Biosci Trends. 2026 Jun 20.
Rare diseases impose a disproportionate clinical burden, and yet therapeutic progress is hindered by small cohorts, biological heterogeneity, and limited disease-specific options. Stem cell-derived extracellular vesicles (EVs), and especially exosome-enriched products, are emerging as adaptable cell-free therapeutics that preserve key paracrine activities of parent cells while offering improved controllability, engineering flexibility, and potentially lower acute immunogenicity than living-cell products. This review proposes a clinically driven bottleneck-to-mechanism framework for rare-disease translation, matching each disease class to its dominant pathological barrier, mechanism-relevant EV function, route-aware delivery strategy, and measurable potency endpoint. Using this framework, EVs may enable immune circuit rewiring in autoimmune disorders, neuroprotection and toxic-protein clearance in neurodegeneration, osteogenic and matrix-supportive repair in skeletal/connective tissue diseases, and metabolic rescue in lysosomal or mitochondrial disorders. We further highlight a key conceptual distinction between EVs as active biologics and EVs as engineered delivery vehicles. Successful translation will depend on integrating cargo design, surface targeting, biodistribution-aware administration, scalable manufacturing, and quality-by-design control, while anticipating repeat-dose pharmacokinetics/pharmacodynamics (PK/PD), immunogenicity, complement activation, procoagulant risk, impurity control, and off-target organ-accumulation challenges. Multi-omics and artificial intelligence may further refine target selection and precision engineering. Overall, stem cell-derived EVs constitute a versatile platform for treating rare diseases, but clinical success requires closer alignment among mechanism, disease specificity, product definition, and translational endpoints.
Keywords: artificial intelligence; cargo engineering; exosomes; extracellular vesicles; mesenchymal stem cells; rare diseases; surface targeting