Navigating off-target effects in CRISPR-based genome editing for safer gene therapies
Off-target activity remains a significant challenge in the clinical development of CRISPR-based therapies. As programmable nucleases advance from experimental tools toward approved medicines, the ability to predict, detect, and mitigate unintended genomic editing events has acquired direct translational importance. This review examines the molecular basis of off-target cleavage across three nuclease platforms—zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR-Cas systems—with emphasis on mechanistic and structural factors that govern mismatch tolerance, including PAM sampling, seed region thermodynamics, and chromatin accessibility. In silico prediction methods are surveyed from early alignment-based approaches through feature-engineered machine learning models to more recent deep learning architectures, with critical attention to training data limitations, cross-platform generalisability, and the distinction between exhaustive genomic search tools and predictive scoring models. The experimental detection landscape is reviewed with expanded coverage of established and newer unbiased genome-wide assays, including their biological principles, sensitivity characteristics, and suitability for structural variant detection. Particular attention is given to next-generation editing modalities—base editors, prime editors, and Cas12/Cas13 systems—and to epigenome editing approaches using non-cleaving CRISPR platforms, which avoid double-strand breaks but retain specificity concerns. The review also addresses delivery-related off-target risks, including tissue-level biodistribution and germline exposure, alongside mitigation strategies. It closes by outlining directions for improving standardisation, expanding structural variation surveillance, and extending safety characterisation to the full diversity of editing platforms now approaching clinical use.