 |
|
||||
|
|||||
| About | Latest Articles | Back-Issues | Articles
|
Search | Instructions | Online Submission | Subscribe | Etcetera | Contact |
| |||||||||||||||||||||
|
Association of polymorphisms of CYP2C9, CYP2C19, and ABCB1, and activity of P-glycoprotein with response to anti-epileptic drugs SR Taur1, NB Kulkarni1, PP Gandhe1, BK Thelma2, SH Ravat3, NJ Gogtay1, UM Thatte11 Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Mumbai, India 2 Department of Genetics, University of Delhi South Campus, New Delhi, India 3 Department of Neurology, Seth GS Medical College and KEM Hospital, Mumbai, India
Correspondence Address: Source of Support: Grants from the Dept. of Biotechnology, Govt. of India,, Conflict of Interest: The corresponding author, Dr. Nithya Gogtay, is the Editor of the Journal of Postgraduate Medicine. DOI: 10.4103/0022-3859.138739
Background and Objective: Epilepsy, the most common neurological disorder, has treatment failure rate of 20 to 25%. Inter-individual variability in drug response can be attributed to genetic polymorphism in genes encoding different drug metabolizing enzymes, drug transporters (P-gp), and enzymes involved in sodium channel biosynthesis. The present study attempted to evaluate association of polymorphisms of CYP2C9, CYP2C19, and ABCB1, and P-gp activity with treatment response in patients with epilepsy. Materials and Methods: Patients with epilepsy on phenytoin and/or phenobarbital and/or carbamazepine were categorized into responders and non-responders as per the International League Against Epilepsy. Plasma drug concentration was estimated by high-performance liquid chromatography. P-gp activity was measured by flow cytometry using rhodamine efflux. The polymerase chain reaction (PCR-RFLP) was used to study polymorphisms of ABCB1 (C3435T), CYP2C9 (416 C > T, and 1061 A > T), and CYP2C19 (681 G > A and 636 G > A). Results: Of total 117 patients enrolled in this study, genotype data was available for 115 patients. P-gp activity was higher in non-responders (n = 68) compared to responders (n = 47) (P<0.001). No association of 416 C > T and 1061 A > T in CYP2C9 or 681 G > A and 636 G > A in CYP2C19 was observed with response phenotype in genotypic analysis. Significant genotypic (odds ratio, OR = 4.5; 95% CI, 1.04 to 20.99) and allelic association (OR = 1.73; 95% CI, 1.02 to 2.95) was observed with ABCB1 C3435T and response phenotype. Conclusions: The response to antiepileptics seems to be modulated by C3435T in ABCB1 or P-gp activity. At present, role of other genetic factors in treatment responsiveness in epilepsy appears limited, warranting analysis in a larger cohort. Keywords: Drug metabolizing enzymes, efflux transporter, epilepsy, non-responders, pharmacogenomics
Epilepsy is the most common chronic neurological disorder in India with a prevalence rate of ~5 per 1,000 population. [1] Recent studies in both developed and developing countries have shown that up to 70% of newly diagnosed children and adults with epilepsy can be successfully treated (i.e., their seizures completely controlled) with anti-epileptic drugs (AEDs). [2] In India, first line AEDs (phenytoin, phenobarbital, carbamazepine, and valproate) still form the mainstay of treatment. [3] Drug resistance in epilepsy affects about a third of patients and is an important clinical problem, associated with increased morbidity and mortality. [4] A variety of factors including, for example, syndromic diagnosis, etiology, and electrophysiology findings have been shown to contribute to resistance. However, genetic causes for drug resistance (polymorphisms in drug-metabolizing enzymes, transporters or targets) have attracted particular attention because they may allow prediction of drug resistance and therefore allow for optimizing treatment strategies. [5] Genes namely CYP2C9 and CYP2C19 encoding for drug metabolizing enzymes (DMEs), have been widely studied for their influence on toxicity of AEDs [6],[7] including a study by Thakkar et al.,[8] which demonstrated an association between CYP2C9*1/*2 or CYP2C9 *1/*3 genotypes and serum phenytoin levels above the reference range. However, there is sparse data correlating genetic variants of DME genes and clinical responsiveness to AEDs in Indian patients with epilepsy. In addition, polymorphism of the efflux transporter, P-glycoprotein (P-gp), encoded by ABCB1 has been shown to decrease bioavailability of AEDs and limit their brain access. [9] This study simultaneously evaluated the association of polymorphisms of CYP2C9, CYP2C19, and ABCB1, as well as activity of P-gp with treatment response, enabling genotype-phenotype correlations.
