Abstract
Background
Opioid use disorder (OUD) is an emerging and global public health concern, and its management remains inadequate, notably due to a lack of biomarkers, except for the CYP2B6 genetic polymorphisms.
Objectives
Hence, the aim of this study was to assess the influence of genetic polymorphisms of ABCB1, SLC22A1, COMT, and OPRM1 on biological parameters and clinical response in patients receiving methadone maintenance treatment (MMT).
Methods
A subgroup of 72 patients treated by MMT was genotyped for ABCB1 (rs1045642; rs2032582), SLC22A1 (rs12208357; rs72552763; rs113569197), COMT (rs4680), and OPRM1 (rs1799971) from Opioid PhArmacoLogy (OPAL), a clinical survey of patients suffering from OUD. Associations of these polymorphisms and both clinical and pharmacological (plasma methadone levels) responses were investigated.
Results
All polymorphisms tested were not associated with (R,S)‐methadone concentrations/doses (concentrations relative to doses), (R)‐methadone concentrations/doses nor (S)‐methadone concentrations/doses in bivariate analyses with codominant and recessive models. Also, polymorphisms tested were not related to clinical response (opiate cessation) during MMT in treated patients. The main limitations of our study were the sample size and the absence of polygenic analyses.
Conclusion
This study found no evidence to support the use of genotyping for polymorphisms in the ABCB1, SLC22A1, COMT, and OPRM1 genes in a clinical setting for the management of MMT in OUD.
Keywords: methadone maintenance treatment, opioid use disorder, pharmacodynamic targets, pharmacogenetics, pharmacokinetic targets
Abbreviations
- CCTIRS
Advisory Committee on the Processing of Health Research Information
- CNIL
Data Protection Commission
- COMT
catechol‐O‐methyl‐transferase
- CYP
cytochrome P450
- EDDP
2‐ethylidene‐1,5‐dimethyl‐3,3‐diphenylpyrrolidine
- FDA
US Food and Drug Administration
- GNEDS
Local Research Ethics Committee
- hERG
human voltage‐gated potassium channel
- LC/MS‐MS
liquid chromatography coupled with mass spectrometry
- MAF
minor allelic frequency
- MMT
methadone maintenance treatment
- OCT1
organic cation transporter 1
- OPAL
Opioid Pharmacology
- OUD
Opioid use disorder
1. INTRODUCTION
Opioid use disorder (OUD), along with the global opioid crisis, is a current and growing major public health concern with a prevalence of 510 people per 100 000 population (affecting around 40 million people worldwide) [1]. OUD could be defined as a pattern of opioid use (including heroin use, extra‐medical use of prescribed opioids, or use of illicitly manufactured synthetic opioids) associated with a range of mental, physical, social, and legal issues leading to clinically significant impairment [1, 2]. Its management, which has become a priority of public health policies, relies in part on the use of opioid maintenance treatment medication such as buprenorphine (a partial μ‐opioid receptor agonist and κ‐opioid receptor antagonist) or methadone (a full μ‐opioid receptor agonist) and on social and psychological interventions [1, 2, 3, 4].
Methadone, a ligand of the μ‐opioid receptor (a G‐protein coupled receptor coded by OPRM1), is a listed narcotic approved in France as a maintenance treatment for OUD [4, 5]. The methadone used in clinical practice is a racemic mixture of (R)‐ and (S)‐methadone. The (R)‐enantiomer seems to be associated with most of the opioid effect [6] and has a 10‐fold more potent agonist action on the μ‐opioid receptor [7], whereas (S)‐methadone is 3.5 times more potent than (R) in blocking the human voltage‐gated potassium channel (hERG) [8]. Hence, elevated (R)‐methadone may induce respiratory depression, whereas (S)‐methadone more potently blocks the hERG (related to long QT syndrome) [7, 8, 9].
The interindividual variability of methadone is important, and physicians have to titrate each patient progressively according to the clinical response [10]. Indeed, large interindividual variation exists in the response, tolerance to treatment, and onset of withdrawal symptoms, due to interindividual variability in pharmacokinetic (methadone blood level and elimination half‐life for a given dose) and pharmacodynamic parameters [6]. This variability could be related to pharmacogenetic biomarkers.
