RinuaGene Biotechnology Co., Ltd.

Lipid-mRNA adducts form during storage for several types of lipid nanoparticles (LNPs) and impair therapeutic efficacy, yet their structural drivers and functional consequences remain incompletely characterized, especially for novel lipids with distinct structures. Here, we investigated adduct formation between mRNA and several impurities derived from an immunotropic ionizable lipid (4S)-KEL12, which has been used to develop therapeutic mRNA cancer vaccines approved for human clinical studies. Elevated storage temperatures promoted both adduct accumulation and the loss of mRNA integrity with divergent kinetics at 25 °C, suggesting their independence. Mechanistically, degradation impurities of (4S)-KEL12, particularly its aldehyde derivative (Z4) and N-oxide derivative (Z1) dominated adduct generation, with Z4 exhibiting ∼4-fold higher activity than Z1. Moreover, mRNA adduction with Z4 did not reduce mRNA integrity by capillary electrophoresis, further supporting independent pathways. Mass spectrometry characterization unambiguously identified cytidines as the primary target on mRNA for Z4 adduction. Functionally, while adducted mRNAs exhibited poor capacity for protein expression in cultured human 293T cells, they did not stimulate significant gene expression involved in innate immunity for RNA sensing and downstream type I interferon pathway activation in human THP1 cells. These findings not only clarify important functional consequences of adducted mRNAs, but also establish impurity control and thermal management as actionable strategies for advancing mRNA therapeutics.

Poly(A) tail is crucial in regulating mRNA stability and protein translation. Thus, it is an essential element of mRNAs transcribed in vitro for mRNA medicines. However, the repetitive nature of the poly(A) tail can lead to significant poly(A) length variation and compromise the quality of the mRNA drug substance. Previous studies improved poly(A) transmission stability by inserting a non-adenosine spacer. Here, we designed new segmented poly(A) variants and evaluated their transmission stability in bacteria transformation and their ability in supporting protein expression in animals. We identified specific variants with multiple non-adenosine insertions that can maintain high transmission stability with robustness in Escherichia coli and facilitate high protein expression in animals. Among the newly designed poly(A) variants, RG2 showed high and consistent transmission stability comparable to the A30-70 variant that is the industry gold standard but higher protein expression in animals than A30-70. We also isolate new factors that can influence the stable transmission of poly(A), such as poly(A) surrounding sequences and bacteria culture temperature. Thus, our work offers new tools valuable for rapidly developing mRNA vaccines and therapeutics.

Treatment with mRNA-based therapeutics represents a potential strategy for improving outcomes of diverse diseases. Tumor-specific toxins might represent ideal candidates for mRNA-based cancer therapeutics. In this study, we investigated the antitumor potential of lipid nanoparticle (LNP)–encapsulated mRNA encoding the tumor-specific toxin protein neutrophil elastase (ELANE) or porcine pancreatic elastase (PPE). Treatment with either ELANE or PPE mRNA-LNP selectively killed various cancer cell types but not noncancer cells in vitro. Furthermore, ELANE and PPE mRNA-LNP administration significantly inhibited tumor growth in vivo and induced CD8+ T-cell infiltration, whereas no acute toxicity was observed in mice. Several additional elastases from different species were also effective against cancer cells. Altogether, these data support further development of tumor-specific toxin protein mRNA-LNP as a therapeutic strategy for cancer.

Treatment with lipid nanoparticle-encapsulated mRNA encoding tumor-specific toxin proteins reduces tumor growth and activates antitumor immunity, providing an alternative approach for treating tumors that are resistant or unresponsive to immunotherapy.