Introduction: The advanced features of plasmonic nanomaterials enable initial high accuracy detection with different therapeutic intervention. Computational simulations could estimate the plasmonic heat generation with a high accuracy and could be reliably compared to experimental results. This proposed combined theoretical-experimental strategy may help researchers to better understand other nanoparticles in terms of plasmonic efficiency and usability for future nano-theranostic research.
Objectives: To develop innovative computationally-driven approach to quantify any plasmonic nanoparticles photothermal efficiency and effects before their use as therapeutic agents.
Methods: This report introduces drug free plasmonic silver triangular nanoprisms coated with polyvinyl alcohol biopolymer (PVA-SNT), for in vivo photoacoustic imaging (PAI) guided photothermal treatment (PTT) of triple-negative breast cancer mouse models. The synthesized PVA-SNT nanoparticles were characterized and a computational electrodynamic analysis was performed to evaluate and predict the optical and plasmonic photothermal properties. The in vitro biocompatibility and in vivo tumor abalation study was performed with MDA-MB-231 human breast cancer cell line and in nude mice model.
Results: The drug free 140 μg∙mL-1 PVA-SNT nanoparticles with 1.0 W∙cm-2 laser irradiation for 7 min proved to be an effective and optimized theranostic approach in terms of PAI guided triple negative breast cancer treatment. The PVA-SNT nanoparticles exhibits excellent biosafety, photostability, and strong efficiency as PAI contrast agent to visualize tumors. Histological analysis and fluorescence-assisted cell shorter assay results post-treatment apoptotic cells, more importantly, it shows substantial damage to in vivo tumor tissues, killing almost all affected cells, with no recurrence.
Conclusion: This is a first complete study on computational simulations to estimate the plasmonic heat generation followed by drug free plasmonic PAI guided PTT for cancer treatment. This computationally-driven theranostic approach demonstrates an innovative thought regarding the nanoparticles shape, size, concentration, and composition which could be useful for the prediction of photothermal heat generation in precise nanomedicine applications.