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Fisher JL, Wilk EJ, Oza VH, Gary SE, Howton TC, Flanary VL, Clark AD, Hjelmeland AB, Lasseigne BN. Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates. FEBS Open Bio 2024; 14:803-830. [PMID: 38531616 PMCID: PMC11073506 DOI: 10.1002/2211-5463.13796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Vishal H. Oza
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Sam E. Gary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Anita B. Hjelmeland
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
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2
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Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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3
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Singh G, Ramanathan M. Repurposing Artificial Intelligence Tools for Disease Modeling: Case Study of Face Recognition Deficits in Neurodegenerative Diseases. Clin Pharmacol Ther 2023; 114:862-873. [PMID: 37394678 DOI: 10.1002/cpt.2987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/20/2023] [Indexed: 07/04/2023]
Abstract
Face recognition deficits occur in diseases such as prosopagnosia, autism, Alzheimer's disease, and dementias. The objective of this study was to evaluate whether degrading the architecture of artificial intelligence (AI) face recognition algorithms can model deficits in diseases. Two established face recognition models, convolutional-classification neural network (C-CNN) and Siamese network (SN), were trained on the FEI faces data set (~ 14 images/person for 200 persons). The trained networks were perturbed by reducing weights (weakening) and node count (lesioning) to emulate brain tissue dysfunction and lesions, respectively. Accuracy assessments were used as surrogates for face recognition deficits. The findings were compared with clinical outcomes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Face recognition accuracy decreased gradually for weakening factors less than 0.55 for C-CNN, and 0.85 for SN. Rapid accuracy loss occurred at higher values. C-CNN accuracy was similarly affected by weakening any convolutional layer whereas SN accuracy was more sensitive to weakening of the first convolutional layer. SN accuracy declined gradually with a rapid drop when nearly all nodes were lesioned. C-CNN accuracy declined rapidly when as few as 10% of nodes were lesioned. CNN and SN were more sensitive to lesioning of the first convolutional layer. Overall, SN was more robust than C-CNN, and the findings from SN experiments were concordant with ADNI results. As predicted from modeling, brain network failure quotient was related to key clinical outcome measures for cognition and functioning. Perturbation of AI networks is a promising method for modeling disease progression effects on complex cognitive outcomes.
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Affiliation(s)
- Gargi Singh
- Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
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4
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Tayal S, Bhatnagar S. Role of molecular mimicry in the SARS-CoV-2-human interactome for pathogenesis of cardiovascular diseases: An update to ImitateDB. Comput Biol Chem 2023; 106:107919. [PMID: 37463554 DOI: 10.1016/j.compbiolchem.2023.107919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 06/13/2023] [Accepted: 07/06/2023] [Indexed: 07/20/2023]
Abstract
Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.
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Affiliation(s)
- Sonali Tayal
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi 110078, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi 110078, India.
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5
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In silico Methods for Identification of Potential Therapeutic Targets. Interdiscip Sci 2022; 14:285-310. [PMID: 34826045 PMCID: PMC8616973 DOI: 10.1007/s12539-021-00491-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/19/2021] [Accepted: 11/01/2021] [Indexed: 11/01/2022]
Abstract
AbstractAt the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.
Graphical abstract
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6
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Viacava Follis A. Centrality of drug targets in protein networks. BMC Bioinformatics 2021; 22:527. [PMID: 34715787 PMCID: PMC8555226 DOI: 10.1186/s12859-021-04342-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/23/2021] [Indexed: 01/13/2023] Open
Abstract
Background In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target’s ‘fitness’ based on its node characteristics within annotated protein functional networks to complement contingent therapeutic hypotheses. Results We observed that targets of approved, selective small molecule drugs exhibit high node centrality within protein networks relative to a broader set of investigational targets spanning various development stages. Targets of approved drugs also exhibit higher centrality than other proteins within their respective functional class. These findings expand on previous reports of drug targets’ network centrality by suggesting some centrality metrics such as low topological coefficient as inherent characteristics of a ‘good’ target, relative to other exploratory targets and regardless of its functional class. These centrality metrics could thus be indicators of an individual protein’s ‘fitness’ as potential drug target. Correlations between protein nodes’ network centrality and number of associated publications underscored the possibility of knowledge bias as an inherent limitation to such predictions. Conclusions Despite some entanglement with knowledge bias, like structure-oriented ‘druggability’ assessments of new protein targets, centrality metrics could assist early pharmaceutical discovery teams in evaluating potential targets with limited experimental proof of concept and help allocate resources for an effective drug discovery pipeline. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04342-x.
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Affiliation(s)
- Ariele Viacava Follis
- EMD Serono Research and Development Inc., 45A Middlesex Turnpike, Billerica, MA, 01821, USA.
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7
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Beckman MF, Mougeot FB, Mougeot JLC. Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach. J Clin Med 2021; 10:1666. [PMID: 33924631 PMCID: PMC8070572 DOI: 10.3390/jcm10081666] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/29/2021] [Accepted: 04/07/2021] [Indexed: 12/20/2022] Open
Abstract
The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (n = 22) from six disease categories were found to have significant associated pathways, validated by Q-Q plots (p < 0.05). Protein-protein interactions of significant (p < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development.
