ReviewPharmaceutics2025
Artificial Intelligence-Driven Strategies for Targeted Delivery and Enhanced Stability of RNA-Based Lipid Nanoparticle Cancer Vaccines.
Review in Pharmaceutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Advances in improving cancer immunotherapy with nanotechnology: from smart nanoparticles to synergistic combination strategies.Molecular cancer · 2026Review
- BBB-aware stimuli-responsive and biomimetic nanomedicines for glioblastoma.Chinese neurosurgical journal · 2026Review
- Analytical Characterization and Stability Assessment of RNA-Based Vaccines.Pharmaceutics · 2026Review
- Stabilization strategies and advancements in lyophilization to preserve integrity and efficacy of next-generation biologicals.International journal of pharmaceutics: X · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Nanoadjuvant-integrated organic biomaterials for immune engineering: Mechanisms, design strategies, and translational applications.Materials today. Bio · 2026Review
- Machine Learning-Driven QSAR Modeling for pKInternational journal of molecular sciences · 2026Article
- Leveraging the crosstalk between cGAS-STING and pyroptosis by nanomedicine to enhance antitumor immunity.Journal of nanobiotechnology · 2026Review
- Advancements and Challenges in Tissue-Engineered Heart Valves: Integrating Biomechanics, Biomaterials, and Biomimetic Design for Functional Maturity.Biomimetics (Basel, Switzerland) · 2026Review
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Deep Tumor Penetration Using Nanoparticle Delivery Systems: Programmed Design Strategies and Emerging Evaluation Platforms.International journal of nanomedicine · 2026Review
- Construction of artificial organellesChemical science · 2025Review
- Transformative roles of digital twins from drug discovery to continuous manufacturing: pharmaceutical and biopharmaceutical perspectives.International journal of pharmaceutics: X · 2025Review
- High-Throughput Strategies for Streamlining Lipid Nanoparticle Development Pipeline.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Translational Advances in Lipid Nanoparticle Drug Delivery Systems for Cancer Therapy: Current Status and Future Horizons.Pharmaceutics · 2025Review
- Artificial intelligence-, organoid-, and organ-on-chip-powered models to improve pre-clinical animal testing of vaccines and immunotherapeutics: potential, progress, and challenges.Frontiers in artificial intelligence · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI's impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.