Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and...

Epilepsy is a common neurological disorder, with approximately one-third of the affected population developing drug-resistant epilepsy despite the exponentially increasing number of antiseizure medications (ASMs) that are available. Current ASM selection remains largely empirical, lacking reliable biomarkers to predict treatment response. Pharmacogenomics is an important factor underlying drug response. The development of models designed to predict drug response in epilepsy that include genomic information is at an early stage. Advances in artificial intelligence (AI) and deep learning (DL) offer new opportunities for multimodality modelling and, in particular, models that can reason over both a patient's genomic risk factors and their clinical features (e.g. epilepsy type, magnetic resonance imaging [MRI]/electroencephalography [EEG] findings). More broadly, new technologies are driving a paradigm shift towards agentic AI, whereby decision-support agents will coordinate medical information and propose actions. These advances have implications for individualised ASM prescribing in epilepsy. For example, rather than modelling thousands of genetic variants, a DL model can operate with compressed representations of data obtained from AI foundation models that have been trained on huge atlases to learn representations of general biology, enhancing capacity for small epilepsy cohorts. In this review, we summarise how emerging technologies are being harnessed and highlight strategies for building capacity. We specifically focus on clinical, pharmacological, and genomic data, discussing the ways in which drug information can be formatted to take advantage of DL model capabilities. We describe AI foundation models and provide examples of how they can be utilised in epilepsy research. We conclude with a summary of ongoing challenges, including model evaluation, data availability, ethical considerations, and barriers to clinical translation.