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Affiliation(s)
| | - Farah Bahrani Mougeot
- Department of Oral Medicine, Carolinas Medical Center, Atrium Health, Charlotte, NC 28203, USA;
| | - Jean-Luc C. Mougeot
- Department of Oral Medicine, Carolinas Medical Center, Atrium Health, Charlotte, NC 28203, USA;
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8
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Tong Z, Zhou Y, Wang J. Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine. Sci Rep 2019; 9:10442. [PMID: 31320657 PMCID: PMC6639372 DOI: 10.1038/s41598-019-46540-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 06/26/2019] [Indexed: 02/08/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one major cause of cancer-related death worldwide. But now, the systematic therapy for the advanced stages of HCC is rather limited. Thus, the discovery of novel drug targets and thereafter targeted drugs against HCC is continuously needed. In this study, we combined clinical association data, gene expression profiles and manually collected drug target genes with the human protein-protein interaction (PPI) network to establish an in-silico HCC drug target predictor. First, we found drug target genes (DTGs), disease-associated genes (DAGs), prognostic unfavorable genes (PUGs) and cancer up-regulated genes (URGs) have higher degree, betweenness, closeness centrality, while cancer down-regulated genes (DRGs), prognostic favorable genes (PFGs) have lower degrees, in comparison with background genes. Moreover, DTG nodes were shown to be closer to DAG, PUG and URG nodes, but farther away from PFG and DRG nodes. Compared to the background, PFGs and DRGs were shown to have relatively bigger genetic dependency scores, while PUGs and URGs have smaller genetic dependency scores. Finally, based on the observed features of DTGs, we constructed a drug target predictor using one-class support vector machine (one-class SVM). Performance evaluation results suggested our predictor could effectively identify putative drug target genes for further research.
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Affiliation(s)
- Zhan Tong
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.
| | - Juan Wang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.
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9
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Wu J, Wang B, Li M, Shi YH, Wang C, Kang YG. Network pharmacology identification of mechanisms of cerebral ischemia injury amelioration by Baicalin and Geniposide. Eur J Pharmacol 2019; 859:172484. [PMID: 31229537 DOI: 10.1016/j.ejphar.2019.172484] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/17/2019] [Accepted: 06/18/2019] [Indexed: 01/05/2023]
Abstract
Cerebral ischemia is one of the main causes of human neurological dysfunction. Baicalin (BC) and Geniposide (GP) and their combination (BC/GP) have an ameliorative effect on cerebral ischemia. Here, we use network pharmacology to predict the targets of BC, GP and BC/GP, then explored the protective mechanisms of the drugs on cerebral ischemia injury caused by abnormal activation of microglia cells in vitro. The results indicate that 45 targets related to cerebral ischemic injury were predicted by network pharmacology, and 26 cerebral ischemia related pathways were extracted by the KEGG database. In vitro lipopolysaccharide (LPS) stimulated BV-2 cells to establish a model of inflammatory injury induced by microglia. The effects of BC, GP and BC/GP on the expression of TNF-α, IL-1β and IL-10, TGF-β and TNF-α were verified. Network pharmacology predicts the regulation of the 5-LOX/CysLTs inflammatory pathway. Finally, we found that GP and BC/GP exert anti-inflammatory and neuroprotective effects by regulating the polarization state of microglia and down-regulating 5-LOX/CysLTs, and has certain protective effects on nerve damage following cerebral ischemia.
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Affiliation(s)
- Jie Wu
- Shaanxi University of Chinese Medicine, Xianyang, 712046, China.
| | - Bin Wang
- Shaanxi University of Chinese Medicine, Xianyang, 712046, China.
| | - Min Li
- Shaanxi University of Chinese Medicine, Xianyang, 712046, China
| | - Yong-Heng Shi
- Shaanxi University of Chinese Medicine, Xianyang, 712046, China
| | - Chuan Wang
- Shaanxi University of Chinese Medicine, Xianyang, 712046, China
| | - Ya-Guo Kang
- Shaanxi University of Chinese Medicine, Xianyang, 712046, China
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10
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Chamberlin SR, Blucher A, Wu G, Shinto L, Choonoo G, Kulesz-Martin M, McWeeney S. Natural Product Target Network Reveals Potential for Cancer Combination Therapies. Front Pharmacol 2019; 10:557. [PMID: 31214023 PMCID: PMC6555193 DOI: 10.3389/fphar.2019.00557] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 05/03/2019] [Indexed: 12/20/2022] Open
Abstract
A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.
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Affiliation(s)
- Steven R Chamberlin
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States
| | - Aurora Blucher
- OHSU Knight Cancer Institute, Portland, OR, United States
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States.,Oregon Clinical and Translational Research Institute, Portland, OR, United States
| | - Lynne Shinto
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Gabrielle Choonoo
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States
| | - Molly Kulesz-Martin
- OHSU Knight Cancer Institute, Portland, OR, United States.,Departments of Dermatology and Cell, Developmental and Cancer Biology, Oregon Health and Sciences University, Portland, OR, United States
| | - Shannon McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States.,Oregon Clinical and Translational Research Institute, Portland, OR, United States
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11
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Abstract
Background For treating a complex disease such as cancer, some effective means are needed to control biological networks that underlies the disease. The one-target one-drug paradigm has been the dominating drug discovery approach in the past decades. Compared to single target-based drugs, combination drug targets may overcome many limitations of single drug target and achieve a more effective and safer control of the disease. Most of existing combination drug targets are developed based on clinical experience or text-and-trial strategy, which cannot provide theoretical guidelines for designing and screening effective drug combinations. Therefore, systematic identification of multiple drug targets and optimal intervention strategy needs to be developed. Results We developed a strategy to screen the synergistic combinations of two drug targets in disease networks based on the classification of single drug targets. The method tried to identify the sensitivity of single intervention and then the combination of multiple interventions that can restore the disease network to a desired normal state. In our strategy of screening drug target combinations, we first classified all drug targets into sensitive and insensitive single drug targets. Then, we identified the synergistic and antagonistic of drug target combinations, including the combinations of sensitive drug targets, the combinations of insensitive drug target and the combination of sensitive and insensitive targets. Finally, we applied our strategy to Arachidonic Acid (AA) metabolic network and found 18 pairs of synergistic drug target combinations, five of which have been proven to be viable by biological or medical experiments. Conclusions Different from traditional methods for judging drug synergy and antagonism, we propose the framework of how to enhance the efficiency by perturbing two sensitive targets in a combinatorial way, how to decrease the drug dose and therefore its side effect and cost by perturbing combinatorially a main sensitive target and an auxiliary insensitive target, and how to perturb two insensitive targets to realize the transition from a disease state to a healthy one which cannot be realized by perturbing each insensitive target alone. Although the idea is mainly applied to an AA metabolic network, the strategy holds for more general molecular networks such as combinatorial regulation in gene regulatory networks. Electronic supplementary material The online version of this article (10.1186/s12859-019-2730-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Min Luo
- Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China
| | - Jianfeng Jiao
- Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China
| | - Ruiqi Wang
- Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China.
