Evaluation of systemic inflammation in seizure phenotypes following meningioma resection.

PURPOSE: To investigate the association between perioperative peripheral blood inflammatory markers and seizures in patients who have undergone meningioma resection. MATERIALS AND METHODS: A single neurosurgery tertiary centre blood bank database was screened to extract pre-operative and post-operative white cell count (WCC), neutrophils, lymphocytes, monocytes, platelets and neutrophil-lymphocyte ratio (NLR) and derived NLR (dNLR). All patients who underwent resection of meningioma from 2012 to 2020 were eligible. Patients were excluded if they had an inflammatory condition, peri-operative infection, medical illness or operative complication. RESULTS: 30 patients suffered pre-operative seizures only, 16 experienced de novo post-operative seizures within 1 year and 42 patients did not experience seizures throughout their treatment timeline. Patients with post-operative de novo seizures had a significantly higher WCC when compared those who never had a seizure (7.1 vs. 4.8x109/L, p =.048, 95 % 1.96 to 5.60). However, this difference of WCC was poorly predictive of de novo seizures at one year (AUC 0.61). dNLR was significantly higher in patients with continued post-operative seizures than in patients in which seizures were terminated with tumour resection (1.2 vs. 0.1, p =.035, 95 % 1.47 to 2.29). dNLR was predictive of seizures at one year with an 87.5 % sensitivity and 82.1 % specificity. CONCLUSIONS: There is a significantly higher post-operative systemic white cell count response in patients who suffered de novo seizures after meningioma resection. Peripheral blood markers have the potential to predict seizures in patients with meningioma.

PCGA: Polynomial collocation genetic algorithm for singular Poisson-Boltzmann equation arising in thermal explosions.

Heat generation as a result of the exothermic reaction reaches the environment mainly due to the conduction through the walls of the vessel. The balance between the heat generated and the heat conducted away, resulting in the explosion is described by the Frank-Kamenetzkii (FK) parameter ρ. The critical value of FK for which the explosion occurs depends upon the shape of the vessel, which requires the solution of governing singular nonlinear Poisson-Boltzmann equation. Owing to the exponential nonlinearity and singularity the analytical exact solution for the non-integer k values does not exist. This work focuses on implementing the polynomial collocation by exploiting the global optimization features of the genetic algorithm to solve the Poisson-Boltzmann equation for integer and non-integer shape factors (k). The governing equation was converted into coupled nonlinear algebraic equations and an objective function was formulated. The method was examined for six different configurations of the control parameters of GA to find the best set of parameters. The solution for temperature distribution is obtained for cylindrical (k = 1), parallelepiped (k = 0.438, 0.694), and an arbitrary shape (k = 0.5) respectively. The solution obtained from Polynomial Collocation Genetic Algorithm (PCGA) remained in good agreement with the corresponding analytical results for k = 1, with the minimum absolute error of 10 - 10 . The critical values of the FK are obtained as 1.5 , 1.4 , a n d 1.7 for shape factor k = 0.438 , 0.5 , a n d 0.694 respectively with the convergence of the order of 10 - 6 t o 10 - 5 . The obtained solution is fairly stable over appropriate independent runs with the variation in the fitness value ranging from 10 - 05 t o 10 - 03 . Further simulations were performed to validate the results through statistical error indices. The diminutive errors of the order of 10 - 6 confirm reliable optimum solution, accuracy, and stability.

Do transportation taxes promote pro-environmental behaviour? An empirical investigation.

The transportation sector is a crucial driver of energy intensity and environmental degradation. Therefore, we aim to explore the nexus of transportation taxes, energy intensity, and CO2 emissions for the BICS economies. The econometric approaches, CS-ARDL and PMG-ARDL, have been employed to compute the estimates. The long-run estimates of the green transportation tax variable are negatively significant in both energy intensity and CO2 emissions models irrespective of the estimation technique. These findings imply that green transportation taxes help reduce energy intensity and CO2 emissions in BICS economies. Conversely, in the short-run, the effects of transportation taxes on energy intensity and CO2 emissions are mixed and inconclusive. Hence, transportation taxes are necessary to keep the polluters under control not only from the transport sector but also serve as a deterrent for other sectors as well.

