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

Authors:
Debleena Guin at Institute of Genomics and Integrative Biology, Delhi Technological University
Debleena Guin
  • Institute of Genomics and Integrative Biology, Delhi Technological University
Yasha Hasija at Delhi Technological University
Yasha Hasija
Ritushree Kukreti at Institute of Genomics and Integrative Biology
Ritushree Kukreti
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Abstract and Figures

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.
Evolution of pharmacogenomics of anti-epileptic drugs in clinical practice PHT phenytoin; PK pharmacokinetics, CBZ carbamazepine, VPA valproic acid, AED Anti-epileptic drugs, CYP cytochrome P450 enzyme, PharmGKB The pharmacogenomic knowledgebase, NIHPRN National institute of health Pharmacogenomics Research Network, PGx pharmacogenomics, FDA Food and drug administration, CPIC Clinical Pharmacogenetics Implementation Consortium, PWE patient with epilepsy, EPIPGX epilepsy pharmacogenomics consortium, CPNDS The Canadian Pharmacogenomics Network for Drug Safety, DPWG The Dutch Pharmacogenetics Working Group, SJS/TEN Stevens-Johnson syndrome/toxic epidermal necrolysis toxic epidermal necrolysis [43, 98, 99]. The first ever PGx implementation guidelines, CPIC guideline introduced in 2009 [100]. Pharmacogenomics of ASMs and its implementation in clinical practice [80, 101, 102].
Evolution of pharmacogenomics of anti-epileptic drugs in clinical practice PHT phenytoin; PK pharmacokinetics, CBZ carbamazepine, VPA valproic acid, AED Anti-epileptic drugs, CYP cytochrome P450 enzyme, PharmGKB The pharmacogenomic knowledgebase, NIHPRN National institute of health Pharmacogenomics Research Network, PGx pharmacogenomics, FDA Food and drug administration, CPIC Clinical Pharmacogenetics Implementation Consortium, PWE patient with epilepsy, EPIPGX epilepsy pharmacogenomics consortium, CPNDS The Canadian Pharmacogenomics Network for Drug Safety, DPWG The Dutch Pharmacogenetics Working Group, SJS/TEN Stevens-Johnson syndrome/toxic epidermal necrolysis toxic epidermal necrolysis [43, 98, 99]. The first ever PGx implementation guidelines, CPIC guideline introduced in 2009 [100]. Pharmacogenomics of ASMs and its implementation in clinical practice [80, 101, 102].
… 
This diagram shows all the pharmacogenes of ASM associations with p-value ≤ 0.05 in human autosomes All the loci are obtained from published studies with reference human genome assembly of GRCh38/hg38. A Ideogram mapping genes implicate chromosomal locations associated with several drug response phenotype of different ASMs. The different ASMs are colour coded. B The zoom in view of Chr 6: 29779092-33806617 is represented for better resolution. This figure was generated using PhenoGram software [103].
This diagram shows all the pharmacogenes of ASM associations with p-value ≤ 0.05 in human autosomes All the loci are obtained from published studies with reference human genome assembly of GRCh38/hg38. A Ideogram mapping genes implicate chromosomal locations associated with several drug response phenotype of different ASMs. The different ASMs are colour coded. B The zoom in view of Chr 6: 29779092-33806617 is represented for better resolution. This figure was generated using PhenoGram software [103].
… 
The ROC for risk allele count on pharmacogenetic SNPs used to measure AUC in PWE The different phenotypes are: (a) Carbamazepine-induced hypersensitivity reaction, (b) Carbamazepine efficacy, (c) Carbamazepine resistance, (d) Phenobarbital resistance, (e) Phenytoin resistance, (f) Phenytoin response.
The ROC for risk allele count on pharmacogenetic SNPs used to measure AUC in PWE The different phenotypes are: (a) Carbamazepine-induced hypersensitivity reaction, (b) Carbamazepine efficacy, (c) Carbamazepine resistance, (d) Phenobarbital resistance, (e) Phenytoin resistance, (f) Phenytoin response.