Ethics This study was approved by the Ethics Committee for Research on Human Subjects (EC/GOVT-17/2007) at Seth GS Medical College and KEM Hospital, Mumbai. Written informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki, 2008 (available at http://www.wma.net/en/30publications/10policies/b3/17c.pdf) and the "Ethical Guidelines for Biomedical Research on Human Participants" (2006) by the Indian Council of Medical Research (ICMR). [10] Participants and eligibility criteria Adult patients (18 to 65 years of age) of either sex with epilepsy receiving phenytoin and/or phenobarbital and/or carbamazepine attending the therapeutic drug monitoring or Neurology outpatient department between June 2010 and August 2011 were enrolled. Exclusions were poor compliance (based on history and plasma concentrations), history of use of inducers (e.g., rifampicin, efavirenz) or inhibitors (e.g., isoniazid, ketoconazole) of AED-metabolizing enzymes, concomitant use of either substrates or inhibitors of P-glycoprotein (e.g., verapamil, nifedipine, digoxin), and history of organic or progressive neurological disorders. Study design and case definitions This was a cross-sectional study. Patients were classified as responders and non-responders based on criteria instituted by the International League against Epilepsy (ILAE), task force meeting 2009. [11] Drug responder had the complete cessation of seizures for 1 year or 3 times the longest inter-seizure interval during the recent active phase of epilepsy, whereas drug non-responder had the failure of adequate trials of 2 tolerated and appropriately chosen and used antiepileptic drug schedules. Laboratory protocols A total of 15 ml blood was collected from each participant for plasma concentrations (in heparin tubes), for genotyping, and measuring the activity of p-glycoprotein (in tube containing 100 μl of 10% disodium EDTA). Genomic DNA was extracted from leucocytes by the phenol-chloroform method and genotyping [CYP2C9 *2 (416 C > T) and *3 (1061 A > T); CYP2C19 *2 (681 G > A) and *3 (636 G > A); ABCB1 C3435T] was performed by a polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) as per methods described earlier. [12],[13],[14] Trough plasma concentration of phenytoin, phenobarbital, and carbamazepine were determined using high-performance liquid chromatography (HPLC) as reported previously. [15] The activity of P-gp was measured by flow cytometry using rhodamine efflux assay using a modification of the method described by Velez et al. [16] The P-gp activity of CD56+ natural killer cells was determined as ratio of the quantities of Rho123 remaining in the presence of the P-gp inhibitor, verapamil, to that without verapamil. The quantity of Rho123 remaining inside the cells was calculated as the addition of mean fluorescence intensity x (% cellular events) of M1 (cells that effectively pumped Rho123) and M2 (cells that did not). Statistical analysis No formal sample size calculation was done. Numerical data was tested for normality using Kolmogorov-Smirnov test, and between groups comparison was done using either unpaired t test (if normally distributed) or Mann-Whitney U test (if not normally distributed). Jonckheere-Terpstra test was used for comparison of numerical data between 3 or more ordered groups. Categorical data was compared using chi square test. Association was expressed as P value and odds ratio (OR) with 95% confidence interval. Binary logistic regression was used to evaluate the effect of various genotypes, plasma AED levels, and P-gp activity on treatment responsiveness. Statistical analyses were considered significant at P < 0.05. All analyses were performed using SPSS software, version 17.0 (SPSS, Chicago, IL, USA).
Of the 117 participants enrolled in this study, genotype data was available for 115 participants (47 responders and 68 non-responders). Among responders, 33 patients were on monotherapy (25 patients on phenytoin, 5 on carbamazepine, and 3 on phenobarbital), 11 were on 2 AEDs (7 on phenytoin + phenobarbital; 4 on phenytoin + carbamazepine) and 3 patients received all 3 AEDs. The number of non-responders on 2 AEDs was 66 (32 on phenytoin + phenobarbital, 22 on phenytoin + carbamazepine, and 12 on phenobarbital + carbamazepine), whereas 4 non-responders received all 3 AEDs. Baseline characteristics Patients in responder and non-responder groups were comparable with respect to baseline characteristics [Table 1], except for phenytoin dose, which was expectedly significantly higher in non-responders.
CYP2C9 and CYP2C19 genotypes The frequency distribution of CYP2C9 and CYP2C19 gene polymorphisms in the present study was in Hardy-Weinberg equilibrium. The frequency of variants of CYP2C9 or CYP2C19 genes was comparable in responders and non-responders [Table 2].
Responders with CYP2C19 mutation required significantly lower doses of phenytoin compared to those with wild genotypes (dose in mg/kg/day, 4.3 + 1 vs. 5.3 + 0.98; P = 0.02). ABCB1 C3435T genotype In the present study, we found significantly higher frequency of ABCB1 CC genotype as well as ABCB1 3435C allele in non-responders compared to responders [Table 3].
P-glycoprotein (P-gp) activity P-gp activity was significantly higher in non-responders compared to responders [1.37 (0.68, 4.76) vs. 1.07 (0.56, 5.95); P < 0.001] [Figure 1].
Correlation between P-gp activity and ABCB1 genotype In total study population (n = 115) irrespective of treatment response, patients with ABCB1 CC genotype had significantly higher P-gp activity than those with CT and TT genotypes (Jonckheere-Terpstra test; P < 0.001) [Figure 2].
