AI-Assisted versus Rule-Based CRISPR-Cas9 gRNA Design Targeting the HBB IVS-1-110 G To A Mutation in β-Thalassemia : An In Silico Study

The β–globin gene (HBB) intronic mutation IVS-1-110 G to A disrupts normal pre–mRNA splicing, leading to reduced β–globin synthesis and β-thalassemia. In this study, the mutation was simulated in silico to evaluate CRISPR–Cas9 guide RNA (gRNA) design strategies using an artificial intelligence–based platform, CRISPRon (RTH), in comparison with traditional rule based tools, Benchling and CHOPCHOP. gRNAs were prioritized based on proximity to the mutation site and predicted on-target efficiency, resulting in the selection of two candidates located within 13–15 bp of the variant. Cross-validation using CHOPCHOP and Benchling confirmed their cut–site positioning and comparable efficiency scores. Off–target assessment using Cas–OFFinder identified a single potential off–target site with one mismatch for each gRNA under conservative search conditions. These findings indicate that AI–assisted gRNA design can generate mutation–proximal and specific candidates, while highlighting the importance of integrating multiple computational tools and the necessity of experimental validation before therapeutic application.