Next-Generation Gene Editing: A Conceptual Framework for Precision Genomic Engineering Beyond CRISPR-Cas Systems

CRISPR-Cas systems have revolutionized genetic engineering by facilitating targeted genomic modifications. However, persistent limitations include off-target effects, protospacer adjacent motif (PAM) sequence dependency, delivery inefficiencies, and immunogenicity. This paper proposes a conceptual framework for a next-generation gene editing platform, the Adaptive Nuclease-Free Programmable Editor (ANFPE). ANFPE integrates computational design of biomolecular components, targeted epigenetic modifications, and AI-optimized nanomaterial delivery systems to mitigate these limitations. It employs a nuclease-independent mechanism utilizing sequence-specific molecular recognition and chromatin remodeling, directed by machine learning algorithms. The framework delineates theoretical underpinnings, engineering methodologies, computational simulations including detailed Sobol sensitivity and uncertainty analyses, and a developmental roadmap aimed at attaining superior precision, efficacy, and safety relative to CRISPR-based approaches. A case study on the HBB gene locus for sickle cell disease validates the model using real-world kinetic and chromatin data from published literature and ENCODE datasets, bridging computational predictions to biological realism.