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Guo R, Yu J, Guo Z. Virtual Screening and Binding Analysis of Potential CD58 Inhibitors in Colorectal Cancer (CRC). Molecules 2023; 28:6819. [PMID: 37836662 PMCID: PMC10574072 DOI: 10.3390/molecules28196819] [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: 09/05/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Human cell surface receptor CD58, also known as lymphocyte function-associated antigen 3 (LFA-3), plays a critical role in the early stages of immune response through interacting with CD2. Recent research identified CD58 as a surface marker of colorectal cancer (CRC), which can upregulate the Wnt pathway and promote self-renewal of colorectal tumor-initiating cells (CT-ICs) by degradation of Dickkopf 3. In addition, it was also shown that knockdown of CD58 significantly impaired tumor growth. In this study, we developed a structure-based virtual screening pipeline using Autodock Vina and binding analysis and identified a group of small molecular compounds having the potential to bind with CD58. Five of them significantly inhibited the growth of the SW620 cell line in the following in vitro studies. Their proposed binding models were further verified by molecular dynamics (MD) simulations, and some pharmaceutically relevant chemical and physical properties were predicted. The hits described in this work may be considered interesting leads or structures for the development of new and more efficient CD58 inhibitors.
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Affiliation(s)
- Rong Guo
- Computational Biology, Bioinformatics and Genomics Program, Department of Biological Sciences, University of Maryland, College Park, MD 20742, USA
| | - Jiangnan Yu
- International Cancer Center, Shenzhen University Medical School, Shenzhen 518054, China
| | - Zhikun Guo
- International Cancer Center, Shenzhen University Medical School, Shenzhen 518054, China
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2
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Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7035634. [PMID: 36262874 PMCID: PMC9576438 DOI: 10.1155/2022/7035634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/23/2022] [Indexed: 11/17/2022]
Abstract
Drug-disease correlations play an important role in revealing the mechanism of disease, finding new indications of available drugs, or drug repositioning. A variety of computational approaches were proposed to find drug-disease correlations and achieve good performances. However, these methods used a variety of network information, but integrated networks were rarely used. In addition, the role of known drug-disease association data has not been fully played. In this work, we designed a combination algorithm of random walk and supervised learning to find the drug-disease correlations. We used an integrated network to update the model and selected a gene set as the start of random walk based on the known drug-disease correlations data. The experimental results show that the proposed method can effectively find the correlation between drugs and diseases, and the prediction accuracy is 82.7%. We found that there are 8 pairs of drug-disease relationships that have not yet been reported, and 5 of them have pharmacodynamic effects on Parkinson's disease. We also found that a key linkage between Parkinson's disease and phenylhexol, a drug for the treatment of Parkinson's disease α-synuclein and tau protein, provides a useful exploration for the effectiveness of the treatment of Parkinson's disease.
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Abstract
Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.
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4
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Wu Z, Li W, Liu G, Tang Y. Network-Based Methods for Prediction of Drug-Target Interactions. Front Pharmacol 2018; 9:1134. [PMID: 30356768 PMCID: PMC6189482 DOI: 10.3389/fphar.2018.01134] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Accepted: 09/18/2018] [Indexed: 01/10/2023] Open
Abstract
Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.
