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Soldatos TG, Kim S, Schmidt S, Lesko LJ, Jackson DB. Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports. CPT Pharmacometrics Syst Pharmacol 2022; 11:540-555. [PMID: 35143713 PMCID: PMC9124355 DOI: 10.1002/psp4.12765] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/20/2021] [Accepted: 01/17/2022] [Indexed: 12/15/2022] Open
Abstract
Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient‐centered approaches for the assessment of drug safety. We anticipate that such advances in real‐world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug‐induced molecular perturbations and adverse events.
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Affiliation(s)
| | - Sarah Kim
- Department of PharmaceuticsCenter for Pharmacometrics and Systems PharmacologyUniversity of FloridaOrlandoFloridaUSA
| | - Stephan Schmidt
- Department of PharmaceuticsCenter for Pharmacometrics and Systems PharmacologyUniversity of FloridaOrlandoFloridaUSA
| | - Lawrence J. Lesko
- Department of PharmaceuticsCenter for Pharmacometrics and Systems PharmacologyUniversity of FloridaOrlandoFloridaUSA
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2
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Satalkar V, Swamy KV. Pathophysiology of acute kidney injury on a molecular level: A brief review. MGM JOURNAL OF MEDICAL SCIENCES 2022. [DOI: 10.4103/mgmj.mgmj_161_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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3
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Zhang S, Wang J, Lin Z, Liang Y. Application of Machine Learning Techniques in Drug-target Interactions Prediction. Curr Pharm Des 2021; 27:2076-2087. [PMID: 33238865 DOI: 10.2174/1381612826666201125105730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 08/06/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field. RESULTS The machine learning methods can be divided into three categories basically: Supervised methods, Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category. CONCLUSION Every prediction model has both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.
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Affiliation(s)
- Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Jiesheng Wang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Zhenhui Lin
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Yunyun Liang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
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4
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Liu Z, Chen Q, Lan W, Liang J, Chen YPP, Chen B. A Survey of Network Embedding for Drug Analysis and Prediction. Curr Protein Pept Sci 2020; 22:CPPS-EPUB-107859. [PMID: 32614745 DOI: 10.2174/1389203721666200702145701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 04/05/2020] [Accepted: 05/21/2020] [Indexed: 11/22/2022]
Abstract
Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.
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Affiliation(s)
- Zhixian Liu
- School of Medical, Guangxi University, Nanning. China
| | - Qingfeng Chen
- School of Computer, Electronic and Information, Guangxi University, Nanning. China
| | - Wei Lan
- School of Computer, Electronic and Information, Guangxi University, Nanning. China
| | - Jiahai Liang
- School of Electronics and Information Engineering, Beibu Gulf University, Qinzhou. China
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Melbourne. Australia
| | - Baoshan Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, Nanning. China
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5
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Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform 2020; 22:247-269. [PMID: 31950972 PMCID: PMC7820849 DOI: 10.1093/bib/bbz157] [Citation(s) in RCA: 161] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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Affiliation(s)
- Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Elyas Sabeti
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kai Wang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maureen A Sartor
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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6
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Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform 2019; 22:451-462. [PMID: 31885041 DOI: 10.1093/bib/bbz152] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 12/18/2022] Open
Abstract
Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | - Xiangeng Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Wei Wang
- Mathematical Sciences, Shanghai Jiao Tong University
| | - Yufang Zhang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | | | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
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7
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8
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Abbott KL, Flannery PC, Gill KS, Boothe DM, Dhanasekaran M, Mani S, Pondugula SR. Adverse pharmacokinetic interactions between illicit substances and clinical drugs. Drug Metab Rev 2019; 52:44-65. [PMID: 31826670 DOI: 10.1080/03602532.2019.1697283] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Adverse pharmacokinetic interactions between illicit substances and clinical drugs are of a significant health concern. Illicit substances are taken by healthy individuals as well as by patients with medical conditions such as mental illnesses, acquired immunodeficiency syndrome, diabetes mellitus and cancer. Many individuals that use illicit substances simultaneously take clinical drugs meant for targeted treatment. This concomitant usage can lead to life-threatening pharmacokinetic interactions between illicit substances and clinical drugs. Optimal levels and activity of drug-metabolizing enzymes and drug-transporters are crucial for metabolism and disposition of illicit substances as well as clinical drugs. However, both illicit substances and clinical drugs can induce changes in the expression and/or activity of drug-metabolizing enzymes and drug-transporters. Consequently, with concomitant usage, illicit substances can adversely influence the therapeutic outcome of coadministered clinical drugs. Likewise, clinical drugs can adversely affect the response of coadministered illicit substances. While the interactions between illicit substances and clinical drugs pose a tremendous health and financial burden, they lack a similar level of attention as drug-drug, food-drug, supplement-drug, herb-drug, disease-drug, or other substance-drug interactions such as alcohol-drug and tobacco-drug interactions. This review highlights the clinical pharmacokinetic interactions between clinical drugs and commonly used illicit substances such as cannabis, cocaine and 3, 4-Methylenedioxymethamphetamine (MDMA). Rigorous efforts are warranted to further understand the underlying mechanisms responsible for these clinical pharmacokinetic interactions. It is also critical to extend the awareness of the life-threatening adverse interactions to both health care professionals and patients.
