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Zhao Y, Yin J, Zhang L, Zhang Y, Chen X. Drug-drug interaction prediction: databases, web servers and computational models. Brief Bioinform 2023; 25:bbad445. [PMID: 38113076 PMCID: PMC10782925 DOI: 10.1093/bib/bbad445] [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: 07/21/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023] Open
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi 214122, China
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2
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Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks. Pharmaceutics 2020; 12:pharmaceutics12090879. [PMID: 32947845 PMCID: PMC7557376 DOI: 10.3390/pharmaceutics12090879] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/09/2020] [Accepted: 09/10/2020] [Indexed: 01/19/2023] Open
Abstract
Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.
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3
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Wang YY, Cui C, Qi L, Yan H, Zhao XM. DrPOCS: Drug Repositioning Based on Projection Onto Convex Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:154-162. [PMID: 29993698 DOI: 10.1109/tcbb.2018.2830384] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.
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Ehrt C, Brinkjost T, Koch O. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Comput Biol 2018; 14:e1006483. [PMID: 30408032 PMCID: PMC6224041 DOI: 10.1371/journal.pcbi.1006483] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 09/02/2018] [Indexed: 11/24/2022] Open
Abstract
The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge. Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- Department of Computer Science, TU Dortmund University, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- * E-mail: ,
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5
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Abstract
Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.
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Affiliation(s)
- Weilin Zhang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Luhua Lai
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University , Beijing 100871, People's Republic of China
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6
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Luo H, Zhang P, Cao XH, Du D, Ye H, Huang H, Li C, Qin S, Wan C, Shi L, He L, Yang L. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome. Sci Rep 2016; 6:35996. [PMID: 27805045 PMCID: PMC5090963 DOI: 10.1038/srep35996] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 10/10/2016] [Indexed: 02/06/2023] Open
Abstract
The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational “drug candidate positioning” or “drug positioning”, to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.
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Affiliation(s)
- Heng Luo
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Ping Zhang
- Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - Xi Hang Cao
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA 19122, USA
| | - Dizheng Du
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hao Ye
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hui Huang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Can Li
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shengying Qin
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Chunling Wan
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Leming Shi
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Lin He
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Lun Yang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
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7
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Shi X, Lin X, Hu R, Sun N, Hao J, Gao C. Toxicological Differences Between NMDA Receptor Antagonists and Cholinesterase Inhibitors. Am J Alzheimers Dis Other Demen 2016; 31:405-12. [PMID: 26769920 PMCID: PMC10852557 DOI: 10.1177/1533317515622283] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2024]
Abstract
Cholinesterase inhibitors (ChEIs), represented by donepezil, rivastigmine, and galantamine, used to be the only approved class of drugs for the treatment of Alzheimer's disease. After the approval of memantine by the Food and Drug Administration (FDA), N-methyl-d-aspartic acid (NMDA) receptor antagonists have been recognized by authorities and broadly used in the treatment of Alzheimer's disease. Along with complementary mechanisms of action, NMDA antagonists and ChEIs differ not only in therapeutic effects but also in adverse reactions, which is an important consideration in clinical drug use. And the number of patients using NMDA antagonists and ChEIs concomitantly has increased, making the matter more complicated. Here we used the FDA Adverse Event Reporting System for statistical analysis , in order to compare the adverse events of memantine and ChEIs. In general, the clinical evidence confirmed the safety advantages of memantine over ChEIs, reiterating the precautions of clinical drug use and the future direction of antidementia drug development.
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Affiliation(s)
- Xiaodong Shi
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Xiaotian Lin
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Rui Hu
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Nan Sun
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Jingru Hao
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
| | - Can Gao
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical College, Jiangsu, China
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8
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Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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9
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Wang K, Wan M, Wang RS, Weng Z. Opportunities for Web-based Drug Repositioning: Searching for Potential Antihypertensive Agents with Hypotension Adverse Events. J Med Internet Res 2016; 18:e76. [PMID: 27036325 PMCID: PMC4833875 DOI: 10.2196/jmir.4541] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Revised: 12/02/2015] [Accepted: 01/04/2016] [Indexed: 12/21/2022] Open
Abstract
Background Drug repositioning refers to the process of developing new indications for existing drugs. As a phenotypic indicator of drug response in humans, clinical side effects may provide straightforward signals and unique opportunities for drug repositioning. Objective We aimed to identify drugs frequently associated with hypotension adverse reactions (ie, the opposite condition of hypertension), which could be potential candidates as antihypertensive agents. Methods We systematically searched the electronic records of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) through the openFDA platform to assess the association between hypotension incidence and antihypertensive therapeutic effect regarding a list of 683 drugs. Results Statistical analysis of FAERS data demonstrated that those drugs frequently co-occurring with hypotension events were more likely to have antihypertensive activity. Ranked by the statistical significance of frequent hypotension reporting, the well-known antihypertensive drugs were effectively distinguished from others (with an area under the receiver operating characteristic curve > 0.80 and a normalized discounted cumulative gain of 0.77). In addition, we found a series of antihypertensive agents (particularly drugs originally developed for treating nervous system diseases) among the drugs with top significant reporting, suggesting the good potential of Web-based and data-driven drug repositioning. Conclusions We found several candidate agents among the hypotension-related drugs on our list that may be redirected for lowering blood pressure. More important, we showed that a pharmacovigilance system could alternatively be used to identify antihypertensive agents and sustainably create opportunities for drug repositioning.
