AUTHOR=Truong Nina , Zahra Abir , Lintao Ryan C. V. , Chauhan Rahul , Bento Giovana Fernanda , Vidal Jr. Manuel , Kim Sungjin , Lam Po Yi , Conrads Thomas , Conrads Kelly , Han Arum , Menon Ramkumar , Richardson Lauren S. 

TITLE=Modeling reproductive and pregnancy-associated tissues using organ-on-chip platforms: challenges, limitations, and the high throughput data frontier

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1568389

DOI=10.3389/fbioe.2025.1568389

ISSN=2296-4185

ABSTRACT=Over the past decade, organ-on-chip technology (microphysiological systems or tissue chips) has reshaped in-vitro physiological and pathological modeling and pharmaceutical drug assessment. FDA Modernization Act 2.0 allows for alternatives to animal testing or the use of appropriate non-animal models/new approach methods (NAMs), such as Organ-on-chips (OC) platforms or in silico simulation models, to generate pre-clinical in-vitro drug trial data for regulatory purposes primes the microfluidic field to have exponential growth in the coming years. The changes in the approaches of regulatory agencies could significantly impact the development of therapeutics for use during pregnancy. However, limitations of the devices and molecular and biochemical assay shortfalls hinder the progress of the OOC field. This review describes available reproductive and pregnancy-related OOC platforms, and the current methodologies utilized to generate endpoint datasets (e.g., microscopic imaging, immunocytochemistry, real-time polymerase chain reaction, cytokine multiplex analysis). Microfluidic platform limitations, such as fewer number of cells or low supernatant volumes and restrictions regarding fabrication materials, are described. Novel approaches (e.g., spatial transcriptomics, imaging cytometry by time of flight, exosomes analysis using Exoview) to overcome these challenges are described. OOC platforms are primed to provide biologically relevant and clinically translational data that can revolutionize in-vitro physiological modeling, drug discovery, and toxicologic risk assessment. However, engineering adaptations to increase the throughput of devices (i.e., device arrays) and biological advancements to improve data throughput are both needed for these platforms to reach their full potential.