Next-generation cancer therapeutics: unveiling the potential of liposome-based nanoparticles through bioinformatics

Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer treatments, such as chemotherapy, radiotherapy, and surgery, have limitations like off-target release, toxicity, and inefficient drug delivery. This study explains the role of bioinformatics and AI in optimizing and analyzing liposomal formulations for innovative and better cancer therapy. Molecular docking (MD), molecular dynamics simulations, and machine learning models are the computational techniques that can help to design stable liposomal carriers for drugs, predict receptor-ligand interactions, and can improve drug release efficiency. Improved liposome nanoparticles (LNPs) surface functionalization, the discovery of tumor-specific biomarkers, and the improvement of receptor-ligand interactions for accurate drug targeting are all made possible by bioinformatics tools and methodologies. Moreover, AI-assisted predictions and in silico modeling can speed up drug discovery and processing while eliminating the experimental expenditures and time. In the present review, we conducted MD studies to complement the discussed literature. MD was performed between cyclic RGD peptides (liposomal ligands) and the GPR116 receptor in triple-negative breast cancer, and between folic acid (liposomal ligand) and the Axl tyrosine kinase receptor for lung cancer, revealing strong and stable interactions and highlighting the amino acid residues involved. Notwithstanding current obstacles, computational tools have shown notable progress in nanomedicine, exploring more options for more individualized and effective cancer therapies. The combination of AI, machine learning, and multi-omics techniques to improve therapeutic efficacy and reduce side effects is a substantial key to the future of LNP-based cancer treatment.