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REVIEW ARTICLE
Role of Pharmacogenomics in Antiepileptic Drug Therapy: Current Status and Future Perspectives Antonio Gambardella*,a,b, Angelo Labateab, Laura Mumolia, Iscia Lopes-Cendesc, and Fernando Cendes*d a
Institute of Neurology, University Magna Græcia, Catanzaro, Italy; bInstitute of Molecular Bioimaging and Physiology of the National Research Council, Catanzaro, Italy; cNeurogenetics Laboratory, Department of Medical Genetics, University of Campinas – UNICAMP, Campinas – SP, Brasil, and dEpilepsy Program and Neuroimaging Laboratory, Department of Neurology University of Campinas – UNICAMP, Campinas - SP Brasil Abstract: Background. Growing evidence indicates that pharmacogenomics will positively impact treatment for patients with epilepsy in the near future, leading to the implementation of a precision-based use of antiepileptic drug (AED) therapy, thereby providing a cornerstone for precision medicine. Objective: In this review, we briefly summarize the studies of pharmacogenomics in epilepsy, recent advances, and how it may progress in the future. ARTICLE HISTORY Received: June 20, 2017 Accepted: May 5, 2017 DOI: 10.2174/1381612823666170911111536
Methods: We subdivided the review into two main sections: genetic variants that may modulate response to AEDs through pharmacokinetics or pharmacodynamics mechanisms; and gene variants that may affect tolerability and safety of AEDs. Results: Results from most studies have been contradictory, due to several flaws, including small sample sizes, inaccurate phenotyping, and genotyping strategies. However, even with these limitations, very recent developments indicate that the goal of incorporating genetic data into clinical practice may be attainable in the near future. In addition, recent pharmacogenomic studies of hypersensitivity reactions to AEDs have also made important strides, as its prevention appears attainable with the identification of HLA-A genotypes for patients at high risk of carbamazepine hypersensitivity. Conclusion: To better clarify the relationship between genetic factors and AEDs, future studies will require more precise epilepsy phenotypes, larger sample sizes, and astute use of new genotyping strategies. Reasonably, this will lead to novel therapeutic approaches in drug targeting and antiepileptogenesis.
Keywords: AEDs, epilepsy, pharmacogenomics, pharmacokinetics, pharmacodynamics, precision medicine, drug resistance, drug response, adverse drug reactions. 1. INTRODUCTION It is well known that different patients respond in different ways to the same medication [1]. This may be related to either pharmacokinetics that is, how the drug is absorbed, distributed, metabolized or eliminated, or to pharmacodynamics, by modifying its target or by perturbing the biological pathways. Growing evidence indicates that genetic variants are major factors that influence these inter-individual differences, accounting for an estimated 20%-95% of variability in drug disposition and effects [2]. Moreover, over the past decades a multitude of genetic variations have been discovered and validated, and have shown promising potential as biomarkers of clinical response and toxicity associated with medical therapy [3]. Unfortunately, despite the increasing emphasis placed upon this area known as pharmacogenomics, which parallels the compelling need to tailor therapeutic options to individualize and optimize drug therapy, this intersection of genomics and medicine is rarely used in clinical practice today, even at major academic medical centers [4]. The reasons that prohibit broad application of genetic information in the clinical use include scientific, economic, educational, legal, and commercial barriers, especially the inconsistent interpretation *Address correspondence to this author at the Institute of Neurology, University Magna Græcia of Catanzaro, Viale Europa, 88100 Catanzaro, Italy; Tel: +39-0961-3647270; Fax: +39-0961-3647177; E-mail:
[email protected] Epilepsy Program and Neuroimaging Laboratory, Department of Neurology University of Campinas – UNICAMP, Campinas - SP Brasil; E-mail:
[email protected] 1381-6128/17 $58.00+.00
of pharmacogenomics test results [4]. Other difficulties include the lack of clinical guidelines for prescribing on the basis of test results, in addition to concerns about incidental or secondary findings from genetic testing [2-4]. 1.1. Pharmacogenomics in Epilepsy Strategies for investigating the role of pharmacogenomics in epilepsy have been even more challenging, and many findings aimed to assess accurately the relationship between gene variants and antiepileptic drug (AED) therapy are controversial [5-8]. A main limitation is that AED therapy in patients with epilepsy is usually challenging, since the pathophysiology underlying seizures is complex, heterogeneous and largely unknown. AED choice is both difficult and empirical, and is mostly based on the evaluation of the balance between benefits and risks of the available AEDs [9]. To date, evidence points to the fact that response rates seem to be mainly related to the epilepsy syndrome, underlying cause, and other factors [10], even though a striking characteristic of the epilepsies is their heterogeneous prognosis, even within the same epileptic syndrome or type of seizures. For instance, some patients with MRI evidence of hippocampal sclerosis are drug responsive [11], whereas others with usual drug-responsive juvenile myoclonic epilepsy may find resistance to all available AED options [12]. Importantly, resistance to AEDs is frequent in epilepsy; about a third of patients continue to have seizures and become refractory to drug treatment despite trying multiple drugs [9-10]. Furthermore, all of the AEDs have associated adverse effects, which are a frequent cause of AEDs failure and are a principal source of disability, © 2017 Bentham Science Publishers