Methadone is intensively metabolized by the liver and intestine by several cytochromes P450 (CYP). Thus, our team and others previously showed the impacts of genetic polymorphisms of CYP2B6 on the methadone concentration [5, 6, 8, 11, 12, 13]. Other polymorphisms of drug‐metabolizing enzymes such as CYP2D6 [5, 14], and CYP3A4 [14, 15], and of carriers such as ABCB1 (P‐gp transporter) [11, 12, 16] have been highlighted to be associated with plasma methadone levels. The influence of genetic polymorphisms on the treatment response (illicit opiate cessation) has been less explored and is not yet supported by much evidence [6, 14, 17]. Interestingly, other carriers have been explored in prescribed opioids, such as the organic cation transporter 1 (OCT1), coded by SLC22A1, with the response to tramadol. OCT1 is primarily present on the sinusoidal membrane of hepatocytes and in the blood–brain barrier. It is responsible for the uptake of positively charged compounds at physiological pH, such as morphine, and the active metabolite of tramadol, O‐desmethyltramadol [18].
Concerning genes involved in pharmacodynamic targets or properties, associations between genetic polymorphisms and the response to methadone have been reported for OPRM1 [19, 20] and its biological cascade (such as ARRB2 coding for β‐arrestin 2) [21]. Obviously, associations were evidenced with polymorphisms of monoaminergic systems (dopamine D2 receptor (DRD2) [22], catechol‐O‐methyl‐transferase (COMT) [23] and functional polymorphisms related to synaptic levels of dopamine or serotonin [20]).
Somogyi et al concluded that the pharmacogenomics of methadone can be considered to reside in its various targets, especially the OPRM1 gene and its efflux transporter (ABCB1) [24]. This conclusion was further confirmed in a genome‐wide pharmacogenomics study [25]. These genetic elements contribute to the response or dosage stratification [26]. Nonetheless, publications summarizing previously studied pharmacological targets have concluded that drawing clear conclusions about the influence of these genes on the treatment response is currently impossible and that results should be interpreted with caution [27, 28].
In view of these misted results, our study aimed to evaluate the impacts of the ABCB1, SLC22A1, COMT, and OPRM1 polymorphisms on methadone enantiomer levels and clinical outcome to methadone maintenance treatment (MMT) in patients with OUD.
2. MATERIAL AND METHODS
2.1. Study oversight and patients
OPAL (Clinical trials: NCT01847729) was an observational transversal multicenter study involving 10 care centers in France which was approved by the local Research Ethics Committee (GNEDS), Advisory Committee on the Processing of Health Research Information (CCTIRS) and Data Protection Commission (CNIL). The sample consisted of 263 patients aged 18 years or older receiving maintenance treatment for OUD for at least 6 months. The primary objective was to determine the prevalence of addictive comorbidities [29]. The secondary objectives of the study included association studies aimed at identifying genetic biomarkers of methadone response in the context of OUD. Hence, the present work is an ancillary study performed solely on patients treated with methadone [5]. Thus, a total of 72 patients were included in this nested study.
2.2. Study procedure
The complete study procedure has been described and published previously [29]. Briefly, the clinical assessment included a clinical structured interview carried out by a physician during a medical consultation with the patient and a set of standardized self‐report questionnaires. Blood samples were collected immediately before administration from subjects who had been on MMT for at least 6 months without any change in dosage in the 5 days prior, to performing the ancillary study. Sociodemographic data included opioid dependence, data relating to the MMT, and psychopathological data. The characteristics of MMT were collected as follows: duration; MMT initiation with daily supervised dosing by a qualified health professional; maximal daily dose; current daily dose; compliance; withdrawal symptoms; current opioid use despite MMT or opioid abstinence, defined as the self‐reported absence of opioid use over the previous 6 months. The 6‐month period was chosen because it corresponds to the time usually required to stabilize the treatment dosage and because each patient could be asked about this period.
2.3. Variant selection
For each gene, variants were selected for this pharmacogenetic study using the PharmGKB database [30]. The selected variants had to be polymorphisms (MAF > 0.01), known to be associated with response to other treatments, and to impact the biological function of the protein or transporter. Thus, the chosen variants are: rs1045642 and rs2032582 for ABCB1; rs12208357, rs72552763 and rs113569197 for SLC22A1; rs4680 for COMT; rs1799971 for OPRM1.
