Epilepsy is a common neurological disorder characterised by recurrent epileptic seizures. Treatment generally proceeds by trial and error, and it can take years to achieve effective seizure control. The pathophysiology of epilepsy is still poorly understood, and its clinical variety is considerable. This makes it difficult to predict how a patient will respond to medication, creating significant challenges in prognosis and treatment planning. The advent of human induced pluripotent stem cells (iPSCs) has made it possible to study, in vitro and in a patient-specific manner, the phenotype, molecular abnormalities and response to medication in epilepsy. However, little is yet known about how to translate these in vitro findings to humans; consequently, there is a wide gap between clinical practice and research in the field of epilepsy.

The main objective of this project is to bridge this gap by improving the prediction of personalised treatments for epilepsy through the translation of findings from in vitro models to patients. To achieve this, the project is developing a multi-level strategy, termed ‘from the neuron, to the network, to the brain’, in which clinical characteristics, neural network phenotypes and single-cell-level abnormalities can be studied for each individual patient.

The project characterises the phenotype and response to medication at these different levels of complexity and utilises approaches based on the concept of the digital twin to link information across the various levels, elucidating the cellular mechanisms underpinning the dynamics of neural networks and brain activity. For each patient, the project identifies biomarkers useful for predicting treatment response, defining the cellular mechanisms that show correlations across the different levels of analysis. This approach enables us to understand the extent to which results obtained in vitro are truly relevant to patients. Ultimately, it enables the study of individual phenotypes and the estimation of treatment efficacy in vitro, ensuring a reliable translation of results to the patient. This bioengineering tool helps to reduce the burden of drug screening on patients and significantly improve the efficacy of treatments.