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morbidity, and continue to be a major public health problem that poses a huge toll in lives and in healthcare costs [13]. Likewise, there is evidence that more than 1 in 3 patients treated with AEDs experienced at least 1 adverse event (AE), determined mostly by individual susceptibility, type of AEDs used, and physicians' skills [14]. There is now general consensus that pharmacogenomics will positively impact treatment of patients with epilepsy in the near future, leading to evidenced-based strategies for the use of AEDs, thereby providing a cornerstone for precision medicine [5-7]. As a result, the clinical implementation of pharmacogenomics at the point of care will make it possible to avoid adverse drug reactions, maximize drug efficacy, reduce drug-drug interactions, and select medications based on the genetic profile of individual patients [23]. Probably, the major challenge for personalized care in epilepsy is related to the complex and continuous refinement of the classification of epilepsies. Epilepsy has a long history of being divided into “lumpers and splitters” [15]; lumpers tend to group epilepsies into broad categories, while splitters like to apply more precise definitions and thereby subdivide them into smaller entities. It is reasonable to hypothesize that the advances in the genetics of epilepsies will shift this balance in favor of the splitters. Moreover, the exponential increase of elucidated genetic causes in both extremely rare and common epileptic disorders has also challenged our understanding of how low frequency and rare variants influence the disease traits [16-17]. Although some of these genetic variants are clearly pathogenic being related to specific epilepsy syndromes, it is still problematic to assess the exact role for other genetic variants found [16-17]. In addition, one cannot exclude the possibility that different epilepsy-related genes may act by similar biological mechanisms. [16-17]. In this review, we briefly summarize the studies of pharmacogenomics in epilepsy, recent advances, and their future impact on individualizing epilepsy care. For this purpose, we subdivided the review into two main sections: (a) Genetic variants that may modulate response to AEDs through pharmacokinetics or pharmacodynamics mechanisms, including also genes capable of causing epilepsy itself (‘epilepsy genes’); and (b) gene variants that may affect tolerability and safety of AEDs. 2. INFLUENCE OF GENETIC VARIANTS ON THE RESPONSE TO AEDS In shaping pharmacological treatment for any patient with epilepsy, efficacy, tolerability, ease of use, pharmacokinetics properties, and safety are the main criteria for selection of AEDs [9-10]. This approach should ideally maximize therapeutic efficacy, and avoid unnecessary costs and side effects. Based on clinical trials, mechanisms of action, and clinical experience, certain AEDs, such as carbamazepine (CBZ), are first line therapy for focal epilepsy and other AEDs (e.g.; valproate, VPA) are preferred for generalized epilepsy [18]. With few exceptions, such as vigabatrin as the preferred AED for infantile spasms related to tuberous sclerosis [19] ] and the recommendation to avoid sodium channel blockers in Dravet syndrome, etiology of epilepsy including identified ‘epilepsy genes’ play a minimal role in AED selection [20]. Studying drug efficacy from a pharmacogenomic perspective is far more complicated, as it is difficult to distinguish reduced drugtarget sensitivity in epileptogenic brain tissue from insufficient drug access to targets even in the presence of adequate AED serum levels [21]. Obviously, both pathophysiological mechanisms are not exclusive but may coexist in the same patient [21]. Therefore, the role of genetic variation in both drug targets and pharmacokinetic parameters must be studied.
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2.1. Genetic Determinants of AEDs Pharmacokinetics and Pharmacodynamics Good evidence from studies of the pharmacokinetics and pharmacodynamics of AEDs indicates that genetic variation probably influences the response to AEDs through various mediating effector systems, including pharmacokinetics and pharmacodynamics clinically effective drug dose in any given patient, and mutations in ‘epilepsy genes’. It is therefore possible that the AED dosing may be based on patient genotype. The cytochromes P450 (CYPs) constitute the major enzyme family capable of catalyzing the oxidative biotransformation of many AEDs. The human CYP superfamily contains 57 functional genes and 58 pseudogenes, most of them playing an important role in the metabolism of therapeutic drugs [22]. Individuals with poor metabolizer alleles of CYP2C9 or CYP2C19 genes were shown to have a reduced metabolism of phenobarbital, phenytoin (PHT), and VPA compared with those with normal alleles [22-24]. There is, however, a large interethnic variability in the proportion of poor metabolizers and ultrametabolizers. The expanding list of CYPs genetic variants that alter the rate of drug metabolism has also led to investigate drug dose concentrations and maximum drug doses. It has been shown that individuals carrying CYP2C9 alleles encoding variant with reduced enzymatic activity metabolize PHT at much slower rate than individuals homozygous for the normal allele, and therefore have a greater risk of developing concentration-dependent neurotoxicity [24]. The maximum dose of PHT reported in epilepsy patients with the poor metabolizer alleles was about 50 mg less than the dosage utilized in those with the normal extensive metabolizer alleles [24]. Other than PHT, CYP2C19 is also responsible for the metabolism of phenobarbital (minor), diazepam, and desmethyldiazepam, the active metabolite of diazepam [24]. A genome-wide association study gave some evidence that CYP2C variants, including CYP2C9*3, known to reduce PHT clearance, may increase the risk of severe cutaneous adverse reactions to PHT [25]. The mechanism underlying this predisposition could be related, at least in part, to a delayed clearance of PHT with consequent accumulated PHT in these patients with adverse cutaneous reactions [25]. Overall, all these findings would imply that studying the genotype of patients prior to drug administration might prevent high serum drug concentrations. Despite the available evidence, however, expert committees have not endorsed pre-treatment pharmacogenetic testing for CYP2C9 variants, since we still lack evidence that clinical outcomes improve. There are a number of other genetic polymorphisms of interest in relation to drug metabolism, which have the potential for altering absorption, distribution, transport, metabolism, clearance, and sites of action of AEDs. UDP-glucuronosyltransferase (UGT) enzymes are a group of 16 separate isozymes consisting of two major subfamilies, UGT1 and UGT2, which are responsible for the metabolism of a wide variety of endogenous substrates, including steroids and bile acids, as well as drugs including AEDs [26]. The metabolism of VPA and lamotrigine occurs mostly via this glucuronidation pathway, but the pharmacogenetic impact of specific isozymes of UGT has lagged behind [27]. Zonisamide is the only new AED that is eliminated by a polymorphic metabolic pathway, NAT2 [28]. About 50% of whites and 10% of Asians or blacks are slow acetylators (i.e., they are homozygous carriers of NAT2 mutant alleles). Only 15% of zonisamide is metabolized by NAT2 [28]. Therefore, the acetylation pathway will affect only a fraction of the total elimination of zonisamide without great impact on the pharmacokinetics or dosing. It is important to recognize that there are many confounders that make it difficult to interpret most of these pharmacogenetic results.