2.4. Genotyping
The patients were genotyped for different genetic polymorphisms of ABCB1 (rs1045642; rs2032582), SLC22A1 (rs12208357; rs72552763; rs113569197), COMT (rs4680) and OPRM1 (rs1799971) using TaqMan allelic discrimination (ABI Prism® 7900HT Sequence Detection System [Applied Biosystem, Courtaboeuf, France]) according to the manufacturer recommendations as previously described [31].
2.5. Plasma methadone measures
Plasma concentration of methadone and its enantiomers, i.e., (R)‐methadone and (S)‐methadone, were measured by liquid chromatography coupled with mass spectrometry (LC/MS–MS) [32]. The analysis combined straightforward sample preparation, consisting of protein precipitation with acetonitrile, and an online enrichment by a flush/back‐flush cycle before the second‐dimension chromatography. Using D3‐deuterated internal standards allows overcoming significant relative matrix effect. This method was linear up to 2000 ng/ml. This simple sample preparation provides sensitive (the limit of quantitation is 25 ng/ml for (R,S)‐methadone. Concentration results by patient were dose adjusted (ng/ml/mg).
2.6. Statistical analyses
The statistical analyses were performed using Stata 15 software®. Deviation from the Hardy–Weinberg equilibrium was assessed using the chi‐square test. The different genotypes of patients were compared, in bivariate analyses, according to the plasma concentrations as well as opiate cessation (defined as was defined by opioid abstinence [self‐reported absence of opioid use over the previous 6 months] AND the stability or improvement of other substance use or gambling practice) by chi‐square tests or Fisher's exact tests for qualitative variables or Kruskall‐Wallis tests for quantitative variables. Then, multivariate analyses, including all genetic polymorphisms, were realized. ANOVAs were performed to explain log (R) and (S)‐methadone concentration/dose, taking into account all genotypes simultaneously. The logarithmic transformation was performed to obtain a distribution close to the Gaussian distribution. As we have already shown that CYP2B6 polymorphism (G516T; rs56308434) is related to clinical and biological response to MMT in this cohort, this variable was included in the multivariate analyses [5]. All tests were two‐tailed. The significance threshold retained was p < 0.05 and no correction for multiple testing was used according to Bender and Lange [33].
3. RESULTS
3.1. Description of the population
The present results are based on 72 patients treated with MMT for OUD. Sociodemographic and clinical characteristics of patients are resumed in Table 1. Briefly, patients were mainly men (78.0%; n = 56) and their mean age was 33.8 ± 5.7 years. The mean age for their first intake of opioids was 19.8 ± 4.5 years, 22.5 ± 5.2 years for the onset of dependence, and 25.3 ± 5.7 years for the first attempt to stop opioids. The current actual consumption of patients is presented in Table 2. Of note, among the 72 patients, 69 (95.8%) present at least one co‐addiction in addition to their OUD. Frequencies of each polymorphism studied in the 72 MMT patients are provided in Tables 3 and 4. Genotype distributions for all polymorphisms are in agreement with Hardy–Weinberg equilibrium.
TABLE 1.
Sociodemographic characteristics, history and consequences of the pathology.
| Characteristic | All patients (n = 72) |
|---|---|
| Men (n [%]) | 56 (78%) |
| Age (years [m ± SD]) | 33.8 ± 5.7 |
| Graduate studies (n [%]) | 29 (40%) |
| Marital life (n [%]) | 33 (46%) |
| Dependent children (n [%]) | 21 (29%) |
| Stable housing (n [%]) | 61 (85%) |
| Professional activity (n [%]) | 33 (46%) |
| Debt (n [%]) | 29 (40%) |
| Consumption in the patient's circle or family (n [%]) | 59 (82%) |
| Age at first intake (years [m ± SD]) | 19.8 ± 4.5 |
| Age at onset of dependence (years [m ± SD]) | 22.4 ± 5.2 |
| Age at the first attempt to stop (years [m ± SD]) | 25.3 ± 5.7 |
| Heroine as the principal drug (n [%]) | 67 (93%) |
| Nasal route (n [%]) | 42 (58%) |
| Other psychotropic drug consumption (n [%]) | 60 (83%) |
m: mean – SD: Standard deviation – n: number of patients.
TABLE 2.
