RESUMO
Evidence is mounting that there is a significant gap between the antitumor efficacy of nanodrugs in preclinical mouse tumor models and in clinical human tumors, and that differences in tumor models are likely to be responsible for this gap. Herein, we investigated the enhanced permeability and retention (EPR) effect in mouse lung cancer models with different tumor growth rates, volumes and locations, and analyzed the nanodrug tumor targeting behaviors limited by tumor vascular pathophysiological characteristics in various tumor models. The results showed that the fast-growing tumors were characterized by lower vascular tight junctions, leading to higher vascular paracellular transport activity and nanodrug tumor accumulation. The paracellular transport activity increased with the growth of tumor, but the vascular density and transcellular transport activity decreased, and as a result, the average tumor accumulation of passive targeting nanodrugs decreased. Orthotopic tumors were rich in blood vessels, but had low vascular transcellular and paracellular transport activities, making it difficult for nanodrug accumulation in orthotopic tumors via passive targeting strategies. The antitumor efficacy of passive targeting nanodrugs in various lung cancer-bearing mice validated the aforementioned nanodrug accumulation behavior, and nanodrugs based on the angiogenesis-tumor sequential targeting strategy achieved obviously improved efficacy in orthotopic lung cancer-bearing mice. These results suggest that the EPR effect varies in different tumor models and should not be used as a universal targeting strategy for antitumor nanodrugs. Besides, attention should be paid to the animal tumor models in the evaluation of nanodrugs so as to avoid exaggerating the antitumor efficacy.
Assuntos
Neoplasias Pulmonares , Nanopartículas , Humanos , Camundongos , Animais , Nanopartículas/uso terapêuticoRESUMO
For randomized clinical trials, subjects' variance structures may vary over time among treatment groups, resulting in the heteroscedasticity of residuals in a regression analysis. Commonly used methods that assume equal variance among all treatment groups may not be able to control for a type I error. When the variances are indeed the same across treatment groups, an equal randomization allocation ratio will yield the greatest study power. However, out of ethical concern or urgent need for rare disease clinical trials, more patients may have to be allocated to the study drug arm. In these situations, an unequal randomization ratio should be considered. We propose a group variance-covariance and structures-based method to adapt the randomization ratio after interim analysis. We use simulations to compare commonly used statistical methods for continuous endpoints in assessing the impact of heteroscedasticity in equal and unequal randomization ratios and examine the extent to which the findings are affected by missing data.