David Luna-Cerralbo

Background/Objectives: Lipid nanoparticles (LNPs) are leading mRNA delivery vehicles, with ionizable lipids (ILs) as their key component. However, the relationship between the IL structure and LNP endogenous organ-targeting is not well understood. In this study, we developed a novel library of biodegradable ILs featuring beta-propionate linkers, which, when incorporated into a four-component LNP formulation, show excellent extrahepatic selectivity and high protein expression. Methods: We explored the impact of structural modifications in the hydrophobic chains and polar-head groups in the ILs while keeping the linkers unchanged. In vivo results were evaluated to examine how structural changes influence the biodistribution to spleen or lungs. LNP formulations were assessed for their protein expression levels and organ-specific targeting. Additionally, protein corona formation by the best-performing LNPs was examined to provide further mechanistic insights. Results: Organ targeting was significantly influenced by structural changes in the ILs, allowing for precise control of the biodistribution between the spleen and lungs. Branched hydrophobic chains demonstrated a higher propensity for spleen targeting, while modifications in the polar-head group could drastically shift biodistribution from the lung to the spleen. This led to the identification of LNPs’ zeta potential as a key determinant of their extrahepatic targeting properties. Notably, ionizable lipid A3T2C7, also known as CP-LC-1495, displayed strong lung selectivity (97%) and high protein expression in lung tissue (1.21 × 10⁸ p/s). Similarly, several promising candidates for spleen-targeting LNPs displayed protein expression levels exceeding 1 × 10⁷ p/s (selectivity >80%). Conclusions: This study elucidates the structure–function relationships of ILs in passive organ-specific mRNA delivery, highlighting how the fine-tuning of hydrophobic chains, polar-head groups, and surface charge (zeta potential) allows for the precise control of LNP endogenous biodistribution, a mechanism influenced by protein corona formation. These findings enable the rational design of targeted LNP systems, enhancing their therapeutic potential for specific organs, such as the spleen and lungs.

The design of oligonucleotides with uniform hybridisation temperatures is essential for successful gene synthesis. However, current computational tools for oligonucleotide design face significant limitations, including difficulties in processing long DNA sequences, poor adaptability to specific experimental conditions, limited control over oligonucleotide length, and challenges in minimising spurious dimer formation. To address these issues, we developed CertPrime (https://oligodesign.bifi.es), an innovative tool designed for scalable and efficient handling of long DNA sequences. CertPrime enables precise customisation of experimental parameters, provides flexibility to limit the maximum oligonucleotide length, and generates designs with reduced deviations in melting temperatures across overlapping regions compared to existing tools. We experimentally compared CertPrime designs with benchmark design methods for a complex DNA sequence and found that CertPrime designs led to more efficient gene assembly, significantly reducing the occurrence of non-specific bands. These results make CertPrime a powerful and versatile tool for oligonucleotide design in gene synthesis applications.

The concept of “codon optimisation” involves adjusting the coding sequence of a target protein to account for the inherent codon preferences of a host species and maximise protein expression in that species. However, there is still a lack of consensus on the most effective approach to achieve optimal results. Existing methods typically depend on heuristic combinations of different variables, leaving the user with the final choice of the sequence hit. In this study, we propose a new statistical-physics model for codon optimisation. This model, called the Nearest-Neighbour interaction (NN) model, links the probability of any given codon sequence to the “interactions” between neighbouring codons. We used the model to design codon sequences for different proteins of interest, and we compared our sequences with the predictions of some commercial tools. In order to assess the importance of the pair interactions, we additionally compared the NN model with a simpler method (Ind) that disregards interactions. It was observed that the NN method yielded similar Codon Adaptation Index (CAI) values to those obtained by other commercial algorithms, despite the fact that CAI was not explicitly considered in the algorithm. By utilising both the NN and Ind methods to optimise the reporter protein luciferase, and then analysing the translation performance in human cell lines and in a mouse model, we found that the NN approach yielded the highest protein expression in vivo . Consequently, we propose that the NN model may prove advantageous in biotechnological applications, such as heterologous protein expression or mRNA-based therapies.