551
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Mishev K, Dejonghe W, Russinova E. Small Molecules for Dissecting Endomembrane Trafficking: A Cross-Systems View. ACTA ACUST UNITED AC 2013; 20:475-86. [DOI: 10.1016/j.chembiol.2013.03.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2013] [Revised: 03/19/2013] [Accepted: 03/20/2013] [Indexed: 01/31/2023]
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552
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Schenone M, Dančík V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol 2013; 9:232-40. [PMID: 23508189 PMCID: PMC5543995 DOI: 10.1038/nchembio.1199] [Citation(s) in RCA: 635] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Accepted: 01/28/2013] [Indexed: 12/12/2022]
Abstract
Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.
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Affiliation(s)
- Monica Schenone
- Proteomics Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Vlado Dančík
- Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Bridget K Wagner
- Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Paul A Clemons
- Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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553
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Abstract
There is a wide gap between the generation of large-scale biological data sets and more-detailed, structural and mechanistic studies. However, recent studies that explicitly combine data from systems and structural biological approaches are having a profound effect on our ability to predict how mutations and small molecules affect atomic-level mechanisms, disrupt systems-level networks, and ultimately lead to changes in organismal fitness. In fact, we argue that a shared framework for analysis of nonadditive genetic and thermodynamic responses to perturbations will accelerate the integration of reductionist and global approaches. A stronger bridge between these two areas will allow for a deeper and more-complete understanding of complex biological phenomenon and ultimately provide needed breakthroughs in biomedical research.
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Affiliation(s)
- James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.
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554
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Wu Z, Wang Y, Chen L. Network-based drug repositioning. MOLECULAR BIOSYSTEMS 2013; 9:1268-81. [PMID: 23493874 DOI: 10.1039/c3mb25382a] [Citation(s) in RCA: 100] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Network-based computational biology, with the emphasis on biomolecular interactions and omics-data integration, has had success in drug development and created new directions such as drug repositioning and drug combination. Drug repositioning, i.e., revealing a drug's new roles, is increasingly attracting much attention from the pharmaceutical community to tackle the problems of high failure rate and long-term development in drug discovery. While drug combination or drug cocktails, i.e., combining multiple drugs against diseases, mainly aims to alleviate the problems of the recurrent emergence of drug resistance and also reveal their synergistic effects. In this paper, we unify the two topics to reveal new roles of drug interactions from a network perspective by treating drug combination as another form of drug repositioning. In particular, first, we emphasize that rationally repositioning drugs in the large scale is driven by the accumulation of various high-throughput genome-wide data. These data can be utilized to capture the interplay among targets and biological molecules, uncover the resulting network structures, and further bridge molecular profiles and phenotypes. This motivates many network-based computational methods on these topics. Second, we organize these existing methods into two categories, i.e., single drug repositioning and drug combination, and further depict their main features by three data sources. Finally, we discuss the merits and shortcomings of these methods and pinpoint some future topics in this promising field.
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Affiliation(s)
- Zikai Wu
- Business School, University of Shanghai for Science and Technology, Shanghai, China
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555
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Significance estimation for sequence-based chemical similarity searching (PhAST) and application to AuroraA kinase inhibitors. Future Med Chem 2013; 4:1897-906. [PMID: 23088272 DOI: 10.4155/fmc.12.148] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Chemical similarity searching allows the retrieval of preferred screening molecules from a compound database. Candidates are ranked according to their similarity to a reference compound (query). Assessing the statistical significance of chemical similarity scores helps prioritizing significant hits, and identifying cases where the database does not contain any promising compounds. METHOD Our text-based similarity measure, Pharmacophore Alignment Search Tool (PhAST), employs pair-wise sequence alignment. We adapted the concept of E-values as significance estimates and employed a sampling technique that incorporates the principle of importance sampling in a Markov chain Monte Carlo simulation to generate distributions of random alignment scores. These distributions were used to compute significance estimates for similarity scores in a preliminary prospective virtual screen for inhibitors of Aurora A kinase. CONCLUSION Assessing the significance of compound similarity computed with PhAST allows for a statistically motivated identification of candidate screening compounds. Inhibitors of Aurora A kinase were retrieved from a large compound library.