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Affiliation(s)
| | | | | | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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5
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Lu L, Yu H. DR2DI: a powerful computational tool for predicting novel drug-disease associations. J Comput Aided Mol Des 2018; 32:633-642. [DOI: 10.1007/s10822-018-0117-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 04/01/2018] [Indexed: 01/01/2023]
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6
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Li J, Lei K, Wu Z, Li W, Liu G, Liu J, Cheng F, Tang Y. Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs. Oncotarget 2018; 7:45584-45596. [PMID: 27329603 PMCID: PMC5216744 DOI: 10.18632/oncotarget.10052] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 05/28/2016] [Indexed: 02/05/2023] Open
Abstract
As the recent development of high-throughput technologies in cancer pharmacogenomics, there is an urgent need to develop new computational approaches for comprehensive identification of new pharmacogenomic biomarkers, such as microRNAs (miRNAs). In this study, a network-based framework, namely the SMiR-NBI model, was developed to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs on the basis of a heterogeneous network connecting drugs, miRNAs and genes. A high area under the receiver operating characteristic curve of 0.820 ± 0.013 was yielded during 10-fold cross validation. In addition, high performance was further validated in identifying new anticancer mechanism-of-action for natural products and non-steroidal anti-inflammatory drugs. Finally, the newly predicted miRNAs for tamoxifen and metformin were experimentally validated in MCF-7 and MDA-MB-231 breast cancer cell lines via qRT-PCR assays. High success rates of 60% and 65% were yielded for tamoxifen and metformin, respectively. Specifically, 11 oncomiRNAs (e.g. miR-20a-5p, miR-27a-3p, miR-29a-3p, and miR-146a-5p) from the top 20 predicted miRNAs were experimentally verified as new pharmacogenomic biomarkers for metformin in MCF-7 or MDA-MB-231 cell lines. In summary, the SMiR-NBI model would provide a powerful tool to identify potential pharmacogenomic biomarkers characterized by miRNAs in the emerging field of precision cancer medicine, which is available at http://lmmd.ecust.edu.cn/database/smir-nbi/.
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Affiliation(s)
- Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Kecheng Lei
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jianwen Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Feixiong Cheng
- State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.,Current address: Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA.,Current address: Center for Complex Networks Research, Northeastern University, Boston, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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Cheng F, Hong H, Yang S, Wei Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief Bioinform 2017; 18:682-697. [PMID: 27296652 DOI: 10.1093/bib/bbw051] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Indexed: 12/12/2022] Open
Abstract
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.
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Wu Z, Cheng F, Li J, Li W, Liu G, Tang Y. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform 2017; 18:333-347. [PMID: 26944082 DOI: 10.1093/bib/bbw012] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Indexed: 01/11/2023] Open
Abstract
Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Feixiong Cheng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
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Fang J, Liu C, Wang Q, Lin P, Cheng F. In silico polypharmacology of natural products. Brief Bioinform 2017; 19:1153-1171. [DOI: 10.1093/bib/bbx045] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Indexed: 12/16/2022] Open
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10
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Proteome-Scale Investigation of Protein Allosteric Regulation Perturbed by Somatic Mutations in 7,000 Cancer Genomes. Am J Hum Genet 2017; 100:5-20. [PMID: 27939638 DOI: 10.1016/j.ajhg.2016.09.020] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 09/27/2016] [Indexed: 02/05/2023] Open
Abstract
The allosteric regulation triggering the protein's functional activity via conformational changes is an intrinsic function of protein under many physiological and pathological conditions, including cancer. Identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes that they alter during tumor initiation and progression is a central challenge for cancer genomes in the post-genomic era. Here, we mapped more than 47,000 somatic missense mutations observed in approximately 7,000 tumor-normal matched samples across 33 cancer types into protein allosteric sites to prioritize the mutated allosteric proteins and we tested our prediction in cancer cell lines. We found that the deleterious mutations identified in cancer genomes were more significantly enriched at protein allosteric sites than tolerated mutations, suggesting a critical role for protein allosteric variants in cancer. Next, we developed a statistical approach, namely AlloDriver, and further identified 15 potential mutated allosteric proteins during pan-cancer and individual cancer-type analyses. More importantly, we experimentally confirmed that p.Pro360Ala on PDE10A played a potential oncogenic role in mediating tumorigenesis in non-small cell lung cancer (NSCLC). In summary, these findings shed light on the role of allosteric regulation during tumorigenesis and provide a useful tool for the timely development of targeted cancer therapies.
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11
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Yu L, Wang B, Ma X, Gao L. The extraction of drug-disease correlations based on module distance in incomplete human interactome. BMC SYSTEMS BIOLOGY 2016; 10:111. [PMID: 28155709 PMCID: PMC5260043 DOI: 10.1186/s12918-016-0364-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Extracting drug-disease correlations is crucial in unveiling disease mechanisms, as well as discovering new indications of available drugs, or drug repositioning. Both the interactome and the knowledge of disease-associated and drug-associated genes remain incomplete. RESULTS We present a new method to predict the associations between drugs and diseases. Our method is based on a module distance, which is originally proposed to calculate distances between modules in incomplete human interactome. We first map all the disease genes and drug genes to a combined protein interaction network. Then based on the module distance, we calculate the distances between drug gene sets and disease gene sets, and take the distances as the relationships of drug-disease pairs. We also filter possible false positive drug-disease correlations by p-value. Finally, we validate the top-100 drug-disease associations related to six drugs in the predicted results. CONCLUSION The overlapping between our predicted correlations with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrate our approach can not only effectively identify new drug indications, but also provide new insight into drug-disease discovery.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China.
