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Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Background  Azathioprine (AZA) pharmacogenetics are complex and much studied. Genetic polymorphism in TPMT is known to influence treatment outcome. Xanthine oxidase/dehydrogenase (XDH) and aldehyde oxidase (AO) compete with TPMT to inactivate AZA.

Aim  To assess whether genetic polymorphism in AOX1, XDH and MOCOS (the product of which activates the essential cofactor for AO and XDH) is associated with AZA treatment outcome in IBD.

Methods  Real-time PCR was conducted for a panel of single nucleotide polymorphism (SNPs) in AOX1, XDH and MOCOS using TaqMan SNP genotyping assays in a prospective cohort of 192 patients receiving AZA for IBD.

Results  Single nucleotide polymorphism AOX1 c.3404A > G (Asn1135Ser, rs55754655) predicted lack of AZA response (P = 0.035, OR 2.54, 95%CI 1.06–6.13) and when combined with TPMT activity, this information allowed stratification of a patient’s chance of AZA response, ranging from 86% in patients where both markers were favourable to 33% where they were unfavourable (P < 0.0001). We also demonstrated a weak protective effect against adverse drug reactions (ADRs) from SNPs XDH c.837C > T (P = 0.048, OR 0.23, 95% CI 0.05–1.05) and MOCOS c.2107A > C, (P = 0.058 in recessive model, OR 0.64, 95%CI 0.36–1.15), which was stronger where they coincided (P = 0.019).

Conclusion  These findings have important implications for clinical practice and our understanding of AZA metabolism.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Immunosuppressive drugs have become the mainstay of treatment for inflammatory bowel disease (IBD), with proven efficacy in reducing relapses, permitting steroid withdrawal and closing fistulas.1, 2 Indeed, as many as 60% of patients with Crohn’s disease (CD) now receive azathioprine (AZA) or mercaptopurine (MP).3 This reflects changing goals of treatment in IBD, away from symptom control alone, towards mucosal healing and altered natural history, particularly early in the disease course.4, 5

As the importance of rapid and effective disease control, and therefore more aggressive use of immunomodulation, has been established, so it has become increasingly urgent to seek pharmacogenetic predictors of response to AZA and 6MP. Ideally, such predictors would prospectively identify patients who will suffer adverse drug reactions (ADRs) or derive no benefit from thiopurines and thus require alternative therapy. They would also assist physicians in dose-optimization and treatment monitoring or facilitate the decision to switch between immunosuppressants.

Azathioprine is a pro-drug with complex metabolism. Once ingested, it is broken down to release MP by both enzymatic6–8 and non-enzymatic9 conjugation with glutathione. According to the accepted model, there are then three different pathways competing to act on MP, (Figure 1). The first, hypoxanthine-guanine phosphoribosyltransferase, (HGPRT) is anabolic and constitutes the first step towards the production of the active end-product [thioguanine nucleotides, TGNs]. The other two pathways, thiopurine methyltransferase (TPMT) and xanthine oxidase/dehydrogenase (XDH) both produce metabolites which are thought to be inactive and are the first steps in eliminating thiopurines from the body. An additional pathway, diversion of the TGN path into thio-ITP, is also relevant because it is regulated by a polymorphic enzyme ITP phosphohydrolase, (ITPase, encoded by the gene ITPA).10 The activity of these various pathways is thought to determine the amount of ingested drug that becomes activated11, 12 and contribute to the inter-patient variability in response to thiopurines.

image

Figure 1.  The metabolism of the thiopurines drugs. Enzymes targeted in this study are indicated by a yellow star. Thiopurine drugs (shown in circles): AZA: azathioprine, MP: mercaptopurine, 6-TG: 6-thioguanine. Metabolites (shown in bold, active metabolites in boxes): tIMP: thioinosine monophosphate, tXMP: thioxanthine monophosphate, tGMP: thioguanine monosphosphate, tGDP: thioguanine diphosphate, tGTP: thioguanine triphosphate, ITP: inosine triphosphate, tITP: thioinosine triphosphate, 6-MeMP: 6-methylmercaptopurine, 6-Me-tIMP: 6-methyl thioinosine monophosphate, Me-tGMP: methylthioguanine monophosphate, deoxy-tGTP: deoxythioguanine triphosphate, 8-OH AZA: 8-hydroxy azathioprine, 8-OH 6MP: 8-hydroxy mercaptopurine, 8-OH TG: 8-hydroxy thioguanine, 6-MeM-8-OHP: 6 methylmercapto-8-hydroxypurine. Enzymes: TPMT: thiopurine methyltransferase, XDH: xanthine dehydrogenase, PK: phosphokinase, IMPDH: inosine monophosphate dehydrogenase, HGPRT: hypoxanthine guanine phosphoribosyltransferase, GMPS: guanosine monophosphate synthetase.