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Affiliation(s)
- Kodye L Abbott
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA.,Auburn University Research Initiative in Cancer, Auburn University, Auburn, AL, USA
| | - Patrick C Flannery
- College of Osteopathic Medicine, Rocky Vista University, Parker, CO, USA
| | - Kristina S Gill
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA.,Auburn University Research Initiative in Cancer, Auburn University, Auburn, AL, USA
| | - Dawn M Boothe
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA.,Auburn University Research Initiative in Cancer, Auburn University, Auburn, AL, USA
| | - Muralikrishnan Dhanasekaran
- Auburn University Research Initiative in Cancer, Auburn University, Auburn, AL, USA.,Department of Drug Discovery and Development, Harrison School of Pharmacy, Auburn University, AL, USA
| | - Sridhar Mani
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Satyanarayana R Pondugula
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA.,Auburn University Research Initiative in Cancer, Auburn University, Auburn, AL, USA
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9
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Soldatou V, Soldatos A, Soldatos T. Examining Socioeconomic and Computational Aspects of Vaccine Pharmacovigilance. BIOMED RESEARCH INTERNATIONAL 2019; 2019:6576483. [PMID: 30911546 PMCID: PMC6399563 DOI: 10.1155/2019/6576483] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 11/26/2018] [Accepted: 12/24/2018] [Indexed: 12/30/2022]
Abstract
BACKGROUND Vaccine pharmacovigilance relates to the detection of adverse events, their assessment, understanding, and prevention, and communication of their risk to the public. These activities can be tedious and long lasting for regulatory authority scientists and may be affected by community practices and public health policies. To better understand underlying challenges, we examined vaccine adverse event reports, assessed whether data-driven techniques can provide additional insight in safety characterization, and wondered on the impact of socioeconomic parameters. METHODS First, we integrated VAERS content with additional sources of drug and molecular data and examined reaction and outcome occurrence by using disproportionality metrics and enrichment analysis. Second, we reviewed social and behavioral determinants that may affect vaccine pharmacovigilance aspects. RESULTS We describe our experience in processing more than 607000 vaccine adverse event reports and report on the challenges to integrate more than 95500 VAERS medication narratives with structured information about drugs and other therapeutics or supplements. We found that only 12.6% of events were serious, while 8.97% referred to polypharmacy cases. Exacerbation of serious clinical patient outcomes was observed in 8.88% VAERS cases in which drugs may interact with vaccinations or with each other, regardless of vaccine activity interference. Furthermore, we characterized the symptoms reported in those cases and summarized reaction occurrence among vaccine-types. Last, we examine socioeconomic parameters and cost-management features, explore adverse event reporting trends, and highlight perspectives relating to the use and development of digital services, especially in the context of personalized and collaborative health-care. CONCLUSIONS This work provides an informative review of VAERS, identifies challenges and limitations in the processing of vaccine adverse event data, and calls for the better understanding of the socioeconomic landscape pertaining vaccine safety concerns. We expect that adoption of computational techniques for integrated safety assessment and interpretation is key not only to pharmacovigilance practice but also to stakeholders from the entire healthcare system.
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Affiliation(s)
- Vasiliki Soldatou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece
| | - Anastasios Soldatos
- Department of Business Administration, School of Business, Athens University of Economics and Business, Greece
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10
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Shi H, Liu S, Chen J, Li X, Ma Q, Yu B. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. Genomics 2018; 111:1839-1852. [PMID: 30550813 DOI: 10.1016/j.ygeno.2018.12.007] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 12/06/2018] [Accepted: 12/07/2018] [Indexed: 01/01/2023]
Abstract
The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning methods to predict potential drug-target interactions has attracted widespread attention. This paper presents a novel drug-target interactions prediction method called LRF-DTIs. Firstly, the pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprinting were used to extract the features of drug-target. Secondly, using Lasso to reduce the dimension of the extracted feature information and then the Synthetic Minority Oversampling Technique (SMOTE) method was used to deal with unbalanced data. Finally, the processed feature vectors were input into a random forest (RF) classifier to predict drug-target interactions. Through 10 trials of 5-fold cross-validation, the overall prediction accuracies on the enzyme, ion channel (IC), G-protein-coupled receptor (GPCR) and nuclear receptor (NR) datasets reached 98.09%, 97.32%, 95.69%, and 94.88%, respectively, and compared with other prediction methods. In addition, we have tested and verified that our method not only could be applied to predict the new interactions but also could obtain a satisfactory result on the new dataset. All the experimental results indicate that our method can significantly improve the prediction accuracy of drug-target interactions and play a vital role in the new drug research and target protein development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LRF-DTIs/ for academic use.
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Affiliation(s)
- Han Shi
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Simin Liu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Junqi Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Xuan Li
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.