Role of Pharmacogenomics in Antiepileptic Drug Therapy
Indeed, AEDs are not only substrates, but can also inhibit or induce genes implicated in metabolism [29] with consequent impact not only on the antiepileptic drug itself, but also the metabolism of concomitant medication. The rate of metabolism can also be affected by gene-gene interactions. Additionally, data for confounders, such as ethnic origin, diet, and medication, and for comorbidities are insufficient [30-32]. A genetic factor putatively associated with AED treatment and dosage has also been the sodium channel gene SCN1A [33]. A first study in a large cohort of patients with epilepsy gave evidence that a functional polymorphism (c.603-91G>A or rs3812718) of SCN1A may influence the dosage requirements of CBZ and phenytoin [3335]. Moreover, it was reported an association between the AA genotype and and CBZ-resistant epilepsy [36]. However, further studies failed to replicate all these findings, and it was also shown that the A allele did not influence the average dosage of oxcarbazepine [37]. Plausible explanations of these conflicting results include sample size, sample heterogeneity, selection bias, and other confounders. 2.2. Predicting Drug Resistance in Epilepsy Pharmacoresistance is seen in about 30% of patients with epilepsy despite appropriate therapy with AEDs [9]. Given that resistance is observed with multiple AEDs, it has been hypothesized that it may be related to generic pharmacodynamics or pharmacokinetic mechanisms. So far, genetic studies mainly focused on the role of drug transporters at the blood brain barrier, as large evidence would indicate that an increase of functional expression of multidrug transporters might prevent AEDs from entering the brain. Thus, genetic polymorphisms can overexpress these transporters and might impart AED resistance. 5 P-glycoprotein (P-gp) is the most studied protein among the transmembrane proteins involved in the ATP binding cassette (ABC) efflux of drugs [38]. The ABC subfamily B member 1 transporter (ABCB1) gene, also known as multidrug resistance 1 (MDR1), encodes this transmembrane transporter. Various genetic polymorphisms in the MDR1 gene could be linked with altered in vivo transport activity of P-gp and play a role in drug-resistant epilepsy [38]. It has been postulated that the common SNP c.3435C>T in the MDR1 gene, specifically the 3435CC genotype, is associated with drug resistance to anticonvulsants [38]. Unfortunately, further studies failed to depict any role of the MDR1 gene while others gave some positive evidence, so the data remain unclear and incongruous [38-42]. Of importance, a prospective cohort study of 503 patients with a new diagnosis of epilepsy found that neither the genotype nor haplotype of MDR1 gene was associated with prognosis [43]. Moreover, there was no association with multiple other SNPs that were analyzed within the region of linkage disequilibrium [43]. A key problem of these incongruous results is that the pathophysiological mechanism underlying pharmacoresistance, especially resistance to multiple AEDs, is not well understood in most cases. Moreover, most studies contain methodological flaws in study design and analysis, together with other confounders such as definition of drug resistance, ethnicity, variable medication histories, and a variety of AEDs [44]. Furthermore, there may be a hypothesis in which the outcome variables are continuous and not dichotomous. Additionally, it cannot be excluded the effect of linkage disequilibrium that refers to the non-random association among variant alleles at different loci in the genome. Thus, when a genetic variant is associated with a phenotype, it is possible that the risk allele might also be some close genetic variation nearby or in some other adjacent linked gene [44]. This is the Achilles heel of many of these studies, as the exact localization of casual variants continues to prove challenging in genetic association studies. Another factor that should be considered is the likely polygenic nature of the genetic factors impacting drug resistance in epilepsy. In this way, a recent study [45] examining this issue found that a
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combination of SNPs located in 11 drug-transporters and drugmetabolism genes was far superior in predicting response to AEDs in patients with mesial temporal lobe epilepsy (MTLE) than clinical phenotypic characteristics alone (including presence of hippocampal sclerosis on MRI). Indeed, the study shows that by using only clinical information to predict response to AEDs (hippocampal sclerosis, age of onset of epilepsy, antecedent of febrile seizures and gender) the accuracy of prediction was (area under the curve [AUC]=0.4568), which is not different from a non-informative prediction (AUC=0.5). However, when using genetic data alone (AUC=0.8008) or combined with clinical information (AUC=0.8177), accuracy of prediction increased. Therefore, suggesting that the combination of genetic and clinical information could be used in clinical practice to predict response to AEDs in patients with MTLE [45]. A very recent study by Glauser et al, however, addressed many of these previous studies’ limitations [46]. Using a unique dataset from a double-blind, randomized trial of ethosuximide, valproate, and lamotrigine in childhood absence epilepsy, the authors analyzed associations between 22 polymorphisms (minor allele frequency ≥15%) in four genes (T-type calcium channel genes CACNA1G, CACNA1H, and CACNA1I; and the drug transporter gene ABCB1) and seizure outcome in 357 children [46]. The prospective design with randomization, double blinding, and titrations with prespecified dosing adjustments based on clinical response minimized treatment bias between and within treatment arms. There were four common T-type channel variants and one transporter missense variant associated with differential drug response, primarily affecting ethosuximide and lamotrigine. There were two specific CACNA1 polymorphisms associated with seizure-free status in patients receiving lamotrigine, and two different CACNA polymorphisms were associated with non–seizure free status in patients on ethosuximide [46]. Overall, the data provide an interesting signal for the existence of genomic biomarkers that could predict drug response in childhood absence epilepsy [46]. 