Optimizing intravenous solid tumour treatment through active targeting approaches and polymeric micelles engineering: A meta-analysis in mouse models

The relatively low delivery of nanoparticles to solid tumours remains one of the most critical hurdles for the success of these nanoformulations, representing a significant drawback for clinical translation. Over the years, modulation of nanoparticle design and surface engineering (especially modifications on NPs' physicochemical properties and/or functionalization of the surface with active targeting moieties) has been proposed as a promising strategy. However, only modest improvements have been achieved. This meta-analysis first focused on understanding the impact of active targeting approaches on tumour delivery efficiency and found that active targeting promoted a twofold increase in tumour delivery efficiency. While the general literature benchmarks report a median delivery efficiency of 0.70% of the injected dose (%ID) for all nanoparticles, our meta-analysis reveals that for the studied systems, active targeting approaches promoted a twofold increase in median tumour delivery efficiency, rising from 0.4%ID for passive targeting to 0.9%ID for active targeting. Despite the benefits of active targeting, it alone does not provide a breakthrough for translating nanomedicines to human use. Given the outstanding properties of polymeric micelles, further efforts have focused on correlating their physicochemical properties, targeting strategies, drug characteristics, tumour models, and cancer types with tumour delivery efficiency, pharmacokinetics, and biodistribution. Notably, increased tumour delivery efficiency has been reported for polymeric micelles composed of PEG: PDLLA, especially in combination with drugs such as doxorubicin, and paclitaxel. Despite these encouraging findings, a deeper understanding of the interplay among all factors is needed to guide the rational design of more effective nanoformulations. Moving forward, a more holistic approach should be developed to enable the creation of predictive models of in vivo behavior and accelerate clinical translation.