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11
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Soldatos TG, Taglang G, Jackson DB. In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data. High Throughput 2018; 7:ht7040037. [PMID: 30477159 PMCID: PMC6306940 DOI: 10.3390/ht7040037] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 11/09/2018] [Accepted: 11/19/2018] [Indexed: 12/19/2022] Open
Abstract
We present a novel approach for the molecular transformation and analysis of patient clinical phenotypes. Building on the fact that drugs perturb the function of targets/genes, we integrated data from 8.2 million clinical reports detailing drug-induced side effects with the molecular world of drug-target information. Using this dataset, we extracted 1.8 million associations of clinical phenotypes to 770 human drug-targets. This collection is perhaps the largest phenotypic profiling reference of human targets to-date, and unique in that it enables rapid development of testable molecular hypotheses directly from human-specific information. We also present validation results demonstrating analytical utilities of the approach, including drug safety prediction, and the design of novel combination therapies. Challenging the long-standing notion that molecular perturbation studies cannot be performed in humans, our data allows researchers to capitalize on the vast tomes of clinical information available throughout the healthcare system.
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Affiliation(s)
| | - Guillaume Taglang
- Molecular Health GmbH, Kurfuersten Anlage 21, 69115 Heidelberg, Germany.
| | - David B Jackson
- Molecular Health GmbH, Kurfuersten Anlage 21, 69115 Heidelberg, Germany.
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12
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Zhang W, Chen Y, Li D. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information. Molecules 2017; 22:molecules22122056. [PMID: 29186828 PMCID: PMC6149680 DOI: 10.3390/molecules22122056] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 11/19/2017] [Accepted: 11/20/2017] [Indexed: 11/16/2022] Open
Abstract
Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.
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Affiliation(s)
- Wen Zhang
- School of Computer, Wuhan University, Wuhan 430072, China.
| | - Yanlin Chen
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
| | - Dingfang Li
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
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13
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Stanescu A, Pandey G. LEARNING PARSIMONIOUS ENSEMBLES FOR UNBALANCED COMPUTATIONAL GENOMICS PROBLEMS. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017; 22:288-299. [PMID: 27896983 DOI: 10.1142/9789813207813_0028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Prediction problems in biomedical sciences are generally quite difficult, partially due to incomplete knowledge of how the phenomenon of interest is influenced by the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor(s) for specific problems. In these situations, a powerful approach to improving prediction performance is to construct ensembles that combine the outputs of many individual base predictors, which have been successful for many biomedical prediction tasks. Moreover, selecting a parsimonious ensemble can be of even greater value for biomedical sciences, where it is not only important to learn an accurate predictor, but also to interpret what novel knowledge it can provide about the target problem. Ensemble selection is a promising approach for this task because of its ability to select a collectively predictive subset, often a relatively small one, of all input base predictors. One of the most well-known algorithms for ensemble selection, CES (Caruana et al.'s Ensemble Selection), generally performs well in practice, but faces several challenges due to the difficulty of choosing the right values of its various parameters. Since the choices made for these parameters are usually ad-hoc, good performance of CES is difficult to guarantee for a variety of problems or datasets. To address these challenges with CES and other such algorithms, we propose a novel heterogeneous ensemble selection approach based on the paradigm of reinforcement learning (RL), which offers a more systematic and mathematically sound methodology for exploring the many possible combinations of base predictors that can be selected into an ensemble. We develop three RL-based strategies for constructing ensembles and analyze their results on two unbalanced computational genomics problems, namely the prediction of protein function and splice sites in eukaryotic genomes. We show that the resultant ensembles are indeed substantially more parsimonious as compared to the full set of base predictors, yet still offer almost the same classification power, especially for larger datasets. The RL ensembles also yield a better combination of parsimony and predictive performance as compared to CES.
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Affiliation(s)
- Ana Stanescu
- Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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14
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Abstract
Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.
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Affiliation(s)
- Leo Chiang
- The Dow Chemical Company, Freeport, Texas 77541;
| | - Bo Lu
- The Dow Chemical Company, Freeport, Texas 77541;
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15
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Rezaei Kolahchi A, Khadem Mohtaram N, Pezeshgi Modarres H, Mohammadi MH, Geraili A, Jafari P, Akbari M, Sanati-Nezhad A. Microfluidic-Based Multi-Organ Platforms for Drug Discovery. MICROMACHINES 2016; 7:E162. [PMID: 30404334 PMCID: PMC6189912 DOI: 10.3390/mi7090162] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Revised: 08/23/2016] [Accepted: 08/24/2016] [Indexed: 12/18/2022]
Abstract
Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC) model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.
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Affiliation(s)
- Ahmad Rezaei Kolahchi
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Nima Khadem Mohtaram
- Laboratory for Innovations in MicroEngineering (LiME), Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada.
- Division of Medical Sciences, University of Victoria, Victoria, BC V8P 5C2, Canada.
