Optimising anti-seizure medication timing using a dynamic network model of seizure rhythms

Area of Science:

• Computational Neuroscience
• Clinical Pharmacology
• Epilepsy Research

Background:

• Epileptic seizures and discharges display circadian rhythms.
• The interplay between biological rhythms and anti-seizure medication (ASM) pharmacology is not well understood.

Purpose of the Study:

• To develop a dynamical network model integrating cortical excitability rhythms and ASM pharmacokinetics.
• To investigate how the timing of ASM administration affects treatment efficacy.

Main Methods:

• Simulated ASMs with varying half-lives under different dosing schedules.
• Incorporated a slow, rhythm-generating process into governing equations for endogenous seizure dynamics.
• Analyzed the impact of administration phase on seizure reduction.

Main Results:

• Short half-life ASMs demonstrated significant phase-dependent efficacy, with optimal timing yielding up to 20% greater reduction in epileptiform discharges.
• Minimal phase dependence was observed for long half-life drugs due to flatter concentration profiles.
• Efficacy was strongly influenced by administration timing relative to the peak seizure likelihood.

Conclusions:

• Short half-life ASMs may benefit most from chronotherapeutic timing.
• The developed model offers a tool for optimizing patient-specific treatment timing.
• This work bridges computational neuroscience and clinical pharmacology for epilepsy chronotherapy.