2.3. Epilepsy Genes and Precision Medicine in Epilepsy Precision medicine is an approach for disease treatment and prevention in which health care is individually tailored on the basis of a person's genes, lifestyle and environment [47]. The better understanding of the genetics of epilepsies, and the increasing availability of suitable in-vitro and in-vivo genetic models that may provide biologically relevant drug-screening platforms have brought forth the promise of genome-driven epilepsy care. The increasing identification of genetic causes for epilepsy over the recent years has much improved the understanding of the underlying epileptogenic process, so if a specific gene mutation causes a functional alteration of physiological systems involved in the control of brain excitability, a rational treatment strategy might ideally aim to reverse or circumvent the dysfunction. This approach may not always prove successful, however, for a number of reasons, including the fact that no causal variant acts in isolation, but does so in the context of the rest of the genome and its variation, and because compensatory and adaptive changes may become fixed and difficult, or impossible, to reverse with treatment of the perceived original fault. Important, the current targeted treatment approach in precision medicine requires the identification of those mechanisms underlying causative genetic alteration; otherwise there is the risk that precision treatments, like current anti-seizure medicines, could still target the wrong outcome. Currently established or investigated precision medicine treatments include the ketogenic diet in patients with GLUT1 deficiency, vitamin B6 for pyridoxine-dependent epilepsy, potassium (K(+)) channel opener or blocker in patients with KCNQ2 or KCNT1 mutations respectively, drug enhancer of GABAergic neurotransmission in Dravet syndrome, sodium channel blockers in some SCN2A-related disorders, memantine in
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GRIN2A-associated epileptic encephalopathy, as well as mTORinhibitors in mTORopathies. The ketogenic diet, which is effective in the therapy of refractory childhood epilepsy, has become the treatment of choice for the GLUT1 deficiency syndrome (OMIM 606777) [48]. GLUT1 is highly expressed in erythrocytes and brain, and is exclusively responsible for glucose transport across blood brain barrier [48]. Therefore, its defect into the brain results in hypoglycorrhachia causing epilepsy, developmental delay, and a complex motor disorder in early childhood [48]. In such cases, ketones provided by a high-fat, low-carbohydrate diet are the main reserve fuel for the brain, as they bypass the Glut-1 defect, and enter the brain by a monocarboxylic acid transporter [48]. It is therefore non surprising that ketogenic diet in patients with GLUT1 effectively controls seizures and other paroxysmal activities, even if it seems to be less effective on the cognitive symptoms [48]. Pyridoxine-dependent epilepsy is due to deficiency of alphaaminoadipic semialdehyde dehydrogenase (antiquitin) a multifunctional enzyme that is encoded by ALDH7A1 gene [49]. It is inherited in an autosomal recessive manner and ALDH7A1 analysis may also be used for prenatal diagnosis of pyridoxine-dependent epilepsy. Deficiency of antiquitin causes seizures because it is essential for normal metabolism of neurotransmitters, and seizures are often fully controlled by treatment with pyridoxine [49]. B6responsive seizures may also be due to mutations in pyridox(am)ine 5’-phosphate oxidase (PNPO)gene, and in some cases may be better treated with pyridoxal5-phosphate [49]. In the case of KCNQ2 epileptic encephalopathy [50], a specific and rationale therapeutic approach may be possible by enhancing the remaining activity of KV7.2 channels with the labeled drug retigabine, which specifically acts on KV7.2 channels [51]. In this way, retigabine might overcome the pharmacoresistance in these patients. Electrophysiological studies could be useful to predict the response to retigabine for each genetic mutation, as specific mutations rarely diminish rather than increase the channel sensitivity to retigabine [50]. Despite these theoretical considerations and its potential for innovative personalized therapy in epilepsy [52], retigabine has been permanently discontinued because it causes serious eye problems and other major side effects [53]. KCNT1 mutations have recently been implicated in various epilepsy syndromes including a severe form of autosomal dominant epilepsy with hypermotor seizures during sleep, and epilepsy of infancy with migrating focal seizures [54-55]. KCNT1 gene encodes a class of sodium-activated potassium channel that is widely expressed in the nervous system and heart. As mutations often result in KCNT1 channel gain of function, it has been proposed the use of quinidine, a drug that has been shown to normalize aberrant KCNT1 function in cultured frog cells3, to inhibit KCNT1 mutation-induced seizures [55]. A recent case report described the antiepileptic efficacy of quinidine in a patient with KCNT1-EIMFS, but it was unsuccessful in others [55]. Most important, the authors cautioned that quinidine exhibits drug interactions, inhibiting the metabolism of many antiepileptic medications [55]. Moreover, a common adverse event is the QT prolongation, which necessitates close ECG monitoring. The voltage-gated Na channel alfa1 subunit (SCN1A) (MIM# 182389) is the most clinically relevant epilepsy gene, with the largest number of epilepsy related mutations so far characterized [56]. The majority of known mutations in SCN1A lead to severe myoclonic epilepsy in infancy (SMEI; MIM# 607208), which is probably related to reduced sodium current density in hippocampal inhibitory interneurons, and not in excitatory pyramidal neurons (which upregulated other sodium channel expression) [57]. This pathophysiological mechanism may explain the good efficacy of stiripentol in patients with SMEI, being its anti-convulsant activity due to enhancement of inhibitory, GABAergic neurotransmission [58].
