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Article

Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

by
Aurea Lima
1,2,3,*,
Miguel Bernardes
4,5,
Rita Azevedo
3,6,
Rui Medeiros
2,3 and
Vítor Seabra
1
1
Cooperativa de Ensino Superior Politécnico e Universitário (CESPU), Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde (IINFACTS), 4585-116 Gandra PRD, Portugal
2
Grupo de Oncologia Molecular & Patologia Viral—Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), 4200-072 Porto, Portugal
3
Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto (ICBAS-UP), 4050-313 Porto, Portugal
4
Faculdade de Medicina da Universidade do Porto (FMUP), 4200-319 Porto, Portugal
5
Departamento de Reumatologia do Centro Hospitalar de São João, 4200-319 Porto, Portugal
6
Grupo de Patologia & Terapêutica Experimental do Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), 4200-072 Porto, Portugal
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2015, 16(6), 13760-13780; https://doi.org/10.3390/ijms160613760
Submission received: 9 March 2015 / Revised: 3 June 2015 / Accepted: 4 June 2015 / Published: 16 June 2015
(This article belongs to the Special Issue Pharmacogenetics and Personalized Medicine)
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Versions Notes

Abstract

:
Background: Methotrexate (MTX) is widely used for rheumatoid arthritis (RA) treatment. Single nucleotide polymorphisms (SNPs) could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI) for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment.

Graphical Abstract

1. Introduction

Rheumatoid arthritis (RA) is a systemic autoimmune disease, characterized by chronic inflammation of multiple peripheral joints, which leads to destruction of cartilage and bone, progressive deformity and severe disability [1]. The worldwide prevalence of RA is relatively consistent ranging from 0.5% to 1.1% and the incidence vary between 20 and 60 cases per 100.000 inhabitants [2]. In Portugal, the prevalence of RA is 0.36% and the incidence is between 20 and 40 cases per 100.000 inhabitants [3,4]. Overall, the incidence of RA is higher in females, with a gender ratio ranging from 2:1 to 3:1, and the peak of disease onset occurring in the fifth decade of life [5,6].
Earlier detection of RA and the prompt institution of effective and aggressive therapeutic are key factors to achieve the disease remission and improve survival rates [7,8,9]. Several studies from controlled and uncontrolled clinical trials have established that methotrexate (MTX), an antifolate, is an effective disease modifying antirheumatic drug (DMARD) [10,11,12]. Once inside the cells, MTX is metabolized into methotrexate polyglutamates (MTXPGs) by a sequential addition of glutamic acid residues via the enzyme folylpolyglutamate synthetase (FPGS) [13,14]. Polyglutamation of MTX enhances the intracellular retention of MTX promoting the inhibition of folate, methionine and adenosine pathways and, the de novo synthesis of purines and pyrimidines, which is considered crucial for anti-inflammatory and antiproliferative therapeutic effects of MTX [13,15,16,17]. Nevertheless, gamma-glutamyl hydrolase (GGH) enzyme removes the glutamic acid residues of MTXPGs and, consequently, MTX can be transported out of the cells [13].
The chemical characteristics of MTX, such its structural composition by a polar glutamate side chain, make it dependent of a carrier-mediated transport system to enter and exit cell membranes [18]. The carrier-mediated transport system includes a network of transporters that belong to two major superfamilies: solute carriers (SLC) and ATP-binding cassette (ABC) transporters [19]. These transporters are expressed in several tissues and have a preeminent effect in MTX pharmacokinetics (PK) parameters such as absorption, distribution and/or elimination [20,21,22]. Consequently, MTX carrier-mediated transport system is considered of great importance for its effectiveness and, despite MTX cost-effectiveness, treatment with this drug is not devoid of drawbacks and different clinical response profiles can occur due to interpatient variability [10,23,24,25]. This variability in MTX clinical effects can be a consequence of MTX PK changes and several lines of evidence suggest that patients’ genetic profile may have a significant role in such variability [26,27,28]. Therefore, over the past decade, numerous pharmacogenomics (PGx) studies were undertaken to investigate individual MTX therapeutic outcomes using genetic information from MTX transport pathway [29,30,31]. Since not all patients benefit from specific therapies, there is a strong and unmet need for pretreatment predictions on therapy outcome. Therefore, this work aims to evaluate the influence of single nucleotide polymorphism (SNPs) in genes encoding for MTX membrane transport proteins on the occurrence of non-response to MTX in Portuguese RA patients.

2. Results

2.1. Studied Population

This study included follow-up data from 233 patients, 196 (84.1%) females and 37 (15.9%) males, with a median age of 52 ± 11.9 years old, of which 32 (13.7%) were current smokers. In this population, median of serum creatinine (SCr) was 8.20 mg/L (4.00–19.80), median of estimated glomerular filtration rate (eGFR) was 82.0 mL/min/1.73 m2 (29.00–186.00) and 30 patients (12.9%) presented chronic renal insufficiency (eGFR < 60 mL/min/1.73 m2). Considering disease-related variables, mean age of diagnosis was 40.3 ± 13.2 years old, median disease duration was 8.0 years and mean disease activity score (DAS28) was 4.2 ± 1.3. Only 146 patients (62.7%) used MTX as unique DMARD, while 59 patients (25.3%) were treated with MTX combined with other classic DMARDs (azathioprine, cyclosporine, gold salts, hydroxychloroquine, leflunomide and/or sulphasalazine) and 28 patients (12.0%) were treated with MTX combined with biological DMARDs (adalimumab, etanercept, infliximab, abatacept, anakinra, rituximab and/or tocilizumab). Median MTX treatment duration was 28.0 months with a median dose of 15.0 mg/week (range 2.5–25.0). MTX was administered in 201 patients (86.3%) by per os route while in 32 patients (13.7%) was administered by subcutaneous route. Non-response to MTX was observed in 128 (54.9%) patients, of which 107 patients used MTX as unique DMARD.

2.2. Genotypes and Haplotypes Characteristics

SLC22A6 rs11568626 C>T (CC: 232 patients; CT: 1 patient; TT: 0 patients) and ABCG2 rs2231142 C>A (CC: 200 patients; CA: 33 patients; AA: 0 patients) were excluded from analyses since minor allele frequency was less than 10.0%, and ABCC1 rs2230671 C>G (CC: 117 patients; CG: 9 patients; GG: 0 patients) was excluded from analyses because genotyping call rates were less than 95.0%. Taking this into account, twenty SNPs were considered. For ABCB1 rs2032582, three patients (1.3%) presented AG genotype and three patients (1.3%) presented AT genotype. Given the low frequency, these six patients were excluded from the analysis of this SNP. Genotypes distribution was in Hardy–Weinberg Equilibrium HWE (p > 0.050) except for the SLC19A1 rs1051266. SNPs in SLC16A7, SLC19A1, ABCB1, ABCC1, ABCC2 and ABCG2 were in linkage disequilibrium (LD) (D′ coefficients mean of 0.81 and range from 0.26 to 1.00; p < 0.001), except for ABCC1 rs246240 and ABCC1 rs2074087 (D′ = 0.02; p = 0.690).

2.3. Genotype Approach and Clinical Response to Methotrexate (MTX)

Table 1 represents the relation between clinical response to MTX and SNPs in SLCs and ABCs. Regarding SNPs in SLCs, our results demonstrated that SLC22A11 rs11231809 T carriers (p = 0.031, odds ratio (OR) = 5.37) were significantly associated with more than five-fold increased risk for non-response to MTX. Regarding SNPs in ABCs, ABCC1 rs246240 G carriers (p = 0.008, OR = 5.47) and ABCC1 rs3784864 G carriers (p = 0.015, OR = 4.24) were associated with MTX non-response.

