High-throughput production and AI-based screening of organoids for label-free drug development assay

Purpose and goal:

Aim: To develop a system that combines a high-throughput bioreactor and label-free quantitative microscopy for automated, cost-effective production and screening of organoids. Objective: 1-Design a scalable high-throughput bioreactor 2.Integrate label-free imaging for automated screening 3.Use machine learning for healthy selection of organoids

4.Correlate label-free and traditional assays for faster drug screening and development 5.Adjust results with societal benefit by reducing animal testing and improving healthcare Expected results and effects: 1.Improved throughput and consistency in organoid production 2.Automated, accurate screening of organoids, accelerates drug toxicity and development

3.Improved predictability in drug development, contributing to safer and more effective treatments 4.Reduced reliance on animal testing, in line with ethical practices 5.Widened access biological 3D models for academic and industrial stakeholders

6.Knowledge transfer and interdisciplinary collaborations, promoting innovation in regenerative medicine and drug discovery sectors Approach and implementation:

The project comprises five work packages (WPs), each with a specific role. WP1 refines a 3D bioreactor for efficiency. WP2 sets an experimental base.

WP3, led by experts in microscopy and machine learning, focuses on image data and adjustments. WP4 handles IP strategies, and WP5 manages reporting and future plans. Regular virtual meetings and a balanced budget ensure smooth operation.

A multidisciplinary team with academic experts aims for success. Work on initial WPs is sequential, then parallel for fine-tuning. Teams are set at both sites.