ML-Based Optimal Design of One-Component Ionizable Amphiphilic Janus Dendrimers for Enhanced Dendrimersome Nanoparticle-Mediated mRNA Delivery

Background/Objectives: Ionizable lipid nanoparticles (LNPs) are the mainstream delivery mechanisms for mRNA vaccines. However, LNPs are limited in their mRNA transfection efficiency (TE) into target cells. Dendrimersome nanoparticle (DNP) delivery systems, developed using ionizable amphiphilic Janus dendrimers (IAJDs), were designed to overcome the limitations of earlier approaches. Researchers have found this alternative promising due to their comparatively simple, repeating one-component structure and enhanced stability. This study sought to clarify the impact of particular IAJD structural components on mRNA TE and develop novel IAJD candidates for maximum predicted TE. Methods: Structural constituents (hydrophilic, ionizable amine, & hydrophobic regions) were systematically defined & encoded for computational analysis. Luciferase-induced luminescence was used as a quantitative metric for mRNA transfection. TE prediction models were built using several machine learning algorithms, and the model using eXtreme Gradient Boosting was selected. This prediction model overcame imbalanced datasets and this model was used to find the optimal IAJD designs and formulation conditions. Results: The IAJD optimization process ultimately yielded three novel optimized IAJD candidates and one of existing IAJDs, surpassing previously identified IAJDs. Conclusions: To our knowledge, this study presents the first large-scale computational investigation of IAJD structural optimization using machine learning. The design of IAJD is the primary factor that influences mRNA TE, but there are other impacting factors and more work is needed. This study highlights the potential of ML-driven IAJD optimization. Combined with high-throughput in vitro assays, this method could significantly accelerate mRNA therapeutics development with an improved delivery mechanism.