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
| | - Hassan Pezeshgi Modarres
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
| | - Mohammad Hossein Mohammadi
- Department of Chemical and Petroleum Engineering, Sharif University of Technology, Azadi Ave., Tehran 11155-9516, Iran.
| | - Armin Geraili
- Department of Chemical and Petroleum Engineering, Sharif University of Technology, Azadi Ave., Tehran 11155-9516, Iran.
| | - Parya Jafari
- Department of Electrical Engineering, Sharif University of Technology, Azadi Ave., Tehran 11155-9516, Iran.
| | - Mohsen Akbari
- Laboratory for Innovations in MicroEngineering (LiME), Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada.
- Division of Medical Sciences, University of Victoria, Victoria, BC V8P 5C2, Canada.
| | - Amir Sanati-Nezhad
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
- Center for Bioengineering Research and Education, Biomedical Engineering Program, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
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16
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Vilar S, Hripcsak G. Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations. J Cheminform 2016; 8:35. [PMID: 27375776 PMCID: PMC4930585 DOI: 10.1186/s13321-016-0147-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 06/23/2016] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. RESULTS In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. CONCLUSIONS The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY USA
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17
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Grove LE, Vajda S, Kozakov D. Computational Methods to Support Fragment-based Drug Discovery. FRAGMENT-BASED DRUG DISCOVERY LESSONS AND OUTLOOK 2016. [DOI: 10.1002/9783527683604.ch09] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
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18
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Madhukar NS, Elemento O, Pandey G. Prediction of Genetic Interactions Using Machine Learning and Network Properties. Front Bioeng Biotechnol 2015; 3:172. [PMID: 26579514 PMCID: PMC4620407 DOI: 10.3389/fbioe.2015.00172] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 10/12/2015] [Indexed: 12/04/2022] Open
Abstract
A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.
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Affiliation(s)
- Neel S Madhukar
- Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA
| | - Olivier Elemento
- Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences and Graduate School of Biomedical Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai , New York, NY , USA
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19
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Wang C, Deng ZL, Xie ZM, Chu XY, Chang JW, Kong DX, Li BJ, Zhang HY, Chen LL. Construction of a genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery. FEBS Lett 2014; 589:285-94. [PMID: 25535697 DOI: 10.1016/j.febslet.2014.12.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 12/10/2014] [Accepted: 12/12/2014] [Indexed: 11/17/2022]
Abstract
We reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides.
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Affiliation(s)
- Cheng Wang
- State Key Laboratory of Agricultural Microbiology, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Zhi-Luo Deng
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Zhi-Ming Xie
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Xin-Yi Chu
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Ji-Wei Chang
- State Key Laboratory of Agricultural Microbiology, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - De-Xin Kong
- State Key Laboratory of Agricultural Microbiology, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Bao-Ju Li
- Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Hong-Yu Zhang
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Ling-Ling Chen
- State Key Laboratory of Agricultural Microbiology, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Center for Bioinformatics, Huazhong Agricultural University, Wuhan 430070, PR China.
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20
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Li CW, Yu G, Jiang J, Lee SMY, Yi C, Yue W, Yang M. A microfluidic linear node array for the study of protein-ligand interactions. LAB ON A CHIP 2014; 14:3993-3999. [PMID: 25140880 DOI: 10.1039/c4lc00779d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
We have developed a microfluidic device for the continuous separation of small molecules from a protein mixture and demonstrated its practical use in the study of protein-ligand binding, a crucial aspect in drug discovery. Our results demonstrated dose-dependent binding between bovine serum albumin (BSA) and its small-molecule site marker, Eosin Y (EY), and found that the binding reached a plateau when the BSA : EY ratio was above 1, which agreed with the eosin binding capacity of BSA reported in literature. By streamline control using a combination of two fundamental building blocks (R and L nodes) with a microdevice operated at a high flow rate (up to 1300 μL h(-1)), a solution barrier was created to "filter" off protein/protein-ligand complexes such that the small unbound molecules were isolated and quantified easily. The percentage decrease of small molecules with increasing protein concentration indicated the presence of binding events. Several fluorophores with different molecular weights were used to test the performance of the microfluidic "filter", which was tunable by 1) the total flow rate, and/or 2) the flow distribution ratio between the two device inlets; both were easily controllable by changing the syringe pump settings. Since the microdevice was operated at a relatively high flow rate, aliquots were easily recovered from the device outlets to facilitate off-chip detection. This microfluidic design is a novel and promising tool for preliminary drug screening.
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Affiliation(s)
- Cheuk-Wing Li
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau SAR, China.
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21
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Cao DS, Zhang LX, Tan GS, Xiang Z, Zeng WB, Xu QS, Chen AF. Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features. Mol Inform 2014; 33:669-81. [PMID: 27485302 DOI: 10.1002/minf.201400009] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Accepted: 04/22/2014] [Indexed: 02/02/2023]
Abstract
Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.
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Affiliation(s)
- Dong-Sheng Cao
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, P.R. China.
| | - Liu-Xia Zhang
- The 163rdHospital of The Chinese People's Liberation Army, Changsha 410003, P.R. China
| | - Gui-Shan Tan
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, P.R. China
| | - Zheng Xiang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, P.R. China
| | - Wen-Bin Zeng
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, P.R. China
| | - Qing-Song Xu
- School of Mathematics and Statistics, Central South University, Changsha 410083, P.R. China
| | - Alex F Chen
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, P.R. China.