| | - Bingbo Wang
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
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12
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sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides. Sci Rep 2016; 6:32115. [PMID: 27558848 PMCID: PMC4997263 DOI: 10.1038/srep32115] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 08/02/2016] [Indexed: 12/19/2022] Open
Abstract
Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.
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Moghadam H, Rahgozar M, Gharaghani S. Scoring multiple features to predict drug disease associations using information fusion and aggregation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:609-628. [PMID: 27455069 DOI: 10.1080/1062936x.2016.1209241] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Accepted: 06/30/2016] [Indexed: 06/06/2023]
Abstract
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
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Affiliation(s)
- H Moghadam
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - M Rahgozar
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - S Gharaghani
- b LBD, Institute of Biochemistry and Biophysics , University of Tehran , Tehran , Iran
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14
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Improving drug safety with a systems pharmacology approach. Eur J Pharm Sci 2016; 94:84-92. [PMID: 27287422 DOI: 10.1016/j.ejps.2016.06.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Revised: 05/20/2016] [Accepted: 06/06/2016] [Indexed: 12/26/2022]
Abstract
Systems pharmacology is used to mechanistically analyze drug-adverse drug reaction (ADRs) pairs and is a promising solution to the complex problem of understanding mechanisms of toxicity. In this research, we have explored the feasibility of retrospectively mapping population-level adverse events from the FDA Adverse Event Reporting System (FAERS) to chemical and biological databases to identify drug safety signals and the underlying molecular mechanisms. We used an analytic platform - Molecular Analysis of Side Effects (MASE™). For this purpose, we selected the adverse event of severe and potentially fatal cutaneous reactions (SCARs) that are associated with acetaminophen (APAP). SCARs encompass the continuum between Stevens-Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN). We found a statistically significant association between APAP and TEN, the most severe form of SCARs. We also explored the influence of APAP on other classes of drugs commonly associated with SCARs. We found that APAP significantly reduced the risk of SCARs commonly associated with carbamazepine (CBZ). We used molecular docking simulations to propose a mechanism for APAP's reduction in CBZ-induced SCARs which is competitive inhibition of the binding of CBZ to HLA-B*15:02. We conclude that systems pharmacology can complement established surveillance methodologies by providing a means to undertake an independent investigation and review of the mechanisms by which drugs cause adverse events.
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15
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Discovery of ERBB3 inhibitors for non-small cell lung cancer (NSCLC) via virtual screening. J Mol Model 2016; 22:135. [DOI: 10.1007/s00894-016-3007-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 05/02/2016] [Indexed: 02/06/2023]
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16
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Yauk CL, Buick JK, Williams A, Swartz CD, Recio L, Li H, Fornace AJ, Thomson EM, Aubrecht J. Application of the TGx-28.65 transcriptomic biomarker to classify genotoxic and non-genotoxic chemicals in human TK6 cells in the presence of rat liver S9. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2016; 57:243-60. [PMID: 26946220 PMCID: PMC5021161 DOI: 10.1002/em.22004] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 02/03/2016] [Accepted: 02/04/2016] [Indexed: 05/05/2023]
Abstract
In vitro transcriptional signatures that predict toxicities can facilitate chemical screening. We previously developed a transcriptomic biomarker (known as TGx-28.65) for classifying agents as genotoxic (DNA damaging) and non-genotoxic in human lymphoblastoid TK6 cells. Because TK6 cells do not express cytochrome P450s, we confirmed accurate classification by the biomarker in cells co-exposed to 1% 5,6 benzoflavone/phenobarbital-induced rat liver S9 for metabolic activation. However, chemicals may require different types of S9 for activation. Here we investigated the response of TK6 cells to higher percentages of Aroclor-, benzoflavone/phenobarbital-, or ethanol-induced rat liver S9 to expand TGx-28.65 biomarker applicability. Transcriptional profiles were derived 3 to 4 hr following a 4 hr co-exposure of TK6 cells to test chemicals and S9. Preliminary studies established that 10% Aroclor- and 5% ethanol-induced S9 alone did not induce the TGx-28.65 biomarker genes. Seven genotoxic and two non-genotoxic chemicals (and concurrent solvent and positive controls) were then tested with one of the S9s (selected based on cell survival and micronucleus induction). Relative survival and micronucleus frequency was assessed by flow cytometry in cells 20 hr post-exposure. Genotoxic/non-genotoxic chemicals were accurately classified using the different S9s. One technical replicate of cells co-treated with dexamethasone and 10% Aroclor-induced S9 was falsely classified as genotoxic, suggesting caution in using high S9 concentrations. Even low concentrations of genotoxic chemicals (those not causing cytotoxicity) were correctly classified, demonstrating that TGx-28.65 is a sensitive biomarker of genotoxicity. A meta-analysis of datasets from 13 chemicals supports that different S9s can be used in TK6 cells, without impairing classification using the TGx-28.65 biomarker.