Current actual consumption.
| Characteristic | All patients (n = 72) |
|---|---|
| Methadone posology (mg/day [m ± SD]) | 53 ± 35 |
| Methadone maximal posology (mg/day [m ± SD]) | 80 ± 40 |
| Good compliance (n [%]): | 68 (94%) |
| Substance consumption (n [%]): | |
|
67 (93.1%) |
|
60 (83.3%) |
|
58 (80.6%) |
|
47 (65.3%) |
|
30 (41.7%) |
|
28 (38.9%) |
|
30 (41.7%) |
| Gambling (n [%]): | 33 (45.8%) |
|
10 |
n: number of patients – LSD: Lysergic acid diethylamide – NPS: new psychoactive substances – m: mean – SD: Standard deviation – n: number of patients.
TABLE 3.
Methadone enantiomers concentrations/dose according to SNP genotypes in 72 MMT patients.
| SNP | Genotype | n (%) | (R)‐methadone/dose | p‐value | (S)‐methadone/dose | p‐value | (R,S)‐methadone/dose | p‐value |
|---|---|---|---|---|---|---|---|---|
|
ABCB1 rs1045642 |
TT CT CC |
15 (21) 43 (60) 14 (19) |
2.76 (1.68) 2.90 (3.03) 3.20 (1.28) |
0.22 |
3.11 (1.84) 2.75 (2.12) 3.70 (2.06) |
0.095 |
6.28 (3.44) 5.87 (5.15) 7.24 (3.26) |
0.095 |
|
ABCB1 rs2032582 |
TT TA GG GT GA |
13 (18) 1 (1) 20 (28) 35 (49) 3 (4) |
2.61 (1.86) 2.51 3.00 (1.19) 3.08 (3.31) 2.23 (1.33) |
0.59 |
2.78 (1.89) 1.36 3.36 (1.94) 2.96 (2.26) 2.76 (1.80) |
0.50 |
5.83 (3.72) 3.88 3.65 (3.06) 6.29 (5.57) 5.12 (3.30) |
0.53 |
|
SLC22A1 rs12208357 |
CC CT TT |
57 (79) 14 (19) 1 (1) |
2.95 (2.70) 2.90 (1.77) 2.30 |
0.94 |
3.04 (2.12) 2.96 (1.93) 2.14 |
0.78 |
6.27 (4.72) 6.12 (3.78) 4.66 |
0.82 |
|
SLC22A1 rs72552763 |
GAT/GAT GAT/del |
52 (72) 20 (28) |
2.76 (1.31) 3.37 (4.32) |
0.96 |
2.96 (1.71) 3.14 (2.85) |
0.82 |
5.98 (3.03) 6.85 (7.10) |
0.95 |
|
SLC22A1 rs113569197 |
TGGTAAGT/TGGTAAGT TGGTAAGT/del del/del |
17 (24) 34 (47) 21 (29) |
2.36 (1.05) 2.80 (1.34) 3.61 (4.22) |
0.37 |
2.52 (1.28) 2.92 (1.37) 3.55 (3.18) |
0.54 |
5.06 (2.42) 5.99 (2.71) 7.54 (7.21) |
0.34 |
|
COMT rs4680 |
GG GA AA |
26 (36) 28 (39) 18 (25) |
2.79 (1.20) 3.58 (3.72) 2.13 (0.90) |
0.052 |
2.85 (1.98) 3.50 (2.51) 2.49 (1.09) |
0.17 |
5.87 (3.21) 7.33 (6.24) 5.01 (1.95) |
0.16 |
|
OPRM1 rs1799971 |
AA AG GG |
52 (72) 19 (26) 1 (1) |
2.70 (1.27) 3.63 (4.41) 1.49 |
0.41 |
2.87 (1.67) 3.49 (2.89) 1.16 |
0.31 |
5.81 (2.93) 7.52 (7.25) 2.90 |
0.28 |
n: number of patients – Kruskal‐Wallis tests were performed to compare groups – SNP: Single Nucleotide Polymorphism – Values are in ng/ml/mg.
TABLE 4.