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22
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Mousavian Z, Masoudi-Nejad A. Drug-target interaction prediction via chemogenomic space: learning-based methods. Expert Opin Drug Metab Toxicol 2014; 10:1273-87. [PMID: 25112457 DOI: 10.1517/17425255.2014.950222] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Identification of the interaction between drugs and target proteins is a crucial task in genomic drug discovery. The in silico prediction is an appropriate alternative for the laborious and costly experimental process of drug-target interaction prediction. Developing a variety of computational methods opens a new direction in analyzing and detecting new drug-target pairs. AREAS COVERED In this review, we will focus on chemogenomic methods which have established a learning framework for predicting drug-target interactions. Learning-based methods are classified into supervised and semi-supervised, and the supervised learning methods are studied as two separate parts including similarity-based methods and feature-based methods. EXPERT OPINION In spite of many improvements for pharmacology applications by learning-based methods, there are many over simplification settings in construction of predictive models that may lead to over-optimistic results on drug-target interaction prediction.
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Affiliation(s)
- Zaynab Mousavian
- University of Tehran, Institute of Biochemistry and Biophysics, Laboratory of Systems Biology and Bioinformatics (LBB) , Tehran , Iran +98 21 6695 9256 ; +98 21 6640 4680 ;
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23
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Zhou B, Wang R, Wu P, Kong DX. Drug repurposing based on drug-drug interaction. Chem Biol Drug Des 2014; 85:137-44. [PMID: 24934184 DOI: 10.1111/cbdd.12378] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2014] [Revised: 06/06/2014] [Accepted: 06/10/2014] [Indexed: 12/30/2022]
Abstract
Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug-drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10,835 drug-drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug-drug interaction data.
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Affiliation(s)
- Bin Zhou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China; Center for Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
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24
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Siragusa L, Spyrakis F, Goracci L, Cross S, Cruciani G. BioGPS: The Music for the Chemo- and Bioinformatics Walzer. Mol Inform 2014; 33:446-53. [DOI: 10.1002/minf.201400028] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 05/19/2014] [Indexed: 01/09/2023]
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25
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Pahikkala T, Airola A, Pietilä S, Shakyawar S, Szwajda A, Tang J, Aittokallio T. Toward more realistic drug-target interaction predictions. Brief Bioinform 2014; 16:325-37. [PMID: 24723570 PMCID: PMC4364066 DOI: 10.1093/bib/bbu010] [Citation(s) in RCA: 245] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors.
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26
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Klünemann M, Schmid M, Patil KR. Computational tools for modeling xenometabolism of the human gut microbiota. Trends Biotechnol 2014; 32:157-65. [PMID: 24529988 DOI: 10.1016/j.tibtech.2014.01.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Revised: 01/09/2014] [Accepted: 01/13/2014] [Indexed: 12/24/2022]
Abstract
The gut microbiota is increasingly being recognized as a key site of metabolism for drugs and other xenobiotic compounds that are relevant to human health. The molecular complexity of the gut microbiota revealed by recent metagenomics studies has highlighted the need as well as the challenges for system-level modeling of xenobiotic metabolism in the gut. Here, we outline the possible pathways through which the gut microbiota can modify xenobiotics and review the available computational tools towards modeling complex xenometabolic processes. We put these diverse computational tools and relevant experimental findings into a unified perspective towards building holistic models of xenobiotic metabolism in the gut.
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Affiliation(s)
- Martina Klünemann
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Melanie Schmid
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kiran Raosaheb Patil
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
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27
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Identifying tinnitus-related genes based on a side-effect network analysis. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e97. [PMID: 24477090 PMCID: PMC3910011 DOI: 10.1038/psp.2013.75] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Accepted: 11/24/2013] [Indexed: 12/13/2022]
Abstract
Tinnitus, phantom sound perception, is a worldwide highly prevalent disorder for which no clear underlying pathology has been established and for which no approved drug is on the market. Thus, there is an urgent need for new approaches to understand this condition. We used a network pharmacology side-effect analysis to search for genes that are involved in tinnitus generation. We analyzed a network of 1,313 drug–target pairs, based on 275 compounds that elicit tinnitus as side effect and their targets reported in databases, and used a quantitative score to identify emergent significant targets that were more common than expected at random. Cyclooxigenase 1 and 2 were significant, which validates our approach, since salicylate is a known tinnitus generator. More importantly, we predict previously unknown tinnitus-related targets. The present results have important implications toward understanding tinnitus pathophysiology and might pave the way toward the design of novel pharmacotherapies.
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28
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Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr Top Med Chem 2014; 14:1356-64. [PMID: 24805064 PMCID: PMC5344042 DOI: 10.2174/1568026614666140506121116] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2014] [Revised: 02/05/2014] [Accepted: 02/05/2014] [Indexed: 12/22/2022]
Abstract
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.
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Affiliation(s)
| | | | | | - Alexander Tropsha
- 100K Beard Hall, Campus Box 7568, University of North Carolina, Chapel Hill, NC 27599-7568, USA.
