| Literature DB >> 31366017 |
Giuseppina Daniela Naimo1, Maria Guarnaccia1, Teresa Sprovieri1, Carmine Ungaro1, Francesca Luisa Conforti2, Sebastiano Andò2,3, Sebastiano Cavallaro4.
Abstract
Epilepsy refers to a common chronic neurological disorder that affects all age groups. Unfortunately, antiepileptic drugs are ineffective in about one-third of patients. The complex interindividual variability influences the response to drug treatment rendering the therapeutic failure one of the most relevant problems in clinical practice also for increased hospitalizations and healthcare costs. Recent advances in the genetics and neurobiology of epilepsies are laying the groundwork for a new personalized medicine, focused on the reversal or avoidance of the pathophysiological effects of specific gene mutations. This could lead to a significant improvement in the efficacy and safety of treatments for epilepsy, targeting the biological mechanisms responsible for epilepsy in each individual. In this review article, we focus on the mechanism of the epilepsy pharmacoresistance and highlight the use of a systems biology approach for personalized medicine in refractory epilepsy.Entities:
Keywords: GABAA receptor; drug transporters; epilepsy; functional genomics; pharmacogenomics; pharmacoresistance
Year: 2019 PMID: 31366017 PMCID: PMC6695675 DOI: 10.3390/ijms20153717
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Figure 1Schematic representation of the main pharmacokinetic (a) and pharmacodynamic processes (b) of antiepileptic drugs involved in refractory epilepsy. Mutations in genes encoding molecular targets could cause structural and/or functional alterations responsible for the lack of response to pharmacological treatment.
Figure 2Illustrative representation of pharmacogenomics temporal evolution: from monogenic to complex genetic research analysis. (A) Genetic screening tests reveal variants correlated to the etiology of drug resistance in epilepsy. (B) Application of genetic technologies (NGS and CGH) integrated together with computational strategies allows the identification of casual disease networks and stratification of patients towards the personalized medicine.