study was obtained from each patient (or their parent or legal guardian).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Clinical and Experimental Medicine University of Pisa, Pisa, Italy
(2)
Department of Neuroscience, Psychology, Drug Research and Children’s Health, University of Florence, Florence, Italy
(3)
Pain and Palliative Care Unit, Meyer Children’s University Hospital, Florence, Italy
(4)
Medical Genetics Unit, Meyer Children’s University Hospital, Florence, Italy
(5)
Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Neuroscience Department, Meyer Children’s University Hospital, University of Florence, Florence, Italy
(6)
Department of Paediatric Oncohematology, Meyer Children’s University Hospital, Florence, Italy
(7)
Clinical Trial Office, Meyer Children’s University Hospital, Florence, Italy
(8)
Direzione Generale, Azienda Sanitaria Provinciale, Ragusa, Italy
(9)
Medical Genetics Unit, Department of Clinical and Experimental Biomedical Sciences “Mario Serio”, University of Florence, Florence, Italy
(10)
Department of Biology, University of Pisa, Pisa, Italy
(11)
Center for Integrative Medicine, Department of Experimental and Clinical Medicine, Careggi University Hospital, University of Florence, Largo Brambilla, 3 -, 50134 Florence, Italy

References

  1. World Health Organization. Cancer Pain Relief – with a guide to opioid availability 1996.Google Scholar
  2. World Health Organization. WHO guidelines on the pharmacological treatment of persisting pain in children with medical illnesses 2012.Google Scholar
  3. Droney J, Riley J, Ross JR. Evolving knowledge of opioid genetics in cancer pain. Clin Oncol. 2011;23(6):418–28.View ArticleGoogle Scholar
  4. Kapur BM, Lala PK, Shaw JL. Pharmacogenetics of chronic pain management. Clin Biochem. 2014;47(13–14):1169–87.PubMedView ArticleGoogle Scholar
  5. van Esch AA, de Vries E, Te Morsche RH, van Oijen MG, Jansen JB, Drenth JP. Catechol-O-methyltransferase (COMT) gene variants and pain in chronic pancreatitis. Neth J Med. 2011;69(7):330–4.PubMedGoogle Scholar
  6. Berthele A, Platzer S, Jochim B, Boecker H, Buettner A, Conrad B, Riemenschneider M, Toelle TR. COMT Val108/158Met genotype affects the mu-opioid receptor system in the human brain: evidence from ligand-binding, G-protein activation and preproenkephalin mRNA expression. NeuroImage. 2005;28(1):185–93.PubMedView ArticleGoogle Scholar
  7. Moskovitz J, Walss-Bass C, Cruz DA, Thompson PM, Hairston J, Bortolato M. The enzymatic activities of brain catechol-O-methyltransferase (COMT) and methionine sulphoxide reductase are correlated in a COMT Val/met allele-dependent fashion. Neuropathol Appl Neurobiol. 2015;41(7):941–51.PubMedPubMed CentralView ArticleGoogle Scholar
  8. Smith SB, Reenilä I, Männistö PT, Slade GD, Maixner W, Diatchenko L, Nackley AG. Epistasis between polymorphisms in COMT, ESR1, and GCH1 influences COMT enzyme activity and pain. Pain. 2014;155(11):2390–9.PubMedPubMed CentralView ArticleGoogle Scholar
  9. Zubieta JK, Heitzeg MM, Smith YR, Bueller JA, Xu K, Xu Y, Koeppe RA, Stohler CS, Goldman D. COMT val158met genotype affects mu-opioid neurotransmitter responses to a pain stressor. Science. 2003;299(5610):1240–3.PubMedView ArticleGoogle Scholar
  10. Kurita GP, Ekholm O, Kaasa S, Klepstad P, Skorpen F, Sjogren P. Genetic variation and cognitive dysfunction in opioid-treated patients with cancer. Brain behav. 2016;6(7):e00471.PubMedPubMed CentralView ArticleGoogle Scholar
  11. World Health Organization. GLOBOCAN-estimated cancer incidence, Mortality and prevalence worldwide 2012.Google Scholar
  12. Mirabello L, Troisi RJ, Savage SA. International osteosarcoma incidence patterns in children and adolescents, middle ages and elderly persons. Int J Cancer. 2009;125(1):229–34.PubMedPubMed CentralView ArticleGoogle Scholar
  13. Lucenteforte E, Vagnoli L, Pugi A, Crescioli G, Lombardi N, Bonaiuti R, Aricò M, Giglio S, Messeri A, Mugelli A, et al. A systematic review of the risk factors for clinical response to opioids for all-age patients with cancer-related pain and presentation of the paediatric STOP pain study. BMC Cancer. 2018;18(1):568.PubMedPubMed CentralView ArticleGoogle Scholar
  14. Eastern Metropolitan Region Palliative Care Consortium (Victoria) Clinical Group. Opioid Conversion Ratios – Guide to Practice 2013. Available at: http://www.emrpcc.org.au/wp-content/uploads/2013/10/EMRPCC-Opioid-Conversion-2013-final.pdf. Accessed 24 Jan 2019.
  15. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1, 2011. Available at: http://handbook-5-1.cochrane.org/. Accessed 24 Jan 2019.
  16. National Heart L aBIN. Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Available at: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools [Accessed 04 Apr 2018].
  17. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR, et al. The 1000 genomes project consortium. A global reference for human genetic variation. Nature. 2015;526(7571):68–74.PubMedView ArticleGoogle Scholar
  18. Klepstad P, Fladvad T, Skorpen F, Bjordal K, Caraceni A, Dale O, Davies A, Kloke M, Lundstrom S, Maltoni M, et al. Influence from genetic variability on opioid use for cancer pain: a European genetic association study of 2294 cancer pain patients. Pain. 2011;152(5):1139–45.PubMedView ArticleGoogle Scholar
  19. Barratt DT, Bandak B, Klepstad P, Dale O, Kaasa S, Christrup LL, Tuke J, Somogyi AA. Genetic, pathological and physiological determinants of transdermal fentanyl pharmacokinetics in 620 cancer patients of the EPOS study. Pharmacogenet Genomics. 2014;24(4):185–94.PubMedView ArticleGoogle Scholar
  20. Barratt DT, Klepstad P, Dale O, Kaasa S, Somogyi AA. Innate immune Signalling genetics of Pain, cognitive dysfunction and sickness symptoms in Cancer Pain patients treated with transdermal fentanyl. PLoS One. 2015;10(9):e0137179.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Laugsand EA, Fladvad T, Skorpen F, Maltoni M, Kaasa S, Fayers P, Klepstad P. Clinical and genetic factors associated with nausea and vomiting in cancer patients receiving opioids. Eur J Cancer. 2011;47(11):1682–91.PubMedView ArticleGoogle Scholar
  22. Matsuoka H, Arao T, Makimura C, Takeda M, Kiyota H, Tsurutani J, Fujita Y, Matsumoto K, Kimura H, Otsuka M, et al. Expression changes in arrestin beta 1 and genetic variation in catechol-O-methyltransferase are biomarkers for the response to morphine treatment in cancer patients. Oncol Rep. 2012;27(5):1393–9.PubMedGoogle Scholar
  23. Rakvag TT, Klepstad P, Baar C, Kvam TM, Dale O, Kaasa S, Krokan HE, Skorpen F. The Val158Met polymorphism of the human catechol-O-methyltransferase (COMT) gene may influence morphine requirements in cancer pain patients. Pain. 2005;116(1–2):73–8.PubMedView ArticleGoogle Scholar
  24. Rakvag TT, Ross JR, Sato H, Skorpen F, Kaasa S, Klepstad P. Genetic variation in the catechol-O-methyltransferase (COMT) gene and morphine requirements in cancer patients with pain. Mol Pain. 2008;4:64.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Ross JR, Rutter D, Welsh K, Joel SP, Goller K, Wells AU, Du Bois R, Riley J. Clinical response to morphine in cancer patients and genetic variation in candidate genes. Pharmacogenomics J. 2005;5(5):324–36.PubMedView ArticleGoogle Scholar
  26. Chatti I, Creveaux I, Woillard JB, Langlais S, Amara A, Ben Fatma L, Saad A, Gribaa M, Libert F. Association of the OPRM1 and COMT genes' polymorphisms with the efficacy of morphine in Tunisian cancer patients: impact of the high genetic heterogeneity in Tunisia? Therapie. 2016;71(5):507–13.PubMedView ArticleGoogle Scholar
  27. Reyes-Gibby CC, Shete S, Rakvag T, Bhat SV, Skorpen F, Bruera E, Kaasa S, Klepstad P. Exploring joint effects of genes and the clinical efficacy of morphine for cancer pain: OPRM1 and COMT gene. Pain. 2007;130(1–2):25–30.PubMedView ArticleGoogle Scholar
  28. Ross JR, Riley J, Taegetmeyer AB, Sato H, Gretton S, du Bois RM, Welsh KI. Genetic variation and response to morphine in cancer patients: catechol-O-methyltransferase and multidrug resistance-1 gene polymorphisms are associated with central side effects. Cancer. 2008;112(6):1390–403.PubMedView ArticleGoogle Scholar
  29. Tammimaki A, Mannisto PT. Catechol-O-methyltransferase gene polymorphism and chronic human pain: a systematic review and meta-analysis. Pharmacogenet Genomics. 2012;22(9):673–91.PubMedView ArticleGoogle Scholar
  30. Kambur O, Mannisto PT. Catechol-O-methyltransferase and pain. Int Rev Neurobiol. 2010;95:227–79.PubMedView ArticleGoogle Scholar
  31. Chau CM, Ranger M, Sulistyoningrum D, Devlin AM, Oberlander TF, Grunau RE. Neonatal pain and COMT Val158Met genotype in relation to serotonin transporter (SLC6A4) promoter methylation in very preterm children at school age. Front Behav Neurosci. 2014;8:409.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Elens L, Norman E, Matic M, Rane A, Fellman V, van Schaik RH. Genetic predisposition to poor opioid response in preterm infants: impact of KCNJ6 and COMT polymorphisms on Pain relief after endotracheal intubation. Ther Drug Monit. 2016;38(4):525–33.PubMedView ArticleGoogle Scholar
  33. Mamie C, Rebsamen MC, Morris MA, Morabia A. First evidence of a polygenic susceptibility to pain in a pediatric cohort. Anesth Analg. 2013;116(1):170–7.PubMedView ArticleGoogle Scholar
  34. Matic M, Simons SH, van Lingen RA, van Rosmalen J, Elens L, de Wildt SN, Tibboel D, van Schaik RH. Rescue morphine in mechanically ventilated newborns associated with combined OPRM1 and COMT genotype. Pharmacogenomics. 2014;15(10):1287–95.PubMedView ArticleGoogle Scholar
  35. van Meurs JB, Uitterlinden AG, Stolk L, Kerkhof HJ, Hofman A, Pols HA, Bierma-Zeinstra SM. A functional polymorphism in the catechol-O-methyltransferase gene is associated with osteoarthritis-related pain. Arthritis Rheum. 2009;60(2):628–9.PubMedView ArticleGoogle Scholar
  36. Diatchenko L, Slade GD, Nackley AG, Bhalang K, Sigurdsson A, Belfer I, Goldman D, Xu K, Shabalina SA, Shagin D, et al. Genetic basis for individual variations in pain perception and the development of a chronic pain condition. Hum Mol Genet. 2005;14(1):135–43.PubMedView ArticleGoogle Scholar
  37. Hunt A, Joel S, Dick G, Goldman A. Population pharmacokinetics of oral morphine and its glucuronides in children receiving morphine as immediate-release liquid or sustained-release tablets for cancer pain. J Pediatr. 1999;135(1):47–55.PubMedView ArticleGoogle Scholar
  38. Altamimi MI, Choonara I, Sammons H. Inter-individual variation in morphine clearance in children. Eur J Clin Pharmacol. 2015;71(6):649–55.PubMedPubMed CentralView ArticleGoogle Scholar
  39. Lasky T, Ernst FR, Greenspan J, Wang S, Gonzalez L. Estimating pediatric inpatient medication use in the United States. Pharmacoepidemiol Drug Saf. 2011;20(1):76–82.PubMedView ArticleGoogle Scholar
  40. Friedrichsdorf SJ, Postier A, Eull D, Weidner C, Foster L, Gilbert M, Campbell F. Pain outcomes in a US Children's hospital: a prospective cross-sectional survey. Hospital pediatrics. 2015;5(1):18–26.PubMedView ArticleGoogle Scholar
  41. Dale O, Moksnes K, Kaasa S. European palliative care research collaborative pain guidelines: opioid switching to improve analgesia or reduce side effects. A systematic review. Palliat Med. 2011;25(5):494–503.PubMedView ArticleGoogle Scholar
  42. Drewes AM, Jensen RD, Nielsen LM, Droney J, Christrup LL, Arendt-Nielsen L, Riley J, Dahan A. Differences between opioids: pharmacological, experimental, clinical and economical perspectives. Br J Clin Pharmacol. 2013;75(1):60–78.PubMedView ArticleGoogle Scholar
  43. Shaheen PE, Walsh D, Lasheen W, Davis MP, Lagman RL. Opioid equianalgesic tables: are they all equally dangerous? J Pain Symptom Manag. 2009;38(3):409–17.View ArticleGoogle Scholar
  44. Rabbitts JA, Palermo TM, Zhou C, Mangione-Smith R. Pain and Health-related quality of life after pediatric inpatient surgery. J Pain. 2015;16(12):1334–41.PubMedPubMed CentralView ArticleGoogle Scholar
  45. Voepel-Lewis T, Zanotti J, Dammeyer JA, Merkel S. Reliability and validity of the face, legs, activity, cry, consolability behavioral tool in assessing acute pain in critically ill patients. Am J Crit Care. 2010;19(1):55–61 quiz 62.PubMedView ArticleGoogle Scholar
  46. Garra G, Singer AJ, Taira BR, Chohan J, Cardoz H, Chisena E, Thode HC Jr. Validation of the Wong-Baker FACES Pain rating scale in pediatric emergency department patients. Acad Emerg Med Off J Soc Acad Emerg Med. 2010;17(1):50–4.View ArticleGoogle Scholar

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