… 
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ARTICLE
Assessment of clinically actionable pharmacogenetic markers
to stratify anti-seizure medications
Debleena Guin
1,2
, Yasha Hasija
2
and Ritushree Kukreti
1,3
✉
© The Author(s), under exclusive licence to Springer Nature Limited 2023
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.
The Pharmacogenomics Journal (2023) 23:149–160; https://doi.org/10.1038/s41397-023-00313-y
INTRODUCTION
Epilepsy is one of the most common neurological conditions, with
around 50 million people affected worldwide. The World health
organisation (WHO)’s Global Burden of Disease reports epilepsy to
have the second highest burden of all neurological disorders
worldwide, in terms of disability of adjusted life years (DALY) [1].
Epilepsy includes a number of medical conditions, with recurrent
seizures being the common characteristic feature. The large
number of different epilepsy syndromes and seizure types as well
as highly variable inter-individual response to therapies makes
management of this condition often challenging [2].
Treatment of epilepsy begins with the initial administration of
anti-seizure medication (ASM) based on three level of diagnosis,
starting with seizure type, followed by epilepsy type and epilepsy
syndrome classification, considering some other clinical parameters
like electroencephalogram patterns [3]. In case of failure to the
initial treatment regime, the physician subsequently moves towards
other drugs or additional poly-therapy. Epilepsy treatment outcome
is often characterised by poor drug efficacy, adverse drug response,
and dose optimisation in patients [4]. There are several factors
contributing to variable treatment response like individual drug
metabolism, lifestyle, environmental factors and genetics [5].
Predominantly, variation in response to ASM arise from genetic
variations in genes involved in drug disposition. These genes affect
the pharmacokinetics, or pharmacodynamics of the drug [5–7].
Following the course, from drug absorption, distribution, metabo-
lism and elimination, molecular understanding of the drug action
through this course can be useful in incorporating pharmacoge-
nomic (PGx) principles in clinical practice. In absorption, the role of
transcellular transporters from intestine to blood is largely unknown
[4]. A number of efflux transporters are expressed in enterocytes
which are crucial for absorption of ASM, commonly known are the
P-glycoprotein (P-gp), multidrug resistance-associated proteins
(MDR) and breast cancer related protein (BCRP) [8]. Genetic variant
of ABCB1 gene (rs1045642, C3435T) is widely studied with
decreased expression resulting in pharmacoresistance to ASM
administration [9–11]. For drug distribution, the given ASM has to
cross the blood brain barrier (BBB) to reach its target site in brain.
Efflux transporters like P-gp, can restrict the brain uptake of ASM.
Genetic variants leading to overexpression of P-gp in BBB may limit
the drug penetration to target leading to drug resistance [12]. Once
the drug has reached its target site, which are mainly ion channels
or other synaptic molecules, it alters the channel gates thereby
affecting the seizure activity. So far the most interesting observa-
tions are known for voltage-dependent Na
+
channels (SCN family
genes), which are common targets for most ASMs like carbamaze-
pine, phenytoin, lamotrigine [13]. Mutations in these channels may
affect clinical response to ASMs (e.g., Dravet syndrome) [14].
Functional genetic polymorphisms in other known targets like the
γ-Aminobutyric acid (GABA) receptors (for benzodiazepine drugs)
Received: 9 January 2023 Revised: 22 July 2023 Accepted: 31 July 2023
Published online: 26 August 2023
1
Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)—Institute of Genomics and Integrative Biology (IGIB), New Delhi 110007, India.
2
Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi 110042, India.