Statins as an antiepileptogenic or disease-modifying treatment? A survey of what UK patients and significant others think about repurposing and trialing them for epilepsy.

OBJECTIVE: To identify the views of people with epilepsy (PWE), and their significant others, on the repurposing and trialing of statins as a potential antiepileptogenic or disease-modifying treatment for those who have had the first seizure. METHODS: Online questionnaire. Participants needed to be aged ≥ 16 years, UK residents, and able to independently complete a questionnaire in English. User groups distributed study adverts. Embedded infographics explained repurposing, why anti-seizure treatment is not typically started after a first seizure and the nature of randomized placebo-controlled trials (RCTs). The questionnaire asked participants to reflect and rate their expected willingness to have started an unspecified treatment after their first seizure/s (or that of the person with epilepsy they knew). They also rated willingness if the treatment were a statin, views of statins, the importance of an RCT of statins to their community, the outcomes it should assess, and their willingness to have taken part in it. The estimated number needed for the survey was 324. RESULTS: Responses from 213 persons were analyzed; 111 (52.1%) were PWE and 102 (47.9%) significant others. The median years diagnosed was 10 and PWE suffered from relatively severe epilepsy. One hundred and seventeen (54.9%) said they would have started an unspecified treatment after their first seizure/s (or supported the person with epilepsy they knew to have). A similar proportion (55.4%) said they would have started the treatment if it were a statin. Participants' main concern about statins, expressed by 79%, was their possible side effects. Repurposing was a concern for only 25%. Most (85.8%) rated an RCT of statins as of extreme or high importance; 54.4% said they would have participated. CONCLUSION: The PWE and significant others (SOs) responding to our survey expressed views towards repurposing statins that were generally positive and indicate a trial in those who have had a first seizure might be feasible. Concerns regarding side effects are common. Our findings could help optimize a future trial's design and the case for funding. Limitations include that we did not survey persons who had experienced a first seizure and did not go on to develop epilepsy. Also, persons with uncontrolled epilepsy were overrepresented.

A systems medicine strategy to predict the efficacy of drugs for monogenic epilepsies.

OBJECTIVE: Monogenic epilepsies are rare but often severe. Because of their rarity, they are neglected by traditional drug developers. Hence, many lack effective treatments. Treatments for a disease can be discovered more quickly and economically by computationally predicting drugs that can be repurposed for it. We aimed to create a computational method to predict the efficacy of drugs for monogenic epilepsies, and to use the method to predict drugs for Dravet syndrome, as (1) it is the archetypal monogenic catastrophic epilepsy; (2) few antiseizure medications are efficacious in Dravet syndrome; and (3) predicting the effect of drugs on Dravet syndrome is challenging, because Dravet syndrome is typically caused by an SCN1A mutation, but some antiseizure medications that are efficacious in Dravet syndrome do not affect SCN1A, and some antiseizure medications that affect SCN1A aggravate seizures in Dravet syndrome. METHODS: We have devised a computational method to predict drugs that could be repurposed for a monogenic epilepsy, based on a combined measure of drugs' effects upon (1) the function of the disease's causal gene and other genes predicted to influence its phenotype, (2) the transcriptomic dysregulation induced by the casual gene mutation, and (3) clinical phenotypes. RESULTS: Our method correctly predicts drugs that are more effective, less effective, ineffective, and aggravating for seizures in people with Dravet syndrome. Our method correctly predicts the positive "hits" from large-scale screening of compounds in an animal model of Dravet syndrome. We predict the relative efficacy of 1462 drugs. At least 38 drugs are ranked higher than one or more of the antiseizure drugs currently used for Dravet syndrome and have existing evidence of antiseizure efficacy in animal models. SIGNIFICANCE: Our predictions are a novel resource for identifying new treatments for seizures in Dravet syndrome, and our method can be adapted for other monogenic epilepsies.

The Seizure-Associated Genes Across Species (SAGAS) database offers insights into epilepsy genes, pathways and treatments.