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12
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Li XX, Yin J, Tang J, Li Y, Yang Q, Xiao Z, Zhang R, Wang Y, Hong J, Tao L, Xue W, Zhu F. Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target. Front Pharmacol 2018; 9:1245. [PMID: 30429792 PMCID: PMC6220079 DOI: 10.3389/fphar.2018.01245] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 10/12/2018] [Indexed: 12/14/2022] Open
Abstract
One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.
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Affiliation(s)
- Xiao Xu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yinghong Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Runyuan Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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13
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Md Aksam VK, Chandrasekaran VM, Pandurangan S. Topological alternate centrality measure capturing drug targets in the network of MAPK pathways. IET Syst Biol 2018; 12:226-232. [PMID: 30259868 PMCID: PMC8687289 DOI: 10.1049/iet-syb.2017.0058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 04/04/2018] [Accepted: 04/30/2018] [Indexed: 12/18/2022] Open
Abstract
A new centrality of the nodes in the network is proposed called alternate centrality, which can isolate effective drug targets in the complex signalling network. Alternate centrality metric defined over the network substructure (four nodes - motifs). The nodes involving in alternative activation in the motifs gain in metric values. Targeting high alternative centrality nodes hypothesised to be destructive free to the network due to their alternative activation mechanism. Overlapping and crosstalk among the gene products in the conserved network of MAPK pathways selected for the study. In silico knock-out of high alternate centrality nodes causing rewiring in the network is investigated using MCF-7 breast cancer cell line-based data. Degree of top alternate centrality nodes lies between the degree of bridging and pagerank nodes. Node deletion of high alternate centrality on the centralities such as eccentricity, closeness, betweenness, stress, centroid and radiality causes low perturbation. The authors identified the following alternate centrality nodes ERK1, ERK2, MEKK2, MKK5, MKK4, MLK3, MLK2, MLK1, MEKK4, MEKK1, TAK1, P38alpha, ZAK, DLK, LZK, MLTKa/b and P38beta as efficient drug targets for breast cancer. Alternate centrality identifies effective drug targets and is free from intertwined biological processes and lethality.
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Affiliation(s)
- V K Md Aksam
- School of Advanced Sciences, VIT University, Vellore 632014, India
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Elliott A, Leicht E, Whitmore A, Reinert G, Reed-Tsochas F. A nonparametric significance test for sampled networks. Bioinformatics 2018; 34:64-71. [PMID: 29036452 PMCID: PMC5870844 DOI: 10.1093/bioinformatics/btx419] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 06/30/2017] [Indexed: 12/31/2022] Open
Abstract
Motivation Our work is motivated by an interest in constructing a protein–protein interaction network that captures key features associated with Parkinson’s disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation. Results We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork; in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein–protein interaction network. Availability and implementation The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrew Elliott
- CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford OX1 1HP, UK
| | - Elizabeth Leicht
- CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford OX1 1HP, UK
| | | | - Gesine Reinert
- Department of Statistics, University of Oxford, Oxford, UK
| | - Felix Reed-Tsochas
- CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford OX1 1HP, UK.,Oxford Martin School, University of Oxford, Oxford, UK
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15
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Dos Santos Vasconcelos CR, de Lima Campos T, Rezende AM. Building protein-protein interaction networks for Leishmania species through protein structural information. BMC Bioinformatics 2018; 19:85. [PMID: 29510668 PMCID: PMC5840830 DOI: 10.1186/s12859-018-2105-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 03/01/2018] [Indexed: 12/21/2022] Open
Abstract
Background Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. Results The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. Conclusions The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported. Electronic supplementary material The online version of this article (10.1186/s12859-018-2105-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Crhisllane Rafaele Dos Santos Vasconcelos
- Microbiology Department of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil. .,Genetics Department of Universidade Federal de Pernambuco, Recife, PE, Brazil.
| | - Túlio de Lima Campos
- Microbiology Department of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil.,Bioinformatics Plataform of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil
| | - Antonio Mauro Rezende
- Microbiology Department of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil. .,Bioinformatics Plataform of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil. .,Genetics Department of Universidade Federal de Pernambuco, Recife, PE, Brazil.
