Harnessing artificial intelligence for engineering extracellular vesicles

Abstract

Extracellular vesicles (EVs) are a type of cell-released phospholipid bilayer nanoscale carrier. However, research on EVs encounters several challenges, such as their heterogeneity, the complexities associated with their isolation and identification, the necessity for engineering optimization, and the limitations in exploring their mechanisms. The advancement of artificial intelligence (AI) technologies offers new opportunities for EV research. Here, the definition and brief history of AI, as well as types and common models of machine learning, are first introduced, and the interactions between AI, machine learning, and deep learning are explored. The article then discusses in detail a variety of applications of AI in EV research, including the use of AI for target identification and selective delivery of EVs, the design and optimization of drug delivery systems, the mapping of cellular communication networks, the analysis of multi-omics data, and synthetic biology-based research on EVs. These applications demonstrate the potential of AI in advancing EV research and applications. Finally, we offer an outlook on the major challenges and future prospects of AI. Overall, the introduction of AI technologies has provided new perspectives and tools for the study of EVs, which is expected to enhance the application of EVs in disease diagnosis and treatment.

Keywords

Extracellular vesicles / artificial intelligence / machine learning / drug delivery / targeted therapy

References

[1] Liu H,Wang S,Jing Y.Bacterial extracellular vesicles as bioactive nanocarriers for drug delivery: advances and perspectives.Bioact Mater2022;14:169-81 PMCID:PMC8892084

[2] Liu H,Wang M,Su J.Intestinal organoids and organoids extracellular vesicles for inflammatory bowel disease treatment.Chem Eng J2023;465:142842

[3] Han R,Ji N.Folic acid-modified ginger-derived extracellular vesicles for targeted treatment of rheumatoid arthritis by remodeling immune microenvironment via the PI3K-AKT pathway.J Nanobiotechnology2025;23:41 PMCID:PMC11756199

[4] Cicero A, Stahl PD, Raposo G. Extracellular vesicles shuffling intercellular messages: for good or for bad.Curr Opin Cell Biol2015;35:69-77

[5] Liu H.Organoid and organoid extracellular vesicles for osteoporotic fractures therapy: current status and future perspectives.Interdiscip Med2023;1:e20230011

[6] Zhou G,Sheng S.Organoids and organoid extracellular vesicles-based disease treatment strategies.J Nanobiotechnology2024;22:679 PMCID:PMC11542470

[7] Théry C,Aikawa E.Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines.J Extracell Vesicles2018;7:1535750 PMCID:PMC6322352

[8] Wang S,Song P.Harnessing extracellular vesicles from Lactobacillus reuteri and Lactobacillus paracasei for synergistic osteoporosis therapy.Compos Part B Eng2025;297:112255

[9] Battistelli M.Apoptotic bodies: particular extracellular vesicles involved in intercellular communication.Biology2020;9:21 PMCID:PMC7168913

[10] Atkin-Smith GK,Zanker DJ.Isolation of cell type-specific apoptotic bodies by fluorescence-activated cell sorting.Sci Rep2017;7:39846 PMCID:PMC5216387

[11] Serrano-Heras G,Castro-Robles B.Isolation and quantification of blood apoptotic bodies, a non-invasive tool to evaluate apoptosis in patients with ischemic stroke and neurodegenerative diseases.Biol Proced Online2020;22:17 PMCID:PMC7395395

[12] Tian Y,Song J.A doxorubicin delivery platform using engineered natural membrane vesicle exosomes for targeted tumor therapy.Biomaterials2014;35:2383-90

[13] Kamerkar S,Sugimoto H.Exosomes facilitate therapeutic targeting of oncogenic KRAS in pancreatic cancer.Nature2017;546:498-503 PMCID:PMC5538883

[14] Alvarez-Erviti L,Yin H,Lakhal S.Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes.Nat Biotechnol2011;29:341-5

[15] Batrakova EV.Using exosomes, naturally-equipped nanocarriers, for drug delivery.J Control Release2015;219:396-405 PMCID:PMC4656109

[16] Tian T,He CP.Surface functionalized exosomes as targeted drug delivery vehicles for cerebral ischemia therapy.Biomaterials2018;150:137-49