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556
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Gakh AA, Sosnov AV, Krasavin M, Nguyen TL, Hamel E. Identification of diaryl 5-amino-1,2,4-oxadiazoles as tubulin inhibitors: the special case of 3-(2-fluorophenyl)-5-(4-methoxyphenyl)amino-1,2,4-oxadiazole. Bioorg Med Chem Lett 2013; 23:1262-8. [PMID: 23385208 PMCID: PMC3601769 DOI: 10.1016/j.bmcl.2013.01.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2012] [Revised: 12/29/2012] [Accepted: 01/02/2013] [Indexed: 12/14/2022]
Abstract
The combination of experimental (inhibition of colchicine binding) and computational (COMPARE, docking studies) data unequivocally identified diaryl 5-amino-1,2,4-oxadiazoles as potent tubulin inhibitors. Good correlation was observed between tubulin binding and cytostatic properties for all tested compounds with the notable exception of the lead candidate, 3-(3-methoxyphenyl)-5-(4-methoxyphenyl)amino-1,2,4-oxadiazole (DCP 10500078). This compound was found to be substantially more active in our in vitro experiments than the monofluorinated title compound, 3-(2-fluorophenyl)-5-(4-methoxyphenyl)amino-1,2,4-oxadiazole (DCP 10500067/NSC 757486), which in turn demonstrated slightly better tubulin binding activity. Comparative SAR analysis of 25 diaryl 5-amino-1,2,4-oxadiazoles with other known tubulin inhibitors, such as combretastatin A-4 (CA-4) and colchicine, provides further insight into the specifics of their binding as well as a plausible mechanism of action.
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Affiliation(s)
- Andrei A Gakh
- Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
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557
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Ziegler S, Pries V, Hedberg C, Waldmann H. Identifizierung der Zielproteine bioaktiver Verbindungen: Die Suche nach der Nadel im Heuhaufen. Angew Chem Int Ed Engl 2013. [DOI: 10.1002/ange.201208749] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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558
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Ziegler S, Pries V, Hedberg C, Waldmann H. Target identification for small bioactive molecules: finding the needle in the haystack. Angew Chem Int Ed Engl 2013; 52:2744-92. [PMID: 23418026 DOI: 10.1002/anie.201208749] [Citation(s) in RCA: 356] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Indexed: 01/10/2023]
Abstract
Identification and confirmation of bioactive small-molecule targets is a crucial, often decisive step both in academic and pharmaceutical research. Through the development and availability of several new experimental techniques, target identification is, in principle, feasible, and the number of successful examples steadily grows. However, a generic methodology that can successfully be applied in the majority of the cases has not yet been established. Herein we summarize current methods for target identification of small molecules, primarily for a chemistry audience but also the biological community, for example, the chemist or biologist attempting to identify the target of a given bioactive compound. We describe the most frequently employed experimental approaches for target identification and provide several representative examples illustrating the state-of-the-art. Among the techniques currently available, protein affinity isolation using suitable small-molecule probes (pulldown) and subsequent mass spectrometric analysis of the isolated proteins appears to be most powerful and most frequently applied. To provide guidance for rapid entry into the field and based on our own experience we propose a typical workflow for target identification, which centers on the application of chemical proteomics as the key step to generate hypotheses for potential target proteins.
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Affiliation(s)
- Slava Ziegler
- Max-Planck-Institut für molekulare Physiologie, Abt. Chemische Biologie, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany.
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559
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Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G. Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules. Mol Inform 2013; 32:133-138. [PMID: 23956801 PMCID: PMC3743170 DOI: 10.1002/minf.201200141] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2012] [Accepted: 01/18/2013] [Indexed: 02/04/2023]
Affiliation(s)
- Michael Reutlinger
- ETH, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences Wolfgang-Pauli-Str. 10, CH-8093 Zurich, Switzerland fax: +41 44 633 13 79, tel: +41 44 633 74 38
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560
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Abstract
In contrast to designed polypharmacology that can result in efficient drugs for complex disorders, unintended drug promiscuity has detrimental contribution to side effects and toxicology. Characterization of promiscuous compounds enhances the understanding of complex interaction patterns and aids the design of compounds with broader selectivity against off-targets that has a major impact on medicinal chemistry outcome. In this Miniperspective we provide insights to the effect of physicochemical parameters on promiscuity. Information collected from recent, large-scale in vitro studies enabled us to discuss the relationships between physicochemical properties and promiscuity in detail. In light of these data, lipophilicity and basic character have the highest influence. On the basis of the accumulated knowledge, we propose the extensive use of pre- and postsynthesis metrics, as well as strict control of physicochemical properties during medicinal chemistry optimizations.
