ArticlePloS one2017
A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 29 citations in OpenAlex.
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy.Epilepsy currents · 2024Review
- Multidrug Resistance-1 C3435T Polymorphism and Carbamazepine Plasma Level in Indonesian Temporal Lobe Epilepsy Patients.Current drug safety · 2023Article
- Gene expression profile suggests different mechanisms underlying sporadic and familial mesial temporal lobe epilepsy.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- [Association between single nucleotide genetic polymorphisms in ABC transporter genes with drug-resistant epilepsy in the Spanish population].Revista de neurologia · 2022Article
- Multi-omic strategies applied to the study of pharmacoresistance in mesial temporal lobe epilepsy.Epilepsia open · 2022Review
- Hippocampal Expression of Cytochrome P450 1B1 in Penetrating Traumatic Brain Injury.International journal of molecular sciences · 2022Article
- Factors affecting interictal unilateral and bilateral discharges and ictal diffusion patterns of scalp electroencephalogram in temporal lobe epilepsy.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022Article
- Towards realizing the vision of precision medicine: AI based prediction of clinical drug response.Brain : a journal of neurology · 2021Article
- Racial Disparity in Drug Disposition in the Digestive Tract.International journal of molecular sciences · 2021Review
- Multidimensional Approach Assessing the Role of Interleukin 1 Beta in Mesial Temporal Lobe Epilepsy.Frontiers in neurology · 2021Article
- CpG methylation signature defines human temporal lobe epilepsy and predicts drug-resistant.CNS neuroscience & therapeutics · 2020Article
- Long-term follow-up of a large cohort with focal epilepsy of unknown cause: deciphering their clinical and prognostic characteristics.Journal of neurology · 2020Article
- Vitamin Pharmacogenomics: New Insight into Individual Differences in Diseases and Drug Responses.Genomics, proteomics & bioinformatics · 2017Review
- Gene Variants Associated With Venous Thrombosis: A Replication Study in a Brazilian Multicentre Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mesial temporal lobe epilepsy is the most common form of adult epilepsy in surgical series. Currently, the only characteristic used to predict poor response to clinical treatment in this syndrome is the presence of hippocampal sclerosis. Single nucleotide polymorphisms (SNPs) located in genes encoding drug transporter and metabolism proteins could influence response to therapy. Therefore, we aimed to evaluate whether combining information from clinical variables as well as SNPs in candidate genes could improve the accuracy of predicting response to drug therapy in patients with mesial temporal lobe epilepsy. For this, we divided 237 patients into two groups: 75 responsive and 162 refractory to antiepileptic drug therapy. We genotyped 119 SNPs in ABCB1, ABCC2, CYP1A1, CYP1A2, CYP1B1, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP3A5 genes. We used 98 additional SNPs to evaluate population stratification. We assessed a first scenario using only clinical variables and a second one including SNP information. The random forests algorithm combined with leave-one-out cross-validation was used to identify the best predictive model in each scenario and compared their accuracies using the area under the curve statistic. Additionally, we built a variable importance plot to present the set of most relevant predictors on the best model. The selected best model included the presence of hippocampal sclerosis and 56 SNPs. Furthermore, including SNPs in the model improved accuracy from 0.4568 to 0.8177. Our findings suggest that adding genetic information provided by SNPs, located on drug transport and metabolism genes, can improve the accuracy for predicting which patients with mesial temporal lobe epilepsy are likely to be refractory to drug treatment, making it possible to identify patients who may benefit from epilepsy surgery sooner.
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