Nasir Mirza

Effect of topiramate on acid–base balance: extent, mechanism and effects

Topiramate is licensed for the treatment of epilepsy and for migraine prophylaxis, but is also used off-licence for a wide range of indications. With the increasing use of topiramate, reports have emerged that topiramate can cause metabolic acidosis in some patients. It does this by impairing both the normal reabsorption of filtered HCO(3)(-) by the proximal renal tubule and the excretion of H(+) by the distal renal tubule. This combination of defects is termed mixed renal tubular acidosis (RTA). The mechanism involves the inhibition of the enzyme carbonic anhydrase, which is consistent with the fact that genetic deficiency of carbonic anhydrase is associated with mixed RTA. Topiramate-induced RTA can make patients acutely ill, and chronically, can lead to nephrolithiasis, osteoporosis and, in children, growth retardation. There is no proven method for predicting or preventing the effect of topiramate on acid-base balance, but patients with a history of renal calculi or known RTA should not receive topiramate. The utility of regular monitoring of HCO(3)(-) levels has not been proven and is not routine practice currently. For patients with persistent RTA, topiramate should usually be discontinued as alternative agents are available.

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.

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.

Personalized medicine approaches in epilepsy

Epilepsy affects 50 million persons worldwide, a third of whom continue to experience debilitating seizures despite optimum anti‐epileptic drug (AED) treatment. Twelve‐month remission from seizures is less likely in female patients, individuals aged 11–36 years and those with neurological insults and shorter time between first seizure and starting treatment. It has been found that the presence of multiple seizures prior to diagnosis is a risk factor for pharmacoresistance and is correlated with epilepsy type as well as intrinsic severity. The key role of neuroinflammation in the pathophysiology of resistant epilepsy is becoming clear. Our work in this area suggests that high‐mobility group box 1 isoforms may be candidate biomarkers for treatment stratification and novel drug targets in epilepsy. Furthermore, transporter polymorphisms contributing to the intrinsic severity of epilepsy are providing robust neurobiological evidence on an emerging theory of drug resistance, which may also provide new insights into disease stratification. Some of the rare genetic epilepsies enable treatment stratification through testing for the causal mutation, for example SCN1A mutations in patients with Dravets syndrome. Up to 50% of patients develop adverse reactions to AEDs which in turn affects tolerability and compliance. Immune‐mediated hypersensitivity reactions to AED therapy, such as toxic epidermal necrolysis, are the most serious adverse reactions and have been associated with polymorphisms in the human leucocyte antigen (HLA) complex. Pharmacogenetic screening for HLA‐B*15:02 in Asian populations can prevent carbamazepine‐induced Stevens–Johnson syndrome. We have identified HLA‐A*31:01 as a potential risk marker for all phenotypes of carbamazepine‐induced hypersensitivity with applicability in European and other populations. In this review, we explore the currently available key stratification approaches to address the therapeutic challenges in epilepsy.

Identifying new antiepileptic drugs through genomics-based drug repurposing

&NA; 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.

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.

Genetic regulation of gene expression in the epileptic human hippocampus

&NA; 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 (Alzheimers 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.

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

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.

New uses for old drugs

Low cost generics are an untapped source of therapeutic innovation

PO188 Hereditary spastic paraplegia, epilepsy and motor neurone disease in a family with nipa1 mutation

The hereditary spastic paraplegias (HSP) are a diverse group of conditions affecting the corticospinal tracts. Rarely HSP has been associated with other neurological diseases. NIPA1 mutations are usually associated with pure HSP and rarely implicated in HSP with epilepsy. There exists one report of a patient with HSP, MND and NIPA1 mutation. We present the first report of a patient with HSP, epilepsy, MND and family history, associated with NIPA1 mutation. Our patient presented in her 30’s with progressive leg stiffness and urinary frequency. Her sister, father and paternal grandfather had a diagnosis of HSP; after normal investigations she was diagnosed with HSP. She had childhood fits but at age 38 blank spells recurred and electroencephalography confirmed idiopathic generalised epilepsy. Two decades after HSP diagnosis she developed bulbar symptoms, breathing difficulty and limb weakness. Electromyography was consistent with MND. Her father had HSP and also developed MND in later life. Genetic testing in the index patient revealed a heterozygous NIPA1 mutation [c.316 G>A, p. (Gly106Arg) in exon 3]. A niece with epilepsy and HSP also has a NIPA1 mutation. This case adds to recent evidence suggesting an association between NIPA1 and MND and highlights the complexity of HSP.