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Affiliation(s)
- Ákos Tarcsay
- Discovery Chemistry, Gedeon Richter Plc., 19-21 Gyömrői út, H-1103 Budapest, Hungary
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561
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Troubleshooting and deconvoluting label-free cell phenotypic assays in drug discovery. J Pharmacol Toxicol Methods 2013; 67:69-81. [PMID: 23340025 DOI: 10.1016/j.vascn.2013.01.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Revised: 12/10/2012] [Accepted: 01/04/2013] [Indexed: 01/04/2023]
Abstract
INTRODUCTION Central to drug discovery and development is to comprehend the target(s), potency, efficacy and safety of drug molecules using pharmacological assays. Owing to their ability to provide a holistic view of drug actions in native cells, label-free biosensor-enabled cell phenotypic assays have been emerging as new generation phenotypic assays for drug discovery. Despite the benefits associated with wide pathway coverage, high sensitivity, high information content, non-invasiveness and real-time kinetics, label-free cell phenotypic assays are often viewed to be a blackbox in the era of target-centric drug discovery. METHODS This article first reviews the biochemical and biological complexity of drug-target interactions, and then discusses the key characteristics of label-free cell phenotypic assays and presents a five-step strategy to troubleshooting and deconvoluting the label-free cell phenotypic profiles of drugs. RESULTS Drug-target interactions are intrinsically complicated. Label-free cell phenotypic signatures of drugs mirror the innate complexity of drug-target interactions, and can be effectively deconvoluted using the five-step strategy. DISCUSSION The past decades have witnessed dramatic expansion of pharmacological assays ranging from molecular to phenotypic assays, which is coincident with the realization of the innate complexity of drug-target interactions. The clinical features of a drug are defined by how it operates at the system level and by its distinct polypharmacology, ontarget, phenotypic and network pharmacology. Approaches to examine the biochemical, cellular and molecular mechanisms of action of drugs are essential to increase the efficiency of drug discovery and development. Label-free cell phenotypic assays and the troubleshooting and deconvoluting approach presented here may hold great promise in drug discovery and development.
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562
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Johns MA. ICBS2012 catalyzes robust response from global chemical biology community. ACS Chem Biol 2013; 8:12-8. [PMID: 23273421 DOI: 10.1021/cb3007049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Margaret A. Johns
- Emory Chemical Biology
Discovery Center, Emory University, Atlanta,
Georgia 30322 , United States
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563
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Lee J, Bogyo M. Target deconvolution techniques in modern phenotypic profiling. Curr Opin Chem Biol 2013; 17:118-26. [PMID: 23337810 DOI: 10.1016/j.cbpa.2012.12.022] [Citation(s) in RCA: 106] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2012] [Revised: 12/19/2012] [Accepted: 12/30/2012] [Indexed: 01/12/2023]
Abstract
The past decade has seen rapid growth in the use of diverse compound libraries in classical phenotypic screens to identify modulators of a given process. The subsequent process of identifying the molecular targets of active hits, also called 'target deconvolution', is an essential step for understanding compound mechanism of action and for using the identified hits as tools for further dissection of a given biological process. Recent advances in 'omics' technologies, coupled with in silico approaches and the reduced cost of whole genome sequencing, have greatly improved the workflow of target deconvolution and have contributed to a renaissance of 'modern' phenotypic profiling. In this review, we will outline how both new and old techniques are being used in the difficult process of target identification and validation as well as discuss some of the ongoing challenges remaining for phenotypic screening.
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Affiliation(s)
- Jiyoun Lee
- Department of Global Medical Science, Sungshin Women's University, Seoul 142-732, Republic of Korea.
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564
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What general conclusions can we draw from kinase profiling data sets? BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1834:1425-33. [PMID: 23333421 DOI: 10.1016/j.bbapap.2012.12.023] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2012] [Accepted: 12/21/2012] [Indexed: 11/21/2022]
Abstract
Understanding general selectivity trends across the kinome has implications ranging from target selection, compound prioritization, toxicity and patient tailoring. Several recent publications have described the characterization of kinase inhibitors via large assay panels, offering a range of generalizations that influenced kinase inhibitor research trends. Since a subset of profiled inhibitors overlap across reports, we evaluated the concordance of activity results for the same compound-kinase pairs across four data sources generated from different kinase biochemical assay technologies. Overall, 77% of all results are within 3 fold or qualitatively in agreement across sources. However, the agreement for active compounds is only 37%, indicating that different profiling panels are in better agreement to determine a compound's lack of activity rather than degree of activity. Low concordance is also found when comparing the promiscuity of kinase targets evaluated from different sources, and the pharmacological similarity of kinases. In contrast, the overall promiscuity of kinase inhibitors was consistent across sources. We highlight the difficulty of drawing general conclusions from such data by showing that no significant selectivity difference distinguishes type I vs. type II inhibitors, and limited kinase space similarity that is consistent across different sources. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).
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565
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Abstract
Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.
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566
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Muthas D, Boyer S. Exploiting Pharmacological Similarity to Identify Safety Concerns - Listen to What the Data Tells You. Mol Inform 2013; 32:37-45. [DOI: 10.1002/minf.201200088] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2012] [Accepted: 11/03/2012] [Indexed: 11/06/2022]
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567
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Lin H, Sassano MF, Roth BL, Shoichet BK. A pharmacological organization of G protein-coupled receptors. Nat Methods 2013; 10:140-6. [PMID: 23291723 PMCID: PMC3560304 DOI: 10.1038/nmeth.2324] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Accepted: 11/16/2012] [Indexed: 01/08/2023]
Abstract
Protein classification typically uses structural, sequence, or functional similarity. Here we introduce an orthogonal method that organizes proteins by ligand similarity, focusing here on the class A G protein-coupled receptor (GPCR) protein family. Comparing a ligand-based dendogram to a sequence-based one, we sought examples of GPCRs that were distantly linked by sequence but neighbors by ligand similarity. Experimental testing of compounds predicted to link three of these new pairs confirmed the predicted association, with potencies ranging from the low-nanomolar to low-micromolar. We then identified hundreds of non-GPCRs closely related to GPCRs by ligand similarity, including the CXCR2 chemokine receptor to Casein kinase I, the cannabinoid receptors to epoxide hydrolase 2, and the α2 adrenergic receptor to phospholipase D. These, too, were confirmed experimentally. Ligand similarities among these targets may reflect a chemical integration in the time domain of molecular signaling.
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Affiliation(s)
- Henry Lin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA
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568
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Wang L, Ma C, Wipf P, Liu H, Su W, Xie XQ. TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS JOURNAL 2013; 15:395-406. [PMID: 23292636 DOI: 10.1208/s12248-012-9449-z] [Citation(s) in RCA: 131] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Accepted: 12/10/2012] [Indexed: 02/08/2023]
Abstract
Target identification of the known bioactive compounds and novel synthetic analogs is a very important research field in medicinal chemistry, biochemistry, and pharmacology. It is also a challenging and costly step towards chemical biology and phenotypic screening. In silico identification of potential biological targets for chemical compounds offers an alternative avenue for the exploration of ligand-target interactions and biochemical mechanisms, as well as for investigation of drug repurposing. Computational target fishing mines biologically annotated chemical databases and then maps compound structures into chemogenomical space in order to predict the biological targets. We summarize the recent advances and applications in computational target fishing, such as chemical similarity searching, data mining/machine learning, panel docking, and the bioactivity spectral analysis for target identification. We then described in detail a new web-based target prediction tool, TargetHunter (http://www.cbligand.org/TargetHunter). This web portal implements a novel in silico target prediction algorithm, the Targets Associated with its MOst SImilar Counterparts, by exploring the largest chemogenomical databases, ChEMBL. Prediction accuracy reached 91.1% from the top 3 guesses on a subset of high-potency compounds from the ChEMBL database, which outperformed a published algorithm, multiple-category models. TargetHunter also features an embedded geography tool, BioassayGeoMap, developed to allow the user easily to search for potential collaborators that can experimentally validate the predicted biological target(s) or off target(s). TargetHunter therefore provides a promising alternative to bridge the knowledge gap between biology and chemistry, and significantly boost the productivity of chemogenomics researchers for in silico drug design and discovery.
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Affiliation(s)
- Lirong Wang
- Department of Pharmaceutical Sciences, School of Pharmacy, Computational Chemical Genomics Screening Center, Pittsburgh, PA, USA
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569
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Socher E, Grossmann TN. Chemical biology 2012: from drug targets to biological systems and back. Chembiochem 2013. [PMID: 23184865 DOI: 10.1002/cbic.201200697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Multiple sites sharing a common target: This year's EMBO conference on chemical biology encouraged over 340 researchers to come to Heidelberg, Germany, and discuss the use of diverse chemical strategies and tools to investigate biological questions and better understand cellular processes.
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Affiliation(s)
- Elke Socher
- Department of Biology and Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
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570
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Abstract
The ability of many drugs, unintended most often, to interact with multiple proteins is commonly referred to as polypharmacology. Could this be a reminiscent chemical signature of early protein evolution?
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Affiliation(s)
- Xavier Jalencas
- Chemogenomics Laboratory
- Research Programme on Biomedical Informatics (GRIB)
- IMIM Hospital del Mar Research Institute and University Pompeu Fabra
- Parc de Recerca Biomèdica
- 08003 Barcelona
| | - Jordi Mestres
- Chemogenomics Laboratory
- Research Programme on Biomedical Informatics (GRIB)
- IMIM Hospital del Mar Research Institute and University Pompeu Fabra
- Parc de Recerca Biomèdica
- 08003 Barcelona
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571
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Phatak SS, Zhang S. A novel multi-modal drug repurposing approach for identification of potent ACK1 inhibitors. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2013:29-40. [PMID: 23424109 PMCID: PMC3864554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Exploiting drug polypharmacology to identify novel modes of actions for drug repurposing has gained significant attentions in the current era of weak drug pipelines. From a serendipitous to systematic or rational ways, a variety of unimodal computational approaches have been developed but the complexity of the problem clearly needs multi-modal approaches for better solutions. In this study, we propose an integrative computational framework based on classical structure-based drug design and chemical-genomic similarity methods, combined with molecular graph theories for this task. Briefly, a pharmacophore modeling method was employed to guide the selection of docked poses resulting from our high-throughput virtual screening. We then evaluated if complementary results (hits missed by docking) can be obtained by using a novel chemo-genomic similarity approach based on chemical/sequence information. Finally, we developed a bipartite-graph based on the extensive data curation of DrugBank, PDB, and UniProt. This drug-target bipartite graph was used to assess similarity of different inhibitors based on their connections to other compounds and targets. The approaches were applied to the repurposing of existing drugs against ACK1, a novel cancer target significantly overexpressed in breast and prostate cancers during their progression. Upon screening of ∼1,447 marketed drugs, a final set of 10 hits were selected for experimental testing. Among them, four drugs were identified as potent ACK1 inhibitors. Especially the inhibition of ACK1 by Dasatinib was as strong as IC(50)=1nM. We anticipate that our novel, integrative strategy can be easily extended to other biological targets with a more comprehensive coverage of known bio-chemical space for repurposing studies.
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Affiliation(s)
- Sharangdhar S. Phatak
- Integrated Molecular Discovery Laboratory (iMDL), The University Texas M.D. Anderson Cancer Center, School of Biomedical Informatics, The Univ. Texas Health Science Center, 7000 Fannin St. Ste 600, Houston, Texas, 77030, USA,
| | - Shuxing Zhang
- Integrated Molecular Discovery Laboratory (iMDL), The University Texas M.D. Anderson Cancer Center, 1901 East Road, Unit 1950, Houston, TX, 77030, USA,
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572
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Wang W, Yang S, Li JING. Drug target predictions based on heterogeneous graph inference. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2013:53-64. [PMID: 23424111 PMCID: PMC3605000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
A key issue in drug development is to understand the hidden relationships among drugs and targets. Computational methods for novel drug target predictions can greatly reduce time and costs compared with experimental methods. In this paper, we propose a network based computational approach for novel drug and target association predictions. More specifically, a heterogeneous drug-target graph, which incorporates known drug-target interactions as well as drug-drug and target-target similarities, is first constructed. Based on this graph, a novel graph-based inference method is introduced. Compared with two state-of-the-art methods, large-scale cross-validation results indicate that the proposed method can greatly improve novel target predictions.
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Affiliation(s)
| | | | - JING Li
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, 44106, USA
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573
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Cortes-Ciriano I, Koutsoukas A, Abian O, Glen RC, Velazquez-Campoy A, Bender A. Experimental validation of in silico target predictions on synergistic protein targets. MEDCHEMCOMM 2013. [DOI: 10.1039/c2md20286g] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Two relatively recent trends have become apparent in current early stage drug discovery settings: firstly, a revival of phenotypic screening strategies and secondly, the increasing acceptance that some drugs work by modulating multiple targets in parallel (‘multi-target drugs’).
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Affiliation(s)
- Isidro Cortes-Ciriano
- Institute of Biocomputation and Physics of Complex Systems (BIFI)
- Unidad Asociada IQFR-CSIC-BIFI, and Department of Biochemistry and Molecular and Cellular Biology
- Universidad de Zaragoza
- Zaragoza
- Spain
| | - Alexios Koutsoukas
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
| | - Olga Abian
- Institute of Biocomputation and Physics of Complex Systems (BIFI)
- Unidad Asociada IQFR-CSIC-BIFI, and Department of Biochemistry and Molecular and Cellular Biology
- Universidad de Zaragoza
- Zaragoza
- Spain
| | - Robert C. Glen
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
| | - Adrian Velazquez-Campoy
- Institute of Biocomputation and Physics of Complex Systems (BIFI)
- Unidad Asociada IQFR-CSIC-BIFI, and Department of Biochemistry and Molecular and Cellular Biology
- Universidad de Zaragoza
- Zaragoza
- Spain
| | - Andreas Bender
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
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574
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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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575
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Forum. Pharmaceut Med 2012. [DOI: 10.1007/bf03262385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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576
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Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM, Huang XP, Norval S, Sassano MF, Shin AI, Webster LA, Simeons FRC, Stojanovski L, Prat A, Seidah NG, Constam DB, Bickerton GR, Read KD, Wetsel WC, Gilbert IH, Roth BL, Hopkins AL. Automated design of ligands to polypharmacological profiles. Nature 2012; 492:215-20. [PMID: 23235874 PMCID: PMC3653568 DOI: 10.1038/nature11691] [Citation(s) in RCA: 599] [Impact Index Per Article: 49.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Accepted: 10/19/2012] [Indexed: 12/22/2022]
Abstract
The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.
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Affiliation(s)
- Jérémy Besnard
- Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK
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577
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The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov Today 2012. [PMID: 23207804 DOI: 10.1016/j.drudis.2012.11.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).
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578
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Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat Rev Drug Discov 2012. [DOI: 10.1038/nrd3845] [Citation(s) in RCA: 470] [Impact Index Per Article: 39.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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579
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Langguth B, Elgoyhen AB. Current pharmacological treatments for tinnitus. Expert Opin Pharmacother 2012; 13:2495-509. [DOI: 10.1517/14656566.2012.739608] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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580
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Sturm N, Desaphy J, Quinn RJ, Rognan D, Kellenberger E. Structural insights into the molecular basis of the ligand promiscuity. J Chem Inf Model 2012; 52:2410-21. [PMID: 22920885 DOI: 10.1021/ci300196g] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Selectivity is a key factor in drug development. In this paper, we questioned the Protein Data Bank to better understand the reasons for the promiscuity of bioactive compounds. We assembled a data set of >1000 pairs of three-dimensional structures of complexes between a "drug-like" ligand (as its physicochemical properties overlap that of approved drugs) and two distinct "druggable" protein targets (as their binding sites are likely to accommodate "drug-like" ligands). Studying the similarity between the ligand-binding sites in the different targets revealed that the lack of selectivity of a ligand can be due (i) to the fact that Nature has created the same binding pocket in different proteins, which do not necessarily have otherwise sequence or fold similarity, or (ii) to specific characteristics of the ligand itself. In particular, we demonstrated that many ligands can adapt to different protein environments by changing their conformation, by using different chemical moieties to anchor to different targets, or by adopting unusual extreme binding modes (e.g., only apolar contact between the ligand and the protein, even though polar groups are present on the ligand or at the protein surface). Lastly, we provided new elements in support to the recent studies which suggest that the promiscuity of a ligand might be inferred from its molecular complexity.
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Affiliation(s)
- Noé Sturm
- UMR 7200 CNRS/Université de Strasbourg, MEDALIS Drug Discovery Center, 74 Route du Rhin, 67401 Illkirch, France
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581
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The determination and interpretation of the therapeutic index in drug development. Nat Rev Drug Discov 2012; 11:751-61. [PMID: 22935759 DOI: 10.1038/nrd3801] [Citation(s) in RCA: 349] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
A key part of drug discovery and development is the characterization and optimization of the safety and efficacy of drug candidates to identify those that have an appropriately balanced safety-efficacy profile for a given indication. The therapeutic index (TI)--which is typically considered as the ratio of the highest exposure to the drug that results in no toxicity to the exposure that produces the desired efficacy--is an important parameter in efforts to achieve this balance. Various types of safety and efficacy data are generated in vitro and in vivo (in animals and in humans), and these data can be used to predict the clinical TI of a drug candidate at an early stage. However, approaches to systematically and quantitatively compare these types of data and to apply this knowledge more effectively are needed. This article critically discusses the various aspects of TI determination and interpretation in drug development for both small molecule drugs and biotherapeutics.
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582
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Liu Z, Fang H, Reagan K, Xu X, Mendrick DL, Slikker W, Tong W. In silico drug repositioning: what we need to know. Drug Discov Today 2012; 18:110-5. [PMID: 22935104 DOI: 10.1016/j.drudis.2012.08.005] [Citation(s) in RCA: 111] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Revised: 08/09/2012] [Accepted: 08/20/2012] [Indexed: 01/31/2023]
Abstract
Drug repositioning, exemplified by sildenafil and thalidomide, is a promising way to explore alternative indications for existing drugs. Recent research has shown that bioinformatics-based approaches have the potential to offer systematic insights into the complex relationships among drugs, targets and diseases necessary for successful repositioning. In this article, we propose the key bioinformatics steps essential for discovering valuable repositioning methods. The proposed steps (repurposing with a purpose, repurposing with a strategy and repurposing with confidence) are aimed at providing a repurposing pipeline, with particular focus on the proposed Drugs of New Indications (DNI) database, which can be used alongside currently available resources to improve in silico drug repositioning.
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Affiliation(s)
- Zhichao Liu
- ICF International at FDA National Center for Toxicological Research, Jefferson, AR 72079, USA
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583
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Biggin PC. An interview with Philip Biggin, Section Editor for Computational, in silico and modelling studies. BMC Pharmacol Toxicol 2012; 13:2. [PMID: 22947036 PMCID: PMC3506266 DOI: 10.1186/2050-6511-13-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Accepted: 08/13/2012] [Indexed: 11/10/2022] Open
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584
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Computational model predicts side effects. Nat Rev Drug Discov 2012. [DOI: 10.1038/nrd3813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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585
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Abstract
Notwithstanding their key roles in therapy and as biological probes, 7% of approved drugs are purported to have no known primary target, and up to 18% lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to "de-orphanize" drugs that lack primary targets. Surprisingly, targets could be easily predicted for many: Whereas these targets were not known to us nor to the common databases, most could be confirmed by literature search, leaving only 13 Food and Drug Administration-approved drugs with unknown targets; the number of drugs without molecular targets likely is far fewer than reported. The number of worldwide drugs without reasonable molecular targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, there remained at least seven drugs for which reasonable mechanism-of-action targets were unknown but could be predicted, including the antitussives clemastine, cloperastine, and nepinalone; the antiemetic benzquinamide; the muscle relaxant cyclobenzaprine; the analgesic nefopam; and the immunomodulator lobenzarit. For each, predicted targets were confirmed experimentally, with affinities within their physiological concentration ranges. Turning this question on its head, we next asked which drugs were specific enough to act as chemical probes. Over 100 drugs met the standard criteria for probes, and 40 did so by more stringent criteria. A chemical information approach to drug-target association can guide therapeutic development and reveal applications to probe biology, a focus of much current interest.
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586
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