Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. EXPERIMENTAL APPROACHIn this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. KEY RESULTSHigh performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC 50 or EC 50 values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. CONCLUSION AND IMPLICATIONSIn summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.Abbreviations bSDTNBI, balanced substructure-drug-target network-based inference; DTI, drug-target interaction; e P , precision enhancement; e R , recall enhancement; ERα, oestrogen receptor α; E2, estradiol; MoA, mechanism of action; NBI, network-based inference; P, precision; R, recall; ROC, receiver operating characteristic

Thymoquinone (TQ, 2‐methyl‐5‐isopropyl‐1,4‐benzoquinone), a bioactive constituent extracted from the seeds of Nigella sativa, has been proved to exert anti‐tumor efficiency in various cancers. Autophagy is a self‐digestion phenomenon, and its role in tumor formation and progression remains controversial. In the present study, we investigated the effects of TQ on renal cell cancer (RCC) cell lines (786‐O and ACHN) using wound healing assay, transwell assay and western blot analysis. We found that TQ effectively inhibited the metastatic capacity of RCC cells in vitro, which was also verified in a xenograft model. Meanwhile, we observed LC3 puncta and detected the expression of LC3 in TQ‐treated RCC cells, and then found that autophagy was induced by TQ in 786‐O and ACHN cell lines. In addition, TQ inhibited the migration and invasion as well as the EMT in RCC cells in an autophagy‐dependent manner. To further explore the underlying mechanism, we detected the AMPK/mTOR signaling pathway. The results indicated that TQ inhibited the metastasis of RCC cells by inducing autophagy via AMPK/mTOR signaling pathway. In conclusion, our findings provide a novel therapeutic strategy that aims at TQ‐induced autophagy in RCC treatment.

The chemokine (C-C motif) ligand 2 (CCL2) with its cognate receptor chemokine (C-C motif) receptor 2 (CCR2) plays important roles in tumor invasion and metastasis. However, the mechanisms and mediators for autocrine CCL2 and CCL2-CCR2 axis remain elusive in breast cancer. Here we examined the levels of CCL2 in 4 breast cancer cell lines along with 57 human breast cancer specimens and found them significantly increased with presence of 17β-estradiol (E2) in estrogen receptor (ER)-positive breast cancer cells, while anti-estrogen treatment weakened this enhancement. CCL2 expression positively correlated with Twist staining and aggressiveness of breast cancer. Estrogen exposure facilitated the proliferation, invasion and metastasis of hormone-dependent breast cancer and promoted angiogenesis via the increased secretion of CCL2 in vitro and in vivo, which could be suppressed by disruption of CCL2-CCR2 axis with CCR2 antagonist RS102895. Knockdown of Twist in MCF-7 cells significantly inhibited E2-induced CCL2 production, indicating an essential role of Twist in CCL2 regulation under estrogenic condition. Our data show the hormonal regulation on CCL2-CCR2 axis is associated with enhanced Twist expression via activation of ERα and PI3K/AKT/NF-κB signaling. Thus, CCL2-CCR2 axis may represent as a novel therapeutic target eagerly needed for hormone-dependent breast cancer.

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The cell membrane is a major barrier for drug transport. Given that many cancer drugs must passively cross the cell membrane, understanding drug-membrane interactions is crucial. We used fluorescence-activated cell sorting to investigate how cholesterol influences the transport of the cancer drugs ellipticine and pirarubicin across cell membranes. We showed that cholesterol depletion helped pirarubicin cross the membranes of nonsmall cell lung carcinoma and Chinese hamster ovary cells. In contrast, the uptake of ellipticine was not strongly influenced by cholesterol depletion. To study the microscopic origins of these observations, atomistic molecular dynamics simulations were performed. Doxorubicin (similar in structure to pirarubicin) and ellipticine were simulated in model membranes of POPC and POPC with 40 mol % cholesterol. Atomistic free energy calculations for the translocation of a single ellipticine and doxorubicin across the lipid bilayers qualitatively matched the experiment results. The free energy barrier for doxorubicin crossing the bilayer was strongly increased when cholesterol was present, while for ellipticine the barrier remained similar with and without cholesterol. Molecular dynamics simulations showed that the different hydrogen-bonding propensities of the two drugs are likely the major factor for the different behaviors. The qualitative agreement between cell experiments and atomistic computer simulations illustrates the potential to link observed biological phenomena and single molecule mechanisms of actions. Our results suggest that the traditional understanding of drug permeation and the influence of cholesterol on the small molecule transport is naïve and needs to be re-examined.