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Affiliation(s)
- Kejian Wang
- CoMed Technology & Consulting Co., Ltd., Hong Kong, China
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10
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Shu M, Zai X, Zhang B, Wang R, Lin Z. Hypothyroidism Side Effect in Patients Treated with Sunitinib or Sorafenib: Clinical and Structural Analyses. PLoS One 2016; 11:e0147048. [PMID: 26784451 PMCID: PMC4718448 DOI: 10.1371/journal.pone.0147048] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Accepted: 12/28/2015] [Indexed: 12/30/2022] Open
Abstract
Tyrosine kinase inhibitors (TKIs) provide more effective targeted treatments for cancer, but are subject to a variety of adverse effects, such as hypothyroidism. TKI-induced hypothyroidism is a highly complicated issue, because of not only the unrealized toxicological mechanisms, but also different incidences of individual TKI drugs. While sunitinib is suspected for causing thyroid dysfunction more often than other TKIs, sorafenib is believed to be less risky. Here we integrated clinical data and in silico drug-protein interactions to examine the pharmacological distinction between sunitinib and sorafenib. Statistical analysis on the FDA Adverse Event Reporting System (FAERS) confirmed that sunitinib is more concurrent with hypothyroidism than sorafenib, which was observed in both female and male patients. Then, we used docking method and identified 3 proteins specifically binding to sunitinib but not sorafenib, i.e., retinoid X receptor alpha, retinoic acid receptors beta and gamma. As potential off-targets of sunitinib, these proteins are well known to assemble with thyroid hormone receptors, which can explain the profound impact of sunitinib on thyroid function. Taken together, we established a strategy of integrated analysis on clinical records and drug off-targets, which can be applied to explore the molecular basis of various adverse drug reactions.
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Affiliation(s)
- Mao Shu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Xiaoli Zai
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Beina Zhang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Rui Wang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Zhihua Lin
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
- School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China
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11
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Sawada R, Iwata H, Mizutani S, Yamanishi Y. Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data. J Chem Inf Model 2015; 55:2717-30. [PMID: 26580494 DOI: 10.1021/acs.jcim.5b00330] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Drug repositioning, or the identification of new indications for known drugs, is a useful strategy for drug discovery. In this study, we developed novel computational methods to predict potential drug targets and new drug indications for systematic drug repositioning using large-scale chemical-protein interactome data. We explored the target space of drugs (including primary targets and off-targets) based on chemical structure similarity and phenotypic effect similarity by making optimal use of millions of compound-protein interactions. On the basis of the target profiles of drugs, we constructed statistical models to predict new drug indications for a wide range of diseases with various molecular features. The proposed method outperformed previous methods in terms of interpretability, applicability, and accuracy. Finally, we conducted a comprehensive prediction of the drug-target-disease association network for 8270 drugs and 1401 diseases and showed biologically meaningful examples of newly predicted drug targets and drug indications. The predictive model is useful to understand the mechanisms of the predicted drug indications.
