Pharmacogenomics and epilepsy: the road ahead

Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

Towards realizing the vision of precision medicine: AI based prediction of clinical drug response

Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and combine a hybrid data/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). We constructed a model that successfully predicts clinical drug response [area under the curve (AUC) = 0.76] and show that even with limited sample size, integrating high-dimensional genetics data with clinical data can inform drug response prediction. After further validation on data collected from an independently conducted clinical study (AUC = 0.75), we extensively explore our model to gain insights into the determinants of drug response, and identify various clinical and genetic characteristics predisposing to poor response. Finally, we assess the potential impact of our model on clinical trial design and demonstrate that, by enriching for probable responders, significant reductions in clinical study sizes may be achieved. To our knowledge, our model represents the first retrospectively validated machine learning model linking drug mechanism of action and the genetic, clinical and demographic background in epilepsy patients to clinical drug response. Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.

Genetic Association of Epilepsy and Anti-Epileptic Drugs Treatment in Jordanian Patients

Purpose

The aim of this study was to investigate the possible effects of single-nucleotide polymorphisms (SNPs) within SLC1A1, SLC6A1, FAM131B, GPLD1, F2, GABRG2, GABRA1, and CACNG5 genes on response to anti-epileptic drugs (AEDs) and the genetic predisposition of epilepsy in Jordanian patients.

Patients and Methods

A total of 299 healthy individuals and 296 pediatric patients from the Jordanian population were recruited. Blood samples are collected, and genotyping was performed using a custom platform array analysis.

Results

The SLC1A1 rs10815018 and FAM131B rs4236482 polymorphisms found to be associated with epilepsy susceptibility. Moreover, SLC1A1 rs10815018 and GPLD1 rs1126617 polymorphisms were associated with generalized epilepsy (GE), while FAM131B rs4236482 is associated with the focal phenotype. Regarding the therapeutic response, the genetic polymorphisms of FAM131B rs4236482, GABRA1 rs2279020, and CACNG5 rs740805 are conferred poor response (resistance) to AEDs. There was no linkage of GLPD1 haplotypes to epilepsy, its subtypes, and treatment responsiveness.

Conclusion

Our findings suggested that SLC1A1, FAM131B, and GPLD1 polymorphisms increasing the risk of generating epilepsy, while FAM131B, GABRA1, and CACNG5 variants may play a role in predicting drug response in patients with epilepsy (PWE).

Possible Genetic Determinants of Response to Phenytoin in a Group of Colombian Patients With Epilepsy

Background

Epilepsy is a serious health problem worldwide. Despite the introduction of new antiepileptic drugs (AEDs) almost 30% of these patients have drug-resistant forms of the disease (DRE), with a significant increase in morbi-mortality.

Objective

Our objective was to assess the impact of some genetic factors and its possible association with treatment response and adverse drug reactions (ADRs) to phenytoin in 67 adult Colombian patients with epilepsy.

Methods

We conducted an analytical, observational, prospective cohort study to screen four polymorphisms in pharmacogenes: CYP2C9*2-c.430C>T (rs1799853), CYP2C9*3-c.1075A>C (rs1057910), ABCB1-c.3435T>C (rs1045642), and SCN1A-IVS5-91G>A (rs3812718), and their association with treatment response. Patients were followed for 1 year to confirm the existence of DRE (non-response) and ADRs using an active pharmacovigilance approach, followed by a consensus in order to classify ADRs according to causality, preventability, intensity and their relation with phenytoin dose, the duration of treatment, and susceptibility factors (DoTS methodology).

Results

A little more than half of evaluated subjects (52.2%) were non-responding to phenytoin. Regarding the genotype-phenotype correlation there was no association between polymorphisms of SCN1A and ABCB1 and DRE (non-response) (p = 0.34), and neither with CYP2C9 polymorphisms and the occurrence of ADRs (p = 0.42). We only found an association between polymorphic alleles of CYP2C9 and vestibular-cerebellar ADRs (dizziness, ataxia, diplopia, and dysarthria) (p = 0.001). Alleles CYP2C9*2-c.430C>T and CYP2C9*3-c.1075A>C were identified as susceptibility factors to ADRs in 24% of patients.