[17] Xu M,Liu B.Engineered exosomes: desirable target-tracking characteristics for cerebrovascular and neurodegenerative disease therapies.Theranostics2021;11:8926-44 PMCID:PMC8419041

[18] Liao Z,Ma L.Engineering extracellular vesicles restore the impaired cellular uptake and attenuate intervertebral disc degeneration.ACS Nano2021;15:14709-24

[19] Gujrati V,Malekzadeh-Najafabadi J.Bioengineered bacterial vesicles as biological nano-heaters for optoacoustic imaging.Nat Commun2019;10:1114 PMCID:PMC6405847

[20] Tran PHL,Tran TTD.Exosomes and nanoengineering: a match made for precision therapeutics.Adv Mater2020;32:e1904040

[21] Wu P,Ocansey DKW,Qian H.Extracellular vesicles: a bright star of nanomedicine.Biomaterials2021;269:120467

[22] Liu H,Yang H.Extracellular vesicles in gut-bone axis: novel insights and therapeutic opportunities for osteoporosis.Small Sci2025;5:2400474

[23] Eisenstein M.Artificial intelligence powers protein-folding predictions.Nature2021;599:706-8

[24] Liu H,Xia Z.Artificial intelligence-enabled studies on organoid and organoid extracellular vesicles.Biomater Transl2024;5:93-94 PMCID:PMC11438610

[25] Kumar S,Tamura K.MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets.Mol Biol Evol2016;33:1870-4 PMCID:PMC8210823

[26] Ronquist F.MrBayes 3: bayesian phylogenetic inference under mixed models.Bioinformatics2003;19:1572-4

[27] Saitou N.The neighbor-joining method: a new method for reconstructing phylogenetic trees.Mol Biol Evol1987;4:406-25

[28] Mortazavi A,McCue K,Wold B.Mapping and quantifying mammalian transcriptomes by RNA-Seq.Nat Methods2008;5:621-8

[29] Smyth GK.Linear models and empirical bayes methods for assessing differential expression in microarray experiments.Stat Appl Genet Mol Biol2004;3:Article3

[30] Wen M,Ou Z.Bacterial extracellular vesicles: a position paper by the microbial vesicles task force of the Chinese society for extracellular vesicles.Interdiscip Med2023;1:e20230017

[31] Liu H,Wang F.Bone-targeted engineered bacterial extracellular vesicles delivering miRNA to treat osteoporosis.Compos Part B Eng2023;267:111047

[32] Ji N,Wang M,Liu H.Engineered bacterial extracellular vesicles for central nervous system diseases.J Control Release2023;364:46-60

[33] Liu H,Zhang T.Engineered bacterial extracellular vesicles for osteoporosis therapy.Chem Eng J2022;450:138309

[34] Liu H,Han Y,Geng Z.Bacterial extracellular vesicles-based therapeutic strategies for bone and soft tissue tumors therapy.Theranostics2022;12:6576-94 PMCID:PMC9516228

[35] Wu Y,Wang M,Jing Y.Extracellular derivatives for bone metabolism.J Adv Res2024;66:329-47 PMCID:PMC11674789

[36] Raposo G.Extracellular vesicles - on the cusp of a new language in the biological sciences.Extracell Vesicles Circ Nucl Acids2023;4:240-54 PMCID:PMC10824536

[37] Kowal J,Colombo M.Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes.Proc Natl Acad Sci U S A2016;113:E968-77 PMCID:PMC4776515

[38] van Niel G, D’Angelo G, Raposo G. Shedding light on the cell biology of extracellular vesicles.Nat Rev Mol Cell Biol2018;19:213-28

[39] Peinado H,Lavotshkin S.Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET.Nat Med2012;18:883-91 PMCID:PMC3645291

[40] Jiang C,Liu G,Davis J.Multiplexed profiling of extracellular vesicles for biomarker development.Nanomicro Lett2021;14:3 PMCID:PMC8638654

[41] Bracht JWP,van der Pol E.Choice of size-exclusion chromatography column affects recovery, purity, and miRNA cargo analysis of extracellular vesicles from human plasma.Extracell Vesicles Circ Nucl Acids2024;5:497-508 PMCID:PMC11648517