3
Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002,
India. ✉email: ritushreekukreti@gmail.com
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Distinguishing Benign Rashes From Severe Skin Reactions From Anti-Seizure Medications
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Purpose of review This review describes risk factors for severe skin reactions to antiseizure medications (ASMs), the usage of updated tests to predict those with increased risk of a severe cutaneous reaction, and guides how to choose specific ASMs and dosing to lower the risk for these reactions. Information is given regarding specific mild versus severe reactions, initial diagnostic evaluation, and treatment. A table listing the risk of mild and severe cutaneous reaction risks as well as the management of potential seizures that may occur while stopping the culprit ASM are provided. Recent findings Five new ASMs have joined the total of 26 FDA-approved ASMs since 2018. Cenobamate had three patients develop a drug reaction with eosinophilia and systemic symptoms. A lower starting dosing and slower titration have resulted in no further published cases. Based on limited data, rash risk is low for fenfluramine, ganaxalone, and stiripentol. It is low-moderate for Epidiolex. Molecular tests can predict severe reactions. Summary Skin reactions are a relatively common side effect of ASMs with aromatic ASMs having the greatest risk. Identifying and informing high-risk patients when to seek medical attention, stopping the culprit ASM when a severe reaction looks possible, and providing appropriate medical triage can reduce morbidity and mortality from severe skin and systemic reactions.
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Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods
Article
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  • Sep 2022
Polygenic risk scores (PRS) have been successfully developed for the prediction of human diseases and complex traits in the past years. For drug response prediction in randomized clinical trials, a common practice is to apply PRS built from a disease genome-wide association study (GWAS) directly to a corresponding pharmacogenomics (PGx) setting. Here, we show that such an approach relies on stringent assumptions about the prognostic and predictive effects of the selected genetic variants. We propose a shift from disease PRS to PGx PRS approaches by simultaneously modeling both the prognostic and predictive effects and further make this shift possible by developing a series of PRS-PGx methods, including a novel Bayesian regression approach (PRS-PGx-Bayes). Simulation studies show that PRS-PGx methods generally outperform the disease PRS methods and PRS-PGx-Bayes is superior to all other PRS-PGx methods. We further apply the PRS-PGx methods to PGx GWAS data from a large cardiovascular randomized clinical trial (IMPROVE-IT) to predict treatment related LDL cholesterol reduction. The results demonstrate substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the treatment-specific predictive effects while compared with the disease PRS approaches. To try to predict an individual’s drug response using genetic data, most studies have used traditional polygenic risk score (PRS) methods. Here, the authors develop a pharmacogenomics-specific PRS method, which can improve drug response prediction and patient stratification in pharmacogenomics studies.
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Associations of HLA genetic variants with carbamazepine‐induced cutaneous adverse drug reactions: An updated meta‐analysis
Article
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Aggregated risk of carbamazepine (CBZ)‐induced cutaneous adverse drug reactions (cADRs) with different HLA variants are unclear and limited in terms of the power of studies. This study aimed to assess the aggregated risk of CBZ‐induced cADRs associated with carrying the following HLA variants: HLA‐B*15:02, HLA‐B*15:11, HLA‐B*15:21, HLA‐B*38:02, HLA‐B*40:01, HLA‐B*46:01, HLA‐B*58:01, HLA‐A*24:02, and HLA‐A*31:01. Literature was searched in different databases following PRISMA guidelines. The outcomes were measured as odds ratio (OR) using RevMan software by a random/fixed effects model, where p < 0.05 was set as statistical significance. In total, 46 case–control studies met the inclusion criteria and were included in this analysis consisting of 1817 cases and 6614 controls. It was found that case‐patients who carried the HLA‐B*15:02 allele were associated with a significantly increased risk of CBZ‐induced Stevens−Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) compared to controls (OR 26.01; 95% CI 15.88–42.60; p < 0.00001). The aggregated risk of cADRs was slightly higher in Asian compared to Caucasian patients (Asians: OR 14.84; 95% CI 8.95–24.61; p < 0.00001; Caucasians: OR 11.65; 95% CI 1.68–80.70; p = 0.01). Further, HLA‐B*15:11, HLA‐B*15:21, or HLA‐A*31:01 allele was also associated with significantly increased risk of CBZ‐induced cADRs (HLA‐B*15:11: OR 6.08; 95% CI 2.28–16.23; p = 0.0003; HLA‐B*15:21: OR 5.37; 95% CI 2.02–14.28; p = 0.0008; HLA‐A*31:01: OR 5.92; 95% CI 4.35–8.05; p < 0.00001). Other HLA variants were not found to have any significant associations with CBZ‐induced cADRs. Strong associations between the HLA‐B*15:02, HLA‐B*15:11, HLA‐B*15:21, or HLA‐A*31:01 allele with CBZ‐induced cADRs have been established in this analysis. Pharmacogenetic testing of particular HLA alleles before initiation of CBZ therapy may be beneficial to patients and may help to eradicate cADRs substantially.