Conclusions

Decreased function alleles of CYP2C9 were highly predictive of vestibular-cerebellar ADRs to phenytoin in our study (p = 0.001). However, the genetic variants CYP2C9*2-c.430C>T, CYP2C9*3-c.1075A>C, ABCB1-c.3435T>C, and SCN1A-IVS5-91G>A, were not associated with treatment response in our study.

[Pharmacogenetic markers of metabolic disorders in the treatment with valproic acid]

The review includes studies on the association between the use of VA drugs and weight gain in patients with epilepsy as well as other valproate-induced adverse drug reactions, including insulin resistance. Understanding the mechanisms of significant weight gain of patients taking VA drugs will help personalize antiepileptic therapy and minimize the risk of valproate-induced obesity.

CYP2C9 polymorphisms in epilepsy: influence on phenytoin treatment

Phenytoin (PHT) is an antiepileptic drug widely used in the treatment of focal epilepsy and status epilepticus, and effective in controlling focal seizures with and without tonic-clonic generalization and status epilepticus. The metabolization of PHT is carried out by two oxidative cytochrome P450 enzymes CYP2C9 and CYP2C19; 90% of this metabolization is done by CYP2C9 and the remaining 10% by CYP2C19. Genetic polymorphism of CYP2C9 may reduce the metabolism of PHT by 25-50% in patients with variants *2 and *3 compared to those with wild-type variant *1. The frequency distribution of CYP2C9 polymorphism alleles in patients with epilepsy around the world ranges from 4.5 to 13.6%, being less frequent in African-Americans and Asians. PHT has a narrow therapeutic range and a nonlinear pharmacokinetic profile; hence, its poor metabolization has significant clinical implications as it causes more frequent and more serious adverse effects requiring discontinuation of treatment, even if it had been effective. There is evidence that polymorphisms of CYP2C9 and the use of PHT are associated with an increase in the frequency of some side effects, such as cerebellar atrophy, gingival hypertrophy or acute cutaneous reactions. The presence of HLA-B*15:02 and CYP2C9 *2 or *3 in the same patient increases the risk of Stevens-Johnson syndrome and toxic epidermal necrolysis; hence, PHT should not be prescribed in these patients. In patients with CYP2C9 *1/*2 or *1/*3 alleles (intermediate metabolizers), the usual PHT maintenance dose (5-10 mg/kg/day) must be reduced by 25%, and in those with CYP2C9 *2/*2, *2/*3 or *3/*3 alleles (poor metabolizers), the dose must be reduced by 50%. It is controversial whether CYP2C9 genotyping should be done before starting PHT treatment. In this paper, we aim to review the influence of CYP2C9 polymorphism on the metabolization of PHT and the clinical implications of poor metabolization in the treatment of epilepsies.

Individualizing Treatment Approaches for Epileptic Patients with Glucose Transporter Type1 (GLUT-1) Deficiency

Monogenic and polygenic mutations are important contributors in patients suffering from epilepsy, including metabolic epilepsies which are inborn errors of metabolism with a good respond to specific dietetic treatments. Heterozygous variation in solute carrier family 2, facilitated glucose transporter member 1 (SLC2A1) and mutations of the GLUT1/SLC2A2 gene results in the failure of glucose transport, which is related with a glucose type-1 transporter (GLUT1) deficiency syndrome (GLUT1DS). GLUT1 deficiency syndrome is a treatable disorder of glucose transport into the brain caused by a variety of mutations in the SLC2A1 gene which are the cause of different neurological disorders also with different types of epilepsy and related clinical phenotypes. Since patients continue to experience seizures due to a pharmacoresistance, an early clinical diagnosis associated with specific genetic testing in SLC2A1 pathogenic variants in clinical phenotypes could predict pure drug response and might improve safety and efficacy of treatment with the initiation of an alternative energy source including ketogenic or analog diets in such patients providing individualized strategy approaches.

Pharmacogenetics of antiepileptic drug efficacy in childhood absence epilepsy

OBJECTIVE

To determine whether common polymorphisms in CACNA1G, CACNA1H, CACNA1I, and ABCB1 are associated with differential short-term seizure outcome in childhood absence epilepsy (CAE).

