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◆ SLAS TECHNOLOGY2026-05-14· Automation

End-to-end automation of organ-on-a-chip culture and drug testing: Enhancing scalability and expandability

Mayu Shibuta, Kazuhiro Tetsuka, Tetsuhiro Kawabe, Ichiji Namatame, H. Asano, Jeroen Heijmans, Nienke R. Wevers, Haruna Sasaki-Iwaoka

一句话结论

2 supports scalability, expandability, and automation of complex workflows that were previously difficult to implement.

原始摘要(原文)
The increasing demand for effective and translatable culture models in drug discovery has driven the development of advanced systems, including organ-on-a-chip (OoC), microphysiological systems (MPS), and complex in vitro models (CIVM). These technologies are recognized for their ability to model human physiological responses, particularly with the Food and Drug Administration (FDA) Modernization Act 2.0 promoting cell-based assays and computer simulations as new approach methodologies (NAMs) to reduce reliance on animal testing. However, the adoption of MPS and OoC systems in drug discovery remains limited by complex culture protocols, low throughput, and difficulties in achieving reproducible results. In this study, we demonstrate that our automated platform, "Screening Station No. 2," addresses these challenges by fully automating the end-to-end process-from cell culture to drug testing-using an angiogenesis model. The system integrates the SCALE12-MR rocking incubator with numerous instruments and dynamic scheduling to enable seamless execution of key operations, including media exchange, compound dosing, and imaging. By minimizing human intervention, it automates OoC cultivation, including gravitational stimulation via rocking culture. Screening Station No. 2 supports scalability, expandability, and automation of complex workflows that were previously difficult to implement. This study highlights the importance of versatile, scalable automated systems capable of adapting to diverse experimental conditions to enhance the efficiency and reliability of drug discovery. The advances demonstrate the potential of automated OoC systems to accelerate drug development, improve preclinical model translatability, and address the demand for innovative laboratory automation methodologies.
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