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Affiliation(s)
- Ryusuke Sawada
- Division of System Cohort, Multi-scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University , 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Hiroaki Iwata
- Division of System Cohort, Multi-scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University , 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Sayaka Mizutani
- Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology , 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Yoshihiro Yamanishi
- Division of System Cohort, Multi-scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University , 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.,Institute for Advanced Study, Kyushu University , 6-10-1, Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
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12
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He W, Shi F, Zhou ZW, Li B, Zhang K, Zhang X, Ouyang C, Zhou SF, Zhu X. A bioinformatic and mechanistic study elicits the antifibrotic effect of ursolic acid through the attenuation of oxidative stress with the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in human hepatic stellate cells and rat liver. Drug Des Devel Ther 2015; 9:3989-4104. [PMID: 26347199 PMCID: PMC4529259 DOI: 10.2147/dddt.s85426] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
NADPH oxidases (NOXs) are a predominant mediator of redox homeostasis in hepatic stellate cells (HSCs), and oxidative stress plays an important role in the pathogenesis of liver fibrosis. Ursolic acid (UA) is a pentacyclic triterpenoid with various pharmacological activities, but the molecular targets and underlying mechanisms for its antifibrotic effect in the liver remain elusive. This study aimed to computationally predict the molecular interactome and mechanistically investigate the antifibrotic effect of UA on oxidative stress, with a focus on NOX4 activity and cross-linked signaling pathways in human HSCs and rat liver. Drug-drug interaction via chemical-protein interactome tool, a server that can predict drug-drug interaction via chemical-protein interactome, was used to predict the molecular targets of UA, and Database for Annotation, Visualization, and Integrated Discovery was employed to analyze the signaling pathways of the predicted targets of UA. The bioinformatic data showed that there were 611 molecular proteins possibly interacting with UA and that there were over 49 functional clusters responding to UA. The subsequential benchmarking data showed that UA significantly reduced the accumulation of type I collagen in HSCs in rat liver, increased the expression level of MMP-1, but decreased the expression level of TIMP-1 in HSC-T6 cells. UA also remarkably reduced the gene expression level of type I collagen in HSC-T6 cells. Furthermore, UA remarkably attenuated oxidative stress via negative regulation of NOX4 activity and expression in HSC-T6 cells. The employment of specific chemical inhibitors, SB203580, LY294002, PD98059, and AG490, demonstrated the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in the regulatory effect of UA on NOX4 activity and expression. Collectively, the antifibrotic effect of UA is partially due to the oxidative stress attenuating effect through manipulating NOX4 activity and expression. The results suggest that UA may act as a promising antifibrotic agent. More studies are warranted to evaluate the safety and efficacy of UA in the treatment of liver fibrosis.
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Affiliation(s)
- Wenhua He
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Feng Shi
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Zhi-Wei Zhou
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Bimin Li
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Kunhe Zhang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Xinhua Zhang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Canhui Ouyang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Shu-Feng Zhou
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Xuan Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
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Chen SJ. A potential target of Tanshinone IIA for acute promyelocytic leukemia revealed by inverse docking and drug repurposing. Asian Pac J Cancer Prev 2015; 15:4301-5. [PMID: 24935388 DOI: 10.7314/apjcp.2014.15.10.4301] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Tanshinone IIA is a pharmacologically active ingredient extracted from Danshen, a Chinese traditional medicine. Its molecular mechanisms are still unclear. The present study utilized computational approaches to uncover the potential targets of this compound. In this research, PharmMapper server was used as the inverse docking tool and the results were verified by Autodock vina in PyRx 0.8, and by DRAR-CPI, a server for drug repositioning via the chemical-protein interactome. Results showed that the retinoic acid receptor alpha (RARα), a target protein in acute promyelocytic leukemia (APL), was in the top rank, with a pharmacophore model matching well the molecular features of Tanshinone IIA. Moreover, molecular docking and drug repurposing results showed that the complex was also matched in terms of structure and chemical-protein interactions. These results indicated that RARα may be a potential target of Tanshinone IIA for APL. The study can provide useful information for further biological and biochemical research on natural compounds.
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Affiliation(s)
- Shao-Jun Chen
- Division of Neurobiology and Physiology, Department of Basic Medical Sciences, School of Medicine, Zhejiang University, Hangzhou, China E-mail :
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14
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Zhang J, Wang Y, Shang D, Yu F, Liu W, Zhang Y, Feng C, Wang Q, Xu Y, Liu Y, Bai X, Li X, Li C. Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways. J Biomed Inform 2015; 54:132-40. [PMID: 25724580 DOI: 10.1016/j.jbi.2015.02.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Revised: 02/16/2015] [Accepted: 02/17/2015] [Indexed: 01/14/2023]
Abstract
One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy.
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Affiliation(s)
- Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Yan Wang
- Majorbio Bio-Pharm Technology Co., Ltd., Shanghai 201203, PR China
| | - Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China
| | - Fulong Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China
| | - Wei Liu
- Department of Mathematics, Heilongjiang Institute of Technology, Harbin 150050, PR China
| | - Yan Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Qiuyu Wang
- School of Nursing, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China
| | - Yuejuan Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Xuefeng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Xuecang Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China.
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15
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Qiu JX, Zhou ZW, He ZX, Zhao RJ, Zhang X, Yang L, Zhou SF, Mao ZF. Plumbagin elicits differential proteomic responses mainly involving cell cycle, apoptosis, autophagy, and epithelial-to-mesenchymal transition pathways in human prostate cancer PC-3 and DU145 cells. DRUG DESIGN DEVELOPMENT AND THERAPY 2015; 9:349-417. [PMID: 25609920 PMCID: PMC4294653 DOI: 10.2147/dddt.s71677] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Plumbagin (PLB) has exhibited a potent anticancer effect in preclinical studies, but the molecular interactome remains elusive. This study aimed to compare the quantitative proteomic responses to PLB treatment in human prostate cancer PC-3 and DU145 cells using the approach of stable-isotope labeling by amino acids in cell culture (SILAC). The data were finally validated using Western blot assay. First, the bioinformatic analysis predicted that PLB could interact with 78 proteins that were involved in cell proliferation and apoptosis, immunity, and signal transduction. Our quantitative proteomic study using SILAC revealed that there were at least 1,225 and 267 proteins interacting with PLB and there were 341 and 107 signaling pathways and cellular functions potentially regulated by PLB in PC-3 and DU145 cells, respectively. These proteins and pathways played a critical role in the regulation of cell cycle, apoptosis, autophagy, epithelial to mesenchymal transition (EMT), and reactive oxygen species generation. The proteomic study showed substantial differences in response to PLB treatment between PC-3 and DU145 cells. PLB treatment significantly modulated the expression of critical proteins that regulate cell cycle, apoptosis, and EMT signaling pathways in PC-3 cells but not in DU145 cells. Consistently, our Western blotting analysis validated the bioinformatic and proteomic data and confirmed the modulating effects of PLB on important proteins that regulated cell cycle, apoptosis, autophagy, and EMT in PC-3 and DU145 cells. The data from the Western blot assay could not display significant differences between PC-3 and DU145 cells. These findings indicate that PLB elicits different proteomic responses in PC-3 and DU145 cells involving proteins and pathways that regulate cell cycle, apoptosis, autophagy, reactive oxygen species production, and antioxidation/oxidation homeostasis. This is the first systematic study with integrated computational, proteomic, and functional analyses revealing the networks of signaling pathways and differential proteomic responses to PLB treatment in prostate cancer cells. Quantitative proteomic analysis using SILAC represents an efficient and highly sensitive approach to identify the target networks of anticancer drugs like PLB, and the data may be used to discriminate the molecular and clinical subtypes, and to identify new therapeutic targets and biomarkers, for prostate cancer. Further studies are warranted to explore the potential of quantitative proteomic analysis in the identification of new targets and biomarkers for prostate cancer.
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Affiliation(s)
- Jia-Xuan Qiu
- School of Public Health, Wuhan University, Wuhan, Hubei, People's Republic of China ; Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People's Republic of China
| | - Zhi-Wei Zhou
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA ; Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, Guizhou, People's Republic of China
| | - Zhi-Xu He
- Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, Guizhou, People's Republic of China
| | - Ruan Jin Zhao
- Center for Traditional Chinese Medicine, Sarasota, FL, USA
| | - Xueji Zhang
- Research Center for Bioengineering and Sensing Technology, University of Science and Technology Beijing, Beijing, People's Republic of China
| | - Lun Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Shu-Feng Zhou
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA ; Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, Guizhou, People's Republic of China
| | - Zong-Fu Mao
- School of Public Health, Wuhan University, Wuhan, Hubei, People's Republic of China
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16
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Su J, Chang C, Xiang Q, Zhou ZW, Luo R, Yang L, He ZX, Yang H, Li J, Bei Y, Xu J, Zhang M, Zhang Q, Su Z, Huang Y, Pang J, Zhou SF. Xyloketal B, a marine compound, acts on a network of molecular proteins and regulates the activity and expression of rat cytochrome P450 3a: a bioinformatic and animal study. Drug Des Devel Ther 2014; 8:2555-602. [PMID: 25548518 PMCID: PMC4271727 DOI: 10.2147/dddt.s73476] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Natural compounds are becoming popular for the treatment of illnesses and health promotion, but the mechanisms of action and safety profiles are often unknown. Xyloketal B (XKB) is a novel marine compound isolated from the mangrove fungus Xylaria sp., with potent antioxidative, neuroprotective, and cardioprotective effects. However, its molecular targets and effects on drug-metabolizing enzymes are unknown. This study aimed to investigate the potential molecular targets of XKB using bioinformatic approaches and to examine the effect of XKB on the expression and activity of rat cytochrome P450 3a (Cyp3a) subfamily members using midazolam as a model probe. DDI-CPI, a server that can predict drug–drug interactions via the chemical–protein interactome, was employed to predict the targets of XKB, and the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the pathways of the predicted targets of XKB. Homology modeling was performed using the Discovery Studio program 3.1. The activity and expression of rat hepatic Cyp3a were examined after the rats were treated with XKB at 7 and 14 mg/kg for 8 consecutive days. Rat plasma concentrations of midazolam and its metabolite 1′-OH-midazolam were determined using a validated high-performance liquid chromatographic method. Bioinformatic analysis showed that there were over 324 functional proteins and 61 related signaling pathways that were potentially regulated by XKB. A molecular docking study showed that XKB bound to the active site of human cytochrome P450 3A4 and rat Cyp3a2 homology model via the formation of hydrogen bonds. The in vivo study showed that oral administration of XKB at 14 mg/kg to rats for 8 days significantly increased the area under the plasma concentration-time curve (AUC) of midazolam, with a concomitant decrease in the plasma clearance and AUC ratio of 1′-OH-midazolam over midazolam. Further, oral administration of 14 mg/kg XKB for 8 days markedly reduced the activity and expression of hepatic Cyp3a in rats. Taken together, the results show that XKB could regulate networks of molecular proteins and related signaling pathways and that XKB downregulated hepatic Cyp3a in rats. XKB might cause drug interactions through modulation of the activity and expression of Cyp3a members. More studies are warranted to confirm the mechanisms of action of XKB and to investigate the underlying mechanism for the regulating effect of XKB on Cyp3a subfamily members.
