Engineering exosome-based gene therapies: Tools, targets, and AI-enabled genomic design

Ali Abuhaliema; Ali Al-Samydai; Tabarek H. Mahmood; Ruqaya Mohammad Ahmed; Tabarak R. Al-Sammarraie; Ahmed S.A. Ali Agha

doi:10.7324/JABB.2026.283092

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Abstract

Exosomes, a category of extracellular vesicles (EVs), are being engineered as biocompatible nanocarriers for gene and genome-editing agents, such as messenger RNA, small interfering RNA/antisense oligonucleotides, plasmid DNA, and CRISPR/Cas (clustered regularly interspaced short palindromic repeats / CRISPR-associated) systems. Their intrinsic membrane composition can safeguard sensitive cargos, diminish immunogenicity, and facilitate repeatable administration; nevertheless, translation is hindered by inconsistent isolation and characterization, variable cargo loading, non-specific biodistribution, and challenges in manufacturing scale-up. This review consolidates recent advancements in the engineering of exosomes for therapeutic gene delivery and introduces an integrated AI-enabled design framework. This framework utilizes AI-assisted whole-exome sequencing variant prioritization to guide target selection, modality determination, and exosome engineering choices, including loading strategies, surface targeting, and mechanism-linked potency assessments. We detail novel exosome-mediated CRISPR delivery methods, critically evaluate safety concerns and potential failure modes, and delineate a pragmatic translational roadmap consistent with expectations for EV standardization. The integration of patient genetics with vector design and manufacturing quality characteristics through AI-exosome convergence provides a pathway to more systematic, reproducible, and clinically scalable gene treatments.

Keyword: Exosome Gene delivery Whole-exome sequencing CRISPR-Cas9 Genomics Artificial intelligence Targeted nanomedicine

Citation:

Abuhaliema A, Al-Samydai A, Mahmood TH, Ahmed RM, Al-Sammarraie TR, Agha ASAA. Engineering exosome-based gene therapies: Tools, targets, and AI-enabled genomic design. J Appl Biol Biotech 2026. Article in Press. http://doi.org/10.7324/JABB.2026.283092

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

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