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16
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A computational network analysis based on targets of antipsychotic agents. Schizophr Res 2018; 193:154-160. [PMID: 28755876 DOI: 10.1016/j.schres.2017.07.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 05/04/2017] [Accepted: 07/18/2017] [Indexed: 11/22/2022]
Abstract
Currently, numerous antipsychotic agents have been developed in the area of pharmacological treatment of schizophrenia. However, the molecular mechanism underlying multi targets of antipsychotics were yet to be explored. In this study we performed a computational network analysis based on targets of antipsychotic agents. We retrieved a total of 96 targets from 56 antipsychotic agents. By expression enrichment analysis, we identified that the expressions of antipsychotic target genes were significantly enriched in liver, brain, blood and corpus striatum. By protein-protein interaction (PPI) network analysis, a PPI network with 77 significantly interconnected target genes was generated. By historeceptomics analysis, significant brain region specific target-drug interactions were identified in targets of dopamine receptors (DRD1-Olanzapine in caudate nucleus and pons (P-value<0.005), DRD2-Bifeprunox in caudate nucleus and pituitary (P-value<0.0005), DRD4-Loxapine in Pineal (P-value<0.00001)) and 5-hydroxytryptamine receptor (HTR2A-Risperidone in occipital lobe, prefrontal cortex and subthalamic nucleus (P-value<0.0001)). By pathway grouped network analysis, 34 significant pathways were identified and significantly grouped into 6 sub networks related with drug metabolism, Calcium signaling, GABA receptors, dopamine receptors, Bile secretion and Gap junction. Our results may provide biological explanation for antipsychotic targets and insights for molecular mechanism of antipsychotic agents.
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17
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Bloomingdale P, Nguyen VA, Niu J, Mager DE. Boolean network modeling in systems pharmacology. J Pharmacokinet Pharmacodyn 2018; 45:159-180. [PMID: 29307099 PMCID: PMC6531050 DOI: 10.1007/s10928-017-9567-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 12/29/2017] [Indexed: 01/01/2023]
Abstract
Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.
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Affiliation(s)
- Peter Bloomingdale
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA
| | - Van Anh Nguyen
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA
| | - Jin Niu
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA
| | - Donald E Mager
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA.
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18
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Wang J, Guo Z, Fu Y, Wu Z, Huang C, Zheng C, Shar PA, Wang Z, Xiao W, Wang Y. Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs. Brief Bioinform 2017; 18:321-332. [PMID: 26962012 DOI: 10.1093/bib/bbw018] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Indexed: 12/16/2022] Open
Abstract
Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery.
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Affiliation(s)
- Jinan Wang
- Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Zihu Guo
- Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Yingxue Fu
- Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Ziyin Wu
- Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Chao Huang
- Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Chunli Zheng
- Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Piar Ali Shar
- College of Life Science, Northwest A & F University, Yangling, Shaanxi, 712100, China; Center of Bioinformatics, Northwest A & F University, Yangling, Shaanxi, China
| | - Zhenzhong Wang
- Jiangsu Kanion Pharmaceutical Co. Ltd., Lianyungang, PR China
| | - Wei Xiao
- State Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Lianyungang, Jiangsu, China
| | - Yonghua Wang
- Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, Yangling, Shaanxi, China, School of Chemical engineering, Dalian University of Technology, Dalian, Liaoning, China, Beijing University of Chinese Medicine, ChaoYang District, Beijing, China and School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
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Abstract
A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the "one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations. The complexity of the molecular and cellular bases of systems responses to drug interventions has fostered an increased interest in systems-oriented approaches to drug discovery. Consonant with this knowledge of the multifactorial mechanistic basis of drug sensitivity and resistance is the application of network-based approaches for the identification of molecular (multi-)feature signatures associated with desired (multi-)drug phenotypic profiles. This chapter illustrates the principal network analysis and inference techniques which have found application in systems-oriented drug design and considers their benefits and drawbacks in relation to the nature of the data produced by network pharmacology.
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20
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Rai S, Bhatnagar S. Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 20:152-68. [DOI: 10.1089/omi.2015.0172] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Sneha Rai
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
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21
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Ke Z, Zhang X, Cao Z, Ding Y, Li N, Cao L, Wang T, Zhang C, Ding G, Wang Z, Xu X, Xiao W. Drug discovery of neurodegenerative disease through network pharmacology approach in herbs. Biomed Pharmacother 2016; 78:272-279. [PMID: 26898452 DOI: 10.1016/j.biopha.2016.01.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 01/08/2016] [Accepted: 01/13/2016] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative diseases, referring to as the progressive loss of structure and function of neurons, constitute one of the major challenges of modern medicine. Traditional Chinese herbs have been used as a major preventive and therapeutic strategy against disease for thousands years. The numerous species of medicinal herbs and Traditional Chinese Medicine (TCM) compound formulas in nervous system disease therapy make it a large chemical resource library for drug discovery. In this work, we collected 7362 kinds of herbs and 58,147 Traditional Chinese medicinal compounds (Tcmcs). The predicted active compounds in herbs have good oral bioavailability and central nervous system (CNS) permeability. The molecular docking and network analysis were employed to analyze the effects of herbs on neurodegenerative diseases. In order to evaluate the predicted efficacy of herbs, automated text mining was utilized to exhaustively search in PubMed by some related keywords. After that, receiver operator characteristic (ROC) curves was used to estimate the accuracy of predictions. Our study suggested that most herbs were distributed in family of Asteraceae, Fabaceae, Lamiaceae and Apocynaceae. The predictive model yielded good sensitivity and specificity with the AUC values above 0.800. At last, 504 kinds of herbs were obtained by using the optimal cutoff values in ROC curves. These 504 herbs would be the most potential herb resources for neurodegenerative diseases treatment. This study would give us an opportunity to use these herbs as a chemical resource library for drug discovery of anti-neurodegenerative disease.
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Affiliation(s)
- Zhipeng Ke
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Xinzhuang Zhang
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Zeyu Cao
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Yue Ding
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Na Li
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Liang Cao
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Tuanjie Wang
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Chenfeng Zhang
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Gang Ding
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Zhenzhong Wang
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China
| | - Xiaojie Xu
- Beijing National Laboratory for Molecular Sciences (BNLMS), State Key Laboratory of Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
| | - Wei Xiao
- National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, China.