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Affiliation(s)
- Carole L. Yauk
- Environmental Health Science and Research Bureau, Health CanadaOttawaOntarioCanada
| | - Julie K. Buick
- Environmental Health Science and Research Bureau, Health CanadaOttawaOntarioCanada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Health CanadaOttawaOntarioCanada
| | - Carol D. Swartz
- Integrated Laboratory Systems IncResearch Triangle ParkNorth Carolina
| | - Leslie Recio
- Integrated Laboratory Systems IncResearch Triangle ParkNorth Carolina
| | - Heng‐Hong Li
- Department of Biochemistry and Molecular and Cellular BiologyGeorgetown University Medical CenterWashingtonDistrict of Columbia
- Department of OncologyGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Albert J. Fornace
- Department of Biochemistry and Molecular and Cellular BiologyGeorgetown University Medical CenterWashingtonDistrict of Columbia
- Department of OncologyGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Errol M. Thomson
- Environmental Health Science and Research Bureau, Health CanadaOttawaOntarioCanada
| | - Jiri Aubrecht
- Drug Safety Research and Development, Pfizer IncGrotonConnecticut
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Yan XY, Zhang SW, Zhang SY. Prediction of drug–target interaction by label propagation with mutual interaction information derived from heterogeneous network. MOLECULAR BIOSYSTEMS 2016; 12:520-31. [DOI: 10.1039/c5mb00615e] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
By implementing label propagation on drug/target similarity network with mutual interaction information derived from drug–target heterogeneous network, LPMIHN algorithm identifies potential drug–target interactions.
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Affiliation(s)
- Xiao-Ying Yan
- Key Laboratory of Information Fusion Technology of Ministry of Education
- School of Automation
- Northwestern Polytechnical University
- Xi'an
- China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education
- School of Automation
- Northwestern Polytechnical University
- Xi'an
- China
| | - Song-Yao Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education
- School of Automation
- Northwestern Polytechnical University
- Xi'an
- China
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18
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Zhang C, Hong H, Mendrick DL, Tang Y, Cheng F. Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science. Biomark Med 2015; 9:1241-52. [PMID: 26506997 DOI: 10.2217/bmm.15.81] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Improved biomarker-based assessment of drug safety is needed in drug discovery and development as well as regulatory evaluation. However, identifying drug safety-related biomarkers such as genes, proteins, miRNA and single-nucleotide polymorphisms remains a big challenge. The advances of 'omics' and computational technologies such as genomics, transcriptomics, metabolomics, proteomics, systems biology, network biology and systems pharmacology enable us to explore drug actions at the organ and organismal levels. Computational and experimental systems pharmacology approaches could be utilized to facilitate biomarker-based drug safety assessment for drug discovery and development and to inform better regulatory decisions. In this article, we review the current status and advances of systems pharmacology approaches for the development of predictive models to identify biomarkers for drug safety assessment.