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Stiripentol was shown to increase the activity of both neuronal and recombinant GABAA receptors at clinically relevant concentrations [58]. Mutations in SCN2A, a gene encoding the voltage-gated sodium channel Nav1.2, have been associated with a spectrum of epilepsies and neurodevelopmental disorders [58]. SCN2A mutations can lead to either augmented or reduced Nav1.2 activity [59], so the downstream result of opposite alterations in sodium currents presumably has different effects on inhibitory and excitatory neuronal networks, leading to the common final pathway of epileptogenesis. Of note, SCN2A mutations causing gain-of-function of Nav1.2 in vitro are more often associated with early infantile epilepsy with onset before 3 months of age, while those causing Nav1.2 loss-of-function lead to later onset epilepsies [59]. These experimental findings may explain the effectiveness of sodium channel blockers in early onset epilepsy linked to gain-of-function SCN2A mutations, and their ineffectiveness in later onset epilepsy, due to loss-of-function mutations [59]. So, the relation of a particular mutation to the corresponding alteration of sodium-channel function may provide an avenue to the targeted therapy for the pharmacological treatment of these disorders. Pathogenic mutations in genes encoding N-methyl-D-aspartate receptors - NMDARs (GRIN2A, and GRIN2B) have been associated with several childhood-onset epilepsy syndromes within the epilepsy-aphasia spectrum [60]. In a patient with a de novo GRIN2A mutation that had a gain-of-function effect on the NMDARs, the NMDAR antagonist memantine inhibited this hyperactivity in vitro and, as an adjunct antiepileptic medication, resulted in a substantial reduction in the seizure burden [61]. This case also exemplifies another potential for personalized genomics and AED therapeutics, even if the complicated and huge variability of functional consequences of NMDARs associated with specific mutations raises important practical issues in the fields of future therapeutic applications [62]. The mammalian target of rapamycin (mTOR) pathway is involved in highly epileptogenic conditions, such as tuberous sclerosis complex (TSC) and represents a reasonable target for antiepileptogenic interventions [63]. Preliminary clinical studies with patients affected by TSC demonstrated seizure reduction and potential disease-modifying effect of mTOR inhibitors [64]. Of note, the DEPDC5 (disheveled, Egl-10 and pleckstrin domain containing protein 5) gene is currently the most common known gene for focal epilepsies, and its mutations encompass many electro-clinical epilepsy syndromes and levels of severity [65]. DEPDC5 is part of the GATOR1 subcomplex, which modulates mTOR activity dependent on the availability of amino acids [65]. All these new data illustrate that the mTOR pathway may be more critical to common focal epilepsy than previously appreciated, and therefore it is tempting to speculate that mTOR inhibitors, which are effective in TSC, may have more widespread application in focal epilepsies [64-65]. 3. INFLUENCE OF GENETIC VARIANTS ON THE SAFETY OF AEDS In a minority of patients, aromatic anticonvulsants can cause severe hypersensitivity reactions (SHRs), of which the most severe form, Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), reach a mortality rate of 30% [66]. SJS is characterized by initial fever and fatigue combined with ulcers on mucous membranes such as mouth and lips. TEN is the worst form of SJS, with a mortality of up to 40% [66]. Recent pharmacogenomic studies of SHRs illustrated that the human leucocyte antigen (HLA) allele HLA-B*1502 was strongly associated with severe allergic skin reactions upon CBZ therapy in the Chinese population [67]. The genetic testing for HLA-B*1502 before CBZ therapy has become a highly potential cost-effective intervention in this population, as avoiding CBZ in patients carrying the HLA-B*1502 allele completely prevented SJS and TEN in a cohort of 4000 individuals
Role of Pharmacogenomics in Antiepileptic Drug Therapy
with epilepsy [68]. This observation well illustrates that the power of pharmacogenomics in the prevention of adverse events [67]. The association of HLA-B*1502 was also found for PHT [69], but not to lamotrigine [70]. In the European population, an association of HLA-A*3101 was found for all hypersensitivity syndromes related to CBZ therapy [71]. In daily practice, the HLA-B*1502 allele should be tested for before the start of CBZ in patients of Asian ancestry, although it remains unclear its cost-effectiveness with current technology [72]. No prospective study has been performed in the European population. THE FUTURE OF PHARMACOGENOMICS AND CONCLUSION So far, the translation of pharmacogenomic discoveries into clinical practice has been surprisingly disappointing, and many genetic biomarkers have not advanced much further beyond identification. However, the many scientific, economic, educational, legal, and commercial barriers that exist need to be overcome before the full potential of pharmacogenomics is achieved. Further efforts are also needed to optimize the use of pharmacogenomics data as care providers may not be comfortable with such data in routine practice, and overrides of electronic pharmacogenomics clinical decision support alerts are common [73]. Indeed, studies are now beginning to provide a foundation for the future establishment of treatment guidelines [67-68]. Recent advances hold promise that pharmacogenomics will positively impact treatment for any epilepsy patient in the near future, towards implementation of an evidenced-based strategy for improving the use of AEDs, thereby providing a cornerstone for precision medicine. CONSENT FOR PUBLICATION Not applicable.