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22
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Rahmani H, Blockeel H, Bender A. Using a Human Drug Network for generating novel hypotheses about drugs. INTELL DATA ANAL 2016. [DOI: 10.3233/ida-150800] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Hossein Rahmani
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
- Department of Knowledge Engineering, Universiteit Maastricht, Maastricht, The Netherlands
| | - Hendrik Blockeel
- Department of Computer Science, KU Leuven, Leuven, Belgium
- Leiden Institute of Advanced Computer Science, Leiden University, CA Leiden, The Netherlands
| | - Andreas Bender
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
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23
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Trinh HC, Kwon YK. Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks. Biosystems 2015; 137:64-72. [DOI: 10.1016/j.biosystems.2015.07.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 07/13/2015] [Accepted: 07/16/2015] [Indexed: 01/18/2023]
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Chua HE, Bhowmick SS, Tucker-Kellogg L, Dewey CF. TENET: topological feature-based target characterization in signalling networks. Bioinformatics 2015; 31:3306-14. [PMID: 26079348 DOI: 10.1093/bioinformatics/btv360] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 06/05/2015] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features. RESULTS Tenet first computes a set of topological features and then leverages a support vector machine-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target. We empirically study the performance of Tenet from a wide variety of aspects, using several signalling networks from BioModels with real-world curated outcomes. Results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION Our software is available freely for non-commercial purposes from: https://sites.google.com/site/cosbyntu/softwares/tenet CONTACT hechua@ntu.edu.sg or assourav@ntu.edu.sg SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Huey Eng Chua
- School of Computer Engineering, Nanyang Technological University
| | | | - Lisa Tucker-Kellogg
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore and
| | - C Forbes Dewey
- Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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25
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van Wijk R, Tans SJ, Wolde PRT, Mashaghi A. Non-monotonic dynamics and crosstalk in signaling pathways and their implications for pharmacology. Sci Rep 2015; 5:11376. [PMID: 26087464 PMCID: PMC5155565 DOI: 10.1038/srep11376] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 05/22/2015] [Indexed: 12/19/2022] Open
Abstract
Currently, drug discovery approaches commonly assume a monotonic dose-response relationship. However, the assumption of monotonicity is increasingly being challenged. Here we show that for two simple interacting linear signaling pathways that carry two different signals with different physiological responses, a non-monotonic input-output relation can arise with simple network topologies including coherent and incoherent feed-forward loops. We show that non-monotonicity of the response functions has severe implications for pharmacological treatment. Fundamental constraints are imposed on the effectiveness and toxicity of any drug independent of its chemical nature and selectivity due to the specific network structure.
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Affiliation(s)
- Roeland van Wijk
- FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
- Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, the Netherlands
| | - Sander J. Tans
- FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
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26
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A survey on the computational approaches to identify drug targets in the postgenomic era. BIOMED RESEARCH INTERNATIONAL 2015; 2015:239654. [PMID: 26060814 PMCID: PMC4427773 DOI: 10.1155/2015/239654] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 08/27/2014] [Indexed: 11/18/2022]
Abstract
Identifying drug targets plays essential roles in designing new drugs and combating diseases. Unfortunately, our current knowledge about drug targets is far from comprehensive. Screening drug targets in the lab is an expensive and time-consuming procedure. In the past decade, the accumulation of various types of omics data makes it possible to develop computational approaches to predict drug targets. In this paper, we make a survey on the recent progress being made on computational methodologies that have been developed to predict drug targets based on different kinds of omics data and drug property data.
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Wang RS, Maron BA, Loscalzo J. Systems medicine: evolution of systems biology from bench to bedside. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:141-61. [PMID: 25891169 DOI: 10.1002/wsbm.1297] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 03/04/2015] [Accepted: 03/06/2015] [Indexed: 12/11/2022]
Abstract
High-throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high-throughput data and predictive computational modeling to understand biological functions as system-level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision.
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Affiliation(s)
- Rui-Sheng Wang
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Cardiology, Veterans Affairs Boston Healthcare System, West Roxbury, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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28
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Large-scale identification of potential drug targets based on the topological features of human protein–protein interaction network. Anal Chim Acta 2015; 871:18-27. [DOI: 10.1016/j.aca.2015.02.032] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2014] [Revised: 01/29/2015] [Accepted: 02/10/2015] [Indexed: 01/17/2023]
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29
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Tao C, Sun J, Zheng WJ, Chen J, Xu H. Colorectal cancer drug target prediction using ontology-based inference and network analysis. Database (Oxford) 2015; 2015:bav015. [PMID: 25818893 PMCID: PMC4375358 DOI: 10.1093/database/bav015] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Revised: 02/04/2015] [Accepted: 02/05/2015] [Indexed: 11/25/2022]
Abstract
Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study, we present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets. We utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data, we inferred 113 potential CRC drug targets. We further prioritized these genes based on their relationships with CRC disease genes in the context of human protein-protein interaction networks. Thus, among the 113 potential drug targets, 15 were selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDA-approved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics.
