5 Salient Points

• Nanocarriers play a vital role in delivering oligonucleotides for gene therapy, particularly for RNA interference (RNAi), which targets mRNA molecules to inhibit gene expression. Various nanoparticle types, such as lipid nanoparticles and polymer nanoparticles, have been tested for siRNA delivery in animal models and clinical trials.

• Computational methods and machine learning algorithms are essential for optimizing nanomedicine properties, predicting drug encapsulation efficiency, and analyzing membrane-nanoparticle interactions. AI models can also identify effective siRNA sequences for RNAi.

• Nanotheranostics, which combine drugs and imaging agents in one nanoparticle, offer a potential solution for prescreening nanomedicine suitability and optimizing treatment outcomes. However, challenges exist in the clinical implementation of these formulations.

• Patient classification based on unsupervised learning methods and the development of easy-to-operate nanosensors for continuous monitoring can improve patient prescreening and follow-up protocols, enhancing precision medicine.

• Despite the advantages of nanomedicine, such as targeted drug delivery and gene silencing, it is not suitable for every patient and cancer type due to variability in the enhanced permeability and retention effect. Translating nanomedicine into personalized treatments requires addressing these challenges and reducing development costs.

🎯Research Appendix