[42] Shami-Shah A,Walt DR.Advances in extracellular vesicle isolation methods: a path towards cell-type specific EV isolation.Extracell Vesicles Circ Nucl Acids2023;4:447-60 PMCID:PMC11648483

[43] Lötvall J,Hochberg F.Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles.J Extracell Vesicles2014;3:26913 PMCID:PMC4275645

[44] Gardiner C,Sahoo S.Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey.J Extracell Vesicles2016;5:32945 PMCID:PMC5090131

[45] Raposo G.Extracellular vesicles: exosomes, microvesicles, and friends.J Cell Biol2013;200:373-83 PMCID:PMC3575529

[46] Liu H,Su J.Engineered mammalian and bacterial extracellular vesicles as promising nanocarriers for targeted therapy.Extracell Vesicles Circ Nucl Acids2022;3:63-86 PMCID:PMC11648430

[47] Witwer KW.Extracellular vesicles or exosomes? On primacy, precision, and popularity influencing a choice of nomenclature.J Extracell Vesicles2019;8:1648167 PMCID:PMC6711079

[48] Smyth T,Payton NM.Surface functionalization of exosomes using click chemistry.Bioconjug Chem2014;25:1777-84 PMCID:PMC4198107

[49] Welsh JA, Goberdhan DCI, O’Driscoll L, et al; MISEV Consortium. Minimal information for studies of extracellular vesicles (MISEV2023): from basic to advanced approaches. J Extracell Vesicles. 2024;13:e12404. PMCID:PMC10850029

[50] Ginini L,Fridman E.Insight into extracellular vesicle-cell communication: from cell recognition to intracellular fate.Cells2022;11:1375 PMCID:PMC9101098

[51] Ovčar A.Biogenesis of extracellular vesicles (EVs) and the potential use of embryo-derived evs in medically assisted reproduction.Int J Mol Sci2024;26:42 PMCID:PMC11719982

[52] Gurunathan S,Kim JH.A comprehensive review on factors influences biogenesis, functions, therapeutic and clinical implications of exosomes.Int J Nanomedicine2021;16:1281-312 PMCID:PMC7898217

[53] Panch T,Atun R.Artificial intelligence, machine learning and health systems.J Glob Health2018;8:020303 PMCID:PMC6199467

[54] Butler KT,Cartwright H,Walsh A.Machine learning for molecular and materials science.Nature2018;559:547-55

[55] Jordan MI.Machine learning: trends, perspectives, and prospects.Science2015;349:255-60

[56] Kwon SH.Flexible sensors and machine learning for heart monitoring.Nano Energy2022;102:107632

[57] Jovel J.An introduction to machine learning approaches for biomedical research.Front Med2021;8:771607 PMCID:PMC8716730

[58] Walker A.Unsupervised learning techniques for the investigation of chronic rhinosinusitis.Ann Otol Rhinol Laryngol2019;128:1170-6

[59] Plemel JR,Michaels NJ.Microglia response following acute demyelination is heterogeneous and limits infiltrating macrophage dispersion.Sci Adv2020;6:eaay6324 PMCID:PMC6962036

[60] Kler P.The inversion factor, by linda bernardi, sanjay sarma and kenneth traub (MIT Press, 2018), 232 pages.Econ Rec2021;97:448-50

[61] Mey A.Improved generalization in semi-supervised learning: a survey of theoretical results.IEEE Trans Pattern Anal Mach Intell2023;45:4747-67

[62] Chato L.Survey of transfer learning approaches in the machine learning of digital health sensing data.J Pers Med2023;13:1703 PMCID:PMC10744730

[63] Thakur A,Abhishek .Quantum machine learning-based electrokinetic mining for the identification of nanoparticles and exosomes with minimal training data.Bioact Mater2025;51:414-30 PMCID:PMC12149654

[64] Yu Y,Jiang L.Quantitative analysis of multiple components based on support vector machine (SVM).Optik2021;237:166759

