DEVELOPING AI-DESIGNED LIPID NANOPARTICLES FOR TARGETED MRNA DELIVERY TO MODULATE SPECIFIC IMMUNE CHECKPOINTS IN SOLID TUMORS

Abstract

Background: Targeted delivery of messenger RNA (mRNA) to modulate immune checkpoints has emerged as a promising approach in cancer immunotherapy. However, the clinical translation of mRNA therapeutics remains limited by challenges in delivery efficiency, specificity, and immunogenicity. Lipid nanoparticles (LNPs) offer a viable solution, yet their design is often empirical and lacks predictive optimization.

Objective: To develop a predictive model that optimizes LNP design for efficient and selective mRNA delivery to solid tumors, minimizing off-target effects and maximizing immune checkpoint modulation.

Methods: A descriptive, simulation-based study was conducted over four months in South Punjab. A total of 1,000 LNP formulations were generated, varying in particle size, zeta potential, PEG density, and ligand modification. Machine learning algorithms—including Random Forest, Gradient Boosting, and Support Vector Regression—were trained on simulated datasets using Python-based libraries. Delivery efficiency, off-target index, immunogenicity score, and mRNA expression duration were used as primary outcome variables. Statistical analyses were performed assuming normal distribution, including ANOVA, multivariate regression, and cross-validation.

Results: Mean delivery efficiency was 75.4% ± 10.2%, with ligand-modified LNPs achieving significantly higher efficiency (up to 80.6%) and reduced off-target indices (as low as 0.18). Zeta potential and PEG density were strongly predictive of performance. Immunogenicity scores remained within acceptable limits, while mRNA expression duration exceeded 60 hours in optimized formulations. The predictive model demonstrated high accuracy and interpretability in forecasting LNP behavior.

Conclusion: The study successfully demonstrated that AI-based predictive modeling can rationalize LNP design for targeted mRNA immunotherapy. This approach enhances delivery outcomes while minimizing systemic risks, offering a scalable strategy for advancing precision nanomedicine in oncology.