CHALLENGES AND FUTURE PERSPECTIVES: AI DESIGNED LNPS

Artificial intelligence has moved from a peripheral computational aid to a central design engine across the lipid nanoparticle (LNP) development pipeline, from ionizable lipid discovery through formulation optimization and, increasingly, patient-specific cargo and dose selection. This maturation has not eliminated the field's foundational difficulties; it has instead recast them in a new, AI-specific form. This chapter examines four such challenges. First, we consider the problem of data scarcity and model validation, which constrains nearly every machine learning application in LNP science and shapes which methods are practically deployable. Second, we examine the immunogenicity and safety profile of AI-designed lipids and formulations, including the possibility that AI-driven exploration of chemical space introduces novel liabilities not captured by existing safety heuristics. Third, we address the ethical and regulatory dimensions of embedding AI into medicine, including algorithmic bias, explainability, informed consent, and the evolving frameworks that regulators are constructing to govern AI-designed drug products. Finally, we look forward to closed-loop, autonomous AI systems capable of iteratively designing, testing, and refining personalized mRNA-LNP therapeutics with progressively less human intervention, and consider what technological and institutional developments would be required to realize this vision responsibly.