METHODS

Four hundred forty-six CAE children in a randomized double-blind trial of ethosuximide, lamotrigine, and valproate had short-term seizure outcome determined. Associations between polymorphisms (minor allele frequency ≥ 15%) in 4 genes and seizure outcomes were assessed. In vitro electrophysiology on transfected CACNA1H channels determined impact of 1 variant on T-type calcium channel responsiveness to ethosuximide.

RESULTS

Eighty percent (357 of 446) of subjects had informative short-term seizure status (242 seizure free, 115 not seizure free). In ethosuximide subjects, 2 polymorphisms (CACNA1H rs61734410/P640L, CACNA1I rs3747178) appeared more commonly among not-seizure-free participants (p = 0.011, odds ratio [OR] = 2.63, 95% confidence limits [CL] = 1.25-5.56; p = 0.026, OR = 2.38, 95% CL = 1.11-5.00). In lamotrigine subjects, 1 ABCB1 missense polymorphism (rs2032582/S893A; p = 0.015, OR = 2.22, 95% CL = 1.16-4.17) was more common in not-seizure-free participants, and 2 CACNA1H polymorphisms (rs2753326, rs2753325) were more common in seizure-free participants (p = 0.038, OR = 0.52, 95% CL = 0.28-0.96). In valproate subjects, no common polymorphisms were associated with seizure status. In vitro electrophysiological studies showed no effect of the P640L polymorphism on channel physiology in the absence of ethosuximide. Ethosuximide's effect on rate of decay of CaV 3.2 was significantly less for P640L channel compared to wild-type channel.

INTERPRETATION

Four T-type calcium channel variants and 1 ABCB1 transporter variant were associated with differential drug response in CAE. The in vivo P640L variant's ethosuximide effect was confirmed by in vitro electrophysiological studies. This suggests that genetic variation plays a role in differential CAE drug response. Ann Neurol 2017;81:444-453.

A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information

Mesial temporal lobe epilepsy is the most common form of adult epilepsy in surgical series. Currently, the only characteristic used to predict poor response to clinical treatment in this syndrome is the presence of hippocampal sclerosis. Single nucleotide polymorphisms (SNPs) located in genes encoding drug transporter and metabolism proteins could influence response to therapy. Therefore, we aimed to evaluate whether combining information from clinical variables as well as SNPs in candidate genes could improve the accuracy of predicting response to drug therapy in patients with mesial temporal lobe epilepsy. For this, we divided 237 patients into two groups: 75 responsive and 162 refractory to antiepileptic drug therapy. We genotyped 119 SNPs in ABCB1, ABCC2, CYP1A1, CYP1A2, CYP1B1, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP3A5 genes. We used 98 additional SNPs to evaluate population stratification. We assessed a first scenario using only clinical variables and a second one including SNP information. The random forests algorithm combined with leave-one-out cross-validation was used to identify the best predictive model in each scenario and compared their accuracies using the area under the curve statistic. Additionally, we built a variable importance plot to present the set of most relevant predictors on the best model. The selected best model included the presence of hippocampal sclerosis and 56 SNPs. Furthermore, including SNPs in the model improved accuracy from 0.4568 to 0.8177. Our findings suggest that adding genetic information provided by SNPs, located on drug transport and metabolism genes, can improve the accuracy for predicting which patients with mesial temporal lobe epilepsy are likely to be refractory to drug treatment, making it possible to identify patients who may benefit from epilepsy surgery sooner.

The right and the wrong with epilepsy and her science

This is a commentary and an opinion paper attempting a critical reassessment of the methods and practices of epilepsy research as we see it. The enormous progress in the field of epilepsy in recent years is a cause of celebration. Advances have been made on most fronts, and the position of patients with epilepsy in society has greatly improved. However, there have also been culs‐de‐sac and dead ends of modern science and clinical practice which are also intriguing. It may be true that we can learn more from our mistakes than from our successes. In this opinion paper, we have listed some of the successes and some of the failures of past epilepsy practice, and also areas of current practice and theory which we feel are likely to prove mistaken. The underlying reasons for misdirected practices and theories include, in our view, the influence of fashion, bad science, and the bureaucracies of practice and academic medicine. As a result, some findings are far from objective. Recognition is the first step to remediation, and hopefully future research will minimize some of the pitfalls mentioned in this article and bring the “End of Epilepsy,” as defined and predicted by Oswei Temkin, closer than it is today.