OBJECTIVE: Decades of genetic studies on people with many different epilepsies, and on many nonhuman species, using many different technologies, have generated a huge body of literature about the genes associated with seizures/epilepsy. Collating these data can help uncover epilepsy genes, pathways, and treatments that would otherwise be overlooked. We aimed to collate and structure these data into a database, and use the database to identify novel epilepsy genes and pathways, and to prioritize promising treatments. METHODS: We collated all the genes associated with all types of seizures/epilepsy in all species, and quantified the supporting evidence for each gene, by manually screening ~10 000 publications, and by extracting data from existing databases. RESULTS: The largest published dataset of epilepsy genes includes only 977 genes, whereas our database (www.sagas.ac) includes 2876 genes, which demonstrates that the number of genes that can potentially contribute to seizures/epilepsy is much higher than previously envisaged. We use our database to identify 12 hitherto unreported polygenic epilepsy genes, 479 high-confidence monogenic epilepsy genes, and 394 more biological pathways than identified using the previously largest epilepsy gene dataset. We use a unique feature of Seizure-Associated Genes Across Species-the number of citations for each gene-to demonstrate that a drug is more likely to affect seizures if there is more evidence that the genes it affects are associated with seizures, and we use these data to identify promising candidate antiseizure drugs. SIGNIFICANCE: This database offers insights into the causes of epilepsy and its treatments, and can accelerate future epilepsy research.

Statins as antiepileptogenic drugs: Analyzing the evidence and identifying the most promising statin.

Many brain insults and injuries are "epileptogenic": they increase the risk of developing epilepsy. It is desirable to identify treatments that are "antiepileptogenic": treatments that prevent the development of epilepsy, if administered after the occurrence of an epileptogenic insult. Current antiepileptic drugs are not antiepileptogenic, but evidence of antiepileptogenic efficacy is accumulating for a growing number of other compounds. From among these candidate compounds, statins are deserving of particular attention because statins are reported to be antiepileptogenic in more published studies and in a wider range of brain insults than any other individual or class of compounds. Although many studies report the antiepileptogenic effect of statins, it is unclear how many studies provide evidence that statins exhibit the following two essential features of a clinically viable antiepileptogenic drug: the drug must exert an antiepileptogenic effect even if it is initiated after the epileptogenic brain insult has already occurred, and the antiepileptogenic effect must endure even after the drug has been discontinued. In the current work, we interrogate published preclinical and clinical studies, to determine if statins fulfill these essential requirements. There are eight different statins in clinical use. To enable the clinical use of one of these statins for antiepileptogenesis, its antiepileptogenic effect will have to be established through future time- and resource-intensive clinical trials. Therefore, it is desirable to review the published literature to determine which of the statins emerges as the most promising candidate for antiepileptogenic therapy. Hence, in the current work, we also collate and analyze published data-clinical and pre-clinical, direct and indirect-that help to answer the question: Which statin is the most promising candidate to take forward into an antiepileptogenesis clinical trial?

Using common genetic variants to find drugs for common epilepsies.

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 developed a novel method for predicting the relative efficacy of drugs against any common epilepsy, by using its Genome-Wide Association Study summary statistics and drugs' activity data. The methodological advancement in our technique is that the drug predictions for a disease are based upon drugs' effects on the function and abundance of proteins, and the magnitude and direction of those effects, relative to the importance, degree and direction of the proteins' dysregulation in the disease. 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. Our method predicts the efficacy of existing antiseizure medications against common epilepsies; in this prediction, our method outperforms the best alternative existing method: area under receiver operating characteristic curve (mean ± standard deviation) 0.83 ± 0.03 and 0.63 ± 0.04, respectively. 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.

Is NIPA1-associated hereditary spastic paraplegia always 'pure'? Further evidence of motor neurone disease and epilepsy as rare manifestations.

Pathogenic variants in the nonimprinted in Prader-Willi/Angelman syndrome (NIPA1) gene typically present with pure hereditary spastic paraplegia (HSP) but complex cases are described. We present a patient with childhood idiopathic generalised epilepsy (IGE) who later developed HSP. She rapidly deteriorated 27 years later with clinically definite amyotrophic lateral sclerosis (ALS). Her family history included HSP, IGE and motor neurone disease. Genetic testing identified a pathogenic variant in the NIPA1 gene associated with spastic paraplegia 6 (SPG6). This case provides the first description of NIPA1 in a family with epilepsy, ALS and thus complex HSP.

The prescribable drugs with efficacy in experimental epilepsies (PDE3) database for drug repurposing research in epilepsy.

OBJECTIVE: Current antiepileptic drugs (AEDs) have several shortcomings. For example, they fail to control seizures in 30% of patients. Hence, there is a need to identify new AEDs. Drug repurposing is the discovery of new indications for approved drugs. This drug "recycling" offers the potential of significant savings in the time and cost of drug development. Many drugs licensed for other indications exhibit antiepileptic efficacy in animal models. Our aim was to create a database of "prescribable" drugs, approved for other conditions, with published evidence of efficacy in animal models of epilepsy, and to collate data that would assist in choosing the most promising candidates for drug repurposing. METHODS: The database was created by the following: (1) computational literature-mining using novel software that identifies Medline abstracts containing the name of a prescribable drug, a rodent model of epilepsy, and a phrase indicating seizure reduction; then (2) crowdsourced manual curation of the identified abstracts. RESULTS: The final database includes 173 drugs and 500 abstracts. It is made freely available at www.liverpool.ac.uk/D3RE/PDE3. The database is reliable: 94% of the included drugs have corroborative evidence of efficacy in animal models (for example, evidence from multiple independent studies). The database includes many drugs that are appealing candidates for repurposing, as they are widely accepted by prescribers and patients-the database includes half of the 20 most commonly prescribed drugs in England-and they target many proteins involved in epilepsy but not targeted by current AEDs. It is important to note that the drugs are of potential relevance to human epilepsy-the database is highly enriched with drugs that target proteins of known causal human epilepsy genes (Fisher's exact test P-value < 3 × 10-5 ). We present data to help prioritize the most promising candidates for repurposing from the database. SIGNIFICANCE: The PDE3 database is an important new resource for drug repurposing research in epilepsy.

Genetic regulation of gene expression in the epileptic human hippocampus.

Epilepsy is a serious and common neurological disorder. Expression quantitative loci (eQTL) analysis is a vital aid for the identification and interpretation of disease-risk loci. Many eQTLs operate in a tissue- and condition-specific manner. We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not only normal (n = 22) but also epileptic (n = 22) samples. We demonstrate that disease-associated variants from an epilepsy GWAS meta-analysis and a febrile seizures (FS) GWAS are significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies. In contrast, GWAS meta-analyses of two other brain diseases associated with hippocampal pathology (Alzheimer's disease and schizophrenia) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs. These observations suggest that an eQTL analysis that includes disease-affected brain tissue is advantageous for detecting additional risk SNPs for the afflicting and closely related disorders, but not for distinct diseases affecting the same brain regions. We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epilepsy cis-gene is significantly more likely to be a causal gene for a Mendelian epilepsy syndrome than to be a causal gene for another Mendelian disorder. Epilepsy cis-genes, compared to normal hippocampal cis-genes, are more enriched within epilepsy-causing genes. Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy disease-risk variants and, thereby, highlight novel potential causal genes for sporadic epilepsy. In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes underlying epilepsy.

Identifying new antiepileptic drugs through genomics-based drug repurposing.

