Grant: Team Grant: ERA-Net PerMed

Epilepsy is a common disease affecting >70 million people worldwide. Patients with epilepsy suffer from recurrent seizures that lead to injuries, death, social stigma as well as job and driving restrictions. Epilepsy is treated with anti-seizure drugs with the aim to stop the seizures. Although many anti-seizure drugs are available, we are currently unable to predict which drug works best in a particular patient. Therefore, we must select the drugs based on trial and error. Unfortunately, though, using this approach typically means that less than one in two patients become seizure free with the first drug, which then leads to a cycle of selecting another drug, testing its anti-seizure effects for several weeks or months before another drug can be tried. It would revolutionize epilepsy treatment if we were able to predict the anti-seizure drug with the highest likelihood of success in an individual patient (personalized medicine). We aim to achieve this by combining two strategies. The first strategy examines human brain cells which are generated from skin cells of adult donors. These cells are treated with four different anti-seizure drugs (carbamazepine, valproate, lacosamide, levetiracetam) and we will investigate how specific genes respond to being exposed to these drugs. We will then look at available genetic data from 1382 patients and examine if changes in these genes correlate with a drugs's ability to stop seizures. The second approach will perform a "big data analysis" incorporating details on the patient's epilepsy, genetic data, brain wave signals and brain imaging to predict treatment effect. Using these large sources of complementary data, we will develop tools to help doctors and patients predict the effect of the different anti-seizure drugs. Subsequently, we will test in an independent cohort of 100 patients if the tool correctly predicts the best anti-seizure drug. Finally, we will prepare a clinical study to establish the tool in clinical practice.