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A pharmacogenomic assessment of psychiatric adverse drug reactions to levetiracetam
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Full-text available
  • Apr 2022
  • EPILEPSIA
Objective Levetiracetam (LEV) is an effective antiseizure medicine, but 10%–20% of people treated with LEV report psychiatric side‐effects, and up to 1% may have psychotic episodes. Pharmacogenomic predictors of these adverse drug reactions (ADRs) have yet to be identified. We sought to determine the contribution of both common and rare genetic variation to psychiatric and behavioral ADRs associated with LEV. Methods This case‐control study compared cases of LEV‐associated behavioral disorder (n = 149) or psychotic reaction (n = 37) to LEV‐exposed people with no history of psychiatric ADRs (n = 920). All samples were of European ancestry. We performed genome‐wide association study (GWAS) analysis comparing those with LEV ADRs to controls. We estimated the polygenic risk scores (PRS) for schizophrenia and compared cases with LEV‐associated psychotic reaction to controls. Rare variant burden analysis was performed using exome sequence data of cases with psychotic reactions (n = 18) and controls (n = 122). Results Univariate GWAS found no significant associations with either LEV‐associated behavioural disorder or LEV‐psychotic reaction. PRS analysis showed that cases of LEV‐associated psychotic reaction had an increased PRS for schizophrenia relative to contr ols (p = .0097, estimate = .4886). The rare‐variant analysis found no evidence of an increased burden of rare genetic variants in people who had experienced LEV‐associated psychotic reaction relative to controls. Significance The polygenic burden for schizophrenia is a risk factor for LEV‐associated psychotic reaction. To assess the clinical utility of PRS as a predictor, it should be tested in an independent and ideally prospective cohort. Larger sample sizes are required for the identification of significant univariate common genetic signals or rare genetic signals associated with psychiatric LEV ADRs.
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Potential role of regulatory DNA variants in modifying the risk of severe cutaneous reactions induced by aromatic anti‐seizure medications
Article
Full-text available
  • Feb 2022
  • EPILEPSIA
Objective Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe cutaneous adverse drug reactions. Antiseizure medications (ASMs) with aromatic ring structure, including carbamazepine, are among the most common culprits. Screening for human leukocyte antigen (HLA) allele HLA‐B*15:02 is recommended prior to initiating treatment with carbamazepine in Asians, but this allele has low positive predictive value. Methods We performed whole genome sequencing and analyzed 6 199 696 common variants among 113 aromatic ASM‐induced SJS/TEN cases and 84 tolerant controls of Han Chinese ethnicity. Results In the primary analysis, nine variants reached genome‐wide significance (p < 5e‐08), one in the carbamazepine subanalysis (85 cases vs. 77 controls) and a further eight identified in HLA‐B*15:02‐negative subanalysis (35 cases and 53 controls). Interaction analysis between each novel variant from the primary analysis found that five increased risk irrespective of HLA‐B*15:02 status or zygosity. HLA‐B*15:02‐positive individuals were found to have reduced risk if they also carried a chromosome 12 variant, chr12.9426934 (heterozygotes: relative risk = .71, p = .001; homozygotes: relative risk = .23, p < .001). All significant variants lie within intronic or intergenic regions with poorly understood functional consequence. In silico functional analysis of suggestive variants (p < 5e‐6) identified through the primary and subanalyses (stratified by HLA‐B*15:02 status and drug exposure) suggests that genetic variation within regulatory DNA may contribute to risk indirectly by disrupting the regulation of pathology‐related genes. The genes implicated were specific either to the primary analysis (CD9), HLA‐B*15:02 carriers (DOCK10), noncarriers (ABCA1), carbamazepine exposure (HLA‐E), or phenytoin exposure (CD24). Significance We identified variants that could explain why some carriers of HLA‐B*15:02 tolerate treatment, and why some noncarriers develop ASM‐induced SJS/TEN. Additionally, this analysis suggests that the mixing of HLA‐B*15:02 carrier status in previous studies might have masked variants contributing to susceptibility, and that inheritance of risk for ASM‐induced SJS/TEN is complex, likely involving multiple risk variants.
