Taweel, Basel 
ORCID: 0000-0002-6157-2438
(2021)
Predicting drugs for epilepsy using genetic and genomic data.
Master of Philosophy thesis, University of Liverpool.
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Abstract
65 million people have epilepsy. Current antiepileptic drugs produce adverse effects in 88% of users and fail to prevent seizures in 30% of people with epilepsy. New drugs for epilepsy are therefore required. Traditional drug development methods are arduous and expensive, taking on average 10-15 years and $2.6 billion per drug. It is estimated that over 90% of drugs have a viable second indication and thus may be used for other purposes, making drug repurposing an attractive alternative. This thesis aims to create drug repurposing resources for epilepsy and generate drug predictions for both monogenic and polygenic epilepsies. We create and present the Seizure Associated Genes Across Species (SAGAS) database, the largest and most comprehensive existing database of epilepsy genes, containing over 9700 pieces of published evidence for the involvement of 3879 genes in the generation and potentiation of seizures across 6 species. We use genetic data from the SAGAS, alongside a publicly available network-based method of drug prediction, to generate drug prediction lists for polygenic focal and generalised epilepsies. A monogenic epileptic syndrome is caused by a single mutant gene. However, knowing the identity of the mutant gene underlying a monogenic epileptic syndrome is not sufficient for predicting the effect of antiseizure medications on the syndrome. Dravet syndrome (DS), the archetypal monogenic epileptic encephalopathy, is typically caused by mutations in SCN1A. Some antiseizure medications that alleviate seizures in Dravet syndrome do not affect SCN1A, whilst some antiseizure medications that affect SCN1A aggravate seizures in Dravet syndrome. We are not aware of any genomics-based methods that can correctly predict the varying effects of different antiseizure medications on Dravet syndrome (or any other monogenic epileptic syndrome). We create a novel method to predict drugs for Dravet syndrome that takes into account not only the gene that causes Dravet syndrome but also other genes that can influence the expression of its phenotype and show that our predictions correctly identify the antiseizure drugs that are effective, aggravating and equivocal for Dravet syndrome.
| Item Type: | Thesis (Master of Philosophy) |
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| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 14 Oct 2022 08:13 |
| Last Modified: | 01 Aug 2023 01:30 |
| DOI: | 10.17638/03151366 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3151366 |
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