Reference : Identification of antiepilepitic drug-target interactions in public databases
Dissertations and theses : Bachelor/master dissertation
Life sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/50246
Identification of antiepilepitic drug-target interactions in public databases
English
Zizovic, Milena[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core >]
16-Nov-2020
University of Luxembourg, Luxembourg
Master in Integrated Systems Biology
67+3
Krause, Roland
de Lange, Nikola Maria
Schymanski, Emma
Ginolhac, Aurélien
[en] Epilepsy ; Antiepileptic drugs ; drug-target
[en] Epilepsy is affecting people of all age and gender. The disease is traditionally treated with application of antiepileptic drugs. The therapy choice mostly relies on the differential diagnosis which is not always easy to be deducted. The treatment guidelines and antiepileptics are diverged according to major epilepsy types – generalized and focal epilepsy. However, prospective studies of antiepileptic drug effectiveness on the European cohort have shown that pharmacoresponse is patient dependent. In this thesis the antiepileptic drug prescription trend in this cohort in generalized and focal epilepsy patients was investigated. Moreover, the use of antiepileptics in the clinics from the EpiPGX database was compared to the findings of their use in general practices in the UK. To explain the difference in patient response to therapy AED-target interactions were investigated on the level of databases. In addition, with the discovery of new genes implicated in epilepsy and success of drugs of other groups such as quinidine and fampridine in treating the symptoms, the drug-repurposing found its application in epilepsy. In this thesis, quinidine-KCNT1 and fampridine-KCNA2 interactions were investigated in order to estimate the feasibility of using public databases to select drug-target interactions for clinical application. The investigation relied mainly on the ChEMBL database. However, these genes were not found among antiepileptic drug targets in the database. Quinidine and fampridine were assay associated with other AED targets. The results suggest that the therapy choice for treatment of rare forms of epilepsy underlined by channelopathies could be significantly expanded, but that database approach requires high level of drug-target selection criteria and text mining.
[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core >]
