Boosting personalized medicine with organoids and artificial intelligence

Research

The response to treatments in epileptic encephalopathies is often very poor or even deleterious. This project proposes the use of brain organoids derived from patients’ induced pluripotent stem cells (iPSC) to evaluate the effect of multiple treatments without risks for the patient. 

Aim

  • To bring to market a recently developed deep learning technology to predict the effect of epilepsy treatments based on image analysis of the patient’s organoids.

Problem to Solve

More than 50 million people worldwide suffer from epilepsy. Almost 30% of these patients have genetic epileptic encephalopathies that are pharmacoresistant. This is associated with reduced quality of life, psychological issues, dependence on others, and social isolation.  Additionally, patients are often exposed to multiple treatments, which tend to deteriorate their cognition.

Innovation

Sandra Acosta and team propose to test multiple drug treatments alone and in combination using in vitro brain organoids, to help the neurologists choose the most suitable treatment for each patient.

Their methodology was developed by combining image-based organoid analysis and a newly generated deep learning algorithm. To date, they have achieved a proof of concept of the algorithm predicting the wellness of organoids, which is a basic step to evaluate potential toxic effects of the treatments and the disease. 

Team

Senior Scientist

Sandra Acosta

Project leader

Research Technologist

Isabel Turpín

Group Leader

Oscar Lao

Validate-20

Scientific Area

Neurosciences

Business area

Diagnostic

Research center

Universitat Pompeu Fabra