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Poster Session
| eCheminfo Predictive ADMET Workshop
Medical Sciences Teaching Center, Oxford University, Oxford, UK
27 - 31 July 2009 |
LIST of POSTERS
1. Barry Hardy (Douglas Connect), Collaborative Development of Predictive Toxicology Applications using the OpenTox Framework
2. Laura Guasch (Universitat Rovira i Virgili), Discovery of Natural Product PPARγ Agonists by a Pharmacophore-Based Virtual Screening Workflow
3. Esther Sala (Rovira & Virgili University), In silico Study of IKKβ Inhibition by Natural Phenolic Compounds
4. Joel Masciocchi (CRS4), MMsPred: a Bioactivity and Toxicology Predictive System
5. Patrik Rydberg (University of Copenhagen), Fast Prediction of Cytochrome P450 Mediated Drug Metabolism
6. Dilip Narayanan (Systems Biology Worldwide), Global and Local Functional Group Signatures of Kinase Inibitors: Implications for Kinase Inhibitor Searching and Design
7. Salmaan Hussain Inayat-Hussain (Universiti Kebangsaan and UKM Medical Molecular Biology Institute , Malaysia), Mechanisms of Apoptosis Induced by Stilbene Derivatives on Human Chronic Myelogenous Leukemic (K562) Cells
Abstracts
Collaborative Development of Predictive Toxicology Applications using the OpenTox Framework
(a)
B. Hardy* (a), N. Douglas (a), C. Helma (b), M. Rautenberg (b), N. Jeliazkova (c), V. Jeliazkov (c), L. Boyanov (c), C. Jiang (c), M. Martinov (c), R. Benigni (d), O. Tcheremenskaia (d), S. Kramer (e), T. Girschick (e), F. Buchwald (e), J. Wicker (e), A. Karwath (f), M. Gütlein (f), A. Maunz (f), H. Sarimveis (g), G. Melagraki (g), A. Afantitis (g), P. Sopasakis (g), D. Gallagher (h), V. Poroikov (i), D. Filimonov (i), A. Zakharov (i), A. Lagunin (i), T. Gloriozova (i), S. Novikov (i), N. Skvortsova (i), S. Chawla (j), S. Bowlus (j), I. Ghosh (k), S. Ray (k), G. Singhai (k), O. Prakash (k), S. Escher (l), S. Weiss (l)
a. Douglas Connect, b. In Silico Toxicology, c. Ideaconsult, d. Istituto Superiore di Sanita', e. Technical University of Munich, f. Albert Ludwigs University Freiburg, g. National Technical University of Athens, h. David Gallagher, i. Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, j. Seascape Learning, k. Jawaharlal Nehru University, l. Fraunhofer Institute for Toxicology & Experimental Medicine
The EC-funded FP7 project “OpenTox” ( www.opentox.org ) is developing an Open Source-based predictive toxicology framework that provides a unified access to toxicological data and (Quantitative) Structure-Activity Relationship i.e., (Q)SAR models. OpenTox provides tools for the integration of data, for the generation and validation of (Q)SAR models for toxic effects, libraries for the development and integration of (Q)SAR algorithms, and scientifically sound validation routines. OpenTox will support the development of applications for non-computational specialists in addition to interfaces for risk assessors, toxicological experts and model and algorithm developers.
OpenTox is relevant for the implementation of REACH as it allows risk assessors to access experimental data, (Q)SAR models and toxicological information from a unified interface that adheres to European and international regulatory requirements including OECD Guidelines for validation and reporting. The OpenTox framework is being populated initially with data and models for chronic, genotoxic and carcinogenic effects. These are the endpoints where computational methods promise the greatest potential reduction in animal testing required under REACH. Initial research has defined the essential components of the framework architecture, approach to data access, schema and management, use of controlled vocabularies and ontologies, web service and communications protocols, and selection and integration of algorithms for predictive modelling. The initial results of this research and next steps will be discussed.
OpenTox has been initiated as a collaborative project involving a combination of 11 different enterprise, university and government research groups to design and build the initial framework. Additionally numerous organizations with industry, regulatory or expert interests are being included from the start in providing guidance and direction. The goal is to expand OpenTox as a community project enabling additional expert and user participants to be involved in developments in as timely a manner as possible. To this end, our agreed upon intention is to carry out developments in an open and transparent manner from the early days of the project, and to open up discussions and development to the global community at large, who may either participate in developments or provide user perspectives. Cooperation on data standards, data integration, ontologies, integration of algorithm predictions from different methods, and testing and validation all have significant collaboration opportunities and benefits for the community. Additionally, practices for building effective collaborations from the OpenTox community approach will be discussed.
About OpenTox
OpenTox - An Open Source Predictive Toxicology Framework, www.opentox.org, is funded under the EU Seventh Framework Program: HEALTH-2007-1.3-3 Promotion, development, validation, acceptance and implementation of QSARs (Quantitative Structure-Activity Relationships) for toxicology, Project Reference Number Health-F5-2008-200787 (2008-2011).
