From Concept to Context-of-Use: A Framework for Organ-on-Chip Development

• A structured OoC development workflow focused on end-user requirements and standard adoption • Unified and harmonized approach for OoC design and testing to identify and mitigate variability • Engage OoC stakeholders on a unified workflow to facilitate decision-making • Strengthen qualification and validation readiness for academically generated OoC knowledge Organ-on-Chip (OoC) models are advanced microengineered systems designed to recapitulate key structural, functional, and dynamic aspects of human tissues at the microscale. By integrating living cells with microfluidic architectures, controlled biochemical and mechanical cues, and real-time sensing, OoC platforms have the potential to capture human-relevant physiological responses that are poorly predicted by conventional cell cultures and animal models. As such, OoCs are increasingly recognised as promising New Approach Methodologies (NAMs) for drug development, disease modelling, and chemical safety assessment. Despite rapid technological progress, broader adoption of OoC systems as predictive NAMs remains limited. A major challenge is the absence of structured development strategies that address biological complexity, technical variability, and clearly defined Contexts-of-Use (CoU). The CoU specifies what the model is intended to do, under which conditions and for which decision-making purpose, thereby guiding validation, performance criteria, and regulatory acceptance. Many academic OoC platforms are developed as proof-of-concept systems, with little focus given to performance accuracy and data robustness, which limits reproducibility and regulatory confidence in the final device. Here, we propose a structured OoC development workflow, based on an established engineering product development framework, adapted to the specific needs of OoC technologies. The approach allows developers to integrate biological rationale, end-user and regulatory requirements, standards, and qualification criteria from early development stages. The framework organizes OoC development into iterative stages encompassing system design, verification, testing and qualification, linking functional and performance requirements to the intended CoU. Adoption of this framework can enhance reproducibility, reduce data variability, and strengthen qualification and validation readiness for regulatory acceptance, facilitating the translation of academic OoC innovations into reliable and predictive NAMs.