Sensor-Integrated Organ-on-a-Chip Platforms: Advances in Drug Evaluation and Disease Modeling
Organ-on-a-chip (OoC) platforms have made remarkable progress in simulating the physiological complexity of human tissues, and provide advanced in vitro models for drug development. These platforms offer superior physiological relevance over conventional cell culture and animal models and thus enhance preclinical drug testing. However, a key challenge is how to align the dynamics of these systems with analytical methods. The wide use of static endpoint assays can restrict the depth of obtainable mechanistic data, and often capture correlative results rather than the basic kinetic processes of a drug effect. This review probes into the premise that functional combination of sensors for real-time non-terminal detection is a critical trend for strengthening the analytical capability of OoC technology. Herein, this perspective is illustrated by systematically examining recent applications in drug toxicity evaluation and disease modeling. The discussion highlights how the focus of sensor-integrated platforms is shifting from physiological endpoint replication to dynamic mechanistic inquiry. These systems enable the continuous detection of key cellular and environmental indices, and thus provide high-resolution temporal data and a more detailed view of pharmacological responses. Finally, the current technological landscape, remaining challenges, and future outlook are summarized, positing that a further integration of sensing technologies is the key to exposing the full potential of OoCs for mechanistic and predictive drug assessment.
Authors
- Feiyang Li
- Lulu Zheng (ORCID: https://orcid.org/0009-0001-3049-5046)
- Ying Li (ORCID: https://orcid.org/0009-0004-5163-7287)
- Mengya Chen (ORCID: https://orcid.org/0000-0002-2252-6068)
- Yule Zhang (ORCID: https://orcid.org/0000-0002-6625-1780)
- Zhiwei Xue
- Songlin Zhuang (ORCID: https://orcid.org/0000-0003-3072-0634)
- Zhaofeng Huang
- Junfei Li
- Wei Li
- Yaohui Zhang
Institutions
- University of Shanghai for Science and Technology (CN)
- Hong Kong University of Science and Technology (HK)
- University of Glasgow (GB)
Publication Details
- Journal
- ACS Sensors
- Published
- 2026-10-06
- DOI
- https://doi.org/10.1021/acssensors.6c00932
- Primary Topic
- 3D Printing in Biomedical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00