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Efficacy and tolerability of antiseizure drugs

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Better drugs are needed for common epilepsies. Drug repurposing offers the potential of significant savings in the time and cost of developing new treatments. In order to select the best candidate drug s to repurpose for a disease, it is desirable to predict the relative clinical efficacy that drugs will have against the disease. Common epilepsy can be divided into different types and syndromes. Different antiseizure medications are most effective for different types and syndromes of common epilepsy. For predictions of antiepileptic efficacy to be clinically translatable, it is essential that the predictions are specific to each form of common epilepsy, and reflect the patterns of drug efficacy observed in clinical studies and practice. These requirements are not fulfilled by previously published drug predictions for epilepsy. We used this method to predict the relative efficacy of all drugs, licensed for any condition, against each of the major types and syndromes of common epilepsy. Our predictions are concordant with findings from real-world experience and randomized clinical trials. Importantly, our method predicts which antiseizure medications are amongst the more efficacious in clinical practice, and which antiseizure medications are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual antiseizure medications in clinical trials of different common epilepsies. We identify promising candidate drugs for each of the major syndromes of common epilepsy. We screen five promising predicted drugs in an animal model: each exerts a significant dose-dependent effect upon seizures. Our predictions are a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major syndromes of common epilepsy. Our method is potentially generalizable to other complex diseases. A total of 50 million people are affected by epilepsy. Drug repurposing—treating a disease using drugs already licensed for other conditions—offers the potential of significant savings in the time and cost of developing new therapies. Numerous drugs licensed for other conditions have the potential of antiepileptic efficacy. One established strategy for discovering potentially effective drugs is to, first, identify the proteins that underlie a disease and, then, identify the drugs that affect the disease-proteins. In such analyses, genes associated with a disease are routinely used as proxies for disease-proteins. Genetic factors can contribute to the development of epilepsies, either as single-gene mutations in rare monogenic epilepsies, or as multiple genetic variants in common epilepsies. Different forms of common epilepsy have important differences in their genetic determinants, 11 clinical manifestations and response to medications. This has not been achieved by any of the published drug prediction studies for epilepsy. Other studies have used genome-wide transcriptomic analysis of human brain tissue from epilepsy surgery 14 , 15 ; such tissue is only available for a very limited number of epilepsy syndromes, and its analysis is hindered by the lack of suitable control brain tissue that is comparable, normal and has been exposed to ASMs. Of course, any transcriptomic changes detected in epileptic brain tissue could be a consequence, rather than a cause, of disease. The Genome-Wide Association Study GWAS is becoming an increasingly powerful tool for revealing the distinct genetic determinants of different common epilepsies. In the standard approach, significant variants from the GWAS are mapped to genes; drugs that are known to affect the protein products of the genes, are predicted to affect the disease. It reflects neither the polygenicity of common diseases, nor the polypharmacology of common drugs. Potential causal variants below the genome-wide disease significance threshold are ignored. Practically, it produces an unordered and unranked pool of drug names, with no indication of the relative predicted efficacy of the compounds, to enable selection of the most promising candidates. Ultimately, it is liable to producing poor results. Some limitations of the standard approach are addressed by recently developed enhanced techniques for using GWAS results to identify effective drugs, 25—28 but these newer methods and their drug predictions for common epilepsy still leave room for improvement. Our aim was to develop such a method, and to use this method to predict the relative efficacy of drugs for each of the major types and syndromes of common epilepsy, and to make our predictions available as a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major types and syndromes of common epilepsy. Methods are summarized below; further details can be found in the Supplementary methods. The common epilepsies are divided into different types, which are further subdivided into different syndromes. All epilepsy, which is comprised of generalized, focal and unclassified epilepsies. The two main types of all epilepsy: generalized