Enabling phenotypic high-throughput drug screening with patient-derived organoids

Patient-derived organoids (PDOs) can recapitulate patient tumors in the lab, making them more realistic models compared to traditional in vitro cell cultures. However, challenges in reproducibility, standardization, and analysis have limited the use of PDOs in drug discovery, especially for high-throughput screening (HTS) applications. To address this, we have developed an efficient workflow, using automation equipment and interoperable computational platforms to facilitate the adoption of PDOs for HTS applications 1 . Our protocol leverages live-cell imaging techniques to capture the dynamic, complex PDO-drug interactions for more comprehensive analysis. Using label-free detection of organoids from brightfield images, we integrated growth-rate-based drug response metrics 2 with drug synergy metrics to significantly improve the identification of synergistic drug interactions 3 . Here, we screened 10 distinct drug combinations on 10 PDOs, sourced from healthy lung, non-small cell lung cancer, and pancreatic ductal adenocarcinoma. In particular, we focused on repurposing the Thioredoxin reductase inhibitor, Auranofin. This screen required a total of 20 384-well plates and resulted in 37,000 images captures. With lab automation, organoid seeding only took 1 hour, and with analysis automation, all 37,000 images were analyzed in under 8 hours. Altogether, we identified drug candidates that can synergistically enhanced the efficacy of Auranofin in a tumor selective manner. Our study highlights the advantage of combining lab automation with computational automation to enable HTS with PDOs. The implementation of our method supports the current push by the FDA to reduce animal experimentation for the discovery of effective therapeutic strategies. We are now further investigating streamlined integration techniques between different lab automation systems. 1 Le Compte, M., et al JoVE 190 (2022) 2 Deben, C., et al Communications Biology 1612 (2024) 3 Deben, C., et al Journal of Experimental & Clinical Cancer Research 88(2024)