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Metabolomic analysis from plasma of patients with mesial temporal lobe epilepsy: searching for drug resistant biomarkers

Grant number: 19/00213-0
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): June 01, 2019
Effective date (End): May 31, 2020
Field of knowledge:Health Sciences - Medicine
Principal Investigator:Iscia Teresinha Lopes Cendes
Grantee:Alexandre Barcia de Godoi
Home Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology, AP.CEPID


Epilepsies are defined as a set of chronic disorders characterized by changes in the pattern of electrical discharges generated by the brain. Among the most diverse types of epilepsies, Mesial Temporal Lobe Epilepsy (MTLE) stands out due to its prevalence and the high presence of resistance to pharmacological treatments. Because of the difficulty in predicting which patients will be resistant to drug therapy, alternative treatments to reduce seizures, such as surgeries, may take many years to be indicated. Therefore, the search for new biomarkers of pharmacological resistance is necessary. The metabolomic approach allows us to access the metabolic products of the phenotypic state and to analyze the drug metabolism profile. It also has high sensitivity and specificity in the detection of these analytes. The aim of this project is to analyze the plasma metabolic profile of controls (individuals without a background of epilepsy) and patients with MTLE, divided into two treatment response groups: responsive and resistant, in order to identify biomarkers that may aid in the prediction of pharmacoresistance. The metabolome of patients will be analyzed using the Nuclear Magnetic Resonance Spectroscopy of H¹ (NMR-H) technique, which allows the analysis of complex samples without the need for chromatographic techniques and allows a simpler quantitative analysis of metabolite concentrations. The profiles obtained from both groups of patients will be compared using MestreNova, Matlab, Metaboanalyst and HMDB databases to identify metabolites that may help in the prediction of drug refractoriness in patients with MTLE.