epilepsy GE and focal epilepsy FE. Genetic variants cause disease by modifying the function or abundance or both of proteins derived from the variant genes. Premise and conceptual explanation of the disease-protein function modulation FM and abundance correction AC scores, which are integrated to form the disease-protein function and abundance modulation FAM score. Before integration, the FM score is adjusted to control for the different number of proteins affected by each drug see Supplementary material for details. Cosine distance is the dis similarity metric used for calculating the AC score. For method development and benchmarking, we used the all epilepsy GWAS. Then, we applied the developed method to the GWAS for specific epilepsy types and syndromes. The steps taken in developing the method for calculating the FAM score are detailed in Supplementary material. Below, we summarize the method Fig. Figure 1 presents a conceptual explanation of how the FM score is calculated from these two types of data. A more detailed explanation can be found in the Supplementary material. The AC score is based upon the following premise: A drug is more likely to be effective for a disease if it is better able to rectify the protein abundance changes underlying the disease. Then, drugs are ranked in accordance with their overall predicted corrective effect on the abundance of all disease-associated proteins. The FM and AC scores were converted into their respective z-scores. We compared our results with the results from two existing and contrasting advanced methods for GWAS-based drug predictions. An approach employed in a number of studies is to identify the drugs that target genome-wide significant disease-proteins and, in addition, the drugs that target the proteins interacting with genome-wide significant disease-proteins. In this method, 36 GWAS gene-based P -values are first converted to z-statistics and, then, a single-sided two-sample t -test is used to determine if the mean z-statistic of the genes that are altered in function by a drug is lower than the mean z-statistic of the genes that are not. For in silico validation of the FAM score, we examined the following hypotheses:. The FAM score for all epilepsy specifically prioritizes the drugs that are effective in people with epilepsy: when drugs are ranked by their FAM score for all epilepsy, drugs used to treat epilepsy are ranked higher than drugs used to treat any other human disease. The FAM score predicts the observed patterns of relative efficacy of individual clinically-effective ASMs for each common epilepsy syndrome studied. The above hypotheses are further detailed in Results and in Supplementary methods. In assessing the discrimination of ASMs from all other drugs, there is a marked class imbalance, because a very small fraction of all drugs are ASMs. To correct for this imbalance, we employed the standard technique of random under-sampling, which is commonly used in published studies see Supplementary material for further details and references. When discriminating more from less effective ASMs, class imbalance is not an issue and, hence, random under-sampling was not employed. Prioritization of effective drugs: amongst all the drug predictions for a phenotype, we determined the average rank of ASMs, or compared the average rank of more clinically-effective and less clinically-effective ASMs. To ease conceptualization and interpretation of results, we converted ranks to percentile ranks. Like numerous published studies, we used the median in order to compute the average of ranks, as it is less liable to skewing by outliers see Supplementary material for further details and references. We determined the statistical significance of drug identification and prioritization results by comparing the results to those from a null distribution generated by performing 10 6 random permutations of the scores assigned to drugs. For each epilepsy, FAM scores were re-calculated after excluding, one at a time, the top 10 most strongly disease-associated proteins Supplementary Table 3. Further details about this analysis can be found in the Supplementary material. To aid the selection of suitable candidate drugs for experimental validation and clinical evaluation, we demarcated the most promising candidate drugs for each phenotype: the topmost drug predictions with the greatest enrichment of more effective ASMs for that phenotype. A manually curated selection of top candidate drugs for different forms of common epilepsy was also produced. As we used complex genetic data to make our drug predictions, we used a complex genetic model to test our drug predictions. Council Directive No. Porsolt facility accreditation for experimentation E 53 , renewed on 19 April ;. The recommendations of the Association for Assessment and Accreditation of Laboratory Animal Care of which the accreditation was granted in June and renewed in Porsolt has an in-house ethics programme, which covers animal care and use within the facility. Additional experimental details about the animal model