Sheng-Yong Yang

SKLB1002, a Novel Potent Inhibitor of VEGF Receptor 2 Signaling, Inhibits Angiogenesis and Tumor Growth In Vivo

Purpose: VEGF receptor 2 (VEGFR2) inhibitors, as efficient antiangiogenesis agents, have been applied in the cancer treatment. However, currently most of these anticancer drugs suffer some adverse effects. Discovery of novel VEGFR2 inhibitors as anticancer drug candidates is still needed. Experimental Design: In this investigation, we adopted a restricted de novo design method to design VEGFR2 inhibitors. We selected the most potent compound SKLB1002 and analyzed its inhibitory effects on human umbilical vein endothelial cells (HUVEC) in vitro. Tumor xenografts in zebrafish and athymic mice were used to examine the in vivo activity of SKLB1002. Results: The use of the restricted de novo design method indeed led to a new potent VEGFR2 inhibitor, SKLB1002, which could significantly inhibit HUVEC proliferation, migration, invasion, and tube formation. Western blot analysis was conducted, which indicated that SKLB1002 inhibited VEGF-induced phosphorylation of VEGFR2 kinase and the downstream protein kinases including extracellular signal-regulated kinase, focal adhesion kinase, and Src. In vivo zebrafish model experiments showed that SKLB1002 remarkably blocked the formation of intersegmental vessels in zebrafish embryos. It was further found to inhibit a new microvasculature in zebrafish embryos induced by inoculated tumor cells. Finally, compared with the solvent control, administration of 100 mg/kg/d SKLB1002 reached more than 60% inhibition against human tumor xenografts in athymic mice. The antiangiogenic effect was indicated by CD31 immunohistochemical staining and alginate-encapsulated tumor cell assay. Conclusions: Our findings suggest that SKLB1002 inhibits angiogenesis and may be a potential drug candidate in anticancer therapy. Clin Cancer Res; 17(13); 4439–50. ©2011 AACR.

ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions.

Scoring functions have been widely used to assess protein-ligand binding affinity in structure-based drug discovery. However, currently commonly used scoring functions face some challenges including poor correlation between calculated scores and experimental binding affinities, target-dependent performance, and low sensitivity to analogues. In this account, we propose a new empirical scoring function termed ID-Score. ID-Score was established based on a comprehensive set of descriptors related to protein-ligand interactions; these descriptors cover nine categories: van der Waals interaction, hydrogen-bonding interaction, electrostatic interaction, π-system interaction, metal-ligand bonding interaction, desolvation effect, entropic loss effect, shape matching, and surface property matching. A total of 2278 complexes were used as the training set, and a modified support vector regression (SVR) algorithm was used to fit the experimental binding affinities. Evaluation results showed that ID-Score outperformed other selected commonly used scoring functions on a benchmark test set and showed considerable performance on a large independent test set. ID-Score also showed a consistent higher performance across different biological targets. Besides, it could correctly differentiate structurally similar ligands, indicating higher sensitivity to analogues. Collectively, the better performance of ID-Score enables it as a useful tool in assessing protein-ligand binding affinity in structure-based drug discovery as well as in lead optimization.

Chloroquine Inhibits Colon Cancer Cell Growth In Vitro and Tumor Growth In Vivo via Induction of Apoptosis

The present study was to investigate the anticancer effect of chloroquine on proliferation of mouse colon cancer cell line CT26 in vivo and in vitro and the possible mechanism. We found that chloroquine inhibited CT26 proliferation by concentration- and time-dependent manner. This effect was associated with apoptosis induction and decreased level of phosphorylated p42/44 mitogen-activated protein kinase and phosphorylated Akt. The in vivo study showed chloroquine-reduced tumor volume and prolonged survival time in CT26-bearing mice. These observations indicated chloroquine could inhibit CT26 proliferation by inducing apoptosis both in vitro and in vivo, providing its chemotherapeutic potential of human cancers.

Pharmacophore modeling study based on known spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors.

