A quantitative framework based on five key performance dimensions: dynamic range, temporal kinetics, output efficiency, spatial fidelity, and cycle retention is proposed, which discusses the emerging roles for computational modeling and artificial intelligence in enabling multiobjective optimization of these metrics and guiding the development of programmable mRNA therapeutics.
Abstract
Messenger RNA (mRNA) nanomedicine has rapidly progressed from a conceptual platform to a clinically established modality for vaccination. However, extending mRNA therapeutics beyond vaccines into oncology, regenerative medicine, immunotherapy, and protein replacement exposes persistent constraints in systemic delivery and functional control. In particular, in vivo applications remain limited by inefficient and heterogeneous biodistribution, suboptimal cytosolic delivery, and off-target protein expression in healthy tissues. Even when intracellular delivery is achieved, mRNA function is not assured, as translation is shaped by innate immune sensing pathways, cellular stress responses, and context-dependent regulatory networks that impose intrinsic limits on expression magnitude and duration. Thus, therapeutic precision and safety are compromised. To address these challenges, emerging strategies are shifting from passive delivery toward programmable mRNA nanomedicine, in which gene expression is dynamically regulated by defined endogenous or exogenous inputs. In this framework, mRNA therapeutics are no longer static cargoes but rather are components of integrated systems that sense, process, and respond to these inputs. We conceptualize these systems as modular logic switches composed of input, signal processing, and output layers, enabled through the co-design of nanocarriers and mRNA cargo architectures. To systematically evaluate such systems, we propose a quantitative framework based on five key performance dimensions: dynamic range, temporal kinetics, output efficiency, spatial fidelity, and cycle retention. Finally, we discuss the emerging roles for computational modeling and artificial intelligence in enabling multiobjective optimization of these metrics and guiding the development of programmable mRNA therapeutics.
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