Pharmacogenetics of antiepileptic drugs: A brief review

The goal of pharmacogenetic research is to assist clinicians in predicting patient response to medications when genetic variations are identified. The pharmacogenetic variation of antiepileptic drug response and side effects has yielded findings that have been included in drug labeling and guidelines. The goal of this review is to provide a brief overview of the pharmacogenetic research on antiepileptic drugs. It will focus on findings that have been included in drug labeling, guidelines, and candidate pharmacogenetic variation. Overall, several genes have been included in guidelines by national and international organizations; however, much work is needed to implement and evaluate their use in clinical settings.

Refractory epilepsy in children

Refractory epilepsy, estimated to affect 10-20% children with epilepsy, can have profound effect on the education, social and cognitive functioning and recreational activities of the child. The definitions are still evolving. A detailed clinical evaluation may reveal an accurate syndromic and etiological diagnosis. The recent advances in neuroimaging and electrophysiology have revolutionized the management of children with refractory epilepsy and supplement the clinical evaluation. Genetic and metabolic evaluation may be indicated in selected cases. The rational use of anti-epileptic drugs, epilepsy surgery and dietary therapies are the mainstay in the management. Various experimental treatment options and pharmacogenetics offer hope for future.

Validation of a multigenic model to predict seizure control in newly treated epilepsy

A multigenic classifier based on five single nucleotide polymorphisms (SNPs) was previously reported to predict treatment response in an Australian newly-diagnosed epilepsy cohort using a k-nearest neighbour (kNN) algorithm. We assessed the validity of this classifier in predicting response to initial antiepileptic drug (AED) treatment in two UK cohorts of newly-diagnosed epilepsy and investigated the utility of these five SNPs in predicting seizure control in general. The original Australian cohort constituted the training set for the classifier and was used to predict response to the first well-tolerated AED monotherapy in independently recruited UK cohorts (Glasgow, n=281; SANAD, n=491). A "leave-one-out" cross-validation was also employed, with training sets derived internally from the UK datasets. The multigenic classifier using the Australian cohort as the training set was unable to predict treatment response in either UK cohort. In the "leave-one-out" analysis, the five SNPs collectively predicted treatment response in both Glasgow and SANAD patients prescribed either carbamazepine or valproate (Glasgow OR=3.1, 95% CI=1.4-6.6, p=0.018; SANAD OR=2.8, 95% CI=1.3-6.1, p=0.048), but not those receiving lamotrigine (Glasgow OR=1.3, 95% CI=0.6-2.8, p=1.0; SANAD OR=2.2, 95% CI=0.9-5.4, p=0.36) or other AEDs (Glasgow OR=0.6, 95% CI=0.2-2.0, p=1.0; SANAD OR=1.9, 95% CI=0.9-4.2, p=0.36). The Australian-based multigenic kNN model is not predictive of initial treatment response in UK cohorts of newly-diagnosed epilepsy. However, the five SNPs identified in the original Australian study appear to collectively have a predictive influence in UK patients prescribed either carbamazepine or valproate.

Genetic forms of epilepsies and other paroxysmal disorders

Genetic mechanisms explain the pathophysiology of many forms of epilepsy and other paroxysmal disorders, such as alternating hemiplegia of childhood, familial hemiplegic migraine, and paroxysmal dyskinesias. Epilepsy is a key feature of well-defined genetic syndromes including tuberous sclerosis complex, Rett syndrome, Angelman syndrome, and others. There is an increasing number of single-gene causes or susceptibility factors associated with several epilepsy syndromes, including the early-onset epileptic encephalopathies, benign neonatal/infantile seizures, progressive myoclonus epilepsies, genetic generalized and benign focal epilepsies, epileptic aphasias, and familial focal epilepsies. Molecular mechanisms are diverse, and a single gene can be associated with a broad range of phenotypes. Additional features, such as dysmorphisms, head size, movement disorders, and family history may provide clues to a genetic diagnosis. Genetic testing can impact medical care and counseling. We discuss genetic mechanisms of epilepsy and other paroxysmal disorders, tools and indications for genetic testing, known genotype-phenotype associations, the importance of genetic counseling, and a look toward the future of epilepsy genetics.