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Affiliation(s)
- Junhui Su
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China ; Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China ; The People's Hospital of Shenzhen City, Shenzhen, People's Republic of China
| | - Cui Chang
- The People's Hospital of Shenzhen City, Shenzhen, People's Republic of China
| | - Qi Xiang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China ; Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China
| | - Zhi-Wei Zhou
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Rong Luo
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Lun Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zhi-Xu He
- Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, People's Republic of China
| | - Hongtu Yang
- Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China ; The People's Hospital of Shenzhen City, Shenzhen, People's Republic of China
| | - Jianan Li
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Yu Bei
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Jinmei Xu
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China ; Department of Pharmacy, Jinan University, Guangzhou, People's Republic of China
| | - Minjing Zhang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Qihao Zhang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Zhijian Su
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Yadong Huang
- Institute of Biomedicine and Guangdong Provincial Key Laboratory of Bioengineering Medicine, Jinan University, Guangzhou, People's Republic of China
| | - Jiyan Pang
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Shu-Feng Zhou
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA ; Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, People's Republic of China
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17
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Huang H, Zhang P, Qu XA, Sanseau P, Yang L. Systematic prediction of drug combinations based on clinical side-effects. Sci Rep 2014; 4:7160. [PMID: 25418113 PMCID: PMC4241517 DOI: 10.1038/srep07160] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Accepted: 10/31/2014] [Indexed: 12/12/2022] Open
Abstract
Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.
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Affiliation(s)
- Hui Huang
- 1] Computational Biology, GlaxoSmithKline, Philadelphia, Pennsylvania, United States of America [2] School of Informatics and Computing, Indiana University, Indianapolis, Indiana, United States of America
| | - Ping Zhang
- Healthcare Analytics Research, IBM T.J. Watson Research Center, United States of America
| | - Xiaoyan A Qu
- Computational Biology, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America
| | - Philippe Sanseau
- Computational Biology, GlaxoSmithKline, Stevenage, United Kingdom
| | - Lun Yang
- Computational Biology, GlaxoSmithKline, Philadelphia, Pennsylvania, United States of America
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18
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Santiago JA, Potashkin JA. A network approach to clinical intervention in neurodegenerative diseases. Trends Mol Med 2014; 20:694-703. [PMID: 25455073 DOI: 10.1016/j.molmed.2014.10.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Revised: 09/30/2014] [Accepted: 10/08/2014] [Indexed: 02/07/2023]
Abstract
Network biology has become a powerful tool to dissect the molecular mechanisms triggering neurodegeneration. Recent developments in network biology have led to the discovery of disease-causing genes, diagnostic biomarkers, and therapeutic targets for several neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's diseases. Network-based approaches have provided the molecular rationale for the relationship among cancer, diabetes, and neurodegenerative diseases, and have uncovered unexpected links between apparently unrelated diseases. Here, we summarize the recent advances in network biology to untangle the molecular underpinnings giving rise to the most prevalent neurodegenerative diseases. We propose that network analysis provides a feasible and practical tool for identifying biologically meaningful biomarkers and potential therapeutic targets for clinical intervention in neurodegenerative diseases.
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Affiliation(s)
- Jose A Santiago
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, 3333 Green Bay Road, North Chicago, IL 60064-3037, USA
| | - Judith A Potashkin
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, 3333 Green Bay Road, North Chicago, IL 60064-3037, USA.
