<i>In silico</i> prediction of chemical mechanism of action via an improved network‐based inference method

Wu, Zengrui; Lü, Weiqiang; Wu, Dang; Luo, Anqi; Bian, Hanping; Li, Jie; Li, Weihua; Liu, Guixia; Huang, Jin; Cheng, Feixiong; Tang, Yun

doi:10.1111/bph.13629

Cited by 73 publications

(138 citation statements)

References 71 publications

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“…Recently, we further improved this method by introducing three tunable parameters that were successfully validated with biological assays. Twenty-seven of the 56 commercially available compounds were experimentally confirmed to have binding affinities for estrogen receptor α with IC 50 or EC 50 values ≤10 μmol/L [51] .…”

Section: Discussionmentioning

confidence: 99%

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach

Wang

et al. 2017

Acta Pharmacol Sin

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An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.

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Section: Discussionmentioning

confidence: 99%

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach

Wang

et al. 2017

Acta Pharmacol Sin

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“…In order to increase the data quality of the computationally predicted DTIs, we only used the predicted targets ranked in top 5 candidates from the best network model (bSDTNBI_KR) described in our previous studies. 20, 21 In total, Exp&ComNet contains 1,623 known and 1,259 predicted DTIs (Supporting Information, Table S7) connecting 275 natural products and 525 targets.…”

Section: Resultsmentioning

confidence: 99%

“…Via bSDTNBI, they identified several novel antagonistic or agonistic estrogen receptor alpha (ERα) ligands, and dual-effect ERα ligands by in vitro assays for the development of potential therapies in breast cancer or osteoporosis. 21 Therefore, systematic identification of new target spaces of natural products via network-based approaches would provide unexpected opportunities for drug discovery and development by exploiting the pharmaceutical wealth of natural products.…”

Section: Introductionmentioning

confidence: 99%

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Fang

Cai

et al. 2017

J. Chem. Inf. Model.

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Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein and kaempherol) with high scores were validated by various literatures. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with the known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram and metformin) with new mechanism-of-action were validated by various published experimental evidences. In summary, this study offers powerful computational systems pharmacology approaches and tools for development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

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“…These network-based approaches are useful in understanding the basis for cancer combination therapy [3], discovering treatment regimens for optimal efficacy [78], identifying the origins of drug induced adverse events [79][80][81], and indicating how drug combinations can mitigate serious adverse events [82]. For example, Wu developed an integrated network and cheminformatics tool (SDTNBI) for systematic prediction of drug-target interactions and drug repositioning [83,84]. Wang applied network topologies and dynamics parameters to obtain two potential weak-binding drug candidates whose effects were validated by in vitro experiments so as to provide a feasible way for drug discovery [85].…”

Section: Network Modeling and Pathway Analysismentioning

confidence: 99%

Computational methods and applications for quantitative systems pharmacology

Xie

2019

Quant. Biol.

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Background: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. Results: Various computational methods such as ADME/T evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine. Conclusion: QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.Author summary: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. This review is an attempt to introduce the computational methods for QSP, including ADME/T (absorption, distribution, metabolism, excretion and toxicity) evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient as well as their applications in medical guidance, drug discovery and explorations of pharmacodynamics material basis and mechanism of traditional Chinese medicine.

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

Cited by 73 publications

References 71 publications

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Computational methods and applications for quantitative systems pharmacology

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