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29
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Bai SY, Dai X, Zhao BX, Miao JY. Discovery of a novel fluorescent HSP90 inhibitor and its anti-lung cancer effect. RSC Adv 2014. [DOI: 10.1039/c4ra01800a] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
A novel fluorescent HSP90 inhibitor with strong growth inhibitory effects on lung cancer cells was developed.
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Affiliation(s)
- Su-Yun Bai
- Institute of Developmental Biology
- School of Life Science
- Shandong University
- Jinan 250100, P.R. China
- School of Basic Medical Sciences
| | - Xi Dai
- Institute of Organic Chemistry
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan 250100, P.R. China
| | - Bao-Xiang Zhao
- Institute of Organic Chemistry
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan 250100, P.R. China
| | - Jun-Ying Miao
- Institute of Developmental Biology
- School of Life Science
- Shandong University
- Jinan 250100, P.R. China
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30
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Wang YC, Deng N, Chen S, Wang Y. Computational Study of Drugs by Integrating Omics Data with Kernel Methods. Mol Inform 2013; 32:930-41. [PMID: 27481139 DOI: 10.1002/minf.201300090] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2013] [Accepted: 11/13/2013] [Indexed: 01/02/2023]
Abstract
With the rapid development of genomic and chemogenomic techniques, many omics data sources for drugs have been publicly available. These data sources illustrate drug's biological function in the living cell from different levels and different aspects. One straightforward idea is to learn understandable rules via computational models and algorithms to mine and integrate these data sources. Here, we review our recent efforts on developing kernel-based methods to integrate drug related omics data sources. Three promising applications of our framework are shown to predict drug targets, assign drug's ATC-code annotation, and reveal drug repositioning. We demonstrate that data integration does provide more information and improve the accuracy by recovering more experimentally observed target proteins, ATC-codes, and drug repositioning. Importantly, data integration can indicate novel predictions which are supported by database search and functional annotation analysis and worthy of further experimental validation. In conclusion, kernel methods can efficiently integrate heterogeneous data sources to computationally study drugs, and will promote the further research in drug discovery in a low-cost way.
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Affiliation(s)
- Yongcui C Wang
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, No. 23, Xinning Road, Xining, Qinghai Province, P. R. China
| | - Naiyang Deng
- College of Science, China Agriculture University, No. 17. Qinghua East Road, Beijing, P. R. China
| | - Shilong Chen
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, No. 23, Xinning Road, Xining, Qinghai Province, P. R. China.
| | - Yong Wang
- National Centre for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, N0.55, Zhongguancun East Road, Beijing, P. R. China. .,Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.
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31
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Cao DS, Liang YZ, Yan J, Tan GS, Xu QS, Liu S. PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies. J Chem Inf Model 2013; 53:3086-96. [PMID: 24047419 DOI: 10.1021/ci400127q] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Dong-Sheng Cao
- School of Pharmaceutical Sciences, Central South University, Changsha 410013, P.R. China
| | | | | | - Gui-Shan Tan
- School of Pharmaceutical Sciences, Central South University, Changsha 410013, P.R. China
| | | | - Shao Liu
- Xiangya Hospital, Central South University, Changsha 410008, P.R. China
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32
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Systematic identification of proteins that elicit drug side effects. Mol Syst Biol 2013; 9:663. [PMID: 23632385 PMCID: PMC3693830 DOI: 10.1038/msb.2013.10] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Accepted: 02/17/2013] [Indexed: 01/02/2023] Open
Abstract
Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.
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33
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Ding H, Takigawa I, Mamitsuka H, Zhu S. Similarity-based machine learning methods for predicting drug–target interactions: a brief review. Brief Bioinform 2013; 15:734-47. [DOI: 10.1093/bib/bbt056] [Citation(s) in RCA: 261] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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34
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Liu X, Vogt I, Haque T, Campillos M. HitPick: a web server for hit identification and target prediction of chemical screenings. Bioinformatics 2013; 29:1910-2. [DOI: 10.1093/bioinformatics/btt303] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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35
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Xu H, Tao Y, Lu P, Wang P, Zhang F, Yuan Y, Wang S, Xiao X, Yang H, Huang L. A computational drug-target network for yuanhu zhitong prescription. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2013; 2013:658531. [PMID: 23762151 PMCID: PMC3665234 DOI: 10.1155/2013/658531] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Accepted: 03/10/2013] [Indexed: 12/16/2022]
Abstract
Yuanhu Zhitong prescription (YZP) is a typical and relatively simple traditional Chinese medicine (TCM), widely used in the clinical treatment of headache, gastralgia, and dysmenorrhea. However, the underlying molecular mechanism of action of YZP is not clear. In this study, based on the previous chemical and metabolite analysis, a complex approach including the prediction of the structure of metabolite, high-throughput in silico screening, and network reconstruction and analysis was developed to obtain a computational drug-target network for YZP. This was followed by a functional and pathway analysis by ClueGO to determine some of the pharmacologic activities. Further, two new pharmacologic actions, antidepressant and antianxiety, of YZP were validated by animal experiments using zebrafish and mice models. The forced swimming test and the tail suspension test demonstrated that YZP at the doses of 4 mg/kg and 8 mg/kg had better antidepressive activity when compared with the control group. The anxiolytic activity experiment showed that YZP at the doses of 100 mg/L, 150 mg/L, and 200 mg/L had significant decrease in diving compared to controls. These results not only shed light on the better understanding of the molecular mechanisms of YZP for curing diseases, but also provide some evidence for exploring the classic TCM formulas for new clinical application.
