A modular Safety-by-Design framework for RNA-LNP prioritization and mechanistically informed biological consistency assessment

The development of RNA therapeutics requires formulations that achieve efficient delivery while limiting unwanted immune activation and off-target effects. Computational approaches can help explore these competing objectives before experimental testing, but mechanistic interpretation, formulation prioritization, and biological consistency assessment are often addressed separately. We developed a modular computational framework with two complementary analytical branches. The candidate-prioritization branch combines a heterogeneous virtual-patient cohort, a 500-formulation RNA–lipid nanoparticle (RNA-LNP) design space, five synthetic efficacy- and safety-related endpoints, a composite Safety-by-Design score, a Random Forest surrogate, and Pareto-based multi-objective analysis. In the biological-consistency branch, descriptors derived from previously published Universal Immune System Simulator (UISS) trajectories were condensed into five Mechanistically Informed Immune Signatures (MIIS), used to constrain synthetic multi-omics profiles, and compared at the immune-program level with the independent human single-cell RNA-sequencing dataset GSE171964. MIIS and synthetic multi-omics variables were not used as predictors, endpoint inputs, or Pareto objectives. In formulation-grouped five-fold cross-validation, the Random Forest reproduced the composite score within the synthetic design space (pooled out-of-fold R² = 0.887; RMSE = 0.055; MAE = 0.044). The primary Pareto analysis identified 21 non-dominated formulations. Ranking was generally robust across 3,876 alternative Safety-by-Design weight combinations, and a complete balanced analysis of all 100,000 patient–formulation pairs produced a ranking closely matching the primary analysis (Spearman ρ = 0.998; top-20 overlap 18/20). LNP_0462 ranked first globally and within the Low-, Intermediate-, and High-inflammatory-risk strata. Mean BPCI across seven post-vaccination days was 0.760 and exceeded the exact shuffled-label null ( p = 0.025), although day-specific evidence and donor-level uncertainty were heterogeneous. This proof-of-concept framework supports transparent exploration of efficacy–safety trade-offs and prioritization of RNA-LNP formulations within a fully synthetic design space. The additional sensitivity analyses indicate that the leading candidates are robust to alternative score weights, sampling balance, and inflammatory-risk stratification. The MIIS/BPCI branch provides a separate assessment of immune-program organization rather than validation of individual formulations. Experimental testing and evaluation in additional human datasets are required before drawing conclusions about biological or clinical performance.