Epiloid predicts preclinical therapeutic efficacy to identify candidates most likely to succeed in clinical trials. We do this by applying explainable machine learning approaches to our proprietary human organoid models of induced neurological diseases, including Parkinson's and Epilepsy.
What is the problem?
Neurological diseases such as Parkinson's disease and epilepsy lack reliable preclinical models, leading to high failure rates in drug development. Current animal models fail to accurately replicate human brain physiology, resulting in ineffective treatments going to clinical trials.
What is their solution?
Epiloid uses machine learning–driven analysis of human brain organoid models to predict personalized drug efficacy in neurological disease. Our platform integrates multi-modal functional, cellular, and electrophysiological profiles from our diseased organoids to characterize their responses to therapeutic candidates.
