A translational multimodal machine-learning prototype predicting valproate response in epilepsy treatment.

ABSTRACT: Epilepsy affects around 1% of the global population and often requires long-term treatment with antiseizure medications (ASMs). However, the current treatment strategy is based on clinical acumen and trial and error, resulting in only about 50% of patients remaining seizure-free for at least 12 months with first-line ASMs. Valproic acid (VPA) is a commonly prescribed first-line ASM, yet <50% of patients experience inadequate seizure control (ISC) or unacceptable adverse reactions (UARs), necessitating discontinuation. We developed a predictive algorithm to support VPA treatment decisions in eligible epilepsy patients integrating in-vitro data, genetic data and previous knowledge. Our approach is based on genetic variations in genes associated with VPA pharmacodynamics and -kinetics, as wel

SUBMITTER: Platte S 

PROVIDER: S-EPMC12407597 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature