The high therapeutic resistance of tumor is the primary cause behind tumor recurrence and incurability. In recent years, scientists have devoted themselves to find a variety of treatments to solve this problem. Herein, we propose a multi-hit strategy that is based on the biodegradable hollow mesoporous Prussian blue (HMPB)-based nanosystem for tumor-specific therapy that encapsulated the critical heat shock protein 90 (HSP90) inhibitor 17-dimethylamino-ethylamino-17-demethoxydeldanamycin (17-DMAG). The nanosystem was further modified using thermotropic phase transition material star-PEG-PCL (sPP) and hyaluronic acid (HA), which offers near infrared light (NIR) responsive release characteristic, as well as enhanced tumor cell endocytosis. Upon cell internalization of 17-DMAG-HMPB@sPP@HA and under 808 nm laser irradiation, photothermal-conversion effect of HMPB directly kills cells using hyperthermia, which further causes phase transition of sPP to trigger release of 17-DMAG, inhibits HSP90 activity and blocks multiple signaling pathways, including cell cycle, Akt and HIF pathways. Additionally, the down-regulation of GPX4 protein expression by 17-DMAG and the release of ferric and ferrous ions from gradual degradation of HMPB in the endogenous mild acidic microenvironment in tumors promoted the occurrence of ferroptosis. Importantly, the antitumor effect of 17-DMAG and ferroptosis damage were amplified using photothermal effect of HMPB by accelerating release of ferric and ferrous ions, and reducing HSP90 expression in cells, which induced powerful antitumor effect
in vitro
and
in vivo
. This multi-hit therapeutic nanosystem helps provide a novel perspective for solving the predicament of cancer treatment, as well as a promising strategy for design of a novel cancer treatment nanoplatform.
Toxicogenomics can measure the expression of thousands of genes to identify changes associated with drug induced toxicities. It is expected that toxicogenomics can be an alternative or complementary approach in preclinical drug safety evaluation to identify or predict drug induced toxicities. One of the major concerns in applying toxicogenomics to diagnose or predict drug induced organ toxicity, is how generalizable the statistical classification model is when derived from small datasets? Here we presented that a diagnosis of kidney proximal tubule toxicity, measured by pathology, can successfully be achieved even with a study design of limited number of training studies or samples. We selected a total of ten kidney toxicants, designed the in life study with multiple dose and multiple time points to cover samples at doses and time points with or without concurrent toxicity. We employed SVM (Support Vector Machine) as the classification algorithm for the toxicogenomic diagnosis of kidney proximal tubule toxicity. Instead of applying cross validation methods, we used an independent testing set by dividing the studies or samples into independent training and testing sets to evaluate the diagnostic performance. We achieved a Sn (sensitivity) = 88% and a Sp (specificity) = 91%. The diagnosis performance underscores the potential application of toxicogenomics in a preclinical lead optimization process of drugs entering into development.
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