Currently available antiepileptic drugs (AEDs) fail to control seizures in 30% of patients. Genomics-based drug repurposing (GBR) offers the potential of savings in the time and cost of developing new AEDs. In the current study, we used published data and software to identify the transcriptomic signature of chornic temporal lobe epilepsy and the drugs that reverse it. After filtering out compounds based on exclusion criteria, such as toxicity, 36 drugs were retained. 11 of the 36 drugs identified (>30%) have published evidence of the antiepileptic efficacy (for example, curcumin) or antiepileptogenic affect (for example, atorvastatin) in recognised rodent models or patients. By objectively annotating all ∼20,000 compounds in the LINCS database as either having published evidence of antiepileptic efficacy or lacking such evidence, we demonstrated that our set of repurposable drugs is ∼6-fold more enriched with drugs having published evidence of antiepileptic efficacy in animal models than expected by chance (P-value <0.006). Further, we showed that another of our GBR-identified drugs, the commonly-used well-tolerated antihyperglycemic sitagliptin, produces a dose-dependent reduction in seizures in a mouse model of pharmacoresistant epilepsy. In conclusion, GBR successfully identifies compounds with antiepileptic efficacy in animal models and, hence, it is an appealing methodology for the discovery of potential AEDs.

An integrative in silico system for predicting dysregulated genes in the human epileptic focus: Application to SLC transporters.

OBJECTIVE: Many different gene families are currently being investigated for their potential role in epilepsy and in the response to antiepileptic drugs. A common research challenge is identifying the members of a gene family that are most significantly dysregulated within the human epileptic focus, before taking them forward for resource-intensive functional studies. Published data about transcriptomic changes within the human epileptic focus remains incomplete. A need exists for an accurate in silico system for the prediction of dysregulated genes within the epileptic focus. We present such a bioinformatic system. We demonstrate the validity of our approach by applying it to the solute carrier (SLC) gene family. There are >400 known SLCs. SLCs have never been systematically studied in epilepsy. METHODS: Using our in silico system, we predicted the SLCs likely to be dysregulated in the epileptic focus. We validated our in silico predictions by identifying ex vivo the SLCs dysregulated in epileptic foci, and determining the overlap between our in silico and ex vivo results. For the ex vivo analysis, we used a custom oligonucleotide microarray containing exon probes for all known SLCs to analyze 24 hippocampal samples obtained from surgery for pharmacoresistant mesial temporal lobe epilepsy and 24 hippocampal samples from normal postmortem controls. RESULTS: There was a highly significant (p < 9.99 × 10(-7) ) overlap between the genes identified by our in silico and ex vivo strategies. The SLCs identified were either metal ion exchangers or neurotransmitter transporters, which are likely to play a part in epilepsy by influencing neuronal excitability. SIGNIFICANCE: The identified SLCs are most likely to mediate pharmacoresistance in epilepsy by enhancing the intrinsic severity of epilepsy, but further functional work will be needed to fully evaluate their role. Our successful in silico strategy can be adapted in order to prioritize genes relevant to epilepsy from other gene families.

Identifying the biological pathways underlying human focal epilepsy: from complexity to coherence to centrality.

Numerous diverse biological pathways are dysregulated in the epileptic focus. Which of these pathways are most critical in producing the biological abnormalities that lead to epilepsy? Answering this question is key to identifying the primary causes of epilepsy and for discovering new therapeutic strategies with greater efficacy than currently available antiepileptics (AEDs). We have performed the largest genome-wide transcriptomic analysis to date comparing epileptic with normal human hippocampi. We have identified 118 differentially expressed and, for the first time, differentially connected pathways in the epileptic focus. Using network mapping techniques, we have shown that these dysregulated pathways, though seemingly disparate, form a coherent interconnected central network. Using closeness centrality analysis, we have identified that the most influential hub pathways in this network are signalling through G protein-coupled receptors, in particular opioid receptors, and their downstream effectors PKA/CREB and DAG/IP3. Next, we have objectively demonstrated that genetic association of gene sets in independent genome-wide association studies (GWASs) can be used to identify causally relevant gene sets: we show that proven causal epilepsy genes, which cause familial Mendelian epilepsy syndromes, are associated in published sporadic epilepsy GWAS results. Using the same technique, we have shown that central pathways identified (opioid receptor and PKA/CREB and DAG/IP3 signalling pathways) are genetically associated with focal epilepsy and, hence, likely causal. Published functional studies in animal models provide evidence of a role for these pathways in epilepsy. Our work shows that these pathways play a central role in human focal epilepsy and that they are important currently unexploited antiepileptic drug targets.