testing can be found in the Supplementary material. The code is for non-commercial use only. Our complete set of predictions, listing each drug and its FAM score, for each phenotype. In the standard method, drugs are predicted to be efficacious if they modulate the function of proteins that are associated with the disease, according to the GWAS, at a genome-wide level of significance. For CAE, JME and HS, there are no genes that both i reach genome-wide level of disease-significance and ii produce a protein that is known to be altered in function by any existing compound. The standard method of drug prediction produces an unordered and unranked set of candidate drugs, with no metrics for the relative predicted efficacy of the candidate compounds. This precludes method evaluation based upon predicted drug rankings and AUROC, and hampers the selection of the most promising candidate drugs for experimental validation. The same set of ASMs is predicted to be effective for the two divergent phenotypes of GE and FE, even though some seizure types in the former are aggravated by the ASMs that are most effective for the latter. Hence, for different common epilepsies, this method either fails to identify the majority of known effective drugs, or identifies no candidate drugs at all, or identifies potentially aggravating drugs. By extension, applying the standard approach to common epilepsies will yield no or few candidates for repurposing, will not prioritize amongst the candidates, will fail to identify any or most of the efficacious compounds and will potentially identify aggravating drugs. To predict the relative efficacy of drugs against common epilepsies, we devised the disease-protein FAM score, which is calculated using the method illustrated in Fig. Results of all comparator alternative approaches are shown in Supplementary Table 1. Next, we present results of the analyses performed to test the validity of the predictions made using the FAM score. When drugs are ranked by their FAM score for all epilepsy, drugs used to treat epilepsy are ranked higher than drugs used to treat any other human disease. The median rank of drugs used to treat epilepsy is at least seven percentiles higher than that of drug-sets used to treat other human diseases. Different ASMs are most effective for different syndromes of common epilepsy. Clinical studies and experience show that, for each common epilepsy syndrome, some ASMs can be classified into a more clinically-effective subset and some into a less clinically-effective subset. For each common epilepsy syndrome, the FAM score predicts which ASMs are amongst the more efficacious in clinical practice, and which ASMs are amongst the less efficacious in clinical practice Table 1. Specifically, for each common epilepsy syndrome, the FAM score i distinguishes the more from the less clinically-effective ASMs and ii prioritizes the more clinically-effective ASMs higher than the less clinically-effective ASMs Table 1. Prioritization result shown is the average median rank of AEDs, expressed as a percentile; it is equivalent to the percentage of all drugs ranked below the middle-ranked AED see Supplementary methods. In order to predict which ASMs are more clinically-effective and which ASMs are less clinically-effective for a syndrome, the best results are obtained by using the FAM score for that syndrome. For FE, current ASMs are not readily classified into more clinically-effective and less clinically-effective subsets. We tested our predictions against the following observed patterns of relative efficacy of individual clinically-effective ASMs. In our predictions for FE, lamotrigine is ranked higher than topiramate, while for GE, topiramate is ranked higher than lamotrigine. Valproate is thought to be the most efficacious broad-spectrum ASM for JME 50—52 but this is based on anecdotal data and retrospective analyses. Amongst our predictions for JME, valproate was amongst the highest ranked drugs percentile rank 98 , but not the highest. The highest ranked prediction was primidone. In the longest retrospective cohort study of JME to date, primidone was most effective, with a 5-year terminal remission rate of Ethosuximide is not ranked highly, but higher than average, amongst our predictions median percentile rank Ethosuximide is ascribed a particularly low FM score for CAE, which places it in the 20th percentile of predictions for the phenotype. The relative predicted efficacy of drugs does not change significantly after excluding, one at a time, the top 10 most strongly disease-associated proteins that contribute to the FAM score for that epilepsy. The predicted ranks of drugs for each epilepsy remained significantly stable after excluding, one at a time, the top 10 most strongly disease-associated proteins that contribute to the FAM score for that epilepsy. For each epilepsy, FAM scores were re-calculated after excluding, one at a time, the top 10 most strongly disease-associated proteins Supplementary Table 3 that contribute to the FAM score for that epilepsy. Ranked lists of the top drugs