The consequences of refractory epilepsy and its treatment

Seizures in some 30% to 40% of patients with epilepsy fail to respond to antiepileptic drugs or other treatments. While much has been made of the risks of new drug therapies, not enough attention has been given to the risks of uncontrolled and progressive epilepsy. This critical review summarizes known risks associated with refractory epilepsy, provides practical clinical recommendations, and indicates areas for future research. Eight international epilepsy experts from Europe, the United States, and South America met on May 4, 2013, to present, review, and discuss relevant concepts, data, and literature on the consequences of refractory epilepsy. While patients with refractory epilepsy represent the minority of the population with epilepsy, they require the overwhelming majority of time, effort, and focus from treating physicians. They also represent the greatest economic and psychosocial burdens. Diagnostic procedures and medical/surgical treatments are not without risks. Overlooked, however, is that these risks are usually smaller than the risks of long-term, uncontrolled seizures. Refractory epilepsy may be progressive, carrying risks of structural damage to the brain and nervous system, comorbidities (osteoporosis, fractures), and increased mortality (from suicide, accidents, sudden unexpected death in epilepsy, pneumonia, vascular disease), as well as psychological (depression, anxiety), educational, social (stigma, driving), and vocational consequences. Adding to this burden is neuropsychiatric impairment caused by underlying epileptogenic processes ("essential comorbidities"), which appears to be independent of the effects of ongoing seizures themselves. Tolerating persistent seizures or chronic medicinal adverse effects has risks and consequences that often outweigh risks of seemingly "more aggressive" treatments. Future research should focus not only on controlling seizures but also on preventing these consequences.

Genetic association analysis of ATP binding cassette protein family reveals a novel association of ABCB1 genetic variants with epilepsy risk, but not with drug-resistance

Epilepsy constitutes a heterogeneous group of disorders that is characterized by recurrent unprovoked seizures due to widely different etiologies. Multidrug resistance remains a major issue in clinical epileptology, where one third of patients with epilepsy continue to have seizures. Role of efflux transporters in multidrug resistant epilepsy has been attributed to drug-resistant epilepsy although, with discrepant observation in genetic studies. These discrepancies could be attributed to variety of factors such as variable definition of the anti-epileptic drug (AED)-resistance, variable epilepsy phenotypes and ethnicities among the studies. In the present study we inquired the role of multidrug transporters ABCB1 and ABCG2 variants in determining AED-resistance and susceptibility to epilepsy in three well-characterized cohorts comprising of mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) (prototype for AED-resistant epilepsy); juvenile myoclonic epilepsy (JME) (prototype for AED-responsive epilepsy); and healthy non-epileptic controls, in 738 subjects of Malayalam speaking south Indian ancestry. ABCB1 and ABCG2 variants were not found to be associated with drug resistance when AED-resistant and AED-responsive cohorts were compared. However, a significant association was observed between ABCB1 (C3435T) rs1045642 and risk of having epilepsy (MTLE-HS and JME pooled cohort; genotypic p-value = 0.0002; allelic p-value = 0.004). This association was seen persistent with MTLE-HS (genotypic p-value = 0.0008; allelic p-value = 0.004) and also with JME (genotypic p-value = 0.01; allelic p-value = 0.05) cohort individually. In-silico functional prediction indicated that ABCB1 rs1045642 has a deleterious impact on protein coding function and in splicing regulation. We conclude that the ABCB1 and ABCG2 variants do not confer to AED-resistance in the study population. However, ABCB1 rs1045642 increases vulnerability to epilepsy with greater tendency for MTLE-HS in south Indian ancestry from Kerala.

Implications of pharmacogenetics for the therapeutic use of antiepileptic drugs

INTRODUCTION

Epilepsy is a chronic neurological disease manifesting as recurrent seizures. Despite the availability of numerous antiepileptic drugs (AEDs), one-third of the patients are not responsive to treatment. Such inter-individual variability in the response to AEDs may be partly explained by genetic differences. This review summarizes the pharmacogenetics (PGx) of AEDs. In addition, a model-based approach is presented that enables the integration of PGx data with other relevant sources of variability, such as demographic characteristics and co-medications.