Opiate cessation according to SNP genotypes in 72 MMT patients.
| SNP | Genotype | Number of patients (n (%)) | Opiate cessation (n (%)) | p‐value |
|---|---|---|---|---|
|
ABCB1 rs1045642 |
TT CT CC |
15 (21) 43 (60) 14 (19) |
8 (53.3) 30 (69.8) 10 (71.4) |
0.51 |
|
ABCB1 rs2032582 |
TT TA GG GT GA |
13 (18) 1 (1) 20 (28) 35 (49) 3 (4) |
7 (53.8) 1 (100) 13 (65.0) 24 (68.6) 3 (100) |
0.71 |
|
SLC22A1 rs12208357 |
CC CT TT |
57 (79) 14 (19) 1 (1) |
37 (64.9) 10 (71.4) 1 (100) |
0.84 |
|
SLC22A1 rs72552763 |
GAT/GAT GAT/del |
52 (72) 20 (28) |
36 (69.2) 12 (60.0) |
0.58 |
|
SLC22A1 rs113569197 |
TGGTAAGT/TGGTAAGT TGGTAAGT/del del/del |
17 (24) 34 (47) 21 (29) |
12 (70.6) 19 (55.9) 17 (81.0) |
0.16 |
|
COMT rs4680 |
GG GA AA |
26 (36) 28 (39) 18 (25) |
18 (69.2) 18 (64.3) 12 (66.7) |
0.95 |
|
OPRM1 rs1799971 |
AA AG GG |
52 (72) 19 (26) 1 (1) |
35 (67.3) 13 (68.4) 0 (0.0) |
0.51 |
n: number of patients – Fisher's exact tests were performed to compare groups – SNP: Single Nucleotide Polymorphism.
3.2. Influence of genotype on dose‐adjusted plasma methadone levels
Bivariate analyses for codominant models are presented in Table 3. In these codominant models, levels of (R,S)‐methadone/dose, of (R)‐methadone/dose, and of (S)‐methadone/dose were not significantly different for all polymorphisms tested of ABCB1, SLC22A1, COMT or OPRM1 (Table 3).
Recessive models were also performed for all polymorphisms. For rs1045642 of ABCB1 (TT vs. CT + CC), no significant difference was evidenced between groups for (R,S)‐methadone/dose (6.28 ± 3.44 vs. 6.21 ± 4.76 ng/ml/mg; p = 0.78), for (R)‐methadone/dose (2.76 ± 1.68 vs. 2.98 ± 2.70 ng/ml/mg; p = 0.64), nor for (S)‐methadone/dose (3.11 ± 1.84 vs. 2.98 ± 2.13 ng/ml/mg; p = 0.78). As well, for rs2032582 of ABCB1 (TT + TA + AA vs. GG + GT + GA), no significant difference was evidenced between groups for (R,S)‐methadone/dose (5.69 ± 3.61 vs. 6.35 ± 4.71 ng/ml/mg; p = 0.37), for (R)‐methadone/dose (2.60 ± 1.78 vs. 3.01 ± 2.66 ng/ml/mg; p = 0.30), nor for (S)‐methadone/dose (2.68 ± 1.86 vs. 3.09 ± 2.11 ng/ml/mg; p = 0.29).
Regarding rs12208357 (CC vs. CT + TT) and rs113569197 (TGGTAAGT/TGGTAAGT vs. TGGTAAGT/del + del/del) of SLC22A1, there was no significant difference between groups for (R,S)‐methadone/dose (respectively, 6.27 ± 4.72 vs. 6.02 ± 3.66 ng/ml/mg; p = 0.66 and 5.06 ± 2.42 vs. 6.58 ± 4.93 ng/ml/mg; p = 0.14), for (R)‐methadone/dose (respectively, 2.95 ± 2.70 vs. 2.86 ± 1.71 ng/ml/mg; p = 0.88 and 2.36 ± 1.05 vs. 3.11 ± 2.80 ng/ml/mg; p = 0.19), nor for (S)‐methadone/dose (respectively, 3.04 ± 2.12 vs. 2.90 ± 1.87 ng/ml/mg; p = 0.57 and 2.52 ± 1.28 vs. 3.16 ± 2.23 ng/ml/mg; p = 0.27).
Concerning rs4680 (GG vs. GA + AA) of COMT, no significant difference was found between groups for (R,S)‐methadone/dose, (R)‐methadone/dose and (S)‐methadone/dose (respectively, 5.87 ± 3.21 vs. 6.42 ± 5.11 ng/ml/mg; p = 0.53, 2.79 ± 1.20 vs. 3.01 ± 3.02 ng/ml/mg; p = 0.66 and 2.85 ± 1.98 vs. 3.10 ± 2.12 ng/ml/mg; p = 0.32).