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An Evidence‐Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine
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  • Jul 2021
Clinical annotations are one of the most popular resources available on the Pharmacogenomics Knowledgebase (PharmGKB). Each clinical annotation summarizes the association between variant‐drug pairs, shows relevant findings from the curated literature and is assigned a level of evidence (LOE) to indicate the strength of support for that association. Evidence from the pharmacogenomic literature is curated into PharmGKB as variant annotations, which can be used to create new clinical annotations or added to existing clinical annotations. This means that the same clinical annotation can be worked on by multiple curators over time. As more evidence is curated into PharmGKB, the task of maintaining consistency when assessing all the available evidence and assigning a level of evidence becomes increasingly difficult. To remedy this, a scoring system has been developed to automate LOE assignment to clinical annotations. Variant annotations are scored according to certain attributes including study size, reported p‐value and whether the variant annotation supports or fails to find an association. Clinical guidelines or FDA‐approved drug labels which give variant‐specific prescribing guidance are also scored. The scores of all annotations attached to a clinical annotation are summed together to give a total score for the clinical annotation, which is used to calculate a LOE. Overall, the system increases transparency, consistency and reproducibility in LOE assignment to clinical annotations. In combination with increased standardization of how clinical annotations are written, use of this scoring system helps to ensure that PharmGKB clinical annotations continue to be a robust source of pharmacogenomic information.
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Spectrum of cutaneous adverse reactions to aromatic antiepileptic drugs and human leukocyte antigen genotypes in Thai patients and meta-analysis
Article
Full-text available
  • Dec 2021
  • PHARMACOGENOMICS J
Aromatic antiepileptic drugs (AEDs)-induced cutaneous adverse drug reactions (cADRs) add up to the limited use of the AEDs in the treatment and prevention of seizures. Human leukocyte antigen-B (HLA-B) alleles have been linked to AEDs-induced cADRs. We investigated the association between cADRs (including Stevens–Johnson syndrome; SJS/toxic epidermal necrolysis; TEN, drug reaction with eosinophilia and systemic symptoms; DRESS, and Maculopapular eruption; MPE) caused by AEDs (phenytoin, carbamazepine, lamotrigine, phenobarbital and oxcarbazepine) and HLA-B alleles in Thai population. Through the case-control study, 166 patients with AEDs-induced cADRs, 426 AEDs-tolerant patients (AEDs-tolerant controls), and 470 healthy subjects (Thai population) were collected. The HLA genotypes were detected using the polymerase chain reaction-sequence specific oligonucleotide probe (PCR-SSOP) method. We also performed a meta-analysis with these data and other populations. The carrier rate of HLA-B*15:02 was significantly different between AEDs-induced cADRs group and AEDs-tolerant group (Odds ratio; OR 4.28, 95% Confidence interval; CI 2.64–6.95, p < 0.001), AEDs-induced cADRs group and Thai population (OR 2.15, 95%CI 1.41–3.29, p < 0.001). In meta-analysis showed the strong association HLA-B*15:02 with AEDs-induced cADRs (OR 4.77, 95%CI 1.79–12.73, p < 0.001). Furthermore, HLA-B*15:02 was associated with SJS/TEN induced by AEDs (OR 10.28, 95%CI 6.50–16.28, p < 0.001) Phenytoin (OR 4.12, 95%CI 1.77–9.59, p = 0.001) and carbamazepine (OR 137.69, 95%CI 50.97–371.98, p < 0.001). This study demonstrated that genetic association for AEDs-induced cADRs was phenotype-specific. A strong association between HLA-B*15:02 and AEDs-induced SJS/TEN was demonstrated with an OR of 10.79 (95%CI 5.50–21.16, p < 0.001) when compared with AEDs-tolerant group. On the other hand, the carrier rates of HLA-B*08:01, HLA-B*13:01, and HLA-B*56:02 were significantly higher in the DRESS group compared with the AEDs-tolerant group (p = 0.029, 0.007, and 0.017, respectively). The HLA-B*15:02 allele may represent a risk factor for AEDs-induced cADRs.
