Discovery and structure–activity analysis of selective estrogen receptor modulators via similarity-based virtual screening

Shen, Jie; Jiang, Jing; Kuang, Guanglin; Tan, Chengfang; Liu, Guixia; Huang, Jin; Tang, Yun

doi:10.1016/j.ejmech.2012.04.041

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Readers are encouraged to refer to the excellent reviews by Jiang et al for various computational drug design methods [80,81] . Virtual screening has been widely used for the discovery of NR ligands [62,[82][83][84][85] .…”

Section: Virtual Screening Approachesmentioning

confidence: 99%

ROR nuclear receptors: structures, related diseases, and drug discovery

et al. 2014

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Nuclear receptors (NRs) are ligand-regulated transcription factors that regulate metabolism, development and immunity. The NR superfamily is one of the major classes of drug targets for human diseases. Retinoic acid receptor-related orphan receptor (ROR) α, β and γ belong to the NR superfamily, and these receptors are still considered as 'orphan' receptors because the identification of their endogenous ligands has been controversial. Recent studies have demonstrated that these receptors are regulated by synthetic ligands, thus emerge as important drug targets for the treatment of multiple sclerosis, rheumatoid arthritis, psoriasis, etc. Studying the structural basis and ligand development of RORs will pave the way for a better understanding of the roles of these receptors in human diseases. Here, we review the structural basis, disease relevance, strategies for ligand identification, and current status of development of therapeutic ligands for RORs.

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Section: Virtual Screening Approachesmentioning

confidence: 99%

ROR nuclear receptors: structures, related diseases, and drug discovery

et al. 2014

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“…In the last two decades, a large number of chemicals have been assayed for estrogenic activity by government agencies and academic research groups (Blair et al, 2000;Branham et al, 2002;Shen et al, 2010Shen et al, , 2012. In addition, new chemical entities have been synthesized to target ER for the treatment of various diseases (Komm and Chines, 2012;Minutolo et al, 2011;Silverman, 2010).…”

mentioning

confidence: 99%

EADB: An Estrogenic Activity Database for Assessing Potential Endocrine Activity

Shen

Fang

et al. 2013

Toxicological Sciences

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Endocrine-active chemicals can potentially have adverse effects on both humans and wildlife. They can interfere with the body's endocrine system through direct or indirect interactions with many protein targets. Estrogen receptors (ERs) are one of the major targets, and many endocrine disruptors are estrogenic and affect the normal estrogen signaling pathways. However, ERs can also serve as therapeutic targets for various medical conditions, such as menopausal symptoms, osteoporosis, and ER-positive breast cancer. Because of the decades-long interest in the safety and therapeutic utility of estrogenic chemicals, a large number of chemicals have been assayed for estrogenic activity, but these data exist in various sources and different formats that restrict the ability of regulatory and industry scientists to utilize them fully for assessing risk-benefit. To address this issue, we have developed an Estrogenic Activity Database (EADB; http://www.fda.gov/ScienceResearch/BioinformaticsTools/EstrogenicActivityDatabaseEADB/default.htm) and made it freely available to the public. EADB contains 18,114 estrogenic activity data points collected for 8212 chemicals tested in 1284 binding, reporter gene, cell proliferation, and in vivo assays in 11 different species. The chemicals cover a broad chemical structure space and the data span a wide range of activities. A set of tools allow users to access EADB and evaluate potential endocrine activity of chemicals. As a case study, a classification model was developed using EADB for predicting ER binding of chemicals.

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“…To examine the application of bSDTNBI for identifying new drug targets or lead compounds for drug discovery and development, we showed how to identify novel ligands targeting ERα. Before bSDTNBI prediction, 6116 unique compounds were identified via chemical similarity search as described in our previous study (Shen et al, ). Then, 56 commercially available compounds computationally identified by bSDTNBI were purchased for bioassays.…”

Section: Resultsmentioning

confidence: 99%

In silico prediction of chemical mechanism of action via an improved network‐based inference method

Lü

et al. 2016

British J Pharmacology

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136

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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

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Discovery and structure–activity analysis of selective estrogen receptor modulators via similarity-based virtual screening

Cited by 11 publications

References 22 publications

ROR nuclear receptors: structures, related diseases, and drug discovery

ROR nuclear receptors: structures, related diseases, and drug discovery

EADB: An Estrogenic Activity Database for Assessing Potential Endocrine Activity

In silico prediction of chemical mechanism of action via an improved network‐based inference method

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