Advisory Board
European Centre for the Validation of Alternative Methods, European Chemicals Bureau, U.S Environmental Protection Agency, U.S. Food & Drug Administration, Nestle, Roche, AstraZeneca, LHASA, Leadscope, University of North Carolina, EC Environment Directorate General, Organisation for Economic Co-operation & Development, CADASTER and Bayer Healthcare
*Contact Address: Dr. Barry Hardy, OpenTox Project Coordinator and Director, Community of Practice & Research Activities, Douglas Connect GmbH, Baermeggenweg 14, 4314 Zeiningen, Switzerland. Email: barry.hardy –(at)- douglasconnect.com
Discovery of Natural Product PPARγ Agonists by a Pharmacophore-Based Virtual Screening Workflow
Laura Guasch (1), Patrick Markt (2), Gudrun Spitzer (3), Markus Mühlbacher (3), Esther Sala (1), Montserrat Vaqué (1), Gerard Pujadas (1), Gerhard Wolber (2), Klaus Liedl (3) and Santi Garcia-Vallvé (1)
1 Grup de Recerca en Nutrigenòmica, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Tarragona, Spain
2 Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Austria
3 Department of Theoretical Chemistry, Institute of General, Inorganic, and Theoretical Chemistry, University of Innsbruck, Austria
Peroxisome proliferator-activated receptors (PPARs), members of the superfamily of nuclear receptors, are transcription factors that control the expression of genes involved in fatty acid metabolism. They function as cellular lipid sensors that activate transcription in response to the binding of ligands, generally fatty acids and their eicosanoids metabolites. One important class of synthetic agonist of PPAR-gamma is the thiazolidinediones (TZDs). The ligand binding pocket is quite large and binds several types of ligands. The most essential feature of total agonists is the hydrogen-bonds network involving the carboxylate group of the ligands with Ser289, His323, His 449 and Tyr473 of PPAR-gamma. The rest of the ligand structure is basically hydrophobic. The partial agonists adopt a distinct binding mode and have no H-bonding interactions with PPARg. The 51 crystal structures of PPAR-gamma avaliable give molecular insights for the improved PPARg potency and selectivity. Recently, it has been shown that different molecules from natural extracts from various sources can act as PPARs agonists. Therefore, natural extracts may contain a large number of potential PPAR-agonists yet to be discovered and that would have an important value for the development of new drugs for the treatment of the several diseases. The aim of this study is to identify novel PPAR-gamma agonists from natural compounds, using the nearly 90,000 natural molecules available in the ZINC database to predict their power as PPARs agonists. We applied a virtual screening workflow based on a combination of pharmacophore modeling with 3D shape and electrostatic similarity screening techniques to discover novel scaffolds for PPAR-gamma ligands.
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In silico Study of IKKβ Inhibition by Natural Phenolic Compounds
Sala E (a), Iwaszkiewicz J (b), Zoete V (b), Grosdidier A (b), Guasch L (a), Garcia-Vallve S (a), Michielin O (b) and Pujadas G (a)
a Nutrigenomics Research Group. Rovira & Virgili University. Campus Sescelades. C/ Marcel•lí Domingo s/n, 43007 Tarragona, Catalonia, Spain.
b Swiss Institute of Bioinformatics (SIB), Molecular Modeling Group, Quartier Sorges, Bâtiment Génopode, CH-1015 Lausanne, Switzerland.
The NF-κβ pathway plays an important role in regulating the expression of the cellular genes that control the immune and inflammatory response. IKKβ is a serine-threonine protein kinase that belongs to the IKK complex and is critically involved in activating the transcription factor NF-κβ in response to various inflammatory stimuli, hence the interest in synthesizing molecules that can inhibit IKKβ. The research interest of this study lies in the relationship between natural compounds and health, and particularly in how the phenolic compounds from grape seed extract can prevent cardiovascular diseases. Our hypothesis is that phenolic compounds can inhibit the IKKβ kinase because previous in vitro studies by our group have shown that some of these compounds can inhibit NF-κβ translocation to the nucleus. The same studies suggest that regulating upstream proteins such as IKK may inhibit degradation of Ikβ (regulatory protein). The aim of the present work is to use a docking-based virtual screening approach to identify the inhibitors of IKKβ in a database of natural phenolic compounds. Since no experimental structure of IKKβ has been deposited in the public Protein Data Bank, the homology model of the IKKβ was built using Modeller 9v5 software. The molecular docking studies were performed using EADock v2.0 and GLIDE v5.0. The results of this investigation are expected to elucidate the molecular background of natural phenolic compounds effect on human health.
Keywords: prediction, homology model, docking, IKKβ, natural compounds.
MMsPred: a Bioactivity and Toxicology Predictive System
J. Masciocchi*, L. Piredu, P. Palla, R. Medda, M. Floris, P. Rodriguez-Tomé
*CRS4, Parco tecnologico della Sardegna, Ed1, 09010 Pula (CA) , Italy.
In the last decade, the development and use of new methods in combinatorial chemistry and high-throughput screening has dramatically increased the number of known biologically active compounds. Paradoxically, the number of drugs reaching the market has not followed the same trend, often because many of the candidate drugs present poor qualities in absorption, distribution, metabolism, excretion, and toxicological properties (ADME-Tox). The ability to recognize and discard bad candidates early in the drug discovery steps would save lost investments in time and money. Machine learning techniques could provide solutions to this problem. The goal of my research is to develop classifiers that accurately discriminate between active and inactive molecules for a specific target. To this end, I am comparing the effectiveness of the application of different machine learning techniques to this problem.
With the assistance of my collaborators, we are currently running experiments comparing the performance of Support Vector Machines (SVM), k-Nearest Neighbor (kNN), Random Forests (RF), and Artificial Neural Network (ANN) in this domain. As a source of data we have selected a set of PubChem's public BioAssays [1]. In addition, with the objective of realizing a real-time query service with our predictors, we aim to keep the features describing the chemical compounds relatively simple. At the end of this process, we should better understand how to build statistical models that are able to recognize molecules active in a specific bioassay, including how to select the most appropriate classification technique, and how to describe compounds in such a way that is not excessively resource-consuming to generate, yet contains sufficient information for the classification. We see immediate applications of such technology to recognize compounds with high-risk of toxicity, and also to suggest likely metabolic pathways that would process it.
1. Eric W. Sayers*, Tanya Barrett, Dennis A. Benson, