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Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Pujadas G, Garcia-Vallve S. Tools for in silico target fishing. Methods 2015; 71:98-103. [DOI: 10.1016/j.ymeth.2014.09.006] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2014] [Revised: 09/18/2014] [Accepted: 09/19/2014] [Indexed: 12/17/2022] Open
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102
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Liu Z, Borlak J, Tong W. Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis. Genome Med 2014; 6:94. [PMID: 25484921 PMCID: PMC4256829 DOI: 10.1186/s13073-014-0094-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Accepted: 10/23/2014] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Cystic fibrosis (CF) is a fatal genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene that primarily affects the lungs and the digestive system, and the current drug treatment is mainly able to alleviate symptoms. To improve disease management for CF, we considered the repurposing of approved drugs and hypothesized that specific microRNA (miRNA) transcription factors (TF) gene networks can be used to generate feed-forward loops (FFLs), thus providing treatment opportunities on the basis of disease specific FFLs. METHODS Comprehensive database searches revealed significantly enriched TFs and miRNAs in CF and CFTR gene networks. The target genes were validated using ChIPBase and by employing a consensus approach of diverse algorithms to predict miRNA gene targets. STRING analysis confirmed protein-protein interactions (PPIs) among network partners and motif searches defined composite FFLs. Using information extracted from SM2miR and Pharmaco-miR, an in silico drug repurposing pipeline was established based on the regulation of miRNA/TFs in CF/CFTR networks. RESULTS In human airway epithelium, a total of 15 composite FFLs were constructed based on CFTR specific miRNA/TF gene networks. Importantly, nine of them were confirmed in patient samples and CF epithelial cells lines, and STRING PPI analysis provided evidence that the targets interacted with each other. Functional analysis revealed that ubiquitin-mediated proteolysis and protein processing in the endoplasmic reticulum dominate the composite FFLs, whose major functions are folding, sorting, and degradation. Given that the mutated CFTR gene disrupts the function of the chloride channel, the constructed FFLs address mechanistic aspects of the disease and, among 48 repurposing drug candidates, 26 were confirmed with literature reports and/or existing clinical trials relevant to the treatment of CF patients. CONCLUSION The construction of FFLs identified promising drug repurposing candidates for CF and the developed strategy may be applied to other diseases as well.
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Affiliation(s)
- Zhichao Liu
- />Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Jürgen Borlak
- />Centre for Pharmacology and Toxicology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany
| | - Weida Tong
- />Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079 USA
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103
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Khedr MA, Shehata TM, Mohamed ME. Repositioning of 2,4-Dichlorophenoxy acetic acid as a potential anti-inflammatory agent: In Silico and Pharmaceutical Formulation study. Eur J Pharm Sci 2014; 65:130-8. [DOI: 10.1016/j.ejps.2014.09.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 08/26/2014] [Accepted: 09/12/2014] [Indexed: 12/26/2022]
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104
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Abstract
Reverse or inverse docking is proving to be a powerful tool for drug repositioning and drug rescue. It involves docking a small-molecule drug/ligand in the potential binding cavities of a set of clinically relevant macromolecular targets. Detailed analyses of the binding characteristics lead to ranking of the targets according to the tightness of binding. This process can potentially identify novel molecular targets for the drug/ligand which may be relevant for its mechanism of action and/or side effect profile. Another potential application of reverse docking is during the lead discovery and optimization stages of the drug-discovery cycle. This review summarizes the state-of-the-art and future prospects of the reverse docking with particular emphasis on computational molecular design.
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105
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A phenome-guided drug repositioning through a latent variable model. BMC Bioinformatics 2014; 15:267. [PMID: 25103881 PMCID: PMC4137076 DOI: 10.1186/1471-2105-15-267] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 07/21/2014] [Indexed: 11/23/2022] Open
Abstract
Background The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution. Results We developed our model on the phenome information in Side Effect Resource (SIDER). We first developed a LDA model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown” one at the time and then recovered based on the remaining drug-phenotype pairs. Of the probabilistically significant pairs, 70% was successfully recovered. Next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. We were able to retrieve approved indications of 6 drugs whose indications were not listed in SIDER. For 908 drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. Several of the suggested new uses can be supported with information from the scientific literature. Conclusions The results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. In this regard, LDA serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-267) contains supplementary material, which is available to authorized users.
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106
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Njoroge M, Njuguna NM, Mutai P, Ongarora DSB, Smith PW, Chibale K. Recent approaches to chemical discovery and development against malaria and the neglected tropical diseases human African trypanosomiasis and schistosomiasis. Chem Rev 2014; 114:11138-63. [PMID: 25014712 DOI: 10.1021/cr500098f] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
| | | | | | | | - Paul W Smith
- Novartis Institute for Tropical Diseases , Singapore 138670, Singapore
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107
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Schomburg KT, Bietz S, Briem H, Henzler AM, Urbaczek S, Rarey M. Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model 2014; 54:1676-86. [PMID: 24851945 DOI: 10.1021/ci500130e] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets.
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Affiliation(s)
- Karen T Schomburg
- Center for Bioinformatics, University of Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany
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108
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Amelio I, Gostev M, Knight RA, Willis AE, Melino G, Antonov AV. DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information. Cell Death Dis 2014; 5:e1051. [PMID: 24503543 PMCID: PMC3944280 DOI: 10.1038/cddis.2014.9] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2013] [Revised: 12/11/2013] [Accepted: 12/19/2013] [Indexed: 12/15/2022]
Abstract
The use of existing drugs for new therapeutic applications, commonly referred to as drug repositioning, is a way for fast and cost-efficient drug discovery. Drug repositioning in oncology is commonly initiated by in vitro experimental evidence that a drug exhibits anticancer cytotoxicity. Any independent verification that the observed effects in vitro may be valid in a clinical setting, and that the drug could potentially affect patient survival in vivo is of paramount importance. Despite considerable recent efforts in computational drug repositioning, none of the studies have considered patient survival information in modelling the potential of existing/new drugs in the management of cancer. Therefore, we have developed DRUGSURV; this is the first computational tool to estimate the potential effects of a drug using patient survival information derived from clinical cancer expression data sets. DRUGSURV provides statistical evidence that a drug can affect survival outcome in particular clinical conditions to justify further investigation of the drug anticancer potential and to guide clinical trial design. DRUGSURV covers both approved drugs (∼1700) as well as experimental drugs (∼5000) and is freely available at http://www.bioprofiling.de/drugsurv.
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Affiliation(s)
- I Amelio
- Medical Research Council Toxicology Unit, Leicester University, Leicester, UK
| | - M Gostev
- Wellcome Trust Genome Campus, EBI, Hinxton, Cambridge, UK
| | - R A Knight
- Medical Research Council Toxicology Unit, Leicester University, Leicester, UK
| | - A E Willis
- Medical Research Council Toxicology Unit, Leicester University, Leicester, UK
| | - G Melino
- 1] Medical Research Council Toxicology Unit, Leicester University, Leicester, UK [2] Department of Experimental Medicine and Surgery, University of Rome 'Tor Vergata', Rome, Italy
| | - A V Antonov
- Medical Research Council Toxicology Unit, Leicester University, Leicester, UK
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109
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Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 2013; 19:637-44. [PMID: 24239728 DOI: 10.1016/j.drudis.2013.11.005] [Citation(s) in RCA: 253] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Revised: 10/07/2013] [Accepted: 11/06/2013] [Indexed: 01/04/2023]
Abstract
Recycling old drugs, rescuing shelved drugs and extending patents' lives make drug repositioning an attractive form of drug discovery. Drug repositioning accounts for approximately 30% of the newly US Food and Drug Administration (FDA)-approved drugs and vaccines in recent years. The prevalence of drug-repositioning studies has resulted in a variety of innovative computational methods for the identification of new opportunities for the use of old drugs. Questions often arise from customizing or optimizing these methods into efficient drug-repositioning pipelines for alternative applications. It requires a comprehensive understanding of the available methods gained by evaluating both biological and pharmaceutical knowledge and the elucidated mechanism-of-action of drugs. Here, we provide guidance for prioritizing and integrating drug-repositioning methods for specific drug-repositioning pipelines.
