Publication | Open Access
Automated microfluidic platform for dynamic and combinatorial drug screening of tumor organoids
383
Citations
24
References
2020
Year
Three-dimensional organoid cultures are physiologically relevant models, yet automated microfluidics has not yet been adapted for high‑throughput organoid analysis. The study introduces an automated, high‑throughput microfluidic system for 3D organoid culture and analysis to support preclinical research and personalized therapy. The platform delivers combinatorial and dynamic drug regimens to hundreds of organoids and allows real‑time analysis, validated through individual, combinatorial, and sequential screens on human pancreatic tumor organoids. Patient‑derived organoids showed variable drug responses, with temporally‑modified regimens outperforming constant‑dose monotherapy or combination therapy, demonstrating the platform’s potential to guide personalized treatment decisions.
Abstract Three-dimensional (3D) cell culture technologies, such as organoids, are physiologically relevant models for basic and clinical applications. Automated microfluidics offers advantages in high-throughput and precision analysis of cells but is not yet compatible with organoids. Here, we present an automated, high-throughput, microfluidic 3D organoid culture and analysis system to facilitate preclinical research and personalized therapies. Our system provides combinatorial and dynamic drug treatments to hundreds of cultures and enables real-time analysis of organoids. We validate our system by performing individual, combinatorial, and sequential drug screens on human-derived pancreatic tumor organoids. We observe significant differences in the response of individual patient-based organoids to drug treatments and find that temporally-modified drug treatments can be more effective than constant-dose monotherapy or combination therapy in vitro. This integrated platform advances organoids models to screen and mirror real patient treatment courses with potential to facilitate treatment decisions for personalized therapy.
| Year | Citations | |
|---|---|---|
Page 1
Page 1