Decoding cellular dynamics and microenvironmental responses in organ-on-chip systems through real-time sensing

Area of Science:

• Biomedical Engineering
• Drug Development
• Systems Biology

Background:

• Organ-on-Chip (OoC) systems offer promise for predictive drug development and disease modeling.
• Current limitations include the inability to monitor cellular microenvironments and tissue behavior in real-time without destructive sampling.
• This lack of real-time sensing hinders mechanistic insights and complicates model validation for pre-clinical decision-making.

Purpose of the Study:

• To review the current state of real-time sensing technologies integrated into OoC systems.
• To examine how various sensing modalities capture critical cellular and microenvironmental dynamics.
• To highlight the enhanced predictive potential of OoCs with integrated sensing for drug development and disease modeling.

Main Methods:

• Overview of electrochemical, optical, affinity-based, and mechanical sensing modalities for OoC applications.
• Analysis of data from representative organ systems (brain, gut, liver, kidney) and commercially available platforms.
• Discussion of disease-modeling applications and the impact of multi-modal sensors and AI/ML-based data analysis.

Main Results:

• Real-time sensing enables continuous, non-invasive monitoring of barrier integrity, metabolic shifts, ion dynamics, biomarker secretion, and contractile force.
• Integrated sensing enhances the predictive potential of OoCs compared to conventional pre-clinical models.
• Innovations in multi-modal sensors and AI/ML are advancing OoCs for high-throughput drug safety and efficacy assessments.

Conclusions:

• Persistent challenges include sensor miniaturization, standardization, reproducibility, signal interference, economic barriers, and regulatory guidelines.
• Addressing these challenges is crucial for the scalable translation of OoCs as routine pre-clinical tools.
• Successful implementation of OoCs can reduce animal use, accelerate therapeutic translation, and improve patient outcomes.