Binary logistic regression None of the variables (age, sex, CYP2C9, CYP2C19, ABCB1, and P-gp activity) could accurately predict the treatment response. Hence, a model could not be established.
The present study in 115 Indian patients with epilepsy evaluated simultaneously the polymorphism of CYP2C9, CYP2C19, ABCB1, and the activity of P-glycoprotein and attempted to correlate it with response to AEDs. Non-responders, as defined by the ILAE, [11] received a significantly higher dose of phenytoin, and had significantly higher P-gp activity as compared to patients who were responders. Additionally, the CC genotype of the ABCB1 gene was also found at a significantly higher frequency relative to responders, while the frequency of variants of CYP2C9 or CYP2C19 remained comparable between groups. However, none of the polymorphisms could accurately predict the treatment response. The extent to which polymorphisms in drug metabolizing enzymes (DMEs) alone will contribute to clinical response is difficult to predict as adjusting the dose can circumvent blood level changes affected by genetic variation. In addition, many AEDs are substrates of more than one DME. [9] Between 20 and 30% of metabolism of phenobarbital is through CYP2C9 and CYP2C19, while CYP3A4 is the major isoform responsible for metabolism of carbamazepine. [17] However, a review has shown that no clear association exists between CYP2C9/CYP2C19 polymorphisms and clinical response to AEDs other than phenytoin. [17] CYP2C9 is responsible for the hydroxylation of up to 90% of phenytoin, while CYP2C19 is partially related to phenytoin metabolism. [18] Variants of CYP2C9 and CYP2C19 result in reduced metabolism of phenytoin [19],[20] and increased serum concentrations, [21] thereby leading to either better response or toxicity. [6],[7],[8] Barring studies by Lakhan et al.[22] and Ufer et al.,[23] who have shown that the frequency of heterozygous CYP2C9*3 allele to be lower in non-responders, most studies including ours have not shown any association between CYP2C9 variants and response. The ABCB1 gene, encoding P-glycoprotein (p-gp), has been extensively studied in patients with drug resistant epilepsy. In the present study, non-responders had a higher frequency of CC genotype than TT genotype (odds ratio = 4.5; 95% confidence interval, 1.04 to 20.99). These findings corroborate those of Siddiqui et al.,[24] who have shown that patients with drug-resistant epilepsy were more likely to have the CC genotype at ABCB1 3435 than the TT genotype (odds ratio = 2.66; 95% confidence interval, 1.32 to 5.38). However, our results contrast with several other studies [25],[26],[27] including two Indian studies [28],[29] and a meta-analysis [30] where no association has been shown between the ABCB1 genotype and drug resistance. To complicate this matter, few studies [31],[32] have shown an association of the TT genotype of ABCB1 and drug resistance. Hence, the jury is still out on the role of polymorphism of ABCB1 affecting response to AEDs and more studies with larger sample sizes would be needed. Plasma AED concentration, one of many factors influencing treatment response, may be altered by P-gp activity and variants of ABCB1. However, the present study did not show any correlation between P-gp activity as well as ABCB1 genotype, and dose adjusted plasma AED (phenytoin, phenobarbital, or carbamazepine) concentrations. This indicates that ABCB1 polymorphisms may influence the AED responsiveness without significant changes in plasma concentrations of AEDs. In addition, a study in healthy volunteers (in contrast to patients in our study) demonstrated that ABCB1 CC genotype is more common in participants with low phenytoin plasma concentrations. [21] This shows that apart from plasma concentration and genetic polymorphisms in target genes, many other factors such as patient characteristics and disease activity may influence drug resistance in epilepsy. P-gp is predominantly expressed in organs with excretory functions and at blood-tissue barriers. It has been shown that the CC genotype of ABCB1 is associated with overexpression of P-gp. [24] It has been hypothesized that overexpression of P-gp and other efflux transporters in the cerebrovascular endothelium, in the region of the epileptic focus, also may lead to drug resistance in epilepsy. [31] Our study showed the highest P-gp activity among patients with the CC genotype of ABCB1, intermediate activity in CT variants, and the lowest activity in TT variants. However, non-responders had significantly higher P-gp activity compared to responders. There is scant literature on this association in patients with epilepsy. To date, in epilepsy, there have been a large number of studies that have attempted to use pharmacogenomics to maximize benefits and minimize adverse effects. Approaches have focused on methodologies which have investigated absorption-, distribution-, metabolism-, and elimination (ADME)-related genes (pharmacokinetic pathways), and potential drug targets (pharmacodynamic pathways). [33] The studies as outlined above have thrown up conflicting results. This is likely the result of small sample sizes used, heterogeneity in study designs and case definitions used, varying methodologies used for phenotyping and genotyping among others warranting larger consortium based studies.
P-gp activity and C3435T polymorphism of ABCB1, but not polymorphisms of CYP2C9 and CYP2C19, appear to influence response to AEDs. Genome wide association studies (GWAS) rather than candidate gene studies are needed to comprehensively establish the role of various genes in drug resistant epilepsy.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|||||||
