CBD Effective Regardless of Infantile Spasm History, Fenfluramine Effective in CDD, Wristband Sensor Data May Predict Seizures

This week Neurology News Network covered the GWPCARE6 trial of cannabidiol in patients with and without a history of infantile spasms, evaluations of fenfluramine in patients with CDKL5 Deficiency Disorder, and the use of mulit-modal wristband sensor data to forecast seizures.

Welcome to this special edition of Neurology News Network. I’m Marco Meglio. Please excuse our appearance this week as a majority of the US workforce, including the NeurologyLive team, moves to working remote as we come together to help reduce the spread of the novel coronavirus. This week, NeurologyLive focused on presentations from the American Epilepsy Society 2020 Annual Meeting.

Post-hoc analysis from the phase 3 GWPCARE6 trial of cannabidiol suggest that the treatment is consistent in reducing seizures in patients with tuberous sclerosis complex (TSC) with and without a history of infantile spasms. In patients with a history of IS, percent reduction in seizure count from baseline was 45% for those on CBD 25 mg/kg/day (CBD25), 43% in the CBD 50 mg/kg/day (CBD50) group, and 23% for placebo compared to 54%, 55%, and 32% in the respective dose groups for those without IS history. The placebo-adjusted reduction was 29% for CBD25 and 25% for CBD50 in those with IS history, whereas, for patients without IS history, the placebo-adjusted reduction was 32% for CBD25 and 34% for CBD50, respectively. The treatment effect was comparable on effect modification analysis between patients with and without IS history. CBD was first approved by the FDA in June 2018 for the treatment of seizures associated with Lennox-Gastaut syndrome and Dravet syndrome and was later expanded to include an indication for patients with TSC.

Preliminary results from a study evaluating fenfluramine (Fintepla; Zogenix) in patients with CDKL5 Deficiency Disorder (CDD) revealed the treatment is effective in controlling tonic-clonic seizures and has promise as an antiseizure medication for this patient population.The findings for the study were presented virtually at the American Epilepsy Society (AES) Annual Meeting, by Orrin Devinsky, MD, director, NYU Langone Comprehensive Epilepsy Center, professor of neurology, neurosurgery, and psychiatry, NYU Langone School of Medicine. Devinsky and colleagues found that fenfluramine treatment was associated with a median 90% reduction (range, 86–100) in 5 patients with tonic-clonic seizures. Additionally, there was a 50% to 60% reduction in the frequency of seizures in 2 patients with tonic seizures. Devinksy and colleagues noted that randomized controlled trials are needed to further understand the clinical use of fenfluramine in this patient population.

Multi-modal wristband sensor data from easy-to-use, non-invasive devices in combination with deep learning may provide statistically significant and clinically useful seizure forecasting, according to a study presented at the American Epilepsy Society (AES) Annual Meeting. Lead author Christian Meisel, and colleagues applied deep learning networks such as long short-term memory (LSTM) and 1DConv on multi-modal wristband sensor data from 69 persons with epilepsy (PWE) to assess its capability to forecast seizures. Using evaluations based on sensitivity, time in warning, and improvement over chance (IoC), results showed that the seizure forecasting was significantly better than chance for 43.5% of patients, yielding a mean IoC of 28.5 and a mean sensitivity of 75.6. Researchers also noted that the mean prediction horizon was 1896 seconds, a period that may be long enough to afford reasonable warning of seizures in advance.

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