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

## TL;DR

This paper uses a dynamic model to show how timing anti-seizure medications according to biological rhythms can improve treatment effectiveness.

## Contribution

A novel dynamic network model integrating seizure rhythms and ASM pharmacology to optimize medication timing.

## Key findings

- Short half-life ASMs showed up to 20% greater efficacy when administered 6 hours before seizure likelihood peaks.
- Phase dependence was minimal for longer half-life drugs due to flatter concentration profiles.
- The model offers a mechanistic approach to personalize treatment timing in epilepsy care.

## Abstract

Epileptic seizures and interictal discharges exhibit robust circadian and multidien rhythms, yet the interaction between these biological cycles and anti-seizure medication (ASM) pharmacology remains poorly understood. Here, we present a dynamical network model that integrates rhythmic fluctuations in cortical excitability with pharmacokinetic properties of common ASMs to explore how treatment timing influences efficacy. The framework embeds a slow, rhythm-generating process directly within the governing equations, allowing seizure-like dynamics to emerge endogenously. We simulated ASMs with a range of distinct half-lives under single-daily and twice-daily dosing schedules. For the short half-life ASM, efficacy depended strongly on the phase of administration, with doses delivered approximately 6 h before the peak in seizure likelihood achieving up to 20% greater reduction in epileptiform discharges than suboptimal phases. In contrast, phase dependence was minimal for slower half-life drugs due to their slower elimination and flatter concentration profiles. These findings suggest that short half-life ASMs could benefit most from chronotherapeutic timing. Our framework unifies seizure dynamics, biological rhythms, and ASM pharmacology within a single model, offering a mechanistic tool to explore patient-specific optimization of treatment timing. This work establishes a foundation for translating chronotherapy into epilepsy care and provides a conceptual bridge between computational neuroscience and clinical pharmacology.