Despite progress in the development of anti-seizure drugs, drug-resistant epilepsy (DRE) occurs in a third of patients. DRE is associated with poor quality of life and increased risk of sudden, unexplained death. The autonomic nervous system and chronobiology play a role in DRE. In the present paper, we provide a narrative review the mechanisms that underlie DRE and characterize some of the autonomic- and chronotherapy-associated parameters that contribute to the degree of response to therapy. Variability describes the functions of many biological systems, which are dynamic and continuously change over time. These systems are required for responses to continuing internal and external triggers, in order to maintain homeostasis and normal function. Both intra- and inter-subject variability in biological systems have been described. We present a platform, which comprises a personalized-based machine learning closed loop algorithm built on epilepsy-related signatures, autonomic signals, and chronotherapy, as a means for overcoming DRE, improving the response, and reducing the toxicity of current therapies.
Potruch Assaf, Khoury Salim T, Ilan Yaron
Anti-seizure, Autonomic nervous system, Drug resistant epilepsy, Variability