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CONFLICT OF INTEREST The authors declare no conflict of interest, financial or otherwise.
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ACKNOWLEDGEMENTS Declared none.
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REFERENCES [1] [2] [3] [4] [5] [6] [7]
[8] [9] [10]
Senn S. Individual response to treatment: is it a valid assumption. BMJ 2004; 329(7472): 966-8. Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med 2011; 364(12): 1144-53. Evans WE, Relling MV. Moving towards individualized medicine with pharmacogenomics. Nature 2004; 429(6990): 464–8. McCarthy JJ, McLeod HL, Ginsburg GS. Genomic medicine: a decade of successes, challenges, and opportunities. Sci Transl Med 2013; 5(189): 189sr4. Szoeke CE, Newton M, Wood JM, Goldstein D, Berkovic SF, OBrien TJ, Sheffield LJ. Update on pharmacogenetics in epilepsy: a brief review. Lancet Neurol 2006; 5(2): 189-96. Kasperaviciūte D, Sisodiya SM. Epilepsy pharmacogenetics. Pharmacogenomics 2009; 10(5): 817-36. Lçscher W, Klotz U, Zimprich F, Schmidt D. (2009) The clinical impact of pharmacogenetics on the treatment of epilepsy. Epilepsia 50: 1–23.Cavalleri GL, McCormack M, Alhusaini S, Chaila E, Delanty N. Pharmacogenomics and epilepsy: the road ahead. Pharmacogenomics. 2011; 12(10): 1429-47. Tan NC, Berkovic SF. The Epilepsy Genetic Association Database (epiGAD): analysis of 165 genetic association studies, 1996–2008. Epilepsia 2010; 51(4): 686-9. Brodie MJ, French JA. Management of epilepsy in adolescents and adults. Lancet 2000; 356(9226): 323-9. Glauser T, Ben-Menachem E, Bourgeois B, et al. ILAE treatment guidelines: evidence-based analysis of antiepileptic drug efficacy and effectiveness as initial monotherapy for epileptic seizures and syndromes. Epilepsia 2006; 47(7): 1094-120.
[24]
[25] [26]
[27] [28] [29] [30]
[31]
[32]
5
Labate A, Ventura P, Gambardella A, et al. MRI evidence of mesial temporal sclerosis in sporadic ‘‘benign’’ temporal lobe epilepsy. Neurology 2006; 66(4): 562-5. Kasteleijn-Nolst Trenité DG, Schmitz B, Janz D, et al. Consensus on diagnosis and management of JME: From founder's observations to current trends. Epilepsy Behav 2013; 28(Suppl.1): S87-90. Beghi E, Garattini L, Ricci E, Cornago D, Parazzini F; EPICOS Group. Direct cost of medical management of epilepsy among adults in Italy: a prospective cost-of-illness study (EPICOS). Epilepsia. 2004; 45(2): 171-8. Canevini MP, De Sarro G, Galimberti CA, et al. Relationship between adverse effects of antiepileptic drugs, number of coprescribed drugs, and drug load in a large cohort of consecutive patients with drug-refractory epilepsy. Epilepsia 2010; 51(5): 797804. Berkovic SF, Reutens DC, Andermann E, Andermann F. The epilepsies: specific syndromes or a neurobiological continuum? In: Wolf P, editor. Epileptic Seizures and Syndromes. John Libbey Eurotext; Paris: 1994. chapter 5; pp. 25–37. Epi4K & Epilepsy Phenome/Genome project. Ultra-rare genetic variation in common epilepsies: a case-control sequencing study. Lancet Neurol 2017; 16(2): 135-43. International League Against Epilepsy Consortium on Complex Epilepsies. Genetic determinants of common epilepsies: a metaanalysis of genome-wide association studies. Lancet Neurol 2014; 13(9): 893-903. Goldenberg MM. Overview of Drugs Used For Epilepsy and Seizures: Etiology, Diagnosis, and Treatment. P.T. 2010; 35(7): 392415. Elterman RD, Shields WD, Mansfield KA, Nakagawa J. Randomized trial of vigabatrin in patients with infantile spasms. Neurology 2001; 57(8): 1416–21. Inoue Y, Ohtsuka Y; STP-1 Study Group. Long-term safety and efficacy of stiripentol for the treatment of Dravet syndrome: A multicenter, open-label study in Japan. Epilepsy Res 2015; 113: 90-7. Remy S, Beck H. Molecular and cellular mechanisms of pharmacoresistance in epilepsy. Brain 2006; 129(1): 18-35. Shintani M, Ieiri I, Inoue K, Mamiya K, et al. Genetic polymorphisms and functional characterization of the 5’-flanking region of the human CYP2C9 gene: in vitro and in vivo studies. Clin Pharmacol Ther 2001; 70(2): 175-82. van der Weide J, Steijns LS, van Weelden MJ, deHaan K. The effect of genetic polymorphism of cytochrome P450 CYP2C9 on phenytoin dose requirement. Pharmacogenetics 2001; 11: 287-91. Hung CC, Lin CJ, Chen CC, Chang CJ, Liou HH. Dosage recommendation of phenytoin for patients with epilepsy with different CYP2C9/CYP2C19 polymorphisms. Ther Drug Monit 2004; 26(5): 534-40. Chung WH, Chang WC, Lee YS, et al. Genetic variants associated with phenytoin-related severe cutaneous adverse reactions. JAMA 2014; 312(5): 525-34. Mackenzie PI, Owens IS, Burchell B, et al. The UDP glucuronosyltransferase gene superfamily: recommended nomenclature update based on evolutionary divergence. Pharmacogenetics 1997; 