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19
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Chen HF, Pan XL, Wang JW, Kong HM, Fu YM. Protein–drug interactome analysis of SSRI-mediated neurorecovery following stroke. Biosystems 2014; 120:1-9. [DOI: 10.1016/j.biosystems.2014.03.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Revised: 03/03/2014] [Accepted: 03/18/2014] [Indexed: 11/26/2022]
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20
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Luo H, Zhang P, Huang H, Huang J, Kao E, Shi L, He L, Yang L. DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic Acids Res 2014; 42:W46-52. [PMID: 24875476 PMCID: PMC4086096 DOI: 10.1093/nar/gku433] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug–human protein interactions, it is reasonable to analyze the chemical–protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.
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Affiliation(s)
- Heng Luo
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China University of Arkansas at Little Rock/University of Arkansas for Medical Sciences, Little Rock, AR 72204, USA
| | - Ping Zhang
- Healthcare Analytics Research Group, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - Hui Huang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jialiang Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Heath, Boston, MA 02215, USA
| | - Emily Kao
- Department of Bioengineering, University of California at Berkeley, Berkeley, CA 94720, USA
| | - Leming Shi
- School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Lin He
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Lun Yang
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China
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21
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Distinct DNA-based epigenetic switches trigger transcriptional activation of silent genes in human dermal fibroblasts. Sci Rep 2014; 4:3843. [PMID: 24457603 PMCID: PMC3900999 DOI: 10.1038/srep03843] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 12/18/2013] [Indexed: 12/22/2022] Open
Abstract
The influential role of the epigenome in orchestrating genome-wide transcriptional activation instigates the demand for the artificial genetic switches with distinct DNA sequence recognition. Recently, we developed a novel class of epigenetically active small molecules called SAHA-PIPs by conjugating selective DNA binding pyrrole-imidazole polyamides (PIPs) with the histone deacetylase inhibitor SAHA. Screening studies revealed that certain SAHA-PIPs trigger targeted transcriptional activation of pluripotency and germ cell genes in mouse and human fibroblasts, respectively. Through microarray studies and functional analysis, here we demonstrate for the first time the remarkable ability of thirty-two different SAHA-PIPs to trigger the transcriptional activation of exclusive clusters of genes and noncoding RNAs. QRT-PCR validated the microarray data, and some SAHA-PIPs activated therapeutically significant genes like KSR2. Based on the aforementioned results, we propose the potential use of SAHA-PIPs as reagents capable of targeted transcriptional activation.
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22
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Chen L, Lu J, Luo X, Feng KY. Prediction of drug target groups based on chemical–chemical similarities and chemical–chemical/protein connections. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2014; 1844:207-13. [DOI: 10.1016/j.bbapap.2013.05.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Revised: 05/20/2013] [Accepted: 05/22/2013] [Indexed: 10/26/2022]
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23
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Chen L, Li BQ, Zheng MY, Zhang J, Feng KY, Cai YD. Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways. BIOMED RESEARCH INTERNATIONAL 2013; 2013:723780. [PMID: 24083237 PMCID: PMC3780555 DOI: 10.1155/2013/723780] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 07/24/2013] [Indexed: 12/11/2022]
Abstract
Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.
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Affiliation(s)
- Lei Chen
- Institute of Systems Biology, Shanghai University, Shanghai 200444, China
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Bi-Qing Li
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ming-Yue Zheng
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai 201203, China
| | - Jian Zhang
- Department of Ophthalmology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
| | - Kai-Yan Feng
- Beijing Genomics Institute, Shenzhen Beishan Industrial Zone, Shenzhen 518083, China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai 200444, China
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24
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Qiu JX, He YQ, Wang Y, Xu RL, Qin Y, Shen X, Zhou SF, Mao ZF. Plumbagin induces the apoptosis of human tongue carcinoma cells through the mitochondria-mediated pathway. Med Sci Monit Basic Res 2013; 19:228-36. [PMID: 23982457 PMCID: PMC3762523 DOI: 10.12659/msmbr.884004] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Plumbagin, a quinonoid constituent isolated from the root of Plumbago zeylanica L., has been proven to possess anti-tumor activity both in vitro and in vivo. However, its anti-tumor properties for human tongue carcinoma have not been reported. This study aimed to investigate the inhibitory effect and the underlying mechanism of plumbagin on the growth of human tongue carcinoma cells. MATERIAL AND METHODS Cell proliferation ability was detected by EdU incorporation assay and colony formation assay. Cell-cycle distribution was determined by flow cytometric analysis using propidium iodide (PI) staining. Cellular apoptosis was then evaluated by flow cytometry and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Western blotting was applied to assay the expression of Bax and Bcl-2. RESULTS Plumbagin inhibited the growth and proliferation of Tca8113 cells in vitro in a concentration- and time-dependent manner. The cell cycles of plumbagin-treated Tca8113 cells were arrested at the G2/M phase. Cells treated with plumbagin presented the characteristic morphological changes of apoptosis. The ratio of Bax/Bcl-2 was raised by plumbagin in a concentration-dependent manner. CONCLUSIONS These results indicate that plumbagin induces the apoptosis of Tca8113 cells through mitochondria-mediated pathway.
