Prussian blue nanoparticles (PBNPs) are a versatile platform for administering photothermal therapy (PTT) in cancer therapy applications. PBNPs combine biocompatibility, safety, and clinical translational potential with durable treatment outcomes in preclinical cancer models. In this perspective, we focus on aspects critical to the workflow of implementing PBNP-PTT in cancer treatment, drawing inspiration from adjacent scientific areas that have not been described in the context of PBNPs, but are important for improving the delivery of PBNP-PTT and its translation. Specifically, we will discuss machine learning approaches, multiomics analyses, and clinical strategies pertinent to PBNP-PTT. Machine learning approaches have the potential to enhance PBNP-PTT design, performance, and therapeutic outcomes. Complementing this, multiomics has the potential to describe the responses to PBNP-PTT, particularly its immune effects. By embedding these advances from nanoparticle engineering to therapy monitoring, PBNP-PTT can evolve from empirical tumor ablation toward a precision photothermal platform, enabling highly individualized cancer treatments with improved safety, efficacy, regulatory approval, and clinical predictability. We will also cover clinical strategies pertinent to the translation PBNP-PTT culminating with specific forward-looking perspectives. This convergence of nanotechnology, immunology, and data science positions PBNP-PTT at the forefront of next-generation cancer nanomedicine and immunotherapy.
Merino et al. (Wed,) studied this question.