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Affiliation(s)
- Cui Tao
- Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jingchun Sun
- Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - W Jim Zheng
- Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Junjie Chen
- Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hua Xu
- Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Wu L, Shen Y, Li M, Wu FX. Network output controllability-based method for drug target identification. IEEE Trans Nanobioscience 2015; 14:184-91. [PMID: 25643411 DOI: 10.1109/tnb.2015.2391175] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Biomolecules do not perform their functions alone, but interactively with one another to form so called biomolecular networks. It is well known that a complex disease stems from the malfunctions of corresponding biomolecular networks. Therefore, one of important tasks is to identify drug targets from biomolecular networks. In this study, the drug target identification is formulated as a problem of finding steering nodes in biomolecular networks while the concept of network output controllability is applied to the problem of drug target identification. By applying control signals to these steering nodes, the biomolecular networks are expected to be transited from one state to another. A graph-theoretic algorithm has been proposed to find a minimum set of steering nodes in biomolecular networks which can be a potential set of drug targets. Application results of the method to real biomolecular networks show that identified potential drug targets are in agreement with existing research results. This indicates that the method can generate testable predictions and provide insights into experimental design of drug discovery.
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31
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Zhang X, Gu J, Cao L, Ma Y, Su Z, Luo F, Wang Z, Li N, Yuan G, Chen L, Xu X, Xiao W. Insights into the inhibition and mechanism of compounds against LPS-induced PGE2 production: a pathway network-based approach and molecular dynamics simulations. Integr Biol (Camb) 2014; 6:1162-9. [PMID: 25228393 DOI: 10.1039/c4ib00141a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In comparison to the current target-based screening approach, it is increasingly evident that active lead compounds based on disease-related phenotypes are more likely to be translated to clinical trials during drug development. That is, because human diseases are in essence the outcome of the abnormal function of multiple genes, especially in complex diseases. Therefore, as a conventional technology in the early phase of active lead compound discovery, computational methods that can connect molecular interactions and disease-related phenotypes to evaluate the efficacy of compounds are in urgently required. In this work, a computational approach that integrates molecular docking and pathway network analysis (network efficiency and network flux) was developed to evaluate the efficacy of a compound against LPS-induced Prostaglandin E2(PGE2) production. The predicted results were then validated in vitro, and a correlation with the experimental results was analyzed using linear regression. In addition, molecular dynamics (MD) simulations were performed to explore the molecular mechanism of the most potent compounds. There were 12 hits out of 28 predicted ingredients separated from Reduning injection (RDN). The predicted results have a good agreement with the experimental inhibitory potency (IC50) (correlation coefficient = 0.80). The most potent compounds could target several proteins to regulate the pathway network. This might partly interpret the molecular mechanism of RDN on fever. Meanwhile, the good correlation of the computational model with the wet experimental results might bridge the gap between molecule-target interactions and phenotypic response, especially for multi-target compounds. Therefore, it would be helpful for active lead compound discovery, the understanding of the multiple targets and synergic essence of traditional Chinese medicine (TCM).
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Affiliation(s)
- Xinzhuang Zhang
- State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, Kanion Pharmaceutical Corporation, Lianyungang City 222002, P. R. China.
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32
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Pinto JP, Machado RSR, Xavier JM, Futschik ME. Targeting molecular networks for drug research. Front Genet 2014; 5:160. [PMID: 24926314 PMCID: PMC4045242 DOI: 10.3389/fgene.2014.00160] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2014] [Accepted: 05/14/2014] [Indexed: 01/18/2023] Open
Abstract
The study of molecular networks has recently moved into the limelight of biomedical research. While it has certainly provided us with plenty of new insights into cellular mechanisms, the challenge now is how to modify or even restructure these networks. This is especially true for human diseases, which can be regarded as manifestations of distorted states of molecular networks. Of the possible interventions for altering networks, the use of drugs is presently the most feasible. In this mini-review, we present and discuss some exemplary approaches of how analysis of molecular interaction networks can contribute to pharmacology (e.g., by identifying new drug targets or prediction of drug side effects), as well as list pointers to relevant resources and software to guide future research. We also outline recent progress in the use of drugs for in vitro reprogramming of cells, which constitutes an example par excellence for altering molecular interaction networks with drugs.
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Affiliation(s)
- José P Pinto
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal
| | - Rui S R Machado
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal
| | - Joana M Xavier
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal
| | - Matthias E Futschik
- SysBioLab, Centre for Molecular and Structural Biomedicine, Universidade do Algarve Faro, Portugal ; Centre of Marine Sciences, Universidade do Algarve Faro, Portugal
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33
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Shi SH, Cai YP, Cai XJ, Zheng XY, Cao DS, Ye FQ, Xiang Z. A network pharmacology approach to understanding the mechanisms of action of traditional medicine: Bushenhuoxue formula for treatment of chronic kidney disease. PLoS One 2014; 9:e89123. [PMID: 24598793 PMCID: PMC3943740 DOI: 10.1371/journal.pone.0089123] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Accepted: 01/20/2014] [Indexed: 12/17/2022] Open
Abstract
Traditional Chinese medicine (TCM) has unique therapeutic effects for complex chronic diseases. However, for the lack of an effective systematic approach, the research progress on the effective substances and pharmacological mechanism of action has been very slow. In this paper, by incorporating network biology, bioinformatics and chemoinformatics methods, an integrated approach was proposed to systematically investigate and explain the pharmacological mechanism of action and effective substances of TCM. This approach includes the following main steps: First, based on the known drug targets, network biology was used to screen out putative drug targets; Second, the molecular docking method was used to calculate whether the molecules from TCM and drug targets related to chronic kidney diseases (CKD) interact or not; Third, according to the result of molecular docking, natural product-target network, main component-target network and compound-target network were constructed; Finally, through analysis of network characteristics and literature mining, potential effective multi-components and their synergistic mechanism were putatively identified and uncovered. Bu-shen-Huo-xue formula (BSHX) which was frequently used for treating CKD, was used as the case to demonstrate reliability of our proposed approach. The results show that BSHX has the therapeutic effect by using multi-channel network regulation, such as regulating the coagulation and fibrinolytic balance, and the expression of inflammatory factors, inhibiting abnormal ECM accumulation. Tanshinone IIA, rhein, curcumin, calycosin and quercetin may be potential effective ingredients of BSHX. This research shows that the integration approach can be an effective means for discovering active substances and revealing their pharmacological mechanisms of TCM.
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Affiliation(s)
- Shao-hua Shi
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Yue-piao Cai
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Xiao-jun Cai
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Xiao-yong Zheng
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Dong-sheng Cao
- School of Pharmaceutical Sciences, Central South University, Changsha, China
| | - Fa-qing Ye
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China
- * E-mail: (FY); (ZX)
| | - Zheng Xiang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China
- * E-mail: (FY); (ZX)
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Network-assisted prediction of potential drugs for addiction. BIOMED RESEARCH INTERNATIONAL 2014; 2014:258784. [PMID: 24689033 PMCID: PMC3932722 DOI: 10.1155/2014/258784] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Accepted: 12/30/2013] [Indexed: 12/19/2022]
Abstract
Drug addiction is a chronic and complex brain disease, adding much burden on the community. Though numerous efforts have been made to identify the effective treatment, it is necessary to find more novel therapeutics for this complex disease. As network pharmacology has become a promising approach for drug repurposing, we proposed to apply the approach to drug addiction, which might provide new clues for the development of effective addiction treatment drugs. We first extracted 44 addictive drugs from the NIDA and their targets from DrugBank. Then, we constructed two networks: an addictive drug-target network and an expanded addictive drug-target network by adding other drugs that have at least one common target with these addictive drugs. By performing network analyses, we found that those addictive drugs with similar actions tended to cluster together. Additionally, we predicted 94 nonaddictive drugs with potential pharmacological functions to the addictive drugs. By examining the PubMed data, 51 drugs significantly cooccurred with addictive keywords than expected. Thus, the network analyses provide a list of candidate drugs for further investigation of their potential in addiction treatment or risk.
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35
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Wu L, Shen Y, Li M, Wu FX. Drug Target Identification Based on Structural Output Controllability of Complex Networks. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/978-3-319-08171-7_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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36
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Network-based approaches in drug discovery and early development. Clin Pharmacol Ther 2013; 94:651-8. [PMID: 24025802 DOI: 10.1038/clpt.2013.176] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Accepted: 09/03/2013] [Indexed: 12/20/2022]
Abstract
Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target-drug combination with high potential for yielding clinical success within the efficacy-toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification.
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Zhou X, Chen P, Wei Q, Shen X, Chen X. Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets. ACTA ACUST UNITED AC 2013; 29:2024-31. [PMID: 23782618 DOI: 10.1093/bioinformatics/btt353] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
MOTIVATION A molecular interaction network can be viewed as a network in which genes with related functions are connected. Therefore, at a systems level, connections between individual genes in a molecular interaction network can be used to infer the collective functional linkages between biologically meaningful gene sets. RESULTS We present the human interactome resource and the gene set linkage analysis (GSLA) tool for the functional interpretation of biologically meaningful gene sets observed in experiments. GSLA determines whether an observed gene set has significant functional linkages to established biological processes. When an observed gene set is not enriched by known biological processes, traditional enrichment-based interpretation methods cannot produce functional insights, but GSLA can still evaluate whether those genes work in concert to regulate specific biological processes, thereby suggesting the functional implications of the observed gene set. The quality of human interactome resource and the utility of GSLA are illustrated with multiple assessments. AVAILABILITY http://www.cls.zju.edu.cn/hir/
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Affiliation(s)
- Xi Zhou
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, P.R. China
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38
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Modular pharmacology: deciphering the interacting structural organization of the targeted networks. Drug Discov Today 2013; 18:560-6. [DOI: 10.1016/j.drudis.2013.01.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2012] [Revised: 12/14/2012] [Accepted: 01/16/2013] [Indexed: 12/24/2022]
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 506] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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40
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Network pharmacology: a new approach for chinese herbal medicine research. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2013; 2013:621423. [PMID: 23762149 PMCID: PMC3671675 DOI: 10.1155/2013/621423] [Citation(s) in RCA: 129] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2013] [Revised: 03/28/2013] [Accepted: 05/02/2013] [Indexed: 12/29/2022]
Abstract
The dominant paradigm of "one gene, one target, one disease" has influenced many aspects of drug discovery strategy. However, in recent years, it has been appreciated that many effective drugs act on multiple targets rather than a single one. As an integrated multidisciplinary concept, network pharmacology, which is based on system biology and polypharmacology, affords a novel network mode of "multiple targets, multiple effects, complex diseases" and replaces the "magic bullets" by "magic shotguns." Chinese herbal medicine (CHM) has been recognized as one of the most important strategies in complementary and alternative medicine. Though CHM has been practiced for a very long time, its effectiveness and beneficial contribution to public health has not been fully recognized. Also, the knowledge on the mechanisms of CHM formulas is scarce. In the present review, the concept and significance of network pharmacology is briefly introduced. The application and potential role of network pharmacology in the CHM fields is also discussed, such as data collection, target prediction, network visualization, multicomponent interaction, and network toxicology. Furthermore, the developing tendency of network pharmacology is also summarized, and its role in CHM research is discussed.
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Duran-Frigola M, Mosca R, Aloy P. Structural Systems Pharmacology: The Role of 3D Structures in Next-Generation Drug Development. ACTA ACUST UNITED AC 2013; 20:674-84. [DOI: 10.1016/j.chembiol.2013.03.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2013] [Revised: 02/28/2013] [Accepted: 03/05/2013] [Indexed: 01/12/2023]
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42
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Bánky D, Iván G, Grolmusz V. Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs. PLoS One 2013; 8:e54204. [PMID: 23382878 PMCID: PMC3558500 DOI: 10.1371/journal.pone.0054204] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2011] [Accepted: 12/11/2012] [Indexed: 11/19/2022] Open
Abstract
Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the directed edge structure of the graph.