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Much is now known about the role of genetically determined variation in TPMT activity in toxicity and treatment success with AZA. It is estimated that TPMT deficiency is responsible for up to 30% of all ADRs experienced on AZA, but whilst TPMT deficiency strongly predicts the development of myelotoxicity, the most serious ADR of AZA therapy, it fails to account for over 70% of cases of myelotoxicity.13, 14 Furthermore, those with higher TPMT activity are at increased risk of nonresponse to AZA.15–17 However, it is highly likely that genetic polymorphism in other enzymes involved in thiopurine metabolism also has an impact on each individual’s response to AZA therapy (Figure 1).

As the second major contributor to azathioprine breakdown besides TPMT, a logical candidate enzyme for further study is xanthine oxidase/dehydrogenase (XDH).18, 19 XDH is known to be subject to common genetic polymorphism and, although true deficiency (Type 1 Xanthinuria) is rare, there is considerable inter-individual variation in enzyme activity,20, 21 some of which may be attributable to genetic differences.22 Both increased and decreased XDH activities have been associated with a variety of SNPs in the Japanese population23 and there is preliminary evidence to suggest that XDH SNPs can affect azathioprine metabolite levels24 in a pattern attributable to decreased XDH activity. Blocking XDH activity using allopurinol is known to cause severe toxicity with conventional doses of AZA and safe co-prescription of allopurinol requires an AZA dose-reduction of approximately 80%.25 Indeed, the ability of allopurinol to increase the bioavailability and improve the efficacy of AZA has been well demonstrated in studies of patients failing to respond to full-dose treatment.26–28 Taken together, these observations suggest that the known inter-individual variability in XDH activity (whether attributable to genetic or other factors),22, 29 could have an impact on an individual’s response to AZA.

A molybdenum cofactor30 is essential for the action of three oxidases, XDH, aldehyde oxidase (AO) and sulphite oxidase. Deficiency of the cofactor is associated with severe neurodegeneration resulting in early infant death caused by the loss of sulphite oxidase activity,31 coincident with AO and XDH deficiencies. The final step in the specific adaptation of molybdenum cofactor for XDH and AO requires the action of molybdenum cofactor sulfurase (MOCOS). MOCOS deficiency (which results in the deficiencies of both XDH and AO, but not sulphite oxidase) is, in contrast, relatively benign, causing only a predisposition to renal stones (Type II Xanthinuria).32 The MOCOS gene is also subject to genetic polymorphism, which might also affect AZA metabolism.

The role of AO in human physiology remains unclear. It occurs as a single isoform in humans, is much more widely distributed than XDH and has a broad range of substrates.33–35 It is therefore thought to have additional functions over and above its contribution to purine catabolism.36 AO acts on azathioprine, MP and other thiopurine metabolites, contributing to the catabolism of thiopurines18, 34, 37 (Figure 1). However, the functional significance of the thiopurine metabolites generated by AO is poorly understood. Despite AO products being found in significant quantities, the role of AO in thiopurine metabolism has been thought to be of minimal clinical significance and has not been examined. There is evidence of varying AO activity between individuals,38, 39 but whether this variability relates to the presence of coding SNPs remains unknown. It is possible that this is at least in part because of the complex regulation of gene expression,40 but it could equally be attributable to genetic polymorphism.

We hypothesized that genetic polymorphism in XDH, MOCOS and AOX1 contributes to variation in clinical outcome on AZA therapy and set out to examine a well-defined prospective cohort of patients receiving AZA for IBD for the presence of coding SNPs in XDH, MOCOS and AOX1, seeking associations between the presence of such polymorphism and clinical outcome (response, nonresponse or toxicity) on AZA therapy.