In this investigation, chemical features based 3D pharmacophore models were developed based on the known inhibitors of Spleen tyrosine kinase (Syk) with the aid of hiphop and hyporefine modules within catalyst. The best quantitative pharmacophore model, Hypo1, was used as a 3D structural query for retrieving potential inhibitors from chemical databases including Specs, NCI, MayBridge, and Chinese Nature Product Database (CNPD). The hit compounds were subsequently subjected to filtering by Lipinskis rule of five and docking studies to refine the retrieved hits. Finally 30 compounds were selected from the top ranked hit compounds and conducted an in vitro kinase inhibitory assay. Six compounds showed a good inhibitory potency against Syk, which have been selected for further investigation.

Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2

Pharmacophore modeling, including ligand- and structure-based approaches, has become an important tool in drug discovery. However, the ligand-based method often strongly depends on the training set selection, and the structure-based pharmacophore model is usually created based on apo structures or a single protein-ligand complex, which might miss some important information. In this study, multicomplex-based method has been suggested to generate a comprehensive pharmacophore map of cyclin-dependent kinase 2 (CDK2) based on a collection of 124 crystal structures of human CDK2-inhibitor complex. Our multicomplex-based comprehensive pharmacophore map contains almost all the chemical features important for CDK2-inhibitor interactions. A comparison with previously reported ligand-based pharmacophores has revealed that the ligand-based models are just a subset of our comprehensive map. Furthermore, one most-frequent-feature pharmacophore model consisting of the most frequent pharmacophore features was constructed based on the statistical frequency information provided by the comprehensive map. Validations to the most-frequent-feature model show that it can not only successfully discriminate between known CDK2 inhibitors and the molecules of focused inactive dataset, but also is capable of correctly predicting the activities of a wide variety of CDK2 inhibitors in an external active dataset. Obviously, this investigation provides some new ideas about how to develop a multicomplex-based pharmacophore model that can be used in virtual screening to discover novel potential lead compounds.

Discovery of Novel Pim-1 Kinase Inhibitors by a Hierarchical Multistage Virtual Screening Approach Based on SVM Model, Pharmacophore, and Molecular Docking

In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.

Cationic nanocarriers induce cell necrosis through impairment of Na+/K+-ATPase and cause subsequent inflammatory response

Nanocarriers with positive surface charges are known for their toxicity which has limited their clinical applications. The mechanism underlying their toxicity, such as the induction of inflammatory response, remains largely unknown. In the present study we found that injection of cationic nanocarriers, including cationic liposomes, PEI, and chitosan, led to the rapid appearance of necrotic cells. Cell necrosis induced by cationic nanocarriers is dependent on their positive surface charges, but does not require RIP1 and Mlkl. Instead, intracellular Na+ overload was found to accompany the cell death. Depletion of Na+ in culture medium or pretreatment of cells with the Na+/K+-ATPase cation-binding site inhibitor ouabain, protected cells from cell necrosis. Moreover, treatment with cationic nanocarriers inhibited Na+/K+-ATPase activity both in vitro and in vivo. The computational simulation showed that cationic carriers could interact with cation-binding site of Na+/K+-ATPase. Mice pretreated with a small dose of ouabain showed improved survival after injection of a lethal dose of cationic nanocarriers. Further analyses suggest that cell necrosis induced by cationic nanocarriers and the resulting leakage of mitochondrial DNA could trigger severe inflammation in vivo, which is mediated by a pathway involving TLR9 and MyD88 signaling. Taken together, our results reveal a novel mechanism whereby cationic nanocarriers induce acute cell necrosis through the interaction with Na+/K+-ATPase, with the subsequent exposure of mitochondrial damage-associated molecular patterns as a key event that mediates the inflammatory responses. Our study has important implications for evaluating the biocompatibility of nanocarriers and designing better and safer ones for drug delivery.

Antitumor and antimetastatic activities of chloroquine diphosphate in a murine model of breast cancer.