Finally, with regard to rs1799971 (AA vs. AG + GG) of OPRM1, no difference was also identified between groups (R,S)‐methadone/dose, (R)‐methadone/dose and (S)‐methadone/dose (respectively, 5.81 ± 2.93 vs. 7.29 ± 7.13 ng/ml/mg; p = 0.66, 2.70 ± 1.27 vs. 3.52 ± 4.32 ng/ml/mg; p = 0.89 and 2.87 ± 1.67 vs. 3.37 ± 2.86 ng/ml/mg; p = 0.79).
In multivariate analyses, none of the polymorphisms explained the (R)‐methadone concentration/dose values. Nevertheless, in a stepwise elimination process, the latest eliminated variable was rs4680 of COMT (p = 0.054). Also, for the (S)‐methadone concentration/dose values, there was no association identified (except for rs56308434 of CYP2B6; p = 0.003). In the stepwise elimination process, the latest eliminated variable was rs1045642 of ABCB1 (p = 0.11). Finally, no association was identified between studied polymorphisms and the (R,S)‐methadone concentration/dose values (except for rs56308434 of CYP2B6; p = 0.023). However, it should be noted that in a stepwise elimination process, the latest eliminated variable was rs113569197 of SLC22A1 (p = 0.060).
3.3. Influence of genotype on opiate cessation
Bivariate analyses for codominant models are presented in Table 4. In these codominant models, opiate abstinence during treatment by methadone was not associated with all polymorphisms tested of ABCB1, SLC22A1, COMT, or OPRM1 (Table 4).
As for the biological parameters, recessive models were performed for opiate abstinence, as previously described. For ABCB1, both polymorphisms (rs1045642 and rs2032582) were not associated with opiate abstinence (respectively, 53.3% vs. 70.2%; p = 0.23, and 57.1% vs. 69.0%; p = 0.53).
For SLC22A1, no significant difference between groups was evidenced for rs12208357 and rs113569197 (respectively, 64.9% vs. 73.3%; p = 0.39, and 70.6% vs. 65.5%; p = 0.78).
Finally, for the pharmacodynamic targets, rs4680 of COMT and rs1799971 of OPRM1 were not associated with opiate abstinence in our sample during MMT (respectively, 69.2% vs. 65.2%; p = 0.80, and 67.3% vs. 65.0%; p = 0.53).
4. DISCUSSION
This study assessed associations between various genetic polymorphisms from ABCB1, SLC22A1, COMT, and OPRM1 with clinical response to MMT and pharmacological characteristics of patients with OUD receiving MMT. We did not find any statistically significant association between these polymorphisms and clinical outcomes (opiate cessation). Moreover, we did not detect any association between dose‐adjusted plasma methadone levels and these genetic variants.
We explored pharmacogenetics variants either pharmacokinetic (ABCB1 and SLC22A1) or pharmacodynamic (COMT and OPRM1) targets. Pharmacokinetic variants affect the bioavailability of the medication in the body, while pharmacodynamic variants directly or indirectly alter the effects of the medication. Among the pharmacokinetic targets, we previously showed that genetic polymorphisms of enzyme proteins such as CYP2B6 could be associated with pharmacological parameters and clinical responses to MMT5. Nevertheless, non‐enzyme proteins can also affect methadone bioavailability; methadone is a substrate of the P‐glycoprotein efflux transporter which is encoded by ABCB1. ABCB1 polymorphisms can influence the transport of opioids at the intestinal level and at the blood–brain barrier [18, 34, 35]. In our study, neither rs1045642 nor rs2032582 of ABCB1 are associated with dose‐adjusted plasma methadone concentrations. These results are globally in concordance with previous research. Indeed, most studies investigating the impact of ABCB1 polymorphisms on methadone response have reported associations with prescribed methadone doses [12, 28, 36], while generally observing no significant association with plasma methadone concentrations, with some exceptions noted [14, 16, 27]. In addition, another publication identified associations between rs1045642 and brain/blood ratios of methadone in methadone‐related deaths [37]. These results suggest that the impact of ABCB1 polymorphisms on methadone pharmacokinetic parameters and methadone responses is related to their effects methadone diffusion into the brain across the blood–brain barrier rather than to their effects on plasma concentrations of methadone [37]. This hypothesis has been tested and validated in rodent models [38].