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Role of Common Genetic Variants for Drug-Resistance to Specific Anti-Seizure Medications
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  • Jun 2021
Objective: Resistance to anti-seizure medications (ASMs) presents a significant hurdle in the treatment of people with epilepsy. Genetic markers for resistance to individual ASMs could support clinicians to make better-informed choices for their patients. In this study, we aimed to elucidate whether the response to individual ASMs was associated with common genetic variation. Methods: A cohort of 3,649 individuals of European descent with epilepsy was deeply phenotyped and underwent single nucleotide polymorphism (SNP)-genotyping. We conducted genome-wide association analyses (GWASs) on responders to specific ASMs or groups of functionally related ASMs, using non-responders as controls. We performed a polygenic risk score (PRS) analyses based on risk variants for epilepsy and neuropsychiatric disorders and ASM resistance itself to delineate the polygenic burden of ASM-specific drug resistance. Results: We identified several potential regions of interest but did not detect genome-wide significant loci for ASM-specific response. We did not find polygenic risk for epilepsy, neuropsychiatric disorders, and drug-resistance associated with drug response to specific ASMs or mechanistically related groups of ASMs. Significance: This study could not ascertain the predictive value of common genetic variants for ASM responder status. The identified suggestive loci will need replication in future studies of a larger scale.
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Antiepileptic drug monotherapy for epilepsy: a network meta‐analysis of individual participant data
Article
  • Mar 2022
Background: This is an updated version of the original Cochrane Review published in 2017. Epilepsy is a common neurological condition with a worldwide prevalence of around 1%. Approximately 60% to 70% of people with epilepsy will achieve a longer-term remission from seizures, and most achieve that remission shortly after starting antiepileptic drug treatment. Most people with epilepsy are treated with a single antiepileptic drug (monotherapy) and current guidelines from the National Institute for Health and Care Excellence (NICE) in the United Kingdom for adults and children recommend carbamazepine or lamotrigine as first-line treatment for focal onset seizures and sodium valproate for generalised onset seizures; however, a range of other antiepileptic drug (AED) treatments are available, and evidence is needed regarding their comparative effectiveness in order to inform treatment choices. Objectives: To compare the time to treatment failure, remission and first seizure of 12 AEDs (carbamazepine, phenytoin, sodium valproate, phenobarbitone, oxcarbazepine, lamotrigine, gabapentin, topiramate, eventrate, zonisamide, eslicarbazepine acetate, lacosamide) currently used as monotherapy in children and adults with focal onset seizures (simple focal, complex focal or secondary generalised) or generalised tonic-clonic seizures with or without other generalised seizure types (absence, myoclonus). Search methods: For the latest update, we searched the following databases on 12 April 2021: the Cochrane Register of Studies (CRS Web), which includes PubMed, Embase, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform (ICTRP), the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Epilepsy Group Specialised Register and MEDLINE (Ovid, 1946 to April 09, 2021). We handsearched relevant journals and contacted pharmaceutical companies, original trial investigators and experts in the field. Selection criteria: We included randomised controlled trials of a monotherapy design in adults or children with focal onset seizures or generalised onset tonic-clonic seizures (with or without other generalised seizure types). Data collection and analysis: This was an individual participant data (IPD) and network meta-analysis (NMA) review. Our primary outcome was 'time to treatment failure', and our secondary outcomes were 'time to achieve 12-month remission', 'time to achieve six-month remission', and 'time to first seizure post-randomisation'. We performed frequentist NMA to combine direct evidence with indirect evidence across the treatment network of 12 drugs. We investigated inconsistency between direct 'pairwise' estimates and NMA results via node splitting. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs) and we assessed the certainty of the evidence using the CiNeMA approach, based on the GRADE framework. We have also provided a narrative summary of the most commonly reported adverse events. Main results: IPD were provided for at least one outcome of this review for 14,789 out of a total of 22,049 eligible participants (67% of total data) from 39 out of the 89 eligible trials (43% of total trials). We could not include IPD from the remaining 50 trials in analysis for a variety of reasons, such as being unable to contact an author or sponsor to request data, data being lost or no longer available, cost and resources required to prepare data being prohibitive, or local authority or country-specific restrictions. No IPD were available from a single trial of eslicarbazepine acetate, so this AED could not be included in the NMA. Network meta-analysis showed high-certainty evidence that for our primary outcome, 'time to treatment failure', for individuals with focal seizures; lamotrigine performs better than most other treatments in terms of treatment failure for any reason and due to adverse events, including the other first-line treatment carbamazepine; HRs (95% CIs) for treatment failure for any reason for lamotrigine versus: eventrate 1.01 (0.88 to 1.20), zonisamide 1.18 (0.96 to 1.44), lacosamide 1.19 (0.90 to 1.58), carbamazepine 1.26 (1.10 to 1.44), oxcarbazepine 1.30 (1.02 to 1.66), sodium valproate 1.35 (1.09 to 1.69), phenytoin 1.44 (1.11 to 1.85), topiramate 1.50 (1.23 to 1.81), gabapentin 1.53 (1.26 to 1.85), phenobarbitone 1.97 (1.45 to 2.67). No significant difference between lamotrigine and eventrate was shown for any treatment failure outcome, and both AEDs seemed to perform better than all other AEDs. For people with generalised onset seizures, evidence was more limited and of moderate certainty; no other treatment performed better than first-line treatment sodium valproate, but there were no differences between sodium valproate, lamotrigine or eventrate in terms of treatment failure; HRs (95% CIs) for treatment failure for any reason for sodium valproate versus: lamotrigine 1.06 (0.81 to 1.37), eventrate 1.13 (0.89 to 1.42), gabapentin 1.13 (0.61 to 2.11), phenytoin 1.17 (0.80 to 1.73), oxcarbazepine 1.24 (0.72 to 2.14), topiramate 1.37 (1.06 to 1.77), carbamazepine 1.52 (1.18 to 1.96), phenobarbitone 2.13 (1.20 to 3.79), lacosamide 2.64 (1.14 to 6.09). Network meta-analysis also showed high-certainty evidence that for secondary remission outcomes, few notable differences were shown for either seizure type; for individuals with focal seizures, carbamazepine performed better than gabapentin (12-month remission) and sodium valproate (six-month remission). No differences between lamotrigine and any AED were shown for individuals with focal seizures, or between sodium valproate and other AEDs for individuals with generalised onset seizures. Network meta-analysis also showed high- to moderate-certainty evidence that, for 'time to first seizure,' in general, the earliest licensed treatments (phenytoin and phenobarbitone) performed better than the other treatments for individuals with focal seizures; phenobarbitone performed better than both first-line treatments carbamazepine and lamotrigine. There were no notable differences between the newer drugs (oxcarbazepine, topiramate, gabapentin, eventrate, zonisamide and lacosamide) for either seizure type. Generally, direct evidence (where available) and network meta-analysis estimates were numerically similar and consistent with confidence intervals of effect sizes overlapping. There was no important indication of inconsistency between direct and network meta-analysis results. The most commonly reported adverse events across all drugs were drowsiness/fatigue, headache or migraine, gastrointestinal disturbances, dizziness/faintness and rash or skin disorders; however, reporting of adverse events was highly variable across AEDs and across studies. Authors' conclusions: High-certainty evidence demonstrates that for people with focal onset seizures, current first-line treatment options carbamazepine and lamotrigine, as well as newer drug eventrate, show the best profile in terms of treatment failure and seizure control as first-line treatments. For people with generalised tonic-clonic seizures (with or without other seizure types), current first-line treatment sodium valproate has the best profile compared to all other treatments, but lamotrigine and eventrate would be the most suitable alternative first-line treatments, particularly for those for whom sodium valproate may not be an appropriate treatment option. Further evidence from randomised controlled trials recruiting individuals with generalised tonic-clonic seizures (with or without other seizure types) is needed.