2.4. Haplotype Approach and Clinical Response to MTX

Table 2 represents the relation between MTX transporters genes haplotypes and clinical response to MTX. Since ABCC1 A>G (rs246240) and ABCC1 G>C (rs2074087) were not in LD, analyses were performed considering the following haplotypes combination: (1) ABCC1 rs35592, rs246240 and rs3784864; and (2) ABCC1 rs35592, rs2074087 and rs3784864. Results showed that CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864 was statistically significant for non-response compared to TGA haplotype (p = 0.025, OR = 4.12). In addition, CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864 was associated with non-response compared to TAA haplotype (p = 0.010, OR = 7.26).
Table
Table 1. Relation between single nucleotide polymorphisms (SNPs) in methotrexate (MTX) transporters genes and clinical response to MTX.
Table 1. Relation between single nucleotide polymorphisms (SNPs) in methotrexate (MTX) transporters genes and clinical response to MTX.
SLCsAllelesResponseNon-ResponsepOR (95% CI)ABCsAllelesResponseNon-ResponsepOR (95% CI)
SLC16A7 A>T (rs3763980)A carriers96 (45.5)115 (54.5)0.061ReferenceABCB1 C>T (rs1045642)C carriers79 (45.9)93 (54.1)0.255Reference
TT9 (40.9)13 (59.1)0.26 (0.06–1.06)TT26 (42.6)35 (57.4)1.88 (0.63–5.55)
AA53 (44.5)66 (55.5)0.890ReferenceCC29 (46.0)34 (54.0)0.622Reference
T carriers52 (45.6)62 (54.4)0.94 (0.38–2.32)T carriers76 (44.7)94 (55.3)1.29 (0.47–3.50)
SLC16A7 T>G (rs10877333)T carriers104 (45.4)125 (54.6)0.999ReferenceABCB1 C>T (rs1128503)C carriers81 (44.0)103 (56.0)0.496Reference
GG1 (25.0)3 (75.0)1.00 (0.00–0.00)TT24 (49.0)25 (51.0)1.56 (0.43–5.57)
TT72 (44.7)89 (55.3)0.738ReferenceCC35 (45.5)42 (54.5)0.244Reference
G carriers33 (45.8)39 (54.2)1.19 (0.43–3.33)T carriers70 (44.9)86 (55.1)1.80 (0.67–4.87)
SLC19A1 G>A (rs7499)G carriers94 (48.2)101 (51.8)0.851ReferenceABCB1 G>A/T (rs2032582)G carriers80 (44.4)100 (55.6)0.706Reference
AA11 (28.9)27 (71.1)1.14 (0.28–4.58)TT23 (48.9)24 (51.1)1.27 (0.36–4.47)
GG47 (51.6)44 (48.4)0.613ReferenceGG36 (44.4)45 (55.6)0.349Reference
A carriers58 (40.8)84 (59.2)1.28 (0.49-3.30)T carriers67 (45.9)79 (54.1)1.62 (0.59–4.44)
SLC19A1 G>A (rs1051266)G carriers80 (46.8)91 (53.2)0.924ReferenceABCC1 T>C (rs35592)T carriers95 (45.2)115 (54.8)0.630Reference
AA25 (40.3)37 (59.7)1.05 (0.36–3.09)CC10 (43.5)13 (56.5)1.47 (0.31–7.09)
GG37 (46.2)43 (53.8)0.672ReferenceTT56 (45.2)68 (54.8)0.130Reference
A carriers68 (44.4)85 (55.6)1.23 (0.47–3.18)C carriers49 (45.0)60 (55.0)2.12 (0.80–5.58)
SLC19A1 A>G (rs2838956)A carriers91 (47.6)100 (52.4)0.512ReferenceABCC1 A>G (rs246240)A carriers102 (45.3)123 (54.7)0.846Reference
GG14 (33.3)28 (66.7)1.61 (0.39–6.66)GG3 (37.5)5 (62.5)0.76 (0.05–11.46)
AA39 (47.6)43 (52.4)0.813ReferenceAA73 (45.9)86 (54.1)0.008 *Reference
G carriers66 (43.7)85 (56.3)0.89 (0.33–2.39)G carriers32 (43.2)42 (56.8)5.47 (1.56–19.25)
SLC19A1 G>A (rs3788200)G carriers90 (47.1)101 (52.9)0.504ReferenceABCC1 G>C (rs2074087)G carriers101 (45.5)121 (54.5)0.419Reference
AA15 (35.7)27 (64.3)1.62 (0.39–6.68)CC4 (36.4)7 (63.6)0.42 (0.05–3.42)
GG41 (50.0)41 (50.0)0.285ReferenceGG62 (42.5)84 (57.5)0.104Reference
A carriers64 (42.4)87 (57.6)1.69 (0.65–4.42)C carriers43 (49.4)44 (50.6)0.46 (0.18–1.18)
SLC22A11 T>A (rs11231809)T carriers86 (43.0)114 (57.0)0.031 *ReferenceABCC1 G>A (rs3784864)G carriers76 (42.7)102 (57.3)0.015 *Reference
AA19 (57.6)14 (42.4)0.19 (0.04–0.86) (a)AA29 (52.7)26 (47.3)0.24 (0.07–0.76) (b)
TT29 (36.2)51 (63.8)0.116ReferenceGG31 (46.3)36 (53.7)0.402Reference
A carriers76 (49.7)77 (50.3)0.44 (0.16–1.22)A carriers74 (44.6)92 (55.4)0.64 (0.23–1.80)
SLC46A1 G>A (rs2239907)G carriers89 (47.8)97 (52.2)0.429ReferenceABCC2 G>A (rs717620)G carriers102 (45.1)124 (54.9)0.486Reference
AA16 (34.0)31 (66.0)1.61 (0.49–5.28)AA3 (42.9)4 (57.1)0.30 (0.01–8.89)
GG42 (48.3)45 (51.7)0.986ReferenceGG59 (43.7)76 (56.3)0.576Reference
A carriers63 (43.2)83 (56.8)1.01 (0.39–2.61)A carriers46 (46.9)52 (53.1)0.77 (0.31–1.91)
SLCO1B1 T>C (rs4149056)T carriers86 (46.5)99 (53.5)0.812ReferenceABCC2 C>T (rs4148396)C carriers81 (44.0)103 (56.0)0.677Reference
CC19 (39.6)29 (60.4)0.87 (0.28–2.69)TT24 (49.0)25 (51.0)0.78 (0.25–2.45)
TT82 (48.5)87 (51.5)0.935ReferenceCC29 (38.7)46 (61.3)0.265Reference
C carriers23 (35.9)41 (64.1)1.05 (0.36–3.07)T carriers76 (48.1)82 (51.9)0.55 (0.20-1.56)
ABCG2 T>C (rs13120400)T carriers95 (44.8)117 (55.2)0.188Reference
CC10 (47.6)11 (52.4)0.36 (0.08–1.65)
TT52 (44.8)64 (55.2)0.226Reference
C carriers53 (45.3)64 (54.7)1.84 (0.68–4.96)
ABCG2 G>A (rs17731538)G carriers101 (45.1)123 (54.9)0.898Reference
AA4 (44.4)5 (55.6)1.16 (0.13–10.55)
GG63 (46.0)74 (54.0)0.994Reference
A carriers42 (43.8)54 (56.2)1.00 (0.39–2.55)
* p value <0.05 is considered to be of statistically significance. p value, odds ratio (OR) and 95% confidence intervals (CI) corresponds to multivariate logistic regression adjusted to patient-related variables (gender, age, smoking, eGFR and SCr), disease-related variables (diagnosis age and disease duration), and treatment-related variables (folic acid, corticosteroids, NSAIDs, other concomitant DMARDs and MTX administration characteristics such as dose, treatment duration and administration route); (a) When reference was AA genotype: OR = 5.37, 95% CI: 1.17–24.71; (b) When reference was AA genotype: OR = 4.24, 95% CI: 1.32–13.65; A: adenine; C: cytosine; CI: confidence interval; DMARDs: disease modifying antirheumatic drugs; eGFR: estimated glomerular filtration rate; G: guanine; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; OR: odds ratio; SLC: solute carrier; SLCO: solute carrier organic anion transporter; SCr: serum creatinine concentration; SNP: single nucleotide polymorphism; T: thymine.
Table
Table 2. Relation between MTX transporters genes haplotypes and clinical response to MTX.
Table 2. Relation between MTX transporters genes haplotypes and clinical response to MTX.
HaplotypeEstimated Frequency (%)p OR (95% CI)
SLC16A7 A>T (rs3763980)SLC16A7 T>G (rs10877333)
AT 54.5 Reference
TT 29.20.3600.72 (0.36–1.44)
AG 16.30.8901.11 (0.38–3.06)
SLC19A1 G>A (rs7499)SLC19A1 G>A (rs1051266)SLC19A1 A>G (rs2838956)SLC19A1 G>A (rs3788200)
GGAG48.7 Reference
AAGA33.60.4301.38 (0.62–3.03)
GAAG5.80.8600.88 (0.21–3.66)
GAGA4.40.8301.22 (0.19–7.70)
AGAG2.00.3303.80 (0.26–55.41)
ABCB1 C>T (rs1045642)ABCB1 C>T (rs1128503)ABCB1 G>A/T (rs2032582)
CCG 43.7 Reference
TTT 37.50.4701.32 (0.63–2.76)
TCG 10.70.7200.82 (0.28–2.44)
CTT 3.90.5300.49 (0.05–4.55)
CTG 2.70.8200.77 (0.08–7.56)
ABCC1 T>C (rs35592)ABCC1 G>C (rs2074087)ABCC1 G>A (rs3784864)
TGA 42.6 Reference
CGG 18.10.025 *4.12 (1.20–14.09)
TGG 17.70.0613.60 (0.95–13.65)
CCG 9.40.7000.77 (0.21–2.86)
TCG 7.40.6900.72 (0.15–3.54)
ABCC1 T>C (rs35592)ABCC1 A>G (rs246240)ABCC1 G>A (rs3784864)
TAA 46.3 Reference
TAG 18.90.1502.05 (0.78–5.38)
CAG 16.40.6201.26 (0.50–3.16)
CGG 11.10.010 *7.26 (1.64–32.14)
TGG 6.20.3702.24 (0.39–12.83)
ABCC2 G>A (rs717620)ABCC2 C>T (rs4148396)
GC 55.3 Reference
AT 22.20.3700.66 (0.26–1.64)
GT 22.20.3900.72 (0.34–1.52)
ABCG2 T>C (rs13120400)ABCG2 G>A (rs17731538)
TG 48.6 Reference
CG 28.70.7301.15 (0.53–2.49)
TA 21.70.8701.07 (0.46-2.51)
* p value <0.05 is considered to be of statistically significance. p value, odds ratio (OR) and 95% confidence intervals (CI) corresponds to multivariate logistic regression adjusted to patient-related variables (gender, age, smoking, eGFR and SCr), disease-related variables (diagnosis age and disease duration), and treatment-related variables (folic acid, corticosteroids, NSAIDs, other concomitant DMARDs and MTX administration characteristics such as dose, treatment duration and administration route); A: adenine; ABC: ATP-binding cassette; C: cytosine; CI: confidence interval; DMARDs: disease modifying antirheumatic drugs; eGFR: estimated glomerular filtration rate; G: guanine; MTX: methotrexate; NSAIDs: non-steroidal anti-inflammatory drugs; OR: odds ratio; SCr: serum creatinine concentration; SLC: solute carrier; T: thymine.