7(4): 255-69. Mackenzie PI, Bock KW, Burchell B, et al. Nomenclature update for the mammalian UDP-glucuronosyltransferase (UGT) gene superfamily. Pharmacogenet Genomics 2005; 15(10): 677-85. Baulac M. Introduction to zonisamide, Epilepsy Res 2006; 68(Suppl.2): S3–9. Anderson GA. Pharmacogenetics and enzyme induction/inhibition properties of antiepileptic drugs. Neurology 2004; 63(Suppl.4): S38. Gopaul S, Farrell K, Abbott F. Effects of age and polytherapy, risk factors of valproic acid (VPA) hepatotoxicity, on the excretion of thiol conjugates of (E)-2,4-diene VPA in people with epilepsy taking VPA. Epilepsia 2003; 44(3): 322-28. Floyd M, Gervasini G, Masica A, et al. Genotype-phenotype associations for common CYP3A4 and CYP3A5 variants in the basal and induced metabolism of midazolam in European- and AfricanAmerican men and women. Pharmacogenetics 2003; 13(10): 595606. Fukuda K, Ohta T, Oshima Y, Ohashi N, Yoshikawa M, Yamazoe Y. Specific CYP3A4 inhibitors in grapefruit juice: furocoumarin dimers as components of drug interaction. Pharmacogenetics 1997; 7(5): 391-6.
6 Current Pharmaceutical Design, 2017, Vol. 23, No. 00 [33]
[34] [35]
[36] [37]
[38] [39] [40]
[41] [42]
[43]
[44] [45]
[46] [47] [48] [49] [50] [51] [52]
Tate SK, Depondt C, Sisodiya SM, Cavalleri GL, Schorge S, Soranzo N et al. Genetic predictors of the maximum doses patients receive during clinical use of the anti-epileptic drugs carbamazepine and phenytoin. Proc Natl Acad Sci USA 2005; 102(15): 5507-12. Tate SK, Singh R, Hung CC, et al. A common polymorphism in the SCN1A gene associates with phenytoin serum levels at maintenance dose. Pharmacogenet Genomics 2006; 16(10): 721-6. Zimprich F, Stogmann E, Bonelli S, et al. A functional polymorphism in the SCN1A gene is not associated with carbamazepine dosages in Austrian patients with epilepsy. Epilepsia 2008; 49(6): 1108-9. Abe T, Seo T, Ishitsu T, Nakagawa T, Hori M, Nakagawa K. Association between SCN1A polymorphism and carbamazepineresistant epilepsy. Br J Clin Pharmacol 2008; 66(2): 304-7. Manna I, Gambardella A, Bianchi A, et al. A functional polymorphism in the SCN1A gene does not influence antiepileptic drug responsiveness in Italian patients with focal epilepsy. Epilepsia 2011; 52(5): e40-4. Siddiqui A, Kerb R, Weale ME, et al. Association of multidrug resistance in epilepsy with a polymorphism in the drug-transporter gene ABCB1. N Engl J Med 2003; 348(15): 1442-48. Zimprich F, Sunder-Plassmann R, Stogmann E, et al. Association of an ABCB1 gene haplotype with pharmacoresistance in temporal lobe epilepsy. Neurology 2004; 63(6): 1087-9. Sills GJ, Mohanraj R, Butler E, McCrindle S, Collier L, Wilson EA et al. Lack of association between the C3435 T polymorphism in the human multidrug resistance (MDR1) gene and response to antiepileptic drug treatment. Epilepsia 2005; 46(5): 643-7. Tan NC, Heron SE, Scheffer IE, et al. Failure to confirm association of a polymorphism in ABCB1 with multidrug-resistant epilepsy. Neurology 2004; 63(6): 1090-92. Manna I, Gambardella A, Labate A, et al. Polymorphism of the multidrug resistance 1 gene MDR1/ABCB1 C3435T and response to antiepileptic drug treatment in temporal lobe epilepsy. Seizure 2015; 24: 124-6. Leschziner G, Jorgensen A, Andrew T, et al. Clinical factors and ABCB1 polymorphisms in prediction of antiepileptic drug response: a prospective cohort study. Lancet Neurol 2006; 5(8): 66876. Szoeke CE, Newton M, Wood JM, et al. Update on pharmacogenetics in epilepsy: a brief review. Lancet Neurol 2006; 5(2): 189-96. Silva-Alves MS, Secolin R, Carvalho BS, Yasuda CL, Bilevicius E, Alvim MKM, et al. A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information. PLoS ONE 2017; 12(1): e0169214. Glauser TA, Holland K, O'Brien VP, et al. Pharmacogenetics of antiepileptic drug efficacy in childhood absence epilepsy. Ann Neurol 2017; 81(3): 444-53. Cardon LH, Harris T. Precision medicine, genomics and drug discovery. Hum Mol Genet 2016; 25(R2): R166-72. Leen WG, Klepper J, Verbeek MM, et al. Glucose transporter-1 deficiency syndrome: the expanding clinical and genetic spectrum of a treatable disorder. Brain 2010; 133(3): 655-70. Mills PB, Struys E, Jakobs C, et al. Mutations in antiquitin in individuals with pyridoxine-dependent seizures. Nat Med 2006; 12(3): 307-9. Weckhuysen S, Mandelstam S, Suls A, et al. KCNQ2 encephalopathy: emerging phenotype of a neonatal encephalopathy. Ann Neurol. 2012; 71(1): 15-25. Gunthorpe MJ, Large CH, Sankar R. The mechanism of action of retigabine (ezogabine), a first-in-class K+ channel opener for the treatment of epilepsy. Epilepsia 2012; 53(3): 412-24. Millichap JJ, Park KL, Tsuchida T, et al. KCNQ2 encephalopathy: Features, mutational hot spots, and ezogabine treatment of 11 patients. Neurol Genet. 2016; 2(5): e96.