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Affiliation(s)
- Jia-xuan Qiu
- Department of Stomatology, Fourth Affiliated Hospital of Nanchang University, Nanchang, PR China
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25
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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: 512] [Impact Index Per Article: 46.5] [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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26
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Sun Y, Zhu R, Ye H, Tang K, Zhao J, Chen Y, Liu Q, Cao Z. Towards a bioinformatics analysis of anti-Alzheimer's herbal medicines from a target network perspective. Brief Bioinform 2012; 14:327-43. [DOI: 10.1093/bib/bbs025] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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27
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Lin X, Huang XP, Chen G, Whaley R, Peng S, Wang Y, Zhang G, Wang SX, Wang S, Roth BL, Huang N. Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors. J Med Chem 2012; 55:5749-59. [PMID: 22694093 DOI: 10.1021/jm300338m] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Of great interest in recent years has been computationally predicting the novel polypharmacology of drug molecules. Here, we applied an "induced-fit" protocol to improve the homology models of 5-HT(2A) receptor, and we assessed the quality of these models in retrospective virtual screening. Subsequently, we computationally screened the FDA approved drug molecules against the best induced-fit 5-HT(2A) models and chose six top scoring hits for experimental assays. Surprisingly, one well-known kinase inhibitor, sorafenib, has shown unexpected promiscuous 5-HTRs binding affinities, K(i) = 1959, 56, and 417 nM against 5-HT(2A), 5-HT(2B), and 5-HT(2C), respectively. Our preliminary SAR exploration supports the predicted binding mode and further suggests sorafenib to be a novel lead compound for 5HTR ligand discovery. Although it has been well-known that sorafenib produces anticancer effects through targeting multiple kinases, carefully designed experimental studies are desirable to fully understand whether its "off-target" 5-HTR binding activities contribute to its therapeutic efficacy or otherwise undesirable side effects.
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Affiliation(s)
- Xingyu Lin
- National Institute of Biological Sciences, Beijing, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
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Guo W, Liu S, Peng J, Wei X, Sun Y, Qiu Y, Gao G, Wang P, Xu Y. Examining the interactome of huperzine A by magnetic biopanning. PLoS One 2012; 7:e37098. [PMID: 22615909 PMCID: PMC3353884 DOI: 10.1371/journal.pone.0037098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2011] [Accepted: 04/18/2012] [Indexed: 11/25/2022] Open
Abstract
Huperzine A is a bioactive compound derived from traditional Chinese medicine plant Qian Ceng Ta (Huperzia serrata), and was found to have multiple neuroprotective effects. In addition to being a potent acetylcholinesterase inhibitor, it was thought to act through other mechanisms such as antioxidation, antiapoptosis, etc. However, the molecular targets involved with these mechanisms were not identified. In this study, we attempted to exam the interactome of Huperzine A using a cDNA phage display library and also mammalian brain tissue extracts. The drugs were chemically linked on the surface of magnetic particles and the interactive phages or proteins were collected and analyzed. Among the various cDNA expressing phages selected, one was identified to encode the mitochondria NADH dehydrogenase subunit 1. Specific bindings between the drug and the target phages and target proteins were confirmed. Another enriched phage clone was identified as mitochondria ATP synthase, which was also panned out from the proteome of mouse brain tissue lysate. These data indicated the possible involvement of mitochondrial respiratory chain matrix enzymes in Huperzine A's pharmacological effects. Such involvement had been suggested by previous studies based on enzyme activity changes. Our data supported the new mechanism. Overall we demonstrated the feasibility of using magnetic biopanning as a simple and viable method for investigating the complex molecular mechanisms of bioactive molecules.
