A Pressure Regulator Platform for Applying Biomechanical Stimuli on Organ-on-A-Chip Systems with Physiological and Pathological Relevancy.

PURPOSE: The organ-on-a-chip (OOC) technology has transformed in vitro modeling by replicating human organ microenvironments with high fidelity, offering improved platforms for drug discovery and disease modeling. However, existing biomechanical stretch-compression platforms are often costly, rely on proprietary chip designs, and lack flexibility in generating (patho)physiological waveforms. These limitations hinder the accurate replication of dynamic biomechanical cues experienced by tissues and organs in vivo. This study presents the Pressure Regulator Platform (PRP), a low-cost, chip-agnostic system designed to deliver customizable and patient-specific stretch-compression biomechanical stimuli to OOC devices. METHODS: The PRP integrates hardware, electronics, and software to enable real-time generation of user-defined mechanical waveforms. Users can input patient-derived waveform profiles or select predefined waveforms, modifying frequency and amplitude to match physiological and pathological conditions. The PRP was tested on a blood vessel-on-a-chip model, evaluating its ability to replicate vascular biomechanics by applying controlled strain through vacuum-induced membrane deformation. RESULTS: The PRP successfully reproduced patient-derived waveform profiles with high accuracy. The chip-agnostic design approach allowed seamless integration with multiple OOC configurations. Furthermore, this platform-maintained error levels below 1% for stabilized generic waveforms and achieved controlled vascular biomechanics in the OOC model, facilitating unidirectional alignment of vascular smooth muscle cells. CONCLUSION: The PRP provides a flexible and accessible platform for customizable and patient-derived biomechanical stimulation, enhancing the physiological relevance of in vitro models. Its capability to replicate patient-specific biomechanical conditions paves the way for applications in drug discovery, disease modeling, and personalized medicine.