AI-guided design and biomaterial integration for next-generation extracellular vesicle therapeutics: an evidence-weighted translational framework.
6 affiliations
- 1Clinical Research Center for Obstetrics and Gynecology, Key Laboratory of Maternal & Fetal Medicine of National Health Commission of China, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, 250001, China.
- 2School of Public Health, North China University of Science and technology, Tangshan, 063200, China.
- 3School of Public Health, Shandong First Medical University, Jinan, 250001, China.
- 4Clinical Research Center for Obstetrics and Gynecology, Key Laboratory of Maternal & Fetal Medicine of National Health Commission of China, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, 250001, China. [email protected].
- 5Shandong Provincial Key Medical and Health Laboratory of Women's Occupational Exposure and Fertility Preservation, Jinan, 250001, China. [email protected].
- 6Jinan (Preparatory) Key Laboratory of Women's Diseases and Fertility Preservation, Jinan, 250001, China. [email protected].
Abstract
Abractst
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.
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BibTeX
@article{tian2026aiguided,
title = {{AI-guided design and biomaterial integration for next-generation extracellular vesicle therapeutics: an evidence-weighted translational framework.}},
author = {Jiaqi Tian and Qiang Li and Ning Li and Dandan Song and Zengfeng Li and Xiaotan Shi and Huiyu Sun and Shuyin Duan and Lin Zhang},
journal = {Journal of nanobiotechnology},
year = {2026},
month = jul,
doi = {10.1186/s12951-026-04831-2},
url = {https://europepmc.org/article/MED/42902374},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - AI-guided design and biomaterial integration for next-generation extracellular vesicle therapeutics: an evidence-weighted translational framework.
AU - Jiaqi Tian
AU - Qiang Li
AU - Ning Li
AU - Dandan Song
AU - Zengfeng Li
AU - Xiaotan Shi
AU - Huiyu Sun
AU - Shuyin Duan
AU - Lin Zhang
PY - 2026
DA - 2026/07/01
JO - Journal of nanobiotechnology
DO - 10.1186/s12951-026-04831-2
UR - https://europepmc.org/article/MED/42902374
AB - Abractst 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.
SN - 1477-3155
ER -