Towards AI-Integrated Digital Twin Frameworks for Nanocarrier-Mediated CRISPR Gene Editing: A Perspective

Abstract

Non-viral nanocarrier delivery remains a major translational bottleneck for clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing because functional outcome emerges from coupled losses across biodistribution, cell uptake, intracellular trafficking, endosomal escape, access to the relevant cellular compartment, editing, and tolerability. This Perspective develops three complementary conceptual tools. The Delivery-Specificity-Toxicity (DST) framework treats formulation design as a constrained multi-objective problem. The Composite Nano-Bio Delivery Efficiency (CNBDE) framework represents functional delivery as a chain of conditional probabilities rather than a product of statistically independent barriers, with shared formulation and biological variables allowed to alter multiple stages simultaneously. We explicitly distinguish the chain-rule formulation from the simpler independence approximation and clarify that the novelty of CNBDE lies in CRISPR-specific mechanistic decomposition and its linkage to measurable state variables, not in multiplication itself. Heterogeneous literature values are reclassified by evidence maturity instead of being treated as universal cross-platform constants. An explicitly hypothetical in silico stress test is added to demonstrate uncertainty propagation and structural sensitivity; importantly, it shows that the bare multiplicative form cannot by itself prove that any single stage, including endosomal escape, is universally dominant. A worked hepatic lipid-nanoparticle (LNP) example, contrasted with lungtargeted LNP studies, illustrates why organ-specific parameterisation is required. Finally, the Nano- Bio Digital Twin System (NB-DTS) is defined as a digital-twin-ready multiscale architecture requiring a biological counterpart, iterative data assimilation, uncertainty-aware state updating, prediction, and feedback. CNBDE and NB-DTS remain hypothesis-generating frameworks and require prospective experimental and external validation before predictive or clinical use.