Clinical translation of carbon nanomaterials: the clinical translation paradox, regulatory barriers, and future pathways.
Carbon-based nanomaterials (CNMs) have generated intense research interest owing to their exceptional physicochemical versatility and broad biomedical potential. However, despite two decades of rapid innovation, the clinical translation of CNMs remains strikingly limited, owing to a persistent mismatch between performance-driven material innovation and predictability-driven clinical and regulatory demands-an enduring clinical translation paradox that urgently requires systematic resolution. This review examines four representative CNM classes-carbon dots, carbon nanotubes, carbon nanofibers, and graphene-family nanomaterials-and systematically analyzes their structure-function relationships, translational status, and clinical positioning within a four-stage framework encompassing material rational design and engineering, preclinical evaluation, clinical translation strategy, and clinical applications with post-market surveillance. We identify three interrelated, system-level bottlenecks that underpin this translational stagnation: (1) immunogenicity and long-term biosafety, (2) biodegradation and metabolic clearance, and (3) challenges in large-scale manufacturing and chemistry, manufacturing, and control consistency. Collectively, these challenges reveal a structural mismatch between performance-driven nanomaterial innovation and predictability-driven regulatory evaluation. To address this translational paradox, we discuss emerging strategies, including the development of degradable carbon frameworks, AI-assisted material optimization, and closer integration of materials science with lifecycle-based regulatory science. By reframing CNMs translation as a problem of system-level alignment rather than incremental performance enhancement, this review outlines a practical roadmap toward safe, reproducible, and clinically actionable carbon-based nanotechnologies.
Authors
Guo Guo, Liu Liu, Zhao Zhao, Tao Tao, Bao Bao, Shi Shi, Li Li, Cui Cui, Guo Guo
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