The University of Catania introduces a modular computational framework to rank safer RNA-lipid nanoparticle formulations before any lab testing. The method uses virtual patients, machine learning, and a mechanistic check against human single-cell data to estimate safety and efficacy tradeoffs across formulations. For biotech, the immediate impact is reducing trial-and-error in formulation development, where candidate selection can be a major bottleneck for RNA therapeutics. The approach also reflects a broader shift toward integrating mechanistic biology with predictive modeling to de-risk early-stage CMC and translational decision-making.