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<p>Extracellular vesicles (EVs) are promising therapeutic and diagnostic platforms, yet their clinical translation remains constrained by population heterogeneity, limited target-site retention, uncertain biodistribution, and variable potency. These persistent limitations have motivated the convergence of artificial intelligence (AI), biomaterials, synthetic biology, and advanced validation systems into what we describe as a next-generation EV framework. The evidence supporting these enabling technologies is, however, uneven. AI methods have demonstrated clear value for retrospective classification and feature discovery but have rarely been prospectively validated for therapeutic design; biomaterial carriers provide measurable gains in local retention and release control yet show inconsistent reporting of EV integrity, dose normalization, and controls; and programmable or trigger-responsive EV systems remain largely preclinical. A similar pattern is evident in the clinical landscape, where trial activity in EV-based interventions continues to grow but most studies remain in early or exploratory phases and no FDA-approved EV therapy is currently available. We therefore present next-generation EVs as an evidence-weighted translational agenda with future progress contingent on validated potency assays, reproducible manufacturing, predictable biodistribution, and a clear separation of demonstrated systems from longer-term concepts.</p> Graphical abstract <p></p>

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AI-guided design and biomaterial integration for next-generation extracellular vesicle therapeutics: an evidence-weighted translational framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月25日

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  • Jiaqi Tian,
  • Qiang Li,
  • Ning Li,
  • Dandan Song,
  • Zengfeng Li,
  • Xiaotan Shi,
  • Huiyu Sun,
  • Shuyin Duan,
  • Lin Zhang

摘要

Extracellular vesicles (EVs) are promising therapeutic and diagnostic platforms, yet their clinical translation remains constrained by population heterogeneity, limited target-site retention, uncertain biodistribution, and variable potency. These persistent limitations have motivated the convergence of artificial intelligence (AI), biomaterials, synthetic biology, and advanced validation systems into what we describe as a next-generation EV framework. The evidence supporting these enabling technologies is, however, uneven. AI methods have demonstrated clear value for retrospective classification and feature discovery but have rarely been prospectively validated for therapeutic design; biomaterial carriers provide measurable gains in local retention and release control yet show inconsistent reporting of EV integrity, dose normalization, and controls; and programmable or trigger-responsive EV systems remain largely preclinical. A similar pattern is evident in the clinical landscape, where trial activity in EV-based interventions continues to grow but most studies remain in early or exploratory phases and no FDA-approved EV therapy is currently available. We therefore present next-generation EVs as an evidence-weighted translational agenda with future progress contingent on validated potency assays, reproducible manufacturing, predictable biodistribution, and a clear separation of demonstrated systems from longer-term concepts.

Graphical abstract