Artificial intelligence (AI) is revolutionizing the design of ionizable lipids, the pivotal components of lipid nanoparticles (LNPs) for messenger RNA (mRNA) delivery, enabling efficient exploration of vast chemical space of ionizable lipids beyond the reach of traditional methods. This mini-review explores the burgeoning field of AI-powered design and optimization of ionizable lipids for mRNA delivery. We also discuss the critical role of high-throughput experimental strategies, particularly barcoding coupled with next-generation sequencing, in generating the large-scale in vivo datasets for model training. Finally, we discuss current challenges, including data quality and the necessity for domain-specific modeling strategies, and present a future outlook on the integration of AI with scientific computing for LNP research.

Tumor-associated macrophages (TAMs) are pivotal regulators of the immunosuppressive tumor microenvironment and major contributors to resistance against conventional and immunotherapeutic interventions. Rather than eliminating TAMs, emerging strategies aim to functionally reprogram them toward an antitumor phenotype, a therapeutic objective uniquely enabled by the precise, transient, and non-integrating nature of mRNA, which allows reversible modulation without genomic risk. Recent progress in nanocarrier design has improved selective delivery to TAMs through both passive uptake and active targeting, with administration routes tailored to tumor location. Precise immunomodulatory interventions in macrophages, accomplished by mRNA payloads designed to induce pro-inflammatory polarization, enhance phagocytosis, or block immunosuppressive signals, thereby remodel the tumor immune microenvironment and generate synergy with established treatments. Future efforts might concentrate on macrophage heterogeneity, carrier immunogenicity, and scalable formulation development to advance clinical translation, not only in cancer but also in other diseases shaped by dysregulated macrophage function.

Antibody-targeted lipid nanoparticles (Ab-LNPs) represent a highly promising delivery platform for precision therapy, enabling efficient in vivo targeted delivery of nucleic acid drugs such as mRNA, DNA and siRNA. Currently, the two primary strategies for antibody functionalization of LNPs are the post-insertion method and direct surface conjugation. This review outlines the key chemistry, manufacturing, and controls challenges associated with scaling up of Ab-LNP production, with a focus on antibody modification strategies, process scale-up challenges, and quality control considerations. It aims to provide practical guidance for translating Ab-LNP technology from laboratory research to scalable manufacturing.