Biotechnology advancesOct 26, 2026

Designing precise gene editors for accurate and controlled DNA changes

CRISPR Gene Editing Weekly Brief ↗PubMed ↗DOI ↗

Updated Oct 27, 2026

Abstract

Gene-editing tools, particularly deaminase-based base editors, have the potential to achieve precise single-base substitutions without inducing DNA double-strand breaks.

The clinical application of base editors faces challenges beyond achieving the intended genetic edits.
Reliable and predictable outcomes in various sequence contexts and delivery conditions are critical for the success of gene editing.
Four major dimensions affecting clinical translation are editing efficiency, editing precision, safety, and delivery.
Artificial intelligence and computational methods play a role in the design, screening, and risk assessment of base editors.
Both ex vivo and in vivo pathways have specific requirements for product quality and safety evaluation in clinical settings.
Editing performance, safety, and delivery factors significantly influence development decisions for base-editing systems in various therapeutic contexts.

Simplified

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.

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Designing precise gene editors for accurate and controlled DNA changes

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