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110
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Zhang Y, Tao C, He Y, Kanjamala P, Liu H. Network-based analysis of vaccine-related associations reveals consistent knowledge with the vaccine ontology. J Biomed Semantics 2013; 4:33. [PMID: 24209834 PMCID: PMC4177205 DOI: 10.1186/2041-1480-4-33] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 11/04/2013] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Ontologies are useful in many branches of biomedical research. For instance, in the vaccine domain, the community-based Vaccine Ontology (VO) has been widely used to promote vaccine data standardization, integration, and computer-assisted reasoning. However, a major challenge in the VO has been to construct ontologies of vaccine functions, given incomplete vaccine knowledge and inconsistencies in how this knowledge is manually curated. RESULTS In this study, we show that network-based analysis of vaccine-related networks can identify underlying structural information consistent with that captured by the VO, and commonalities in the vaccine adverse events for vaccines and for diseases to produce new hypotheses about pathomechanisms involving the vaccine and the disease status. First, a vaccine-vaccine network was inferred by applying a bipartite network projection strategy to the vaccine-disease network extracted from the Semantic MEDLINE database. In total, 76 vaccines and 573 relationships were identified to construct the vaccine network. The shortest paths between all pairs of vaccines were calculated within the vaccine network. The correlation between the shortest paths of vaccine pairs and their semantic similarities in the VO was then investigated. Second, a vaccine-gene network was also constructed. In this network, 4 genes were identified as hubs interacting with at least 3 vaccines, and 4 vaccines were identified as hubs associated with at least 3 genes. These findings correlate with existing knowledge and provide new hypotheses in the fundamental interaction mechanisms involving vaccines, diseases, and genes. CONCLUSIONS In this study, we demonstrated that a combinatorial analysis using a literature knowledgebase, semantic technology, and ontology is able to reveal important unidentified knowledge critical to biomedical research and public health and to generate testable hypotheses for future experimental verification. As the associations from Semantic MEDLINE remain incomplete, we expect to extend this work by (1) integrating additional association databases to complement Semantic MEDLINE knowledge, (2) extending the neighbor genes of vaccine-associated genes, and (3) assigning confidence weights to different types of associations or associations from different sources.
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Affiliation(s)
- Yuji Zhang
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
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111
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Rodrigues FAR, Oliveira ACA, Cavalcanti BC, Pessoa C, Pinheiro AC, de Souza MVN. Biological evaluation of isoniazid derivatives as an anticancer class. Sci Pharm 2013; 82:21-8. [PMID: 24634839 PMCID: PMC3951230 DOI: 10.3797/scipharm.1307-25] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Accepted: 09/22/2013] [Indexed: 11/25/2022] Open
Abstract
A series of thirty-two isoniazid derivatives have been evaluated for their activity against four human cancer cell lines with potent cytotoxicity (IC50 ranging from 0.61 to 3.36 μg/mL). The structure-activity relationship (SAR) analysis indicated the number, the positions, and the types of substituents attached to the aromatic ring as being critical factors for the biological activity. Briefly, we observed that the presence of a hydroxyl group on the benzene ring plays an important role in the anticancer activity of this series, especially when it is located in ortho-position. Among the thirty-two compounds, three displayed good cytotoxic activity when compared to the reference drug doxorubicin and are thus being considered leading compounds of this new class.
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Affiliation(s)
- Felipe A R Rodrigues
- Laboratório de Oncologia Experimental, Universidade Federal do Ceará, Fortaleza, CE, Brazil
| | - Augusto C A Oliveira
- Laboratório de Oncologia Experimental, Universidade Federal do Ceará, Fortaleza, CE, Brazil
| | - Bruno C Cavalcanti
- Laboratório de Oncologia Experimental, Universidade Federal do Ceará, Fortaleza, CE, Brazil
| | - Claudia Pessoa
- Laboratório de Oncologia Experimental, Universidade Federal do Ceará, Fortaleza, CE, Brazil
| | - Alessandra C Pinheiro
- FioCruz-Fundação Oswaldo Cruz, Instituto de Tecnologia em Fármacos-Far-Manguinhos, Rua Sizenando Nabuco, 100, Manguinhos, 21041-250 Rio de Janeiro, RJ, Brazil
| | - Marcus V N de Souza
- FioCruz-Fundação Oswaldo Cruz, Instituto de Tecnologia em Fármacos-Far-Manguinhos, Rua Sizenando Nabuco, 100, Manguinhos, 21041-250 Rio de Janeiro, RJ, Brazil
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112
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Dy GK, Adjei AA. Understanding, recognizing, and managing toxicities of targeted anticancer therapies. CA Cancer J Clin 2013; 63:249-79. [PMID: 23716430 DOI: 10.3322/caac.21184] [Citation(s) in RCA: 231] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Revised: 02/21/2013] [Accepted: 02/25/2013] [Indexed: 12/11/2022] Open
Abstract
Answer questions and earn CME/CNE Advances in genomics and molecular biology have identified aberrant proteins in cancer cells that are attractive targets for cancer therapy. Because these proteins are overexpressed or dysregulated in cancer cells compared with normal cells, it was assumed that their inhibitors will be narrowly targeted and relatively nontoxic. However, this hope has not been achieved. Current targeted agents exhibit the same frequency and severity of toxicities as traditional cytotoxic agents, with the main difference being the nature of the toxic effects. Thus, the classical chemotherapy toxicities of alopecia, myelosuppression, mucositis, nausea, and vomiting have been generally replaced by vascular, dermatologic, endocrine, coagulation, immunologic, ocular, and pulmonary toxicities. These toxicities need to be recognized, prevented, and optimally managed.
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Affiliation(s)
- Grace K Dy
- Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York, USA
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