Metastatic breast cancers are hard to treat and almost always fatal. Chloroquine diphosphate, a derivative of quinine, has long been used as a potent and commonly used medicine against different human diseases. We therefore investigated the effects of chloroquine diphosphate on a highly metastatic mouse mammary carcinoma cell line. In vitro treatment of 4T1 mouse breast cancer cells with chloroquine diphosphate resulted in significant inhibition of cellular proliferation and viability, and induction of apoptosis in 4T1 cells in a time- and dose-dependent manner. Further analysis indicated that induction of apoptosis was associated with the loss of mitochondrial membrane potential, release of cytochrome c, and activation of caspase-9 and caspase-3, and cleavage of poly(ADP-ribose) polymerase. The effect of chloroquine diphosphate was then examined using a mice model in which 4T1 cells were implanted subcutaneously. Chloroquine diphosphate (25mg/kg and 50mg/kg, respectively) significantly inhibited the growth of the implanted 4T1 tumor cells and induced apoptosis in the tumor microenvironment. Moreover, the metastasis of tumor cells to the lungs was inhibited significantly and the survival of the mice enhanced. These data suggested that chloroquine diphosphate might have chemotherapeutic efficacy against breast cancer including inhibition of metastasis.

Cell growth inhibition, G2/M cell cycle arrest, and apoptosis induced by chloroquine in human breast cancer cell line Bcap-37.

Chloroquine is an antimalarial drug that has been used in the treatment and prophylaxis of malaria since the 1950s. The present study was undertaken to examine the effects of chloroquine on Bcap-37 human breast cancer cells’ growth, cell cycle modulation, apoptosis induction, and associated molecular alterations in vitro. The chloroquine treatment decreased the viability of Bcap-37 cells in a concentration- and time-dependent manner, which correlated with G2/M phase cell cycle arrest. The chloroquine-mediated cell cycle arrest was associated with a decrease in protein levels/activity of polo-like kinase 1 (Plk1), phosphorylated cell division cycle 25C (Cdc25C), phosphorylated extracellular signal-regulated kinase 1/2 (ERK1/2), phosphorylated Akt. The chloroquine-treated Bcap-37 cells exhibited a marked decrease in the level of mitochondrial transmembrane potential (ΔΨm), which was accompanied by the activation of caspase-3 and cleaved poly(ADP-ribose) polymerase (PARP). Exposure of Bcap-37 cells to chloroquine also resulted in the induction of spindle abnormalities. In conclusion, the findings in this study suggested that chloroquine might have potential anticancer efficacy, which could be attributed, in part, to its proliferation inhibition and apoptosis induction of cancer cells through modulation of apoptosis and cell cycle-related proteins expressions, down-regulation of mitochondrial transmembrane potential (ΔΨm), and induction of spindle abnormalities.

An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs

OBJECTIVE Support vector machine (SVM), a statistical learning method, has recently been evaluated in the prediction of absorption, distribution, metabolism, and excretion properties, as well as toxicity (ADMET) of new drugs. However, two problems still remain in SVM modeling, namely feature selection and parameter setting. The two problems have been shown to have an important impact on the efficiency and accuracy of SVM classification. In particular, the feature subset choice and optimal SVM parameter settings influence each other; this suggested that they should be dealt with simultaneously. In this paper, we propose an integrated scheme to account for both feature subset choice and SVM parameter settings in concert. METHOD In the proposed scheme, a genetic algorithm (GA) is used for the feature selection and the conjugate gradient (CG) method for the parameter optimization. Several classification models of ADMET related properties have been built for assessing and testing the integrated GA-CG-SVM scheme. They include: (1) identification of P-glycoprotein substrates and nonsubstrates, (2) prediction of human intestinal absorption, (3) prediction of compounds inducing torsades de pointes, and (4) prediction of blood-brain barrier penetration. RESULTS Compared with the results of previous SVM studies, our GA-CG-SVM approach significantly improves the overall prediction accuracy and has fewer input features. CONCLUSIONS Our results indicate that considering feature selection and parameter optimization simultaneously, in SVM modeling, can help to develop better predictive models for the ADMET properties of drugs.