From off-target to on-demand: Precision engineering and translational evaluation of next-generation base editors.

Authors

Wenju Wang, Jiajie Song, Yuchen Du, Changli Zhou, Xiangxiang Zuo, Guojia You, Chengjian Luo, Yaqian Huang, Zichen Jiang, Xue Li, Qiuyan Wang

Published in

Biotechnology advances. Pages 109083. Oct 26, 2026. Epub Oct 26, 2026.

Abstract

Due to the increasing demand for clinical gene therapy and the limitations of current treatment methods, there is an urgent need to develop gene-editing tools that are safer, more precise, and more clinically implementable. Deaminase-based base editors have emerged as gene-editing platforms with significant therapeutic potential because they can achieve precise single-base substitutions without inducing DNA double-strand breaks. However, the challenges of applying base editing in clinical practice extend beyond whether the intended edit can be achieved. The critical question is whether the editing system can reliably provide predictable, controllable, and verifiable outcomes across complex sequence contexts, diverse cell environments, and actual delivery conditions. This review focuses on the engineering optimization and clinical translation of base editors. We first systematically analyze the major factors affecting the clinical translation of base editors across four dimensions: editing efficiency, editing precision, safety, and delivery, together with the corresponding optimization strategies. We then summarize the applications of artificial intelligence and computational approaches in base editor design, screening, and risk assessment. We further discuss the ex vivo and in vivo pathways for clinical translation, including their respective requirements for product quality and safety evaluation. Finally, based on representative clinical programs, we discuss how editing performance, safety, and delivery factors influence development decisions for candidate base-editing systems across different therapeutic contexts, and propose minimum reporting recommendations to improve the consistency of key data and cross-study comparability.

PMID:
42933584
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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