The second pharmacokinetics target tested is OCT1 (coded by SLC22A1). Recently, loss‐of‐function polymorphisms of SLC22A1 have been reported to be associated with reduced postoperative tramadol consumption [39]. Moreover, loss‐of‐function genetic variants of this gene were associated with higher plasma concentrations of O‐desmethyltramadol and morphine in patients and in healthy subjects [40, 41]. Implication of these polymorphisms for methadone has not been yet evidenced, and to the best of our knowledge, we are the first ones which assess the association between SLC22A1 polymorphisms and clinical/biological responses to MMT and found that there was no relation between them. To date, these results are not unexpected, as methadone does not appear to be a direct substrate of OCT1 [42]. Nevertheless, the influence of SLC22A1 polymorphisms could be indirectly relevant. First, OCT1 is one of the main transporters of EDDP (2‐ethylidene‐1,5‐dimethyl‐3,3‐diphenylpyrrolidine), a primary metabolite of methadone [42]. Thus, an alteration in the transport of EDDP could modify methadone metabolism. Second, OCT1 serves as a primary transporter of acylcarnitines, which have been associated with methadone withdrawal syndrome. This observation could suggest an indirect pathway by which acylcarnitines, potentially mediated by OCT1, may influence methadone management—a connection already observed in depression [43, 44, 45].
Concerning the pharmacodynamic targets, COMT is the enzyme that realizes the methyl conjugation of catecholamines such as dopamine and is, consequently, a key regulator of their concentrations [23]. In the context of addiction, the dopaminergic system was explored given that this pathway explained a part of the rewarding and reinforcing effects of drugs [46]. The most studied polymorphism of COMT is rs4880 and in vitro data suggest that it produces a protein with a 3–4‐fold lower enzymatic activity [23, 47]. As expected, this polymorphism was not associated with dose‐adjusted plasma methadone concentrations, since it is involved in the pharmacokinetic mechanisms of methadone. However, it was not associated with opiate abstinence during MMT in patients. Our data are consistent with previous studies. Indeed, in other cohorts from 81 to 820 patients, this polymorphism was not related to methadone prescribed dose, clinical response during MMT, or adverse events [23, 28, 48]. Only another variant of COMT, rs933271, was described to be associated with opiate cessation in a Han Chinese population [48].
The second studied pharmacodynamic gene is OPRM1, coding for the μ‐opioid receptor which is one of the main targets of methadone for inducing its pharmacological effect making this receptor a major candidate to explain genetic inter‐individual differences in opioid response [49]. The studied polymorphism, rs1799971, is one of the most common genetic variants of OPRM1 and is known to induce a reduced response to opioids [49]. This effect could be due to the removal of a glycosylation site in the N‐terminal domain of the receptor and a reduction in gene expression [49, 50]. This genetic variant was described to be associated with the response to other opioid drugs such as naltrexone or buprenorphine [51, 52]. It was expected that there would be no significant difference in dose‐adjusted plasma methadone concentrations according to the rs1799971 OPRM1 genetic polymorphism. But we equally did not highlight any difference for opiate cessation during MMT according to this last polymorphism. The effect of this polymorphism remains highly controversial both in terms of methadone prescribed dose and therapeutic efficacy [12, 23, 28, 53, 54, 55, 56]. Nevertheless, in concordance with our observed result, an in vitro study suggests that this genetic variant does not alter methadone signaling in a cellular model [52].