Determination of age specific ¹³¹I S-factor values for thyroid using anthropomorphic phantom in Geant4 simulations.

Using anthropomorphic phantom in Geant4, determination of ß- and γ-absorbed fractions and energy absorbed per event due to (131)I activity in thyroid of individuals of various age groups and geometrical models, have been carried out. In the case of (131)I ß-particles, the values of the absorbed fraction increased from 0.88 to 0.97 with fetus age. The maximum difference in absorbed energy per decay for soft tissue and water is 7.2% for γ-rays and 0.4% for ß-particles. The new mathematical MIRD embedded in Geant4 (MEG) and two-lobe ellipsoidal models developed in this work have 4.3% and 2.9% lower value of S-factor as compared with the ORNL data.

Kinetic study of fission product activity released inside containment under loss of coolant transients in a typical MTR system.

Based on continuous release of fission product (FP) activity from fuel to the coolant and then to the containment, a kinetic model is developed for source term after a LOCA in a typical MTR type system. The time dependent source, re-suspension rate, decay of fission products, leakage, deposition on surfaces, and re-circulation of air through filters are employed with a partial prompt source plus a time varying source. Releases of different FP activities are simulated for various release rates.

Exploring the genomic basis of pharmacoresistance in epilepsy: an integrative analysis of large-scale gene expression profiling studies on brain tissue from epilepsy surgery.

Some patients with pharmacoresistant epilepsy undergo therapeutic resection of the epileptic focus. At least 12 large-scale microarray studies on brain tissue from epilepsy surgery have been published over the last 10 years, but they have failed to make a significant impact upon our understanding of pharmacoresistance, because (1) doubts have been raised about their reproducibility, (2) only a small number of the gene expression changes found in each microarray study have been independently validated and (3) the results of different studies have not been integrated to give a coherent picture of the genetic changes involved in epilepsy pharmacoresistance. To overcome these limitations, we (1) assessed the reproducibility of the microarray studies by calculating the overlap between lists of differentially regulated genes from pairs of microarray studies and determining if this was greater than would be expected by chance alone, (2) used an inter-study cross-validation technique to simultaneously verify the expression changes of large numbers of genes and (3) used the combined results of the different microarray studies to perform an integrative analysis based on enriched gene ontology terms, networks and pathways. Using this approach, we respectively (1) demonstrate that there are statistically significant overlaps between the gene expression changes in different publications, (2) verify the differential expression of 233 genes and (3) identify the biological processes, networks and genes likely to be most important in the development of pharmacoresistant epilepsy. Our analysis provides novel biologically plausible candidate genes and pathways which warrant further investigation to assess their causal relevance.

Metabolic acidosis with topiramate and zonisamide: an assessment of its severity and predictors.

OBJECTIVE: Carbonic anhydrase (CA) inhibitors topiramate and zonisamide can induce metabolic acidosis in some patients. Our aims were to assess the prevalence and severity of this acidosis and to determine its predictors. METHODS: For 70 patients established on treatment with topiramate (n=55) or zonisamide (n=14) or both (n=1), we measured electrolytes, and genotyped single nucleotide polymorphisms (SNPs) in the main renal CA isoenzymes (II, IV and XII). RESULTS: Twenty-six percent of patients had a metabolic acidosis (serum bicarbonate <20 mmol/l). The mean serum bicarbonate of patients taking topiramate was significantly lower than those taking zonisamide (P=0.002). We found no association between serum bicarbonate and the dose of drug or the duration of treatment. Serum bicarbonate levels were associated with the CA type XII SNPs rs2306719 (P=0.006 by one-way analysis of variance) and rs4984241 (P=0.015), but this association was not strong enough to survive correction for multiple testing. CONCLUSION: The development of acidosis with topiramate and zonisamide is not determined by drug dose or by treatment duration, but may be influenced by polymorphisms in the gene for CA type XII. The aforementioned SNPs lie 9.8 kb apart in intron 1 of the CA type XII gene, and deserve further study in a larger cohort of patients.