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Affiliation(s)
- Haiyu Xu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
| | - Ye Tao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Peng Lu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Peng Wang
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Fangbo Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
| | - Yuan Yuan
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
| | | | - Xuefeng Xiao
- Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Hongjun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
| | - Luqi Huang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
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36
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Cao DS, Liang YZ, Deng Z, Hu QN, He M, Xu QS, Zhou GH, Zhang LX, Deng ZX, Liu S. Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach. PLoS One 2013; 8:e57680. [PMID: 23577055 PMCID: PMC3618265 DOI: 10.1371/journal.pone.0057680] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2012] [Accepted: 01/27/2013] [Indexed: 11/18/2022] Open
Abstract
The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.
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Affiliation(s)
- Dong-Sheng Cao
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
| | - Yi-Zeng Liang
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
- * E-mail: (YZL); (QNH)
| | - Zhe Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan, P. R. China
| | - Qian-Nan Hu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan, P. R. China
- * E-mail: (YZL); (QNH)
| | - Min He
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
| | - Qing-Song Xu
- School of Mathematics and Statistics, Central South University, Changsha, P. R. China
| | - Guang-Hua Zhou
- The 163rd Hospital of The Chinese People's Liberation Army, Changsha, P. R. China
| | - Liu-Xia Zhang
- The 163rd Hospital of The Chinese People's Liberation Army, Changsha, P. R. China
| | - Zi-xin Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan, P. R. China
| | - Shao Liu
- Xiangya Hospital, Central South University, Changsha, P. R. China
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Wang J. Prediction of the Binding Affinities of PSD95 PDZ Domain in Complex with the CRIPT Peptide. ACTA ACUST UNITED AC 2013. [DOI: 10.12720/jomb.2.2.137-141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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38
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Laenen G, Thorrez L, Börnigen D, Moreau Y. Finding the targets of a drug by integration of gene expression data with a protein interaction network. MOLECULAR BIOSYSTEMS 2013; 9:1676-85. [DOI: 10.1039/c3mb25438k] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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39
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Large-scale prediction of drug–target interactions using protein sequences and drug topological structures. Anal Chim Acta 2012; 752:1-10. [DOI: 10.1016/j.aca.2012.09.021] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2012] [Revised: 09/10/2012] [Accepted: 09/14/2012] [Indexed: 11/19/2022]
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40
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García-Sosa AT, Oja M, Hetényi C, Maran U. DrugLogit: logistic discrimination between drugs and nondrugs including disease-specificity by assigning probabilities based on molecular properties. J Chem Inf Model 2012; 52:2165-80. [PMID: 22830445 DOI: 10.1021/ci200587h] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The increasing knowledge of both structure and activity of compounds provides a good basis for enhancing the pharmacological characterization of chemical libraries. In addition, pharmacology can be seen as incorporating both advances from molecular biology as well as chemical sciences, with innovative insight provided from studying target-ligand data from a ligand molecular point of view. Predictions and profiling of libraries of drug candidates have previously focused mainly on certain cases of oral bioavailability. Inclusion of other administration routes and disease-specificity would improve the precision of drug profiling. In this work, recent data are extended, and a probability-based approach is introduced for quantitative and gradual classification of compounds into categories of drugs/nondrugs, as well as for disease- or organ-specificity. Using experimental data of over 1067 compounds and multivariate logistic regressions, the classification shows good performance in training and independent test cases. The regressions have high statistical significance in terms of the robustness of coefficients and 95% confidence intervals provided by a 1000-fold bootstrapping resampling. Besides their good predictive power, the classification functions remain chemically interpretable, containing only one to five variables in total, and the physicochemical terms involved can be easily calculated. The present approach is useful for an improved description and filtering of compound libraries. It can also be applied sequentially or in combinations of filters, as well as adapted to particular use cases. The scores and equations may be able to suggest possible routes for compound or library modification. The data is made available for reuse by others, and the equations are freely accessible at http://hermes.chem.ut.ee/~alfx/druglogit.html.