2.5. Genetic Risk Index and Clinical Response to MTX

A genetic risk index (GRI) for non-response to MTX was created for each patient by the sum of risk genotypes from the SNPs that revealed to be statistically significant associated with clinical response to MTX (Table 1). The GRI was adjusted for clinicopathological variables possibly influencing clinical response to MTX as described in Methods section. The risk genotypes were as follow: SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers and ABCC1 rs3784864 G carriers. Figure 1 represents the contribution of GRI for the occurrence of MTX non-response in RA patients. The number (%) of patients is given for each incremental unit of the index.
Ijms 16 13760 g001 1024
Figure 1. (A) Contribution of genetic risk index (GRI) for the occurrence of non-response to MTX; (B) Percentage of rheumatoid arthritis (RA) patients with non-response to MTX in relation to GRI.
Figure 1. (A) Contribution of genetic risk index (GRI) for the occurrence of non-response to MTX; (B) Percentage of rheumatoid arthritis (RA) patients with non-response to MTX in relation to GRI.
Ijms 16 13760 g001
Index 0 included SLC22A11 rs11231809 AA + ABCC1 rs246240 AA + ABCC1 rs3784864 AA. Index 1 included SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers or ABCC1 rs3784864 G carriers. Index 2 included SLC22A11 rs11231809 T carriers + ABCC1 rs246240 G carriers; SLC22A11 rs11231809 T carriers + ABCC1 rs3784864 G carriers; ABCC1 rs246240 G carriers + ABCC1 rs3784864 G carriers. Index 3 included SLC22A11 rs11231809 T carriers + ABCC1 rs246240 G carriers + ABCC1 rs3784864 G carriers.
The GRI ranged from 0 to 3. Patients with Index 2 were 6.37-fold (95% CI: 1.45–27.99) more likely to be non-responders than those with Index 1 (p = 0.014), patients with Index 3 were 9.08-fold (95% CI: 1.54–53.48) more likely to be non-responders compared with those with Index 2 (p = 0.015) and patients with Index 3 were 16.06-fold (95% CI: 1.85–139.28) more likely to be non-responders compared with those with Index 1 (p = 0.012).

3. Discussion

Interpatient variability in clinical response observed in RA patients under MTX treatment may be due to SNPs in genes encoding for MTX membrane transport proteins. This study aims to evaluate the influence of twenty-three SNPs in SLCs and ABCs MTX transporters as putative predictors of clinical response to MTX in Portuguese RA patients. Hence, we have performed multivariate logistic regression analysis adjusted to potential confounders, particularly to clinicopathological variables, such as patient-, disease- and treatment-related variables, using genotype- and haplotype-based approaches and considering a genetic risk index. This is the first report analyzing the association of SNPs in genes encoding for MTX membrane transport proteins with clinical response to MTX in the Portuguese RA population. In addition, this study is relevant because it overcomes the lack of data regarding the impact of several SNPs in genes involved in the MTX transport pathway in clinical response to MTX while provides a potential pathophysiological explanation to the findings.
The studied population was homogenous relatively to ethnic origin and characteristics were in accordance with other reported studies, in regard to disease gender epidemiology and to the diagnosis age [6,32]. The evaluated outcome (clinical response to MTX) was measured attending to DAS28 and after, at least, six months of MTX treatment, because the maximum improvement to MTX tends to occur only after approximately six months of starting with MTX therapy [11]. Non-response to MTX was registered in 128 (54.9%) patients, which is in accordance to literature, that reports a 40%–60% of patients fail to achieve a good response profile [33].
Genotypes distribution of studied SNPs were in HWE and/or were similar to those previously described for Caucasian populations [16,34,35] and/or in the National Center for Biotechnology Information (NCBI) database.
Multivariate analyses demonstrated that SLC22A11 rs11231809 T carriers were significantly associated with non-response to MTX. SLC22A11, also known as organic anion transporter 4 (OAT4), is a bidirectional transporter highly expressed at the apical membrane of renal proximal tubular cells [36]. It has been described as having a major role in the tubular reabsorption from urine and secretion of MTX [37,38,39,40]. The rs11231809 occurs in an intronic region of SLC22A11 at chromosome 11 and is characterized by a substitution of a T for an A [41]. To the best of our knowledge, the impact of rs11231809 in SLC22A11 expression and function is currently unknown and this is the first report regarding its influence in clinical response to MTX. Nevertheless, previous PGx studies demonstrated the influence of this SNP in PK of diuretic torsemide [42]; no statistically significant risk for renal toxicity was described for the antiretroviral drug tenofovir [43]; and the impact of this SNP in MTX-related overall and gastrointestinal toxicity in RA patients demonstrated non-significant results [19]. Consequently, the influence of rs11231809 in the interplay between tubular reabsorption of MTX and its secretion, and, consequently, in PK and pharmacodynamics (PD) of MTX deserves further investigation.
Regarding ABCC1, multivariate genotype analysis demonstrated that G carriers for rs246240 and rs3784864 were associated with non-response to MTX. Moreover, and from haplotypes analysis, our results demonstrated that two haplotypes were associated with an increased risk for non-response to MTX, as follow: CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. ABCC1, also known as multidrug resistance-associated protein 1 (MRP1), is encoded by ABCC1, located on chromosome 16p13 [41]. It is a polytopic membrane protein, constituted by seventeen transmembrane regions, present in basolateral plasma membranes of enterocytes [44,45] and has been reported as expressed in cells involved in RA pathophysiology. These include CD3-positive T cells in lymphocytic aggregates, RA synovial tissue macrophages in the intimal lining layer, the synovial sublining, and endothelial cells [46]. The rs35592 is characterized by a substitution of a T to a C; the rs246240 consists in a substitution of an A to a G; the rs2074087 denotes a substitution of a C to a G; and the rs3784864 is characterized by a substitution of a G to an A, all in intronic regions of ABCC1 [41]. The influence of ABCC1 studied polymorphisms in ABCC1 function and/or in clinical response to MTX in RA patients is currently unknown. Nevertheless, and since ABCs are mainly efflux transporters, we hypothesize that these genotypes and haplotypes should provide an increased MTX efflux, leading to lower intracellular MTX levels and, consequently, to non-response to MTX.
Other extensively studied SNPs such as SLC19A1 and SLCO1B1 variants were not significant for clinical response in this study. This could be explained by the existence of other polymorphisms in these transporters that could counterbalance their functionality or the simultaneous expression of other transporters in target cells that could equalize the influx/efflux ratio. Nevertheless, more and larger studies are necessary to support our results.
The calculation of a GRI was used to improve the impact of studied SNPs in the prediction of non-response to MTX. This GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders compared with those with Index 1. This highlights the importance of genotyping the SNPs composing the GRI and the urgency of developing the field of therapy personalization for the prediction of non-response to MTX.
Besides the potential importance of our results, the possible study limitations such as the sample size, study design, and a lack of adjustment of significant results for multiple testing in order to minimize false positive results should be emphasized. Since we analyzed twenty SNPs, a Bonferroni-corrected p-value of 0.0025 would have to be used as threshold for significance. Consequently, none of the SNPs would have remained statistically significant after adjustment [47]. Given the conservative nature of Bonferroni method and implied constraints from its usage [48], in order to minimize potential false positive results, a multivariate analysis adjusted to variables that could influence the measured outcome was performed. In addition, results from haplotype analysis were concordant to the associations obtained in genotype analysis, thus it is unlikely that the reported significant results were false positives. Beyond the multivariate analyses, other strengths of this study can be highlighted such: (1) our population is relatively homogenous regarding ethnic origin and is representative of clinical cohorts of established and well-defined RA patients; (2) patient characteristics were in accordance with other reported studies in regard to gender and age at diagnosis [6,32]; and (3) having studied twenty-three SNPs in genes encoding for MTX membrane transporter proteins, many of which had never been studied before, in both RA and Caucasian populations. As future perspectives, due to the lack of studies analyzing the expression of MTX transporters in cells involved in the pathophysiology of RA and the impact of SNPs in the function of such transporters, clinical response and/or MTX circulating levels, further evidence is necessary, to support the interpretation of our results. Furthermore, it is important to consider if enterohepatic recirculation contributes to major differences in bioavailability, to reinforce the importance of understanding the role of the SNPs in transporters on clinical response to MTX. It should be also considered that MTX retention is dependent of MTX polyglutamation levels and, thus, genetic polymorphisms in genes encoding to enzymes involved in MTX polyglutamation process deserve future evaluation. Also, future studies should consider that carrier-mediated mechanisms have been referred to differently depending on MTX dose (low-doses implicates a membrane transport pathway while high-doses implicates both a membrane transport pathway and passive diffusion [49]) and, consequently, may influence MTX pharmacokinetics and the clinical response to MTX. Thus, it is important to address this issue when comparisons are drawn between “low-dose disease models” and “high-dose disease models”. Finally, further studies should consider that a possible synergetic effect between MTX and other DMARDs could influence the associations between genetic polymorphisms and clinical response to MTX [50,51]. Thus, multicenter, prospective studies including RA patients at the diagnosis time and without previously MTX treatment should be conducted to confirm our results.