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Affiliation(s)
- Chen Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, 130 Meilong Road, Shanghai 200237, China
| | - Huixiao Hong
- National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Donna L Mendrick
- National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, 130 Meilong Road, Shanghai 200237, China
| | - Feixiong Cheng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, 130 Meilong Road, Shanghai 200237, China.,State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, Sichuan, China
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Yu L, Huang J, Ma Z, Zhang J, Zou Y, Gao L. Inferring drug-disease associations based on known protein complexes. BMC Med Genomics 2015; 8 Suppl 2:S2. [PMID: 26044949 PMCID: PMC4460611 DOI: 10.1186/1755-8794-8-s2-s2] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html.
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FXR antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach. Sci Rep 2015; 5:8114. [PMID: 25631039 PMCID: PMC4310094 DOI: 10.1038/srep08114] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Accepted: 01/07/2015] [Indexed: 12/20/2022] Open
Abstract
Non-steroidal anti-inflammatory drugs (NSAIDs) are worldwide used drugs for analgesic, antipyretic, and anti-inflammatory therapeutics. However, NSAIDs often cause several serious liver injuries, such as drug-induced liver injury (DILI), and the molecular mechanisms of DILI have not been clearly elucidated. In this study, we developed a systems pharmacology approach to explore the mechanism-of-action of NSAIDs. We found that the Farnesoid X Receptor (FXR) antagonism of NSAIDs is a potential molecular mechanism of DILI through systematic network analysis and in vitro assays. Specially, the quantitative real-time PCR assay reveals that indomethacin and ibuprofen regulate FXR downstream target gene expression in HepG2 cells. Furthermore, the western blot shows that FXR antagonism by indomethacin induces the phosphorylation of STAT3 (signal transducer and activator of transcription 3), promotes the activation of caspase9, and finally causes DILI. In summary, our systems pharmacology approach provided novel insights into molecular mechanisms of DILI for NSAIDs, which may propel the ways toward the design of novel anti-inflammatory pharmacotherapeutics.
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Yang J, Li Z, Fan X, Cheng Y. Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization. J Chem Inf Model 2014; 54:2562-9. [PMID: 25116798 DOI: 10.1021/ci500340n] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.
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Affiliation(s)
- Jihong Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
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Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology. Sci Rep 2014; 4:5576. [PMID: 24992957 PMCID: PMC4081875 DOI: 10.1038/srep05576] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Accepted: 06/17/2014] [Indexed: 12/25/2022] Open
Abstract
MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.
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Puniya BL, Kulshreshtha D, Verma SP, Kumar S, Ramachandran S. Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets. MOLECULAR BIOSYSTEMS 2014; 9:2798-815. [PMID: 24056838 DOI: 10.1039/c3mb70278b] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis to gain insights into gene expression architecture during log phase growth. The differentially expressed genes between at least one pair of 11 different M. tuberculosis strains as source of biological variability were used for co-expression network analysis. This data included genes with highest coefficient of variation in expression. Five distinct modules were identified using topological overlap based clustering. All the modules together showed significant enrichment in biological processes: fatty acid biosynthesis, cell membrane, intracellular membrane bound organelle, DNA replication, Quinone biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and structural constituents of ribosome and transposition. We then extracted the co-expressed connections which were supported either by transcriptional regulatory network or STRING database or high edge weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico and evaluated by Flux Balance Analysis. The results showed that in 12 out of 15 cases, in 11 more than 50% of reactions catalyzed by genes connected through co-expressed connections also had altered fluxes. The modules 'Turquoise', 'Blue' and 'Red' also showed enrichment in essential genes. We could map 152 of the previously known or proposed drug targets in these modules and identified 15 new potential drug targets based on their high degree of co-expressed connections and strong correlation with module eigengenes.
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Affiliation(s)
- Bhanwar Lal Puniya
- G N Ramachandran Knowledge Centre for Genome Informatics, CSIR - Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India.
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Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation. BMC Med Genomics 2013; 6 Suppl 3:S4. [PMID: 24565337 PMCID: PMC3980383 DOI: 10.1186/1755-8794-6-s3-s4] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue. METHODS We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations. RESULTS We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data. CONCLUSIONS We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.
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Cheng F, Li W, Wu Z, Wang X, Zhang C, Li J, Liu G, Tang Y. Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. J Chem Inf Model 2013; 53:753-62. [PMID: 23527559 DOI: 10.1021/ci400010x] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.
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Affiliation(s)
- Feixiong Cheng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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