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41
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Cami A, Arnold A, Manzi S, Reis B. Predicting adverse drug events using pharmacological network models. Sci Transl Med 2012; 3:114ra127. [PMID: 22190238 DOI: 10.1126/scitranslmed.3002774] [Citation(s) in RCA: 81] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Early and accurate identification of adverse drug events (ADEs) is critically important for public health. We have developed a novel approach for predicting ADEs, called predictive pharmacosafety networks (PPNs). PPNs integrate the network structure formed by known drug-ADE relationships with information on specific drugs and adverse events to predict likely unknown ADEs. Rather than waiting for sufficient post-market evidence to accumulate for a given ADE, this predictive approach relies on leveraging existing, contextual drug safety information, thereby having the potential to identify certain ADEs earlier. We constructed a network representation of drug-ADE associations for 809 drugs and 852 ADEs on the basis of a snapshot of a widely used drug safety database from 2005 and supplemented these data with additional pharmacological information. We trained a logistic regression model to predict unknown drug-ADE associations that were not listed in the 2005 snapshot. We evaluated the model's performance by comparing these predictions with the new drug-ADE associations that appeared in a 2010 snapshot of the same drug safety database. The proposed model achieved an AUROC (area under the receiver operating characteristic curve) statistic of 0.87, with a sensitivity of 0.42 given a specificity of 0.95. These findings suggest that predictive network methods can be useful for predicting unknown ADEs.
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Affiliation(s)
- Aurel Cami
- Children's Hospital Boston, Boston, MA 02115, USA.
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42
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43
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Yeh SH, Yeh HY, Soo VW. A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer. J Clin Bioinforma 2012; 2:1. [PMID: 22239822 PMCID: PMC3285036 DOI: 10.1186/2043-9113-2-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2011] [Accepted: 01/13/2012] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases. METHODS We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets. RESULTS We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method. CONCLUSIONS Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.
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Affiliation(s)
- Shih-Heng Yeh
- Institute of Information Systems and Applications, National Tsing Hua University, HsinChu 300, Taiwan
| | - Hsiang-Yuan Yeh
- Department of Computer Science, National Tsing Hua University, HsinChu 300, Taiwan
| | - Von-Wun Soo
- Department of Computer Science, National Tsing Hua University, HsinChu 300, Taiwan
- Institute of Information Systems and Applications, National Tsing Hua University, HsinChu 300, Taiwan
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44
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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: 145] [Impact Index Per Article: 11.2] [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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45
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Prediction of residues involved in inhibitor specificity in the dihydrofolate reductase family. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2011; 1814:1870-9. [DOI: 10.1016/j.bbapap.2011.08.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2011] [Revised: 07/29/2011] [Accepted: 08/01/2011] [Indexed: 12/11/2022]
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46
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Brouwers L, Iskar M, Zeller G, van Noort V, Bork P. Network neighbors of drug targets contribute to drug side-effect similarity. PLoS One 2011; 6:e22187. [PMID: 21765950 PMCID: PMC3135612 DOI: 10.1371/journal.pone.0022187] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2011] [Accepted: 06/19/2011] [Indexed: 12/31/2022] Open
Abstract
In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.
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Affiliation(s)
- Lucas Brouwers
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Murat Iskar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Georg Zeller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Vera van Noort
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Max-Delbruck-Centre for Molecular Medicine, Berlin-Buch, Germany
- * E-mail:
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47
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Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 2011; 74:2554-74. [PMID: 21621023 DOI: 10.1016/j.jprot.2011.05.011] [Citation(s) in RCA: 186] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 04/10/2011] [Accepted: 05/06/2011] [Indexed: 01/31/2023]
Abstract
Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.
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Affiliation(s)
- Alexios Koutsoukas
- Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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48
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Wendt B, Uhrig U, Bös F. Capturing structure-activity relationships from chemogenomic spaces. J Chem Inf Model 2011; 51:843-51. [PMID: 21410249 DOI: 10.1021/ci100270x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Modeling off-target effects is one major goal of chemical biology, particularly in its applications to drug discovery. Here, we describe a new approach that allows the extraction of structure-activity relationships from large chemogenomic spaces starting from a single chemical structure. Several public source databases, offering a vast amount of data on structure and activity for a large number of different targets, have been investigated for their usefulness in automated structure-activity relationships (SAR) extraction. SAR tables were constructed by assembling similar structures around each query structure that have an activity record for a particular target. Quantitative series enrichment analysis (QSEA) was applied to these SAR tables to identify trends and to transform these trends into topomer CoMFA models. Overall more than 1700 SAR tables with topomer CoMFA models have been obtained from the ChEMBL, PubChem, and ChemBank databases. These models were able to highlight the structural trends associated with various off-target effects of marketed drugs, including cases where other structural similarity metrics would not have detected an off-target effect. These results indicate the usefulness of the QSEA approach, particularly whenever applicable with public databases, in providing a new means, beyond a simple similarity between ligand structures, to capture SAR trends and thereby contribute to success in drug discovery.
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Affiliation(s)
- Bernd Wendt
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, D-69117 Germany.
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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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Perlman L, Gottlieb A, Atias N, Ruppin E, Sharan R. Combining Drug and Gene Similarity Measures for Drug-Target Elucidation. J Comput Biol 2011; 18:133-45. [DOI: 10.1089/cmb.2010.0213] [Citation(s) in RCA: 125] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
| | - Assaf Gottlieb
- The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv, Israel
| | - Nir Atias
- The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv, Israel
| | - Eytan Ruppin
- The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv, Israel
- School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Roded Sharan
- The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv, Israel
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