[65] Cortes C.Support-vector networks.Mach Learn1995;20:273-97

[66] Xu J,Vázquez D.Hierarchical adaptive structural SVM for domain adaptation.Int J Comput Vis2016;119:159-78

[67] Salazar-Rojas T,Calvo-Brenes G.Comparison between machine linear regression (MLR) and support vector machine (SVM) as model generators for heavy metal assessment captured in biomonitors and road dust.Environ Pollut2022;314:120227

[68] Rivera-lopez R,Mezura-montes E.Induction of decision trees as classification models through metaheuristics.Swarm Evol Comput2022;69:101006

[69] Weinberg AI.Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification.J Big Data2019;6:186

[70] Rodríguez JJ,Alonso CJ.Rotation forest: a new classifier ensemble method.IEEE Trans Pattern Anal Mach Intell2006;28:1619-30

[71] Sorokin A,Lee EH.SigOpt Mulch: an intelligent system for AutoML of gradient boosted trees.Knowl Based Syst2023;273:110604

[72] Beniaguev D,London M.Single cortical neurons as deep artificial neural networks.Neuron2021;109:2727-39.e3

[73] Moldoveanu M.Explananda and explanantia in deep neural network models of neurological network functions.Behav Brain Sci2023;46:e403

[74] Zanaty E.Support vector machines (SVMs) versus multilayer perception (MLP) in data classification.Egypt Inform J2012;13:177-83

[75] Lee J.Long-term stagnation monitoring using machine learning: comparison of artificial neural network model and convolution neural network model.Water Resour Manage2022;36:2117-30

[76] Ayub M.Enhanced first-break picking using hybrid convolutional neural network and recurrent neural networks.IEEE Trans Geosci Remote Sensing2024;62:1-16

[77] LeCun Y,Hinton G.Deep learning.Nature2015;521:436-44

[78] Wang Z,Kong Q.Extracellular vesicle preparation and analysis: a state-of-the-art review.Adv Sci2024;11:e2401069 PMCID:PMC11321646

[79] Xiao X,Yan A,Zhang Z.Intelligent probabilistic system for digital tracing cellular origin of individual clinical extracellular vesicles.Anal Chem2021;93:10343-50

[80] Wang YT,Sun LL.A rapid and facile separation-detection integrated strategy for exosome profiling based on boronic acid-directed coupling immunoaffinity.Anal Chem2021;93:16059-67

[81] Del Real Mata C, Jeanne O, Jalali M, Lu Y, Mahshid S. Nanostructured-based optical readouts interfaced with machine learning for identification of extracellular vesicles.Adv Healthc Mater2023;12:e2202123

[82] Jalali M,Montermini L.MoS₂-Plasmonic nanocavities for raman spectra of single extracellular vesicles reveal molecular progression in glioblastoma.ACS Nano2023;17:12052-71 PMCID:PMC10339787

[83] Jin Y,Huang Y.Fluorescence analysis of circulating exosomes for breast cancer diagnosis using a sensor array and deep learning.ACS Sens2022;7:1524-32

[84] Shen WX,Chen Y.AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks.Nucleic Acids Res2022;50:e45 PMCID:PMC9071488

[85] Pandian BJ.Control of a bioreactor using a new partially supervised reinforcement learning algorithm.J Process Control2018;69:16-29

[86] Ma X,Guo J.Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering.J Innov Opt Health Sci2023;16:2244001

[87] Greenberg ZF,He M.Towards artificial intelligence-enabled extracellular vesicle precision drug delivery.Adv Drug Deliv Rev2023;199:114974

[88] Basile AO,Tatonetti NP.Artificial intelligence for drug toxicity and safety.Trends Pharmacol Sci2019;40:624-35 PMCID:PMC6710127

[89] Obrezanova O.Artificial intelligence for compound pharmacokinetics prediction.Curr Opin Struct Biol2023;79:102546

[90] Cohn W,Huang C.Multi-omics analysis of microglial extracellular vesicles from human Alzheimer’s disease brain tissue reveals disease-associated signatures.Front Pharmacol2021;12:766082 PMCID:PMC8675946

[91] Luo HT,Tang J.Dissecting the multi-omics atlas of the exosomes released by human lung adenocarcinoma stem-like cells.NPJ Genom Med2021;6:48 PMCID:PMC8203745

[92] Lam SM,Wang Z.A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19.Nat Metab2021;3:909-22

[93] Lei X, Caiyun H, Hao L, et al. Artificial intelligence for central dogma-centric multi-omics: challenges and breakthroughs. arXiv 2024; arXiv:2412.12668.