Gambardella et al. [53]
[54]
[55] [56] [57]
[58] [59] [60]
[61]
[62] [63] [64] [65] [66] [67] [68] [69] [70]
[71] [72] [73]
Tompson DJ, Crean CS, Reeve R, Berry NS. Efficacy and tolerability exposure-response relationship of retigabine (ezogabine) immediate-release tablets in patients with partial-onset seizures. Clin Ther 2013; 35(8): 1174-85. Heron SE, Smith KR, Bahlo M, et al. Missense mutations in the sodium-gated potassium channel gene KCNT1 cause severe autosomal dominant nocturnal frontal lobe epilepsy. Nat Genet 2012; 44(11): 1188-90. Milligan CJ, Li M, Gazina EV, et al. KCNT1 gain of function in 2 epilepsy phenotypes is reversed by quinidine. Ann Neurol 2014; 75(4): 581-90. Gambardella A, Marini C. Clinical spectrum of SCN1A mutations. Epilepsia. 2009; 50(Suppl.5): 20-3. Yu FH, Mantegazza M, Westenbroek RE, et al. Reduced sodium current in GABAergic interneurons in a mouse model of severe myoclonic epilepsy in infancy. Nat Neurosci 2006; 9(9): 1142-9. Erratum in: Nat Neurosci. 2007; 10(1): 134. Fisher JL. The effects of stiripentol on GABA(A) receptors. Epilepsia. 2011; 52 (Suppl.2): 76-8. Wolff M, Johannesen KM, Hedrich UB, et al. Genetic and phenotypic heterogeneity suggest therapeutic implications in SCN2Arelated disorders. Brain 2017, Epub ahead of print. Endele S, Rosenberger G, Geider K, et al. Mutations in GRIN2A and GRIN2B encoding regulatory subunits of NMDA receptors cause variable neurodevelopmental phenotypes. Nat Genet 2010; 42(11): 1021-6. Pierson TM, Yuan H, Marsh ED, Fuentes-Fajardo K, et al. GRIN2A mutation and early-onset epileptic encephalopathy: personalized therapy with memantine. Ann Clin Transl Neurol. 2014; 1(3): 190-8. Burnashev N, Szepetowski P. NMDA receptor subunit mutations in neurodevelopmental disorders. Curr Opin Pharmacol 2015; 20: 7382. Child NE, Benarroch EE. mTOR: its role in the nervous system and involvement in neurologic disease. Neurology 2014; 83(17): 156272. Krueger DA, Wilfong AA, Holland-Bouley K, et al. Everolimus treatment of refractory epilepsy in tuberous sclerosis complex. Ann Neurol 2013; 74(5): 679-87. Scheffer IE, Heron SE, Regan BM, et al. Mutations in mammalian target of rapamycin regulator DEPDC5 cause focal epilepsy with brain malformations. Ann Neurol. 2014; 75(5): 782-7. Roujeau JC, Stern RS. Severe adverse cutaneous reactions to drugs. N Engl J Med 1994; 331: 1272-85. Chung WH, Hung SI, Hong HS, et al. Medical genetics: a marker for Stevens-Johnson syndrome. Nature 2004; 428: 486. Chen P, Lin JJ, Lu CS, et al. Carbamazepine-induced toxic effects and HLA-B*1502 screening in Taiwan. N Engl J Med 2011; 364(12): 1126-33. Chung WH, Chang WC, Lee YS, et al. Genetic variants associated with phenytoin-related severe cutaneous adverse reactions. JAMA 2014; 312(5): 525-34. Hung SI, Chung WH, Liu ZS, et al. Common risk allele in aromatic antiepileptic-drug induced Stevens-Johnson syndrome and toxic epidermal necrolysis in Han Chinese. Pharmacogenomics 2010; 11(3): 349-56. McCormack M, Alfirevic A, Bourgeois S, et al. HLA- A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans. N Engl J Med 2011; 364(12): 1134-43. Lange V, Böhme I1, Hofmann J, et al. Cost-efficient highthroughput HLA typing by MiSeq amplicon sequencing. BMC Genomics. 2014; 15: 63. St Sauver JL, Bielinski SJ, Olson JE, et al. Integrating pharmacogenomics into clinical practice: promise vs reality. Am J Med 2016; 129(10): 1093-99.