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Patients and samples

Patients were recruited as part of a prospective multi-centre study of AZA for the treatment of IBD undertaken by members of the London IBD Forum.41 The study was initially designed to assess the impact of pre-treatment TPMT genotype and activity and subsequent red cell TGN levels on clinical outcome. All patients were aged between 16 and 80 years of age and had IBD diagnosed by standard criteria. After excluding any patients with zero TPMT activity, all patients received 2 mg/kg of AZA. Complete response was defined as the achievement of each patient’s indication for treatment and stated treatment goal. Steroid withdrawal was defined as complete withdrawal of corticosteroids by 3 months and remaining off corticosteroids for further 3 months. Maintenance of remission was defined as no active disease for 6 months. Remission of active disease was defined by disease activity criteria (Harvey-Bradshaw indices, Truelove and Witts criteria). Requirement for surgery, an alternative immunomodulator or a biologic were considered treatment failure. Adverse drug effects were carefully defined and included only those resulting in AZA withdrawal. The full protocol of this study is reported elsewhere.41 Written informed consent was obtained from all participants. Multicentre research ethical approval for the original study was granted by the Guy’s Hospital Research Ethics Committee (MREC 00/1/33). Ethical approval for the additional pharmacogenetic work was granted by Bexley and Greenwich LREC (06/Q0707/84).

The complete cohort contained 215 patients recruited from a total of 11 centres, of whom 208 were included in the original analysis due to either protocol breaks or withdrawal. One hundred and ninety two patients from this cohort were included in our study, selected on the availability of adequate material for further DNA analysis. The methodology for DNA extraction, measurement of TPMT activity and TGN levels is described elsewhere.41

Pharmacogenetic analysis

Known SNPs in XDH, MOCOS and AOX1 were selected using the Applied Biosystems International database and the online SNP registry: http://www.ncbi.nlm.nih.gov/SNP. Only coding region SNPs with a minor allele frequency greater than 0.02 in the Caucasian population were selected. TaqMan SNP genotyping assays were obtained from Applied Biosystems (Warrington, UK) and details are shown in Table 1.

Table 1.   Single nucleotide polymorphism (SNP) information. Alelle frequencies quoted for our population are for the whole cohort, including non-Caucasians
rs numberGeneExoncDNA base changeAmino-acid substitutionFrequency in dbSNP*Frequency in our population
  1. * Frequencies are those listed in dbSNP for the Caucasian population. http://www.ncbi.nlm.nih.gov/projects/SNP/

rs4407290XDH10837C > TVal279Val0.020.04
rs17323225XDH181936A > GIle646Val0.050.04
rs17011368XDH202107A > GIle703Val0.050.03
rs2295475XDH212211C > TIle737Ile0.310.27
rs1884725XDH273030C > TPhe1010Phe0.230.24
rs207440XDH343717G > AGlu1239Glu0.060.06
rs3744900MOCOS4359G > ASer120Asn0.030.07
rs623053MOCOS4509T > CIle170Thr0.030.07
rs678560MOCOS61072A > GMet358Val0.030.07
rs59445MOCOS112107A > CAsn703His0.340.28
rs1057251MOCOS152600T > GVal867Ala0.100.11
rs55754655AOX1303404A > GAsn1135Ser0.160.11

Patients were genotyped by real-time PCR using a Biorad Miniopticon (Hemel Hempstead, UK). 1.8 μL of DNA was added to Absolute QPCR Mix (Abgene, Epsom, UK) and Taqman SNP genotyping assay and diluted up to a reaction volume of 10 μL with DNA-free water, according to the manufacturers’ instructions. PCR conditions were 15 min enzyme activation at 95 °C, then 42 cycles of: denaturation (15 s at 95 °C) and anneal/extension (1 min at 60 °C).

Sequencing

Due to the highly significant association discovered between one AOX1 SNP and clinical outcome, the coding region of AOX1 was then sequenced in 10 nonresponders (5 with and 5 without the SNP).

Primers for each exon were designed using the web-based tool primer3 (http://frodo.wi.mit.edu/) and synthesized by MWG Biotech, (Ebersberg Germany). The primer sequences are shown in the supplemental information (Table S1) available online. PCR conditions were 1 min denaturation at 94 °C then 35 cycles of (45 s denaturation at 94 °C, then 30 s annealing at 54 °C and 50 s extension at 72 °C) then 5 min extension at 72 °C. PCR product was then purified using QIAquick DNA extraction kits according to the manufacturer’s instructions (Qiagen Ltd. Crawley, UK). PCR products were sequenced using Beckman Coulter Dye Terminator Sequencing Kit, (High Wycombe, UK), according to the manufacturer’s instructions. The sequences obtained were compared with the published sequences obtained from the NCBI website cDNA:BC117179, genomic DNA: AC007163 (http://www.ncbi.nlm.nih.gov/sites/entrez?db=Nucleotide).