4. Experimental Section

4.1. Patients and Study Design

This study was developed as a retrospective study in a cohort of consecutive Caucasian patients (≥18 years) with RA treated with MTX conducted between January 2009 and December 2012 at São João Hospital Center (Porto, Portugal). The Ethics Committee for the Health (Comissão de Ética para a Saúde—CES) had approved this study from an ethical point of view on 19 March 2009, reference 33/2009, and informed written consent was obtained from all patients according to the standards of the Helsinki Declaration. Included patients had to meet the 2010 revised classification criteria of American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) [52] Patients were excluded of the study if had drug abuse history, recent pregnancy or desire to become pregnant during the study.
Therapeutic strategy: All patients were initially treated with 10 mg per os/week of MTX in monotherapy. This dose was increased 5 mg for each three weeks the patients did not meet the EULAR criteria for response, i.e., if presented a Disease Activity Score in 28 joints (DAS28) >3.2. Every 3 months, MTX clinical response was evaluated and therapeutic strategies were defined as follow: (1) first evaluation, if patients have no response or show gastrointestinal toxicity, administration route was changed to subcutaneous; (2) second evaluation, if maximum tolerable dose was used without response, MTX therapy was discontinued or associated with other synthetic DMARD; and (3) third evaluation, in patients without response and other contraindication, therapy was changed by associating a biological DMARD. Folic acid supplementation was prescribed to all patients and its regular compliance was registered. Other concomitant drugs, such as corticosteroids, non-steroidal anti-inflammatory drugs (NSAIDs), and other DMARDs were allowed during the study.
Data collection and variable definition: Patients’ demographics, clinicopathological and treatment characteristics were collected from clinical records. Clinical response was assessed using the DAS28 as described by Prevoo et al. [53]. Estimated glomerular filtration rate (eGFR) was calculated using the follow Modification of Diet in Renal Disease (MDRD) equation: 186 × (creatinine/88.4) − 1.154 × (Age) − 0.203 × (0.742 if female) × (1.210 if black), available on http://egfrcalc.renal.org/.
Clinical response definition: Non-response was defined when patients presented a DAS28 > 3.2 in two consecutive evaluations. Therefore, non-response to MTX was defined only after six months of MTX therapy. Response to MTX was defined when patients presented a DAS28 ≤ 3.2.

4.2. Single Nucleotide Polymorphisms Selection and Genotyping

A total of twenty-three SNPs in ten genes that codify for MTX membrane transporter proteins were selected based on literature attending to their putative effects on MTX transport function and/or MTX clinical response (Table S1) [35,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74]. Whole blood samples from each patient were obtained with standard venipuncture technique. Genomic DNA was extracted with QIAamp DNA Blood Mini Kit (QIAGEN, Hilden, Germany) according to manufacturer instructions and total genomic DNA was quantified, and its purity analyzed, using the NanoDrop 1000 Spectrophotometer v3.7 (Thermo Scientific, Wilmington DE, USA). Sequenom® Assay Design 3.1 software was used to design the primers and genotyping was performed according to standard Sequenom® iPLEX protocol [75]. Results were manually inspected and verified, using the MassARRAY Typer Analyzer v4.0 software (Sequenom®, San Diego, CA, USA). For quality control, 10% of the samples were randomly selected for a second analysis and results were 100% concordant.

4.3. Statistical Analysis

Statistical analysis was performed with either IBM® SPSS® Statistics for Windows, Version 20.0 (IBM Corp, Armonk, NY, USA) and SNPStats software [76]. Genotype frequencies were assessed and tested for HWE. SNPs were excluded from analysis when genotyping call rates were less than 95% and when minor allele frequency was less than 10.0%. Multivariate analysis by binary logistic regression was used to identify which genotypes and haplotypes were associated with clinical response to MTX, by adjusting to clinicopathological variables possibly influencing disease state and clinical response to MTX. Such variables were selected based either in literature review and/or clinical significance [77,78,79,80,81] and included: (1) patient-related: age, gender, smoking status and renal function (eGFR and SCr); (2) disease-related: diagnosis age and disease duration; and (3) treatment-related: folic acid, corticosteroids, NSAIDs, other DMARDs and MTX administration characteristics (dose, treatment duration and administration route). The LD between SNPs in the same gene was estimated and expressed as D′ coefficients. Possible haplotypes were tested for association with clinical response to MTX by taking the most frequent haplotype as reference. A GRI for non-response to MTX was created for each patient. The index included the risk genotypes from the SNPs that revealed to be statistically significant with clinical response to MTX. GRI was tested for association with clinical response to MTX in a multivariate analysis. Results were expressed as OR with 95% confidence intervals (CI) and considering a probability (p) value of 5% or less as statistically significant.

5. Conclusions

In conclusion, this study revealed that genetic polymorphisms in SLC22A11 and ABCC1 could be predictors of clinical response to MTX in Portuguese RA patients. Genotyping patients according to these genetic markers may be helpful to identify which patients will not benefit from MTX treatment, highlighting the relevance of developing the field of personalized medicine. Despite the potential of our findings, translation into clinical practice requires larger and multicentric studies in order to clearly endorse the utility of these SNPs.

Abbreviations

A
adenine
ACR
American College of Rheumatology
ABC
ATP-binding cassette
C
cytosine
CI
confidence interval
DAS28
Disease Activity Score in 28 joints
DMARD
disease modifying antirheumatic drug
eGFR
estimated glomerular filtration rate
EULAR
European League Against Rheumatism
FPGS
folylpolyglutamate synthetase
G
guanine
GGH
gamma-glutamyl hydrolase
GRI
genetic risk index
HWE
Hardy–Weinberg equilibrium
MRP1
multidrug resistance-associated protein 1
MTX
methotrexate
NSAIDs
non-steroidal anti-inflammatory drugs
OAT4
organic anion transporter 4
OR
odds ratio
PD
pharmacodynamics
PGx
pharmacogenomics
PK
pharmacokinetics
RA
rheumatoid arthritis
SLC
solute carrier
SLCO
solute carrier organic anion transporter
SCr
serum creatinine concentration
SNP
single nucleotide polymorphism
T
thymine

Supplementary Materials

Supplementary materials can be found at https://www.mdpi.com/1422-0067/16/06/13760/s1.

Acknowledgments

The authors wish to acknowledge to Fundação para a Ciência e Tecnologia (FCT) for Aurea Lima’s Doctoral Grant (SFRH/BD/64441/2009), to the nursing service of Rheumatology Day Hospital and the physicians from the Rheumatology Department of Centro Hospitalar de São João for the support in patients’ recruitment, to the Genomics Unit-Genotyping Service of Instituto Gulbenkian de Ciência for genotyping service, and to Hugo Sousa (PhD) for the coordination of Grupo de Oncologia Molecular & Patologia Viral.

Author Contributions

Aurea Lima and Vítor Seabra designed the study. Aurea Lima, Miguel Bernardes, Rui Medeiros and Vítor Seabra conceived the study. Aurea Lima and Rita Azevedo performed the methodology. Aurea Lima, Rui Medeiros and Vítor Seabra contributed to reagents and materials. Aurea Lima, Miguel Bernardes, Rita Azevedo, Rui Medeiros and Vítor Seabra wrote the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Smith, H.S.; Smith, A.R.; Seidner, P. Painful rheumatoid arthritis. Pain Phys. 2011, 14, E427–E458. [Google Scholar]
  2. Rahman, L.K.; Chhabra, S.R. The chemistry of methotrexate and its analogues. Med. Res. Rev. 1988, 8, 95–155. [Google Scholar] [CrossRef] [PubMed]
  3. Kasim, N.A.; Whitehouse, M.; Ramachandran, C.; Bermejo, M.; Lennernas, H.; Hussain, A.S.; Junginger, H.E.; Stavchansky, S.A.; Midha, K.K.; Shah, V.P.; et al. Molecular properties of WHO essential drugs and provisional biopharmaceutical classification. Mol. Pharm. 2004, 1, 85–96. [Google Scholar] [CrossRef] [PubMed]
  4. Korell, J.; Stamp, L.K.; Barclay, M.L.; Dalrymple, J.M.; Drake, J.; Zhang, M.; Duffull, S.B. A population pharmacokinetic model for low-dose methotrexate and its polyglutamated metabolites in red blood cells. Clin. Pharm. 2013, 52, 475–485. [Google Scholar] [CrossRef] [PubMed]
  5. Kalliokoski, A.; Niemi, M. Impact of OATP transporters on pharmacokinetics. Br. J. Pharmacol. 2009, 158, 693–705. [Google Scholar] [CrossRef] [PubMed]
  6. Rindfleisch, J.A.; Muller, D. Diagnosis and management of rheumatoid arthritis. Am. Fam. Phys. 2005, 72, 1037–1047. [Google Scholar]
  7. Lima, A.; Azevedo, R.; Sousa, H.; Seabra, V.; Medeiros, R. Current approaches for TYMS polymorphisms and their importance in molecular epidemiology and pharmacogenetics. Pharmacogenomics 2013, 14, 1337–1351. [Google Scholar] [CrossRef] [PubMed]
  8. Lima, A.; Monteiro, J.; Bernardes, M.; Sousa, H.; Azevedo, R.; Seabra, V.; Medeiros, R. Prediction of methotrexate clinical response in Portuguese rheumatoid arthritis patients: Implication of MTHFR rs1801133 and ATIC rs4673993 polymorphisms. BioMed Res. Int. 2014, 2014, 11. [Google Scholar] [CrossRef] [PubMed]
  9. Lima, A.; Bernardes, M.; Azevedo, R.; Monteiro, J.; Sousa, H.; Medeiros, R.; Seabra, V. SLC19A1, SLC46A1 and SLCO1B1 polymorphisms as predictors of methotrexate-related toxicity in Portuguese rheumatoid arthritis patients. Toxicol. Sci. 2014, 142, 196–209. [Google Scholar] [CrossRef] [PubMed]
  10. Benucci, M.; Saviola, G.; Manfredi, M.; Sarzi-Puttini, P.; Atzeni, F. Cost effectiveness analysis of disease-modifying antirheumatic drugs in rheumatoid arthritis. A systematic review literature. Int. J. Rheumatol. 2011, 2011, 845496. [Google Scholar] [CrossRef] [PubMed]
  11. Kremer, J.M.; Lee, J.K. The safety and efficacy of the use of methotrexate in long-term therapy for rheumatoid arthritis. Arthritis Rheum. 1986, 29, 822–831. [Google Scholar] [CrossRef] [PubMed]
  12. Weinblatt, M.E.; Kaplan, H.; Germain, B.F.; Block, S.; Solomon, S.D.; Merriman, R.C.; Wolfe, F.; Wall, B.; Anderson, L.; Gall, E.; et al. Methotrexate in rheumatoid arthritis. A five-year prospective multicenter study. Arthritis Rheum. 1994, 37, 1492–1498. [Google Scholar] [CrossRef] [PubMed]
  13. Tian, H.; Cronstein, B.N. Understanding the mechanisms of action of methotrexate: Implications for the treatment of rheumatoid arthritis. Bull. NYU Hosp. Jt. Dis. 2007, 65, 168–173. [Google Scholar] [PubMed]
  14. Dervieux, T.; Greenstein, N.; Kremer, J. Pharmacogenomic and metabolic biomarkers in the folate pathway and their association with methotrexate effects during dosage escalation in rheumatoid arthritis. Arthritis Rheum. 2006, 54, 3095–3103. [Google Scholar] [CrossRef] [PubMed]
  15. Swierkot, J.; Szechinski, J. Methotrexate in rheumatoid arthritis. Pharmacol. Rep. 2006, 58, 473–492. [Google Scholar] [PubMed]
  16. Chan, E.S.; Cronstein, B.N. Molecular action of methotrexate in inflammatory diseases. Arthritis Res. 2002, 4, 266–273. [Google Scholar] [CrossRef] [PubMed]
  17. Silman, A.; Pearson, J. Epidemiology and genetics of rheumatoid arthritis. Arthritis Res. 2002, 4, S265–S272. [Google Scholar] [CrossRef] [PubMed]
  18. Branco, J.C.; Canhao, H. Epidemiological study of rheumatic diseases in Portugal—EpiReumaPt. Acta Reumatol. Port. 2011, 36, 203–204. [Google Scholar] [PubMed]
  19. Dias, A. Terapêutica anti-inflamatória em reumatologia: Coxibes—parte I: (b) Epidemiologia da osteoartrose e da artrite reumatóide. Anamnesis 2001, 10, 16–19. [Google Scholar]
  20. Mikkelsen, T.S.; Thorn, C.F.; Yang, J.J.; Ulrich, C.M.; French, D.; Zaza, G.; Dunnenberger, H.M.; Marsh, S.; McLeod, H.L.; Giacomini, K.; et al. PharmGKB summary: Methotrexate pathway. Pharm. Genomics 2011, 21, 679–686. [Google Scholar] [CrossRef] [PubMed]
  21. International Transporter Consortium; Giacomini, K.M.; Huang, S.M.; Tweedie, D.J.; Benet, L.Z.; Brouwer, K.L.; Chu, X.; Dahlin, A.; Evers, R.; Fischer, V.; et al. Membrane transporters in drug development. Nat. Rev. Drug Discov. 2010, 9, 215–236. [Google Scholar]
  22. Qiu, A.; Jansen, M.; Sakaris, A.; Min, S.H.; Chattopadhyay, S.; Tsai, E.; Sandoval, C.; Zhao, R.; Akabas, M.H.; Goldman, I.D. Identification of an intestinal folate transporter and the molecular basis for hereditary folate malabsorption. Cell 2006, 127, 917–928. [Google Scholar] [CrossRef] [PubMed]
  23. Kremer, J.M. Toward a better understanding of methotrexate. Arthritis Rheum. 2004, 50, 1370–1382. [Google Scholar] [CrossRef] [PubMed]
  24. Alamanos, Y.; Drosos, A.A. Epidemiology of adult rheumatoid arthritis. Autoimmun Rev. 2005, 4, 130–136. [Google Scholar] [CrossRef] [PubMed]
  25. Romao, V.C.; Canhao, H.; Fonseca, J.E. Old drugs, old problems: Where do we stand in prediction of rheumatoid arthritis responsiveness to methotrexate and other synthetic DMARDs? BMC Med. 2013, 11, 17. [Google Scholar] [CrossRef] [PubMed]
  26. Siva, C.; Yokoyama, W.M.; McLeod, H.L. Pharmacogenetics in rheumatology: The prospects and limitations of an emerging field. Rheumatology (Oxford) 2002, 41, 1273–1279. [Google Scholar] [CrossRef] [PubMed]
  27. Malik, F.; Ranganathan, P. Methotrexate pharmacogenetics in rheumatoid arthritis: A status report. Pharmacogenomics 2013, 14, 305–314. [Google Scholar] [CrossRef] [PubMed]
  28. Rowland, M.; Tozer, T.N. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications, 4th ed.; Wolters Kluwer Health/Lippincott William & Wilkins: Philadelphia, PA, USA, 2011. [Google Scholar]
  29. Kroot, E.J.A.; van Leeuwen, M.A.; van Rijswijk, M.H.; Prevoo, M.L.L.; vanʼt Hof, M.A.; van de Putte, L.B.A.; van Riel, P.L.C.M. No increased mortality in patients with rheumatoid arthritis: Up to 10 years of follow up from disease onset. Ann. Rheum. Dis. 2000, 59, 954–958. [Google Scholar] [CrossRef] [PubMed]
  30. Bjornadal, L.; Baecklund, E.; Yin, L.; Granath, F.; Klareskog, L.; Ekbom, A. Decreasing mortality in patients with rheumatoid arthritis: Results from a large population based cohort in Sweden, 1964–1995. J. Rheumatol. 2002, 29, 906–912. [Google Scholar] [PubMed]
  31. Zhu, H.; Deng, F.Y.; Mo, X.B.; Qiu, Y.H.; Lei, S.F. Pharmacogenetics and pharmacogenomics for rheumatoid arthritis responsiveness to methotrexate treatment: The 2013 update. Pharmacogenomics 2014, 15, 551–566. [Google Scholar] [CrossRef] [PubMed]
  32. Gibofsky, A. Overview of epidemiology, pathophysiology, and diagnosis of rheumatoid arthritis. Am. J. Manag. Care 2012, 18, S295–S302. [Google Scholar] [PubMed]
  33. Klareskog, L.; van der Heijde, D.; de Jager, J.P.; Gough, A.; Kalden, J.; Malaise, M.; Martin Mola, E.; Pavelka, K.; Sany, J.; Settas, L.; et al. Therapeutic effect of the combination of etanercept and methotrexate compared with each treatment alone in patients with rheumatoid arthritis: Double-blind randomised controlled trial. Lancet 2004, 363, 675–681. [Google Scholar] [CrossRef]
  34. Moncrieffe, H.; Hinks, A.; Ursu, S.; Kassoumeri, L.; Etheridge, A.; Hubank, M.; Martin, P.; Weiler, T.; Glass, D.N.; Thompson, S.D.; et al. Generation of novel pharmacogenomic candidates in response to methotrexate in juvenile idiopathic arthritis: Correlation between gene expression and genotype. Pharm. Genomics 2010, 20, 665–676. [Google Scholar] [CrossRef] [PubMed]
  35. De Rotte, M.C.; Bulatovic, M.; Heijstek, M.W.; Jansen, G.; Heil, S.G.; van Schaik, R.H.; Wulffraat, N.M.; de Jonge, R. ABCB1 and ABCC3 gene polymorphisms are associated with first-year response to methotrexate in juvenile idiopathic arthritis. J. Rheumatol. 2012, 39, 2032–2040. [Google Scholar] [CrossRef] [PubMed]
  36. Ekaratanawong, S.; Anzai, N.; Jutabha, P.; Miyazaki, H.; Noshiro, R.; Takeda, M.; Kanai, Y.; Sophasan, S.; Endou, H. Human organic anion transporter 4 is a renal apical organic anion/dicarboxylate exchanger in the proximal tubules. J. Pharmacol. Sci. 2004, 94, 297–304. [Google Scholar] [CrossRef] [PubMed]
  37. Van Aubel, R.A.; Smeets, P.H.; Peters, J.G.; Bindels, R.J.; Russel, F.G. The MRP4/ABCC4 gene encodes a novel apical organic anion transporter in human kidney proximal tubules: Putative efflux pump for urinary cAMP and cGMP. J. Am. Soc. Nephrol. 2002, 13, 595–603. [Google Scholar] [PubMed]
  38. Koepsell, H.; Endou, H. The SLC22 drug transporter family. Pflugers Arch. 2004, 447, 666–676. [Google Scholar] [CrossRef] [PubMed]
  39. Miyazaki, H.; Sekine, T.; Endou, H. The multispecific organic anion transporter family: Properties and pharmacological significance. Trends Pharmacol. Sci. 2004, 25, 654–662. [Google Scholar] [CrossRef] [PubMed]
  40. Burckhardt, B.C.; Burckhardt, G. Transport of organic anions across the basolateral membrane of proximal tubule cells. Rev. Physiol. Biochem. Pharmacol. 2003, 146, 95–158. [Google Scholar] [PubMed]
  41. Haga, S.B.; Burke, W. Using pharmacogenetics to improve drug safety and efficacy. JAMA 2004, 291, 2869–2871. [Google Scholar] [CrossRef] [PubMed]
  42. Vormfelde, S.V.; Schirmer, M.; Hagos, Y.; Toliat, M.R.; Engelhardt, S.; Meineke, I.; Burckhardt, G.; Nurnberg, P.; Brockmoller, J. Torsemide renal clearance and genetic variation in luminal and basolateral organic anion transporters. Br. J. Clin. Pharmacol. 2006, 62, 323–335. [Google Scholar] [CrossRef] [PubMed]
  43. Pirmohamed, M.; Park, B.K. Genetic susceptibility to adverse drug reactions. Trends Pharmacol. Sci. 2001, 22, 298–305. [Google Scholar] [CrossRef]
  44. Berggren, S.; Gall, C.; Wollnitz, N.; Ekelund, M.; Karlbom, U.; Hoogstraate, J.; Schrenk, D.; Lennernas, H. Gene and protein expression of P-glycoprotein, MRP1, MRP2, and CYP3A4 in the small and large human intestine. Mol. Pharm. 2007, 4, 252–257. [Google Scholar] [CrossRef] [PubMed]
  45. Drozdzik, M.; Rudas, T.; Pawlik, A.; Kurzawski, M.; Czerny, B.; Gornik, W.; Herczynska, M. The effect of 3435C>T MDR1 gene polymorphism on rheumatoid arthritis treatment with disease-modifying antirheumatic drugs. Eur. J. Clin. Pharmacol. 2006, 62, 933–937. [Google Scholar] [CrossRef] [PubMed]
  46. Takano, M.; Yumoto, R.; Murakami, T. Expression and function of efflux drug transporters in the intestine. Pharmacol. Ther. 2006, 109, 137–161. [Google Scholar] [CrossRef] [PubMed]
  47. Bland, J.M.; Altman, D.G. Multiple significance tests: The Bonferroni method. BMJ 1995, 310, 170. [Google Scholar] [CrossRef] [PubMed]
  48. Perneger, T.V. Whatʼs wrong with Bonferroni adjustments. BMJ 1998, 316, 1236–1238. [Google Scholar] [CrossRef] [PubMed]
  49. Warren, R.D.; Nichols, A.P.; Bender, R.A. Membrane transport of methotrexate in human lymphoblastoid cells. Cancer Res. 1978, 38, 668–671. [Google Scholar] [PubMed]
  50. Van der Heijde, D.; Klareskog, L.; Rodriguez-Valverde, V.; Codreanu, C.; Bolosiu, H.; Melo-Gomes, J.; Tornero-Molina, J.; Wajdula, J.; Pedersen, R.; Fatenejad, S.; et al. Comparison of etanercept and methotrexate, alone and combined, in the treatment of rheumatoid arthritis: Two-year clinical and radiographic results from the TEMPO study, a double-blind, randomized trial. Arthritis Rheum. 2006, 54, 1063–1074. [Google Scholar] [CrossRef] [PubMed]
  51. Capell, H.A.; Madhok, R.; Porter, D.R.; Munro, R.A.; McInnes, I.B.; Hunter, J.A.; Steven, M.; Zoma, A.; Morrison, E.; Sambrook, M.; et al. Combination therapy with sulfasalazine and methotrexate is more effective than either drug alone in patients with rheumatoid arthritis with a suboptimal response to sulfasalazine: Results from the double-blind placebo-controlled MASCOT study. Ann. Rheum. Dis. 2007, 66, 235–241. [Google Scholar] [CrossRef] [PubMed]
  52. Aletaha, D.; Neogi, T.; Silman, A.J.; Funovits, J.; Felson, D.T.; Bingham, C.O., 3rd; Birnbaum, N.S.; Burmester, G.R.; Bykerk, V.P.; Cohen, M.D.; et al. 2010 rheumatoid arthritis classification criteria: An American College of Rheumatology/European League against Rheumatism collaborative initiative. Ann. Rheum. Dis. 2010, 69, 1580–1588. [Google Scholar] [CrossRef] [PubMed]
  53. Prevoo, M.L.; vanʼt Hof, M.A.; Kuper, H.H.; van Leeuwen, M.A.; van de Putte, L.B.; van Riel, P.L. Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum. 1995, 38, 44–48. [Google Scholar] [CrossRef] [PubMed]
  54. Hinken, M.; Halwachs, S.; Kneuer, C.; Honscha, W. Subcellular localization and distribution of the reduced folate carrier in normal rat tissues. Eur. J. Histochem. 2011, 55, e3. [Google Scholar] [CrossRef] [PubMed]
  55. Owen, S.A.; Hider, S.L.; Martin, P.; Bruce, I.N.; Barton, A.; Thomson, W. Genetic polymorphisms in key methotrexate pathway genes are associated with response to treatment in rheumatoid arthritis patients. Pharm. J. 2013, 13, 227–234. [Google Scholar] [CrossRef] [PubMed]
  56. Baslund, B.; Gregers, J.; Nielsen, C.H. Reduced folate carrier polymorphism determines methotrexate uptake by B cells and CD4+ T cells. Rheumatology (Oxford) 2008, 47, 451–453. [Google Scholar] [CrossRef] [PubMed]
  57. Bohanec Grabar, P.; Logar, D.; Lestan, B.; Dolzan, V. Genetic determinants of methotrexate toxicity in rheumatoid arthritis patients: A study of polymorphisms affecting methotrexate transport and folate metabolism. Eur. J. Clin. Pharmacol. 2008, 64, 1057–1068. [Google Scholar] [PubMed]
  58. Drozdzik, M.; Rudas, T.; Pawlik, A.; Gornik, W.; Kurzawski, M.; Herczynska, M. Reduced folate carrier-1 80G>A polymorphism affects methotrexate treatment outcome in rheumatoid arthritis. Pharm. J. 2007, 7, 404–407. [Google Scholar] [CrossRef] [PubMed]
  59. James, H.M.; Gillis, D.; Hissaria, P.; Lester, S.; Somogyi, A.A.; Cleland, L.G.; Proudman, S.M. Common polymorphisms in the folate pathway predict efficacy of combination regimens containing methotrexate and sulfasalazine in early rheumatoid arthritis. J. Rheumatol 2008, 35, 562–571. [Google Scholar] [PubMed]
  60. Konig, J.; Cui, Y.; Nies, A.T.; Keppler, D. A novel human organic anion transporting polypeptide localized to the basolateral hepatocyte membrane. Am. J. Physiol. Gastrointest. Liver Physiol. 2000, 278, G156–G164. [Google Scholar] [PubMed]
  61. Tirona, R.G.; Leake, B.F.; Merino, G.; Kim, R.B. Polymorphisms in OATP-C: Identification of multiple allelic variants associated with altered transport activity among European- and African-Americans. J. Biol. Chem. 2001, 276, 35669–35675. [Google Scholar] [CrossRef] [PubMed]
  62. Trevino, L.R.; Shimasaki, N.; Yang, W.; Panetta, J.C.; Cheng, C.; Pei, D.; Chan, D.; Sparreboom, A.; Giacomini, K.M.; Pui, C.H.; et al. Germline genetic variation in an organic anion transporter polypeptide associated with methotrexate pharmacokinetics and clinical effects. J. Clin. Oncol. 2009, 27, 5972–5978. [Google Scholar] [CrossRef] [PubMed]
  63. Arceci, R.J.; Stieglitz, K.; Bras, J.; Schinkel, A.; Baas, F.; Croop, J. Monoclonal antibody to an external epitope of the human mdr1 P-glycoprotein. Cancer Res. 1993, 53, 310–317. [Google Scholar] [PubMed]
  64. Takatori, R.; Takahashi, K.A.; Tokunaga, D.; Hojo, T.; Fujioka, M.; Asano, T.; Hirata, T.; Kawahito, Y.; Satomi, Y.; Nishino, H.; et al. ABCB1 C3435T polymorphism influences methotrexate sensitivity in rheumatoid arthritis patients. Clin. Exp. Rheumatol. 2006, 24, 546–554. [Google Scholar] [PubMed]
  65. Kooloos, W.M.; Wessels, J.A.; van der Straaten, T.; Allaart, C.F.; Huizinga, T.W.; Guchelaar, H.J. Functional polymorphisms and methotrexate treatment outcome in recent-onset rheumatoid arthritis. Pharmacogenomics 2010, 11, 163–175. [Google Scholar] [CrossRef] [PubMed]
  66. Fromm, M.F.; Kauffmann, H.M.; Fritz, P.; Burk, O.; Kroemer, H.K.; Warzok, R.W.; Eichelbaum, M.; Siegmund, W.; Schrenk, D. The effect of rifampin treatment on intestinal expression of human MRP transporters. Am. J. Pathol. 2000, 157, 1575–1580. [Google Scholar] [CrossRef]
  67. Keppler, D.; Kartenbeck, J. The canalicular conjugate export pump encoded by the cmrp/cmoat gene. Prog. Liver Dis. 1996, 14, 55–67. [Google Scholar] [PubMed]
  68. Simon, N.; Marsot, A.; Villard, E.; Choquet, S.; Khe, H.X.; Zahr, N.; Lechat, P.; Leblond, V.; Hulot, J.S. Impact of ABCC2 polymorphisms on high-dose methotrexate pharmacokinetics in patients with lymphoid malignancy. Pharmacogenomics J. 2013, 13, 507–513. [Google Scholar] [CrossRef] [PubMed]
  69. Ranganathan, P.; Culverhouse, R.; Marsh, S.; Mody, A.; Scott-Horton, T.J.; Brasington, R.; Joseph, A.; Reddy, V.; Eisen, S.; McLeod, H.L. Methotrexate (MTX) pathway gene polymorphisms and their effects on MTX toxicity in Caucasian and African American patients with rheumatoid arthritis. J. Rheumatol. 2008, 35, 572–579. [Google Scholar] [PubMed]
  70. Maliepaard, M.; van Gastelen, M.A.; Tohgo, A.; Hausheer, F.H.; van Waardenburg, R.C.; de Jong, L.A.; Pluim, D.; Beijnen, J.H.; Schellens, J.H. Circumvention of breast cancer resistance protein (BCRP)-mediated resistance to camptothecins in vitro using non-substrate drugs or the BCRP inhibitor GF120918. Clin. Cancer Res. 2001, 7, 935–941. [Google Scholar] [PubMed]
  71. Zhang, L.; Spencer, K.L.; Voruganti, V.S.; Jorgensen, N.W.; Fornage, M.; Best, L.G.; Brown-Gentry, K.D.; Cole, S.A.; Crawford, D.C.; Deelman, E.; et al. Association of functional polymorphism rs2231142 (Q141K) in the ABCG2 gene with serum uric acid and gout in 4 US populations: The PAGE study. Am. J. Epidemiol. 2013, 177, 923–932. [Google Scholar] [CrossRef] [PubMed]
  72. Lee, S.H.; Lee, M.S.; Lee, J.H.; Kim, S.W.; Kang, R.H.; Choi, M.J.; Park, S.J.; Kim, S.J.; Lee, J.M.; Cole, S.P.; et al. MRP1 polymorphisms associated with citalopram response in patients with major depression. J. Clin. Psychopharmacol. 2010, 30, 116–125. [Google Scholar] [CrossRef] [PubMed]
  73. Leschziner, G.D.; Andrew, T.; Pirmohamed, M.; Johnson, M.R. ABCB1 genotype and PGP expression, function and therapeutic drug response: A critical review and recommendations for future research. Pharm. J. 2007, 7, 154–179. [Google Scholar] [CrossRef] [PubMed]
  74. Lima, A.; Bernardes, M.; Sousa, H.; Azevedo, R.; Costa, L.; Ventura, F.; Seabra, V.; Medeiros, R. SLC19A1 80G allele as a biomarker of methotrexate-related gastrointestinal toxicity in Portuguese rheumatoid arthritis patients. Pharmacogenomics 2014, 15, 807–820. [Google Scholar] [CrossRef] [PubMed]
  75. Bradic, M.; Costa, J.; Chelo, I.M. Genotyping with sequenom. Methods Mol. Biol. 2011, 772, 193–210. [Google Scholar] [PubMed]
  76. Sole, X.; Guino, E.; Valls, J.; Iniesta, R.; Moreno, V. SNPStats: A web tool for the analysis of association studies. Bioinformatics 2006, 22, 1928–1929. [Google Scholar] [CrossRef] [PubMed]
  77. Halilova, K.I.; Brown, E.E.; Morgan, S.L.; Bridges, S.L., Jr.; Hwang, M.H.; Arnett, D.K.; Danila, M.I. Markers of treatment response to methotrexate in rheumatoid arthritis: Where do we stand? Int. J. Rheumatol. 2012, 2012, 978396. [Google Scholar] [CrossRef] [PubMed]
  78. Ronnelid, J.; Wick, M.C.; Lampa, J.; Lindblad, S.; Nordmark, B.; Klareskog, L.; van Vollenhoven, R.F. Longitudinal analysis of citrullinated protein/peptide antibodies (anti-CP) during 5 year follow up in early rheumatoid arthritis: Anti-CP status predicts worse disease activity and greater radiological progression. Ann. Rheum. Dis. 2005, 64, 1744–1749. [Google Scholar] [CrossRef] [PubMed]
  79. Morel, J.; Combe, B. How to predict prognosis in early rheumatoid arthritis. Best Pract. Res. Clin. Rheumatol. 2005, 19, 137–146. [Google Scholar] [CrossRef] [PubMed]
  80. Dervieux, T.; Furst, D.; Lein, D.O.; Capps, R.; Smith, K.; Walsh, M.; Kremer, J. Polyglutamation of methotrexate with common polymorphisms in reduced folate carrier, aminoimidazole carboxamide ribonucleotide transformylase, and thymidylate synthase are associated with methotrexate effects in rheumatoid arthritis. Arthritis Rheum. 2004, 50, 2766–2774. [Google Scholar] [CrossRef] [PubMed]
  81. Anderson, J.J.; Wells, G.; Verhoeven, A.C.; Felson, D.T. Factors predicting response to treatment in rheumatoid arthritis: the importance of disease duration. Arthritis Rheum. 2000, 43, 22–29. [Google Scholar] [CrossRef]
  82. Swierkot, J.; Slezak, R.; Karpinski, P.; Pawlowska, J.; Noga, L.; Szechinski, J.; Wiland, P. Associations between single-nucleotide polymorphisms of RFC-1, GGH, MTHFR, TYMS and TCII genes and the efficacy and toxicity of methotrexate treatment in patients with rheumatoid arthritis. Pol. Arch. Med. Wewn. 2015, 125, 152–161. [Google Scholar] [PubMed]
  83. Muralidharan, N.; Mariaselvam, C.M.; Cb, M.; Negi, V.S. Reduced folate carrier-1 80G>A gene polymorphism is not associated with methotrexate treatment response in South Indian Tamils with rheumatoid arthritis. Clin. Rheumatol. 2015. [Google Scholar] [CrossRef] [PubMed]
  84. Plaza-Plaza, J.C.; Aguilera, M.; Canadas-Garre, M.; Chemello, C.; Gonzalez-Utrilla, A.; Faus Dader, M.J.; Calleja, M.A. Pharmacogenetic polymorphisms contributing to toxicity induced by methotrexate in the southern Spanish population with rheumatoid arthritis. OMICS 2012, 16, 589–595. [Google Scholar] [CrossRef] [PubMed]
  85. Kato, T.; Hamada, A.; Mori, S.; Saito, H. Genetic polymorphisms in metabolic and cellular transport pathway of methotrexate impact clinical outcome of methotrexate monotherapy in Japanese patients with rheumatoid arthritis. Drug Metab. Pharm. 2012, 27, 192–199. [Google Scholar] [CrossRef]
  86. Bohanec Grabar, P.; Leandro-García, L.J.; Inglada-Pérez, L.; Logar, D.; Rodríguez-Antona, C.; Dolžan, V. Genetic variation in the SLC19A1 gene and methotrexate toxicity in rheumatoid arthritis patients. Pharmacogenomics 2012, 13, 1583–1594. [Google Scholar] [CrossRef] [PubMed]
  87. Chatzikyriakidou, A.; Georgiou, I.; Voulgari, P.V.; Papadopoulos, C.G.; Tzavaras, T.; Drosos, A.A. Transcription regulatory polymorphism −43T>C in the 5′-flanking region of SLC19A1 gene could affect rheumatoid arthritis patient response to methotrexate therapy. Rheumatol. Int. 2007, 27, 1057–1061. [Google Scholar] [CrossRef] [PubMed]
  88. Hilgendorf, C.; Ahlin, G.; Seithel, A.; Artursson, P.; Ungell, A.L.; Karlsson, J. Expression of thirty-six drug transporter genes in human intestine, liver, kidney, and organotypic cell lines. Drug Metab. Dispos. 2007, 35, 1333–1340. [Google Scholar] [CrossRef] [PubMed]
  89. Bleasby, K.; Hall, L.A.; Perry, J.L.; Mohrenweiser, H.W.; Pritchard, J.B. Functional consequences of single nucleotide polymorphisms in the human organic anion transporter hOAT1 (SLC22A6). J. Pharm. Exp. Ther. 2005, 314, 923–931. [Google Scholar] [CrossRef] [PubMed]
  90. Erdman, A.R.; Mangravite, L.M.; Urban, T.J.; Lagpacan, L.L.; Castro, R.A.; de la Cruz, M.; Chan, W.; Huang, C.C.; Johns, S.J.; Kawamoto, M.; et al. The human organic anion transporter 3 (OAT3; SLC22A8): Genetic variation and functional genomics. Am. J. Physiol. Ren. Physiol. 2006, 290, F905–F912. [Google Scholar] [CrossRef] [PubMed]
  91. Goricar, K.; Kovač, V.; Jazbec, J.; Zakotnik, B.; Lamovec, J.; Dolžan, V. Influence of the folate pathway and transporter polymorphisms on methotrexate treatment outcome in osteosarcoma. Pharm. Genomics 2014, 24, 514–521. [Google Scholar] [CrossRef] [PubMed]
  92. Ramsey, L.B.; Bruun, G.H.; Yang, W.; Treviño, L.R.; Vattathil, S.; Scheet, P.; Cheng, C.; Rosner, G.L.; Giacomini, K.M.; Fan, Y.; et al. Rare versus common variants in pharmacogenetics: SLCO1B1 variation and methotrexate disposition. Genome Res. 2012, 22, 1–8. [Google Scholar] [CrossRef] [PubMed]
  93. Radtke, S.; Zolk, O.; Renner, B.; Paulides, M.; Zimmermann, M.; Möricke, A.; Stanulla, M.; Schrappe, M.; Langer, T. Germline genetic variations in methotrexate candidate genes are associated with pharmacokinetics, toxicity, and outcome in childhood acute lymphoblastic leukemia. Blood 2013, 121, 5145–5153. [Google Scholar] [CrossRef] [PubMed]
  94. Zhang, H.N.; He, X.L.; Wang, C.; Wang, Y.; Chen, Y.J.; Li, J.X.; Niu, C.H.; Gao, P. Impact of SLCO1B1 521T>C variant on leucovorin rescue and risk of relapse in childhood acute lymphoblastic leukemia treated with high-dose methotrexate. Pediatr. Blood Cancer 2014, 61, 2203–2207. [Google Scholar] [CrossRef] [PubMed]
  95. Niemi, M.; Pasanen, M.K.; Neuvonen, P.J. Organic anion transporting polypeptide 1B1: A genetically polymorphic transporter of major importance for hepatic drug uptake. Pharmacol. Rev. 2011, 63, 157–181. [Google Scholar] [CrossRef] [PubMed]
  96. Spinella, M.J.; Brigle, K.E.; Sierra, E.E.; Goldman, I.D. Distinguishing between folate receptor-alpha-mediated transport and reduced folate carrier-mediated transport in L1210 leukemia cells. J. Biol. Chem. 1995, 270, 7842–7849. [Google Scholar] [CrossRef] [PubMed]
  97. Wang, D.; Johnson, A.D.; Papp, A.C.; Kroetz, D.L.; Sadée, W. Multidrug resistance polypeptide 1 (MDR1, ABCB1) variant 3435C>T affects mRNA stability. Pharm. Genomics 2005, 15, 693–704. [Google Scholar] [CrossRef]
  98. Stamp, L.K.; Chapman, P.T.; OʼDonnell, J.L.; Zhang, M.; James, J.; Frampton, C.; Barclay, M.L.; Kennedy, M.A.; Roberts, R.L. Polymorphisms within the folate pathway predict folate concentrations but are not associated with disease activity in rheumatoid arthritis patients on methotrexate. Pharm. Genomics 2010, 20, 367–376. [Google Scholar] [CrossRef] [PubMed]
  99. Tanabe, M.; Ieiri, L.; Nagata, N.; Inoue, K.; Ito, S.; Kanamori, Y.; Takahashi, M.; Kurata, Y.; Kigawa, J.; Higuchi, S.; et al. Expression of P-glycoprotein in human placenta: Relation to genetic polymorphism of the multidrug resistance (MDR)-1 gene. J. Pharmacol. Exp. Ther. 2001, 297, 1137–1143. [Google Scholar] [PubMed]
  100. Warren, R.B.; Smith, R.L.; Campalani, E.; Eyre, S.; Smith, C.H.; Barker, J.N.; Worthington, J.; Griffiths, C.E. Genetic variation in efflux transporters influences outcome to methotrexate therapy in patients with psoriasis. J. Investig. Dermatol. 2008, 128, 1925–1929. [Google Scholar] [CrossRef] [PubMed]
  101. Nguyen, T.D.; Markova, S.; Liu, W.; Gow, J.M.; Baldwin, R.M.; Habashian, M.; Relling, M.V.; Ratain, M.J.; Kroetz, D.L. Functional characterization of ABCC2 promoter polymorphisms and allele-specific expression. Pharm. J. 2013, 13, 396–402. [Google Scholar] [CrossRef] [PubMed]
  102. Deo, A.K.; Prasad, B.; Balogh, L.; Lai, Y.; Unadkat, J.D. Interindividual variability in hepatic expression of the multidrug resistance-associated protein 2 (MRP2/ABCC2): Quantification by liquid chromatography/tandem mass spectrometry. Drug Metab. Dispos. 2012, 40, 852–855. [Google Scholar] [CrossRef] [PubMed]

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Lima, A.; Bernardes, M.; Azevedo, R.; Medeiros, R.; Seabra, V. Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted? Int. J. Mol. Sci. 2015, 16, 13760-13780. https://doi.org/10.3390/ijms160613760

AMA Style

Lima A, Bernardes M, Azevedo R, Medeiros R, Seabra V. Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted? International Journal of Molecular Sciences. 2015; 16(6):13760-13780. https://doi.org/10.3390/ijms160613760

Chicago/Turabian Style

Lima, Aurea, Miguel Bernardes, Rita Azevedo, Rui Medeiros, and Vítor Seabra. 2015. "Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?" International Journal of Molecular Sciences 16, no. 6: 13760-13780. https://doi.org/10.3390/ijms160613760

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