predicted to be effective for each phenotype, which are most enriched with the drugs that are known to be more effective for the phenotype, are available for download see Data availability. A manually curated selection of top candidate drugs that could potentially be repurposed for different forms of common epilepsy is shown in the Table 2. Manually curated selection of candidate drugs for the phenotypes shown in the table. Candidate drugs for GE, which we tested in an animal model, are listed in Table 3. References, for the evidence cited here, can be found in the Supplementary material. After excluding drugs that are toxic or otherwise unsuitable, the top five predicted drugs for GE were tested in a mouse model with a complex genetic seizure disorder that manifests as audiogenic generalized seizures. Each of the drugs had a significant dose-dependent effect on tonic and clonic convulsions Table 3. Whilst four of the drugs had a significant dose-dependent anti -convulsant effect, one of the compounds betahistine had a significant dose-dependent pro -convulsant effect. After activation of a bell, latency to the occurrence of tonic convulsions and clonic convulsions was measured. We present the relative predicted efficacy of drugs against each of the main types and syndromes of common epilepsy. This dataset is a novel and valuable resource for selecting the best candidate drug s to repurpose for any of the main types and syndromes of common epilepsy. To generate our predictions, we created a novel method. Our method possesses several strengths that are lacking in previously published approaches. Common epilepsies, like other complex diseases, develop when many different proteins display abnormal activity due to pathological changes in their abundance or function. Furthermore, drugs are prioritized on the basis of their ability to correct disease-protein abnormalities that are found in people with the disease, rather than in animal models, and that are not consequential to or compensatory for the disease, as they are driven by germline variations. We use genetic variation data specific to each form of common epilepsy, to make drug predictions specific to that form of common epilepsy. The ASMs that are more clinically-effective for a syndrome and the ASMs that are less clinically-effective for a syndrome are predicted more effective and less effective, respectively, for that syndrome only, but not for any other epilepsy type or syndrome—this suggests that our predictions are not systemically biased in favour of a particular set or type of drugs. The methodology is based upon a polygenic model of disease and a multi-targeted approach to treatment, which are desirable for complex diseases. We utilize conventional canonical low-throughput single-target functional drug activity data, and high-throughput genome-wide transcriptomic drug activity data, so that prioritization of drugs is informed by their on-target and off-target effects, and by their affinities for individual proteins and effects upon genome-wide gene expression. Rather than dichotomous categorization of compounds into drugs that are predicted to be effective or ineffective, our method ranks drugs individually according to relative predicted efficacy, which aids candidate selection for in vivo validation and for development. Our method produces accurate drug predictions for epilepsy syndromes even if their GWAS results include few genome-wide significant loci. This is because our method is not reliant on individual highly disease-associated proteins. Instead, our method leverages the gene-set analysis approach, where each gene-set is the set of genes affected by each drug. The disease association of all the genes in a gene-set, even those below the genome-wide significant threshold, is combined; the gene-sets that are more disease-associated overall are more biologically relevant. The gene-set approach is a long-established and widely-used method in all areas of genomic analysis, 56 including post-GWAS analysis generally 57 and GWAS-based drug repurposing analysis specifically. Alongside these strengths, our method has some limitations, discussed below. Our drug prediction method, like all previously published genetics- or genomics-based drug prediction methods, predicts the efficacy of drugs for a disease. However, the most efficacious drug for a disease in not always the most appropriate drug for every individual with the disease. Important factors to consider when choosing a drug for an individual include the potential of undesirable interactions with other medications and the possible side-effects. Indeed, the success of an ASM is determined as much by its tolerability as by its efficacy. This allows researchers to select for further development those candidate compounds whose side-effects are less deleterious or even desirable. Our method predicts drugs effective for a disease from the proteins underlying the disease, after identifying the proteins underlying the disease from the common genetic variations associated with the disease. However, some