Toward closed-loop engineering of nanomedicines: integrating AI-enabled design, microfluidic manufacturing, and organ-on-a-chip validation

Nanomedicine development is constrained less by a shortage of candidate materials than by weak coupling among formulation design, reproducible manufacturing, and biologically informative validation. This review examines how artificial intelligence (AI)-enabled design, microfluidic manufacturing, and tiered biological testing can be integrated into a design-manufacture-test-learn (DMTL) framework. We focus on the interfaces linking design descriptors and critical material attributes to critical process parameters, distribution-resolved and longitudinal critical quality attributes (CQAs), biological dose metrics, functional endpoints, and model updates. Particular attention is given to feasibility-constrained inverse design; nanoparticle heterogeneity at the single-particle and subpopulation levels; time-dependent channel fouling and process drift; cross-platform dose normalization; storage stability and cold-chain resilience; and the fit-for-purpose use of organ-on-a-chip systems. Quantitative benchmarking of representative primary studies indicates that current evidence is strongest at the component and partial-loop levels, with reported advances in model-guided formulation selection, process-CQA prediction, prospective experimental validation, and mechanism-relevant biological testing. However, direct comparisons remain limited by heterogeneity in datasets, validation strategies, manufacturing conditions, assay formats, and biological endpoints. Repeated end-to-end DMTL cycles integrating uncertainty-aware model updating, controlled and traceable manufacturing, mechanism-relevant testing, independent external validation, and prespecified stopping criteria have not yet been demonstrated. To distinguish conceptual integration from measurable closed-loop performance, we propose a five-level maturity model, a minimum reporting set, and a four-domain scoring framework encompassing design intelligence, process intelligence, biological fidelity, and translational readiness. This evidence-calibrated framework delineates the current evidence boundary and provides practical criteria for designing, comparing, and validating future closed-loop nanomedicine platforms.