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Affiliation(s)
- Dániel Bánky
- Protein Information Technology Group, Eötvös University, Pázmány Péter stny. 1/C, Budapest, Hungary
- Uratim Ltd., Budapest, Hungary
| | - Gábor Iván
- Protein Information Technology Group, Eötvös University, Pázmány Péter stny. 1/C, Budapest, Hungary
- Uratim Ltd., Budapest, Hungary
| | - Vince Grolmusz
- Protein Information Technology Group, Eötvös University, Pázmány Péter stny. 1/C, Budapest, Hungary
- Uratim Ltd., Budapest, Hungary
- * E-mail:
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43
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Mora A, Donaldson IM. Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. BMC Bioinformatics 2012; 13:294. [PMID: 23146171 PMCID: PMC3534413 DOI: 10.1186/1471-2105-13-294] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Accepted: 11/02/2012] [Indexed: 11/21/2022] Open
Abstract
Background Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction. Results Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn. Conclusions These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.
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Affiliation(s)
- Antonio Mora
- Department for Molecular Biosciences, University of Oslo, Norway
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44
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Schmidt BJ, Papin JA, Musante CJ. Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today 2012; 18:116-27. [PMID: 22999913 DOI: 10.1016/j.drudis.2012.09.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 08/17/2012] [Accepted: 09/05/2012] [Indexed: 01/24/2023]
Abstract
A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.
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Affiliation(s)
- Brian J Schmidt
- Department of Bioengineering, University of California at San Diego, La Jolla, CA 92093-0412, USA
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45
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Kotlyar M, Fortney K, Jurisica I. Network-based characterization of drug-regulated genes, drug targets, and toxicity. Methods 2012; 57:499-507. [PMID: 22749929 DOI: 10.1016/j.ymeth.2012.06.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2012] [Revised: 05/30/2012] [Accepted: 06/08/2012] [Indexed: 12/25/2022] Open
Abstract
Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.
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Affiliation(s)
- Max Kotlyar
- The Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network, IBM Life Sciences Discovery Centre, Toronto Medical Discovery Tower, 9-305, 101 College Street, Toronto, Ontario, M5G 1L7, Canada.
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46
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Sun J, Xu H, Zhao Z. Network-Assisted Investigation of Antipsychotic Drugs and Their Targets. Chem Biodivers 2012; 9:900-10. [DOI: 10.1002/cbdv.201100356] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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47
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Jaeger S, Aloy P. From protein interaction networks to novel therapeutic strategies. IUBMB Life 2012; 64:529-37. [DOI: 10.1002/iub.1040] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2012] [Accepted: 03/14/2012] [Indexed: 01/18/2023]
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Edberg A, Soeria-Atmadja D, Bergman Laurila J, Johansson F, Gustafsson MG, Hammerling U. Assessing Relative Bioactivity of Chemical Substances Using Quantitative Molecular Network Topology Analysis. J Chem Inf Model 2012; 52:1238-49. [DOI: 10.1021/ci200429f] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Anna Edberg
- Division of Food
Data, National Food Agency, SE-75126 Uppsala, Sweden
| | - Daniel Soeria-Atmadja
- Division of R&D Information, AstraZeneca Research and Development, SE-15185, Södertälje, Sweden
| | | | - Fredrik Johansson
- Division of Information
Technology,
National Food Agency, SE-75126 Uppsala, Sweden
| | - Mats G. Gustafsson
- Division of Cancer Pharmacology and Computational Medicine, Department of Medical Sciences, Uppsala University and Uppsala Academic Hospital, SE-75185 Uppsala, Sweden
| | - Ulf Hammerling
- Department of Risk Benefit Assessment,
National Food Agency, SE-75126 Uppsala, Sweden
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De Las Rivas J, Prieto C. Protein interactions: mapping interactome networks to support drug target discovery and selection. Methods Mol Biol 2012; 910:279-96. [PMID: 22821600 DOI: 10.1007/978-1-61779-965-5_12] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Proteins are biomolecular structures that build the microscopic working machinery of any living system. Proteins within the cells and biological systems do not act alone, but rather team up into macromolecular structures enclosing intricate physicochemical dynamic connections to undertake biological functions. A critical step towards unraveling the complex molecular relationships in living systems is the mapping of protein-to-protein physical "interactions". The complete map of protein interactions that can occur in a living organism is called the "interactome". Achieving an adequate atlas of all the protein interactions within a living system should allow to build its interaction network and to identity the "central nodes" that can be critical for the function, the homeostasis, and the movement of such system. Focusing on human studies, the data about the human interactome are most relevant for current biomedical research, because it is clear that the location of the proteins in the interactome network will allow to evaluate their centrality and to redefine the potential value of each protein as a drug target. This chapter presents our current knowledge on the human protein-protein interactome and explains how such knowledge can help us to select adequate targets for drugs.
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Affiliation(s)
- Javier De Las Rivas
- Bioinformatics and Functional Genomics Group, Cancer Research Center (IBMCC, CSIC/USAL), Salamanca, Spain.
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Abstract
The era of targeted cancer therapies has arrived. However, due to the complexity of biological systems, the current progress is far from enough. From biological network modeling to structural/dynamic network analysis, network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells. It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.
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Affiliation(s)
- Ting-Ting Zhou
- Department of Immunology, Institute of Basic Medical Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China.
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