proteins become dysfunctional or dysregulated not because of common genetic variations, but because of rare genetic variations, or copy number variations, or abnormalities of epigenetic, post-transcriptional or post-translational mechanisms, or because of environmental insults. We are not aware of any existing drug prediction methods which take into account the multiple potential pathogenic factors that influence proteins; the development of such methods might lead to improved accuracy of drug predictions. Our analysis uses data from a GWAS that employed imputation to improve genomic coverage. The GWAS gene-level data used in this analysis offers coverage of genes across the genome, and it is corrected for the lengths and single nucleotide polymorphism-densities of genes. However, if a gene is not adequately covered by the genotyping array and the imputation, but the gene is of importance in epilepsy and affected by drug s , the accuracy of our drug predictions could be adversely affected. Hence, improved coverage of future epilepsy GWAS analyses could help to improve the accuracy of drug predictions. Our drug predictions are based upon two scores: the FM and AC scores. The FM score relies upon knowledge of the proteins changed in function by drugs. At present, knowledge of the proteins that are changed in function by each drug is incomplete, and it is more incomplete for some drugs than for others. By extension, the FM score is more likely to be underestimated for drugs that are less studied, as their modes of action are less analysed and, hence, knowledge of the proteins changed in function by them is less complete. The AC score is free of this limitation, as the AC score is based upon profiles of drug-induced transcriptomic changes assayed by using the same standardized pipeline for each drug. However, a small number of interesting drugs for example, brivaracetam and cenobamate have not been assayed, which means that an AC score and, hence, a FAM score cannot be calculated for them. Therefore, drugs predicted by the FM score to affect a phenotype may be alleviating or aggravating for the phenotype. This is a recognized limitation of methods that use data for the ability of drugs to alter the function of genetically-associated disease-proteins in order to predict drugs that can affect the disease, 16 , 17 , 65 as the direction of change in protein activity occurring in the disease is unknown. Thereby, the AC score proposes to predict the drugs with a beneficial effect on disease-protein abundance and clinical phenotype. Hence, inclusion of the AC score, with the FM score, in our final FAM score, is expected to help mitigate the risk of deleterious compounds with high FM scores being included in our candidate drugs. Still, it is possible that some aggravating drugs are included in our candidate compounds. Hence, experimental validation of candidate drugs is essential before clinical use, as with all in silico drug prediction methods. We tested five of our candidate compounds in a rodent model: all five compounds had a significant dose-dependent effect on seizures. Interestingly, one of the candidate compounds betahistine had a significant dose-dependent pro-convulsant effect in the animal model. This finding could be explained by the possibility that some of our predicted compounds are aggravating, as discussed. However, it is also possible that the pro-convulsant effect of betahistine in our study is a reflection of species- or model-specific behaviour. Indeed, a recent study published after our animal experiments had ended showed that betahistine has a significant antiepileptogenic and anticonvulsant effect on pentylenetetrazole-induced generalized seizures in a different mouse strain. Whilst acknowledging these limitations and some aberrant predictions, we note that our method outperforms alternative methods for predicting drugs that have efficacy against common epilepsies in clinical studies and experience. Our method also predicts which ASMs are amongst the more efficacious in clinical practice, and which ASMs are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual ASMs in clinical trials of different common epilepsies. This aspect is key to the clinical translation of drug predictions for common epilepsies, but is missing from previously published studies that have predicted drugs for epilepsy. In this study, we have used the tissue-wide association study method to identify the protein abundance changes underlying disease. A closely-related alternative method is to use Mendelian randomization. Mendelian randomization is discussed at greater length in the Supplementary material. As our method uses GWAS data, it cannot be applied to monogenic diseases. It is conceivable that this method could be adapted to make it applicable to monogenic diseases, and we plan to explore this possibility in a future study dedicated to this objective. The latest schizophrenia GWAS, for example, included 36 cases and controls, resulting in the identification of risk loci. In this analysis, we predicted drugs for the main epilepsy