AREAS COVERED

A comprehensive overview is provided of the data available in the literature on the evidence for correlations between genetic mutations and pharmacokinetic (PK) and/or pharmacodynamics (PD) of AEDs. This information is then used in an integrated manner in the second part, where PGx differences are parameterized as covariates in PK and PKPD models.

EXPERT OPINION

Polymorphisms are profuse in the PK and PD of AEDs. However, understanding of their clinical implication remains limited due to the lack of methodologies that discriminate the contribution of other sources of variability in CNS exposure to drugs. A model-based approach, in which other intrinsic (e.g., demographic covariates) and extrinsic (e.g., drug-drug interactions) factors are evaluated concurrently is needed to ensure optimization and individualization of treatment in epileptic patients.

Weight change, genetics and antiepileptic drugs

Weight gain caused by antiepileptic drugs (AEDs) constitutes a serious problem in the management of people with epilepsy. AEDs associated with weight gain include sodium valproate, pregabalin and vigabatrin. Excessive weight gain can lead to non-compliance with treatment and to an exacerbation of obesity-related conditions. The mechanisms by which AEDs cause weight gain are not fully understood. It is likely that weight change induced by some AEDs has a genetic underpinning, and recent developments in DNA sequencing technology should speed the understanding, prediction and thus prevention of serious weight change associated with AEDs. This review focuses on the biology of obesity in the context of AEDs. Future directions in the investigations of the mechanism of weight change associated with these drugs and the use of such knowledge in tailoring the treatment of specific patient groups are explored.

Clinical neurogenetics: recent advances in the genetics of epilepsy

Epilepsy represents a diverse group of disorders with primary and secondary genetic etiologies, as well as non-genetic causes. As more causative genes are identified, genetic testing is becoming increasingly important in the evaluation and management of epilepsy. This article outlines the clinical approach to epilepsy patients, with emphasis on genetic testing. Specific targeted tests are available for numerous individual genetic causes of epilepsy. Broader screening tests, such as chromosome microarray analysis and whole exome sequencing, have also been developed. As a standardized protocol for genetic testing has not been established, individualized diagnostic approaches to epilepsy patients should be used.

Effects of polymorphisms in six candidate genes on phenytoin maintenance therapy in Han Chinese patients

AIM

The present study aimed to investigate the associations between variants in pharmacokinetic- and pharmacodynamic-related genes with the dosages, concentrations and concentration-dose ratios (CDRs) of phenytoin (PHT).

METHODS & RESULTS

Eleven genetic polymorphisms in the six candidate genes were detected in 269 epileptic patients under maintenance PHT monotherapy by real-time PCR and PCR-RFLP. Results of a bivariate analysis demonstrated that among tested polymorphisms, carriers of the variant CYP2C9*3 tended to require significantly lower maintenance PHT dosages than wild-type carriers (p < 0.0001); on the other hand, carriers of the variants CYP2C9*3 or CYP2C19*3 revealed significantly higher CDRs than wild-type carriers (p < 0.004). In a further multivariate analysis, variants in SCN1A, CYP2C9, CYP2C19 and ABCB1 genes were significantly associated with CDRs of PHT under adjustment of age, gender and epilepsy classifications (adjusted r(2) = 20.07%).

CONCLUSION

The results of present study indicated that polygenic analysis may provide useful information in PHT therapy optimization.

Opportunities and challenges for genome sequencing in the clinic

Human genome sequencing technology is developing rapidly. These developments are providing exciting opportunities for genetic mapping of human traits, ranging from accelerated discovery of mutations underlying relatively simple Mendelian disorders to more genetically complex human diseases. This chapter outlines the development of whole-genome sequencing in a historical context of genetic mapping and explores the impact that sequencing is having on gene discovery study design. Using the example of epilepsy, the authors outline the opportunities and barriers for the translation of genetic predictors from discovery to the clinic. Finally, the authors discuss the practical challenges of actual implementation of whole-genome sequencing to the clinic.