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The Association Between HLA-B*15:02 and Phenytoin-Induced Severe Cutaneous Adverse Reactions: A Meta-Analysis
Article
  • Nov 2021
Aim: Phenytoin (PHT) is a common anticonvulsant agent known for inducing severe cutaneous adverse reactions (SCARs). HLA-B*15:02 as a risk factor of PHT-induced SCARs was reported in numerous studies with inconsistent results. This meta-analysis aimed to establish pooling evidence of this association. Materials & methods: Pooled odds ratios (ORs) with 95% CIs were estimated using a random-effects model. Results: A total of 11 studies on 1389 patients, were included for the analyses. There was a significant association between HLA-B*15:02 and PHT-induced SCAR (pooled OR = 2.29, 95% CI: 1.25–4.19, p = 0.008). Furthermore, there was a significant association regarding Stevens–Johnson syndrome/toxic epidermal necrolysis (OR = 3.63, 95% CI: 2.15–6.13, p < 0.001) but no association regarding drug reaction with eosinophilia and systemic symptom. Conclusion: The results supported the recommendations of HLA-B*15:02 screening before treatment with PHT.
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Association of ABCB1 polymorphisms with carbamazepine metabolism and resistance in epilepsy: A meta-analysis
Article
  • Nov 2021
  • EPILEPSY RES
Objective ABCB1 polymorphisms were previously demonstrated to be associated with the metabolism and resistance of carbamazepine (CBZ) in epilepsy, but the results still remained controversial. Therefore, we performed this meta-analysis to further evaluate the impacts of ABCB1 polymorphisms on CBZ metabolism and resistance. Methods The PubMed, EMBASE, Cochrane library, Chinese National Knowledge Infrastructure, Chinese Science and Technique Journals Database and Wan Fang Database were searched for eligible publications up to 5 July 2021. The mean difference (MD), Odds ratio (OR) and 95 % confidence interval (CI) were calculated by Review Manager 5.3 software to assess the strength of the association. Results Twelve studies involving 2126 epilepsy patients were included in this meta-analysis. We found that the TC genotype (heterozygous model: TC vs. CC) of rs1045642 polymorphism was significantly connected with decreased CBZ concentration. Furthermore, this polymorphism was indicated to be associated with concentrations of carbamazepine-10, 11-epoxide (homozygote model: TT vs. CC; heterozygous model: TC vs CC; dominant model: TT + TC vs. CC; over-dominant model: TC vs. TT + CC) and carbamazepine-10, 11-trans dihydrodiol (heterozygous model: TC vs. CC; dominant model: TT + TC vs. CC). Moreover, the AG genotype of rs2032582 polymorphism was related to increased CBZ concentration in heterozygous (AG vs. GG), dominant (AA + AG vs. GG) and over-dominant (AG vs. AA + GG) models. Additionally, rs1128503 was associated with CBZ resistance in heterozygous model (TC vs. CC). Conclusions ABCB1 rs1045642 and rs2032582 polymorphisms were associated with CBZ metabolism for epilepsy, and rs1128503 was related to CBZ resistance. These findings would contribute to improving individualized therapy of epileptic patients.
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