syndromes that had risk loci identified in the latest epilepsy GWAS. It is hoped that future epilepsy GWAS will be large enough to report results for additional epilepsy syndromes, and drugs can be predicted for them using the method presented here. Finally, it is likely that our method can be applied to the GWAS results of other common complex phenotypes. Supplementary material is available at Brain Communications online. Opinions expressed by the authors, however, do not necessarily represent the policy or position of the ILAE. Members are listed in alphabetical order. Author affiliations can be found in the Supplementary material. Berkovic, Katja E. Boysen, Jonathan P. Bradfield, Lawrence C. Brody, Russell J. Buono, Ellen Campbell, Gregory D. Cascino, Claudia B. Catarino, Gianpiero L. Cavalleri, Stacey S. Cherny, Krishna Chinthapalli, Alison J. Dlugos, Colin P. Doherty, Christian E. Elger, Johan G. Eriksson, Thomas N. Kirsch, Robert C. Knowlton, Bobby P. Lowenstein, Alberto Malovini, Anthony G. Reif, Eva M. Reinthaler, Felix Rosenow, Josemir W. Schachter, Christoph J. Schankin, Ingrid E. Sham, Jerry J. Shih, Graeme J. Sills, Sanjay M. Smith, Michael C. Smith, Philip E. Smith, Anja C. Sonsma, Doug Speed, Michael R. Sperling, Bernhard J. Vari, Eileen P. Walley, Yvonne G. GBD Epilepsy Collaborators. Global, regional, and national burden of epilepsy, A systematic analysis for the Global Burden of Disease Study Lancet Neurol. Shorvon SD. The epidemiology and treatment of chronic and refractory epilepsy. Google Scholar. 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The SANAD II study of the effectiveness and cost-effectiveness of levetiracetam, zonisamide, or lamotrigine for newly diagnosed focal epilepsy: An open-label, non-inferiority, multicentre, phase 4, randomised controlled trial. The SANAD study of effectiveness of valproate, lamotrigine, or topiramate for generalised and unclassifiable epilepsy: An unblinded randomised controlled trial. The SANAD II study of the effectiveness and cost-effectiveness of valproate versus levetiracetam for newly diagnosed generalised and unclassifiable epilepsy: An open-label, non-inferiority, multicentre, phase 4, randomised controlled trial. Brodie MJ. Modern management of juvenile myoclonic epilepsy. Expert Rev Neurother. Management of juvenile myoclonic epilepsy. Epilepsy Behav. Nicolson A , Marson AG. When the first antiepileptic drug fails in a patient with juvenile myoclonic epilepsy. Pract Neurol. Prognosis of juvenile myoclonic epilepsy 45 years after onset: Seizure outcome and predictors. Ethosuximide, valproic acid, and lamotrigine in childhood absence epilepsy. N Engl J Med. Describing the genetic architecture of epilepsy through heritability analysis. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Genome-wide gene and pathway analysis. Eur J Hum Genet. Functional mapping and annotation of genetic associations with FUMA. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. Biological interpretation of genome-wide association studies using predicted gene functions. High-throughput gene expression profiles to define drug similarity and predict compound activity. Assay Drug Dev Technol. Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks. BMC Bioinformatics. Cell-specific prediction and application of drug-induced gene expression profiles. Network-based approach to prediction and population-based validation of in silico drug repurposing. Betahistine, prevents kindling, ameliorates the behavioral comorbidities and neurodegeneration induced by pentylenetetrazole. Biological insights from schizophrenia-associated genetic loci. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Advertisement intended for healthcare professionals. Sign in through your institution. Brain Journals. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume 3. Article Contents Abstract. Materials and methods. Supplementary material. Appendix I. Journal Article. Using common genetic variants to find drugs for common epilepsies. Nasir Mirza , Nasir Mirza. Oxford Academic. Remi Stevelink. Basel Taweel. School of Medicine, University of Liverpool. Bobby P C Koeleman. Anthony G Marson. A list of consortium members is provided in Appendix I. Revision received:. Corrected and typeset:. Select Format Select format. Permissions Icon Permissions. Abstract Better drugs are needed for common epilepsies. Graphical Abstract. Open in new tab Download slide. Figure 1. Table 1 Open in new tab. Prioritisation of AEDs average percentile. More from less effective AEDs. More effective AEDs. Less effective AEDs. Table 2 Open in new tab. Evidence of antiseizure efficacy in. Mode of action. Table 3 Open in new tab. Vehicle i. International League against Epilepsy. Download all slides. Supplementary data. Views 2, More metrics information. Total Views 2, Email alerts Article activity alert. Advance article alerts. New issue alert. In progress issue alert. Receive exclusive offers and updates from Oxford Academic. Citing articles via Web of Science 8. 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