[94] Yin H,Xing S.Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer.Cell Rep Med2024;5:101689 PMCID:PMC11384723

[95] Reel PS,Pearson E,Jefferson E.Using machine learning approaches for multi-omics data analysis: a review.Biotechnol Adv2021;49:107739

[96] Asao T,Lucotti S,Matei I.Extracellular vesicles and particles as mediators of long-range communication in cancer: connecting biological function to clinical applications.Extracell Vesicles Circ Nucl Acids2023;4:461-85 PMCID:PMC11067132

[97] Eitan E,Elgart K.Synaptic proteins in neuron-derived extracellular vesicles as biomarkers for Alzheimer’s disease: novel methodology and clinical proof of concept.Extracell Vesicles Circ Nucl Acids2023;4:133-50 PMCID:PMC10568955

[98] Zhou S,Han G.Accurate cancer diagnosis and stage monitoring enabled by comprehensive profiling of different types of exosomal biomarkers: surface proteins and miRNAs.Small2020;16:e2004492

[99] Wen Y,Li YY,Yu YL.Ultramultiplex NaLnF₄ nanosatellites combined with ICP-MS for exosomal multi-miRNA analysis and cancer classification.Anal Chem2022;94:16196-203

[100] Shin H,Hong S.Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes.ACS Nano2020;14:5435-44

[101] Yang Z,Kong J.ChatExosome: an artificial intelligence (AI) agent based on deep learning of exosomes spectroscopy for hepatocellular carcinoma (HCC) diagnosis.Anal Chem2025;97:4643-52

[102] Sanchez-Manas JM,Perales S,Torres C.Potential clinical applications of extracellular vesicles in pancreatic cancer: exploring untapped opportunities from biomarkers to novel therapeutic approaches.Extracell Vesicles Circ Nucl Acids2024;5:180-200 PMCID:PMC11648502

[103] Zheng J,Wang B.Electrochemical detection of extracellular vesicles for early diagnosis: a focus on disease biomarker analysis.Extracell Vesicles Circ Nucl Acids2024;5:165-79 PMCID:PMC11648401

[104] Laine RF,Henriques R.Avoiding a replication crisis in deep-learning-based bioimage analysis.Nat Methods2021;18:1136-44 PMCID:PMC7611896

[105] Heil BJ,Markowetz F,Greene CS.Reproducibility standards for machine learning in the life sciences.Nat Methods2021;18:1132-5 PMCID:PMC9131851

[106] Karimzadeh M.Top considerations for creating bioinformatics software documentation.Brief Bioinform2018;19:693-9 PMCID:PMC6054259

[107] Challen R,Pitt M,Edwards T.Artificial intelligence, bias and clinical safety.BMJ Qual Saf2019;28:231-7 PMCID:PMC6560460

[108] Char DS,Feudtner C.Identifying ethical considerations for machine learning healthcare applications.Am J Bioeth2020;20:7-17 PMCID:PMC7737650

[109] Morley J,Burr C.The ethics of AI in health care: a mapping review.Soc Sci Med2020;260:113172

[110] Boateng D,Smith ZJ,Dai Y.Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection.Biomed Opt Express2024;15:1-13 PMCID:PMC10783894

[111] Dong Z,Zhou S.Microsphere lens array embedded microfluidic chip for SERS detection with simultaneous enhancement of sensitivity and stability.Biosens Bioelectron2024;261:116505

[112] Lin W,Gao Z.An integrated sample-to-answer SERS platform for multiplex phenotyping of extracellular vesicles.Sens Actuators B Chem2023;394:134355

[113] Ma CY,Li CT.Translating mesenchymal stem cell and their exosome research into GMP compliant advanced therapy products: promises, problems and prospects.Med Res Rev2024;44:919-38