This study had some limitations. First, the sample size is relatively small. But this size allowed a great phenotyping of patients with a wealth of clinical data at our disposal. Second, we assessed only methadone whereas other medications are available such as buprenorphine to manage OUD. But this approach reinforces the biological rationale. Third, except for CYP2B6 polymorphisms, no other cofounding factors, such co‐medications or potential hepatic dysfunction, were included in the multivariate analyses as these data were not available. Nonetheless, in clinical practice, few drugs with pharmacokinetic drug–drug interactions are prescribed, given the limited number associated with CYP2B6 induction or inhibition within the pharmacopeia [57]. Similarly, recent data suggest that liver diseases do not significantly influence the pharmacokinetic parameters of methadone [58]. Fourth, we did not perform urine tests to screen substance use in addition to declarative status. We may assume that the unsuccessful OUD treatment rate could be underestimated. Finally , genetic polymorphisms were analyzed separately while some data suggest that the therapeutic effect could be observed with a combination of genetic variants [12, 35, 59]. Hence, their combination could be associated with response to MMT. In accordance, a genetic test that combines several polymorphisms (including ABCB1, COMT, and OPRM1 variants) has been recently approved by the US Food And Drug Administration (FDA) to estimate the risk of developing OUD [59, 60]. But our sample size was not sufficient to perform this type of analysis. Nonetheless, cohorts with a large number of subjects allowing the identification of polygenic biomarkers are rarely available in pharmacogenetic studies, particularly in the context of OUD5. Similarly, the selection of polymorphisms was based on their frequency and the availability of previous data, such as their association with the response to other treatments which remains hypothetical. Consequently, we cannot exclude the possibility that other variants not studied here, particularly rare ones, may have an impact on the phenotypes studied. However, the size of our cohort did not allow us to adopt an alternative approach that would have included them. Of note, the prescribed methadone dosages were relatively low, which may be inherent to the naturalistic approach used in our real‐world study. Furthermore, it is unlikely that this would influence the associations being investigated between concentrations and genetic polymorphisms of the targets, as concentrations were normalized to the received dosage.
5. CONCLUSION
To conclude, in our sample, all the polymorphisms tested in these genes were not associated with clinical responses or pharmacological parameters to methadone. Hence, ABCB1, SLC22A1, COMT, and OPRM1 genotyping cannot yet be considered in a daily clinical setting for the management of MMT in OUD. Further investigations in larger cohorts, exploring other polymorphisms or targets and combining various polymorphisms should be performed to confirm or refute these preliminary findings.
AUTHORS CONTRIBUTION
Abd El Kader Ait Tayeb: Writing—original draft/formal analysis; visualization; resources; validation; writing—review & editing. Edouard‐Jules Laforgue: Investigation; data curation; writing—review & editing. Benoit Schreck: Investigation; data curation; writing—review & editing. Marie Grall‐Bronnec: Conceptualization; investigation; data curation; writing—review & editing. Jean‐Benoit Hardouin: Formal analysis; writing—review & editing. Juliette Leboucher: Conceptualization; writing—review & editing. Caroline Victorri‐Vigneau: Funding acquisition; supervision; methodology; validation; writing—review & editing. Céline Verstuyft: Supervision; methodology; resources; validation; writing—review & editing.
CONFLICT OF INTEREST STATEMENT
Abd El Kader Ait Tayeb, Edouard‐Jules Laforgue, Benoit Schreck, Marie Grall‐Bronnec, Jean‐Benoit Hardouin, Juliette Leboucher, Caroline Victorri‐Vigneau, Céline Verstuyft have no conflict of interest to disclose.
ACKNOWLEDGMENTS
This study was supported jointly by the Mission Interministérielle de Lutte contre les Drogues et les Conduites Addictives (MILDECA) and the Université Paris 13, as part of the call for research projects “PREVDROG” launched by these two organizations in 2011. It was conducted at the initiative of and coordinated by the UIC “Psychiatrie et Santé Mentale” of Nantes University Hospital. Nantes University Hospital is the sponsor of this study.
Ait Tayeb AEK, Laforgue E‐J, Schreck B, et al. ABCB1, SLC22A1, COMT, and OPRM1 genotypes: Study of their influence on plasma methadone levels and clinical response to methadone maintenance treatment in opioid use disorder. Fundam Clin Pharmacol. 2025;39(3):e70013. doi: 10.1111/fcp.70013
Contributor Information
Abd El Kader Ait Tayeb, Email: abdelkader.aittayeb@aphp.fr.
OPAL Group:
DATA AVAILABILITY STATEMENT
The datasets generated and analyzed during the current study are not publicly available because the data generated included sensitive data according to the French Data Protection Authority (CNIL), that could not be transferred to other researchers to guarantee participants' anonymity. But they are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analyzed during the current study are not publicly available because the data generated included sensitive data according to the French Data Protection Authority (CNIL), that could not be transferred to other researchers to guarantee participants' anonymity. But they are available from the corresponding author on reasonable request.