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Affiliation(s)
- Wei Guo
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Shupeng Liu
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Institute of Biomedical Engineering, Shanghai University, Shanghai, People's Republic of China
| | - Jinliang Peng
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiaohui Wei
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Ye Sun
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yangsheng Qiu
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Guangwei Gao
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Peng Wang
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yuhong Xu
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- * E-mail:
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Pathway-pathway network-based study of the therapeutic mechanisms by which salvianolic acid B regulates cardiovascular diseases. ACTA ACUST UNITED AC 2012. [DOI: 10.1007/s11434-012-5142-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Abstract
Human diseases can be caused by complex mechanisms involving aberrations in numerous proteins and pathways. With recent advances in genomics, elucidating the molecular basis of disease on a personalized level has become an attainable goal. In many cases, relevant molecular targets will be identified for which approved drugs already exist, and the potential repositioning of these drugs to a new indication can be investigated. Repositioning is an accelerated route for drug discovery because existing drugs have established clinical and pharmacokinetic data. Personalized medicine and repositioning both aim to improve the productivity of current drug discovery pipelines, which expend enormous time and cost to develop new drugs, only to have them fail in clinical trials because of lack of efficacy or toxicity. Here, we discuss the current state of research in these two fields, focusing on recent large-scale efforts to systematically find repositioning candidates and elucidate individual disease mechanisms in cancer. We also discuss scenarios in which personalized drug repositioning could be particularly rewarding, such as for diseases that are rare or have specific mutations, as well as current challenges in this field. With an increasing number of drugs being approved for rare cancer subtypes, personalized medicine and repositioning approaches are poised to significantly alter the way we diagnose diseases, infer treatments and develop new drugs.
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Affiliation(s)
- Yvonne Y Li
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada
| | - Steven Jm Jones
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada
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Zhao XM, Iskar M, Zeller G, Kuhn M, van Noort V, Bork P. Prediction of drug combinations by integrating molecular and pharmacological data. PLoS Comput Biol 2011; 7:e1002323. [PMID: 22219721 PMCID: PMC3248384 DOI: 10.1371/journal.pcbi.1002323] [Citation(s) in RCA: 147] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2011] [Accepted: 11/10/2011] [Indexed: 11/18/2022] Open
Abstract
Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.
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Affiliation(s)
- Xing-Ming Zhao
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Murat Iskar
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Georg Zeller
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Michael Kuhn
- Biotechnology Center, Technical University Dresden, Dresden, Germany
| | - Vera van Noort
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Peer Bork
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Max-Delbrück-Centre for Molecular Medicine, Berlin, Germany
- * E-mail:
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Sardana D, Zhu C, Zhang M, Gudivada RC, Yang L, Jegga AG. Drug repositioning for orphan diseases. Brief Bioinform 2011; 12:346-56. [PMID: 21504985 DOI: 10.1093/bib/bbr021] [Citation(s) in RCA: 134] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism 'connecting' them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.
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Affiliation(s)
- Divya Sardana
- Department of Computer Science, University of Cincinnati, OH, USA
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Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome--clozapine-induced agranulocytosis as a case study. PLoS Comput Biol 2011; 7:e1002016. [PMID: 21483481 PMCID: PMC3068927 DOI: 10.1371/journal.pcbi.1002016] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2010] [Accepted: 01/25/2011] [Indexed: 12/20/2022] Open
Abstract
In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs. Idiosyncratic drug reactions (IDR) generally cannot be identified until after a drug is taken by a large population, but usually result in restricted use or withdrawal. Clozapine provides the most effective treatment for schizophrenia but its use is limited because of a life-threatening IDR, i.e., the agranulocytosis. A high impact clinical study demonstrated the necessity of moving clozapine from 3rd line to 1st line drug; therefore, intensive research has aimed at identifying genes responsible for clozapine-induced agranulocytosis (CIA). Olanzapine, an analog of clozapine, has much lower incidence of agranulocytosis. Based on this phenomenon, we proposed an in silico methodology termed as antithesis chemical-protein interactome (CPI), which mimics the differences in the drug-protein interactions of the two drugs across a panel of human proteins. e.g., HSPA1A was identified to be targeted by clozapine not olanzapine. Furthermore, the gene expression of the HSPA1A-related gene system was also found up-regulated after clozapine treatment. This approach can examine the system's perturbation in terms of both the off-target and the off-system's interaction with the drug, providing theoretical basis for decoding the adverse drug reactions or the new uses for old drugs.
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Haupt VJ, Schroeder M. Old friends in new guise: repositioning of known drugs with structural bioinformatics. Brief Bioinform 2011; 12:312-26. [DOI: 10.1093/bib/bbr011] [Citation(s) in RCA: 106] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
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Yang L, Wang KJ, Wang LS, Jegga AG, Qin SY, He G, Chen J, Xiao Y, He L. Chemical-protein interactome and its application in off-target identification. Interdiscip Sci 2011; 3:22-30. [PMID: 21369884 DOI: 10.1007/s12539-011-0051-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2010] [Revised: 09/14/2010] [Accepted: 09/19/2010] [Indexed: 01/30/2023]
Abstract
Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.
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Affiliation(s)
- Lun Yang
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China.
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