Statistical analysis

The frequency of each SNP was compared with the published frequencies for Caucasian individuals in dbSNP and all SNPs tested for departure from Hardy–Weinberg equilibrium. Association tests were performed under a dominant model, testing for association between presence of the SNP minor allele and clinical outcome (both response and occurrence of side effects). Chi-square tests were used, unless cells contained values less than 5 in which instance a Fisher’s Exact test was performed. Analysis was performed using Instat version 3.0a for Macintosh, (Graphpad Software, San Diego, CA, USA http://www.graphpad.com). Chi-square test for trend was used for 2 × 2 × 2 contingency tables. Difference in means was analysed using unpaired Student’s t-test. No correction for multiple testing has been applied. Haplotype analysis was performed in XDH and MOCOS using UNPHASED.42

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Of the 192 patients included in the analysis, 112 (58%) were women and the mean age was 39 years (16-84). Fourteen (7%) were non-Caucasian. One hundred and five (55%) had Crohn’s disease, 86 (45%) ulcerative colitis and a single patient had indeterminate colitis. 77 of 192 (40%) patients withdrew from 2 mg/kg AZA because of adverse effects. The overall per protocol clinical response rate was 71/115 (62%) in those completing 6 months treatment.

The allele frequencies identified in the study cohort were similar to those from online databases (Table 1). SNPs MOCOS c.509T > C, c.1072A > G and c.359G > A were linked, c.509T > C and c.1072A > G were in absolute linkage disequilibrium (r2 = 1, HapMap data) with the minor allele of each SNP occurring together, r2 = 0.89 between these SNPs and c.359G > A (HapMap data). Two of these SNPs were situated in exon 4 and the third (MOCOS c.1072A > G) was in exon 6. In the analysis of clinical outcome, these SNPs were therefore analysed together. All genotypes were in Hardy–Weinberg equilibrium. SNP genotyping was successful in greater than 99% of individuals for all SNPs.

XDH and MOCOS

Details of genotyping results are shown in Table 2. Using a dominant model, a weak but significant protection from ADRs was found for the XDH 837T variant (P = 0.048, OR 0.23, 95% CI 0.05–1.05). Interestingly, those individuals carrying the XDH 837T variant had an under-representation of atypical side effects (headache, myalgia, rash, flu-like symptoms, etc.), but this failed to reach statistical significance (P = 0.07), perhaps because of the small number of patients with the XDH 837T variant. A trend towards protection from ADRs was seen with MOCOS 2107C variant, although this did not reach significance (P = 0.058 in a recessive model, P = 0.13 in a dominant model, OR 0.64, 95%CI 0.36–1.15). Interestingly, no patients with a variant MOCOS c.2107A > C and XDH c.837C > T haplotype (n = 7) experienced ADRs (P = 0.019 Chi-square for trend). Analysis of these SNPs, when non-Caucasians had been excluded, had a minor impact on these associations (XDH 837T variant and ADRs P = 0.08, OR 0.26, 95%CI 0.06–1.22, MOCOS 2107C variant and ADRs P = 0.056 in recessive model, OR 0.26, 95%CI 0.36–1.20).

Table 2.   The association between each SNP and clinical outcome
SNP CR with SNP (%) NR with SNP (%) P-valueOR 95% CITolerant with SNP (%) ADRs with SNP (%) P-valueOR 95% CI
  1. CR, complete responders; NR, nonresponders.

  2. Statistics performed for the dominant model unless otherwise stated.

XDH c.837C > T7/71 (10)5/44 (11)0.791.170.35–3.9512/115 (10)2/77 (3)0.0480.230.05–1.05
XDH c.1936A > G6/71 (8)4/44 (9)1.01.080.29–4.0810/115 (9)6/77 (8)0.820.890.31–2.55
XDH c.2107A > G6/71 (8)2/44 (5)0.710.520.10–2.688/115 (7)5/77 (6)0.890.930.29–2.95
XDH c.2211C > T38/71 (53)21/44 (48)0.540.790.37–1.6859/115 (51)33/77 (43)0.290.730.41–1.31
XDH c.3030C > T25/71 (35)17/44 (39)0.711.160.53–2.5242/115 (37)33/77 (43)0.381.30.72–2.35
XDH c.3717G > A7/71 (10)4/44 (9)1.00.910.25–3.3211/115 (10)11/77 (14)0.311.580.65–3.84
MOCOS c.509T > C, 1072A > G & 359G > A13/71 (18)6/44 (14)0.430.70.25–2.0119/115 (17)7/77 (9)0.140.510.20–1.27
MOCOS c.2107A > C38/71 (54)24/44 (55)0.921.040.49–2.2262/115 (54)33/77 (43)0.13 (0.058 recessive)0.640.36–1.15
MOCOS c.2600T > G12/71 (17)10/44 (23)0.431.450.57–3.7022/115 (19)18/77 (23)0.471.290.64–2.61
AOX1 c.3404A > G12/71 (17)15/44 (34)0.0352.541.06–6.1327/115 (23)13/77 (17)0.260.660.32–1.38

Further investigation into the association with ADRs with MOCOS and XDH was carried out using haplotypes across all markers genotyped in these genes, but no increase in signal was detected (P-values of 0.23 and 0.24 respectively).

There was no relationship observed between any of the XDH or MOCOS SNPs tested and AZA treatment success.

AOX1

A significant association was detected between an AOX1 3404G variant and a lack of clinical response to AZA (P = 0.035, OR 2.54, 95%CI 1.06–6.13). The significance was not altered by excluding non-Caucasians from the analysis (P = 0.036).

Failure to respond to therapy could not be attributed to differences in TGN level, which did not differ significantly between those with and those without the SNP (P = 0.46). Likewise, TPMT was not significantly different between those with and those without the AOX1 SNP (P = 0.44). Excluding from the analysis those patients whose failure to respond was likely to be because of non-adherence to treatment (average TGN < 100 pmol/8 × 108 RBC over the 6 month period) strengthened the association (P = 0.012, OR 6.94, 95%CI 1.58–30.43).

As patients with very high TPMT activity are already known to be at increased risk of non-response,15–17, 41 we analysed the effect of combining this information with a patient’s AOX1 c.3404A > G genotype. In patients with both the AOX1 3404G variant and a TPMT greater than 35 pmol/h/mgHb, only 33% (4/12) responded to treatment compared to 44% (24/55) for those with one of these predictors of nonresponse and an 86% (42/49) for those with neither predictor (P < 0.0001 for trend, Figure 2). The P-value remained unaffected by excluding non-Caucasians.

image

Figure 2.  The percentage of patients achieving a complete response to azathioprine treatment. Patients are stratified according to the number of pharmacogenetic predictors of adverse outcome which they carry.

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No association was found between the AOX1 3404G variant and the occurrence of ADRs.

Sequencing

Sequencing of the AOX1 coding region in ten patients (5 with and 5 without the AOX1 3404G variant) confirmed concordance with the real-time SNP genotyping. No additional coding mutations were detected.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

The impact of common polymorphism in TPMT on clinical outcome with azathioprine treatment has become a classic example of the application of pharmacogenetics and is one of the first developments in this field to be widely adopted in clinical practice.43 The only other gene which has been subject to significant enquiry in the context of AZA treatment is ITPA. It seems likely that ITPA polymorphism is responsible for a proportion of ADRs experienced on AZA, including flu-like symptoms10, 41, 44–46 and perhaps some cases of neutropaenia.46, 47 A few other candidate enzymes have been subject to preliminary studies, for example glutathione s-transferase,48 5′nucleotidase,49–51 inosine monophosphate dehydrogenase52 and methylene tetrahydrofolate reductase.53–55 However, the associations discovered have not yet found a place in clinical practice.

Our study has demonstrated that the presence of the coding region SNP AOX1 c.3404A > G predicts non-response to AZA therapy. This information can be usefully combined with the result of TPMT activity testing to provide even more information about an individual’s chance of success on AZA treatment. This finding could have important applications in clinical practice, not just in the field of IBD, but potentially also for the use of thiopurines in rheumatology, dermatology, transplant medicine and oncology. Knowledge of an individual’s chance of response to AZA will prompt an early review of treatment efficacy, allowing a timely switch to an alternative immunosuppressive agent. It could be argued that those with a poor chance of responding to AZA (TPMT activity > 35 pmol/h/mgHb and AOX1 3404G variant) should be offered an alternative treatment as first-line therapy, which might include reduced dose azathioprine in combination with allopurinol, a combination which has been shown to circumvent the problem of hyper-methylation in some patients.28

In patients with Crohn’s disease, the next immunomodulator considered for treatment would usually be methotrexate. It is possible that the same polymorphism AOX1 c.3404A > G could also affect an individual’s chance of response to methotrexate, as AO is known to metabolize methotrexate producing a 7-hydroxy metabolite, thought to be inactive. This relationship should be examined in the light of our data.

Our findings have implications for the current understanding of AZA’s mechanism of action. Genotype variants, which have a functional impact, most commonly decrease the activity of the affected enzyme. If this is true for the AOX1 3404G variant, then the association with lack of clinical response would suggest that AO metabolites of AZA have immunosuppressive activity. The only functional study of the metabolites produced by the action of AO on AZA showed that 8-hydroxy-6MP did not slow the growth of rat sarcoma.56 However, AO produces several other AZA metabolites on which no functional work has been carried out (Figure 1) and rat sarcoma is not a model for immunomodulation in IBD. The other possibility is that AO activity is increased in the presence of the AOX1 3404G variant. In this case, it is possible that overactive AO removes and inactivates a higher proportion of the ingested drug, resulting in decreased efficacy. In this instance however, one would expect carriers of the AOX1 sequence variant to have lower TGN levels. This was not the case in our cohort, but this could have been confounded by the high degree of inter-individual variability in TGN measurements and the relatively small number of individuals carrying the AOX1 3404G variant.

The XDH and MOCOS SNPs examined here are the first examples of protective pharmacogenetic influences reported in AZA treatment. However, these results must be interpreted with caution bearing in mind the borderline significance of the findings. Confirmation of the association should be sought in other cohorts. The two XDH SNPs which have been the subject of previous functional work (c.2107A > G and c.1936A > G) were not associated with significant changes in metabolite concentrations.23 Neither sequence variant was associated with a significant clinical impact in our study. The fact that the SNP XDH c.837C > T does not encode a change in an amino acid raises the possibility that it is not this SNP itself which exerts the effect but rather that it is in linkage disequilibrium (LD) with another polymorphism. From the analysis of HapMap databases, we found 2 other SNPs (rs17011353 and rs17011359) within a 200 kbp region spanning from 50 kbp upstream of the 5′UTR to 69 kbp downstream of the 3′UTR, which were in LD with SNP XDH c.837C > T with an r2 > 0.5. Both of these SNPs are intronic. We cannot exclude the possibility of LD to an SNP located in a regulatory element. It is possible that one of these SNPs or a neighbouring intronic SNP has an effect on mRNA splicing, as seen with ITPA.57

Our findings are important for the understanding of thiopurine pharmacology. We had not expected that polymorphism in XDH or its cofactor gene MOCOS would protect against ADRs. Rather, we had anticipated that by limiting inactivation of AZA, they would improve response or possibly increase the risk of dose-dependent ADRs such as myelotoxicity. The unexpected association with reduced side effects suggests that the products of XDH metabolism of thiopurines may be toxic and attenuated XDH activity is therefore an advantage. This would be consistent with the theory that XDH activity creates damaging free radicals, which would be predicted to cause adverse events.29 This is consistent with in vitro studies which showed oxidative damage resulting from the action of XDH on thiopurines58 and the use of allopurinol in combination with low dose AZA to circumvent hepatotoxicity in some patients.59

Our findings are based on a well-powered and well-documented prospective cohort and should therefore be robust. However, for some of the SNPs we assessed, with a lower allele frequency, it may be that the numbers involved were too small to demonstrate significant effects. The cohort was mostly made up of Caucasians and the relevance of our findings to other ethnic groups is not clear. In particular, the AOX1 3404G variant has a lower reported frequency in other ethnic groups and none of the non-Caucasians in our study had this SNP present.

In conclusion, we have identified a novel pharmacogenetic marker of nonresponse to AZA: AOX1 3404G variant. The ability of this marker, particularly in combination with TPMT activity, to stratify each individual’s chance of responding to AZA therapy could be used in clinical practice, allowing individualized prescription of immunomodulation to improve patient outcomes. We also report a weak protective effect of polymorphism in XDH and MOCOS against ADRs on AZA. These findings require replication in other cohorts and suggest future directions for investigation, particularly the relevance of AO metabolites for the action of thiopurines.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Declaration of personal interests: Guy’s and St Thomas’ NHS Foundation Trust has a patent pending for the reported associations between SNPs AOX1 c.3404A > G (Asn1135Ser, rs55754655), XDH c.837C > T (Val279Val, rs4407290) and MOCOS c.2107A > C> (Asn703His, rs59445) and azathioprine treatment outcome. Declaration of funding interests: We acknowledge the financial support of the Purine Research Fund, Guy’s and St Thomas’ Charity.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Table S1. Details of the primers used to sequence AOX1, listed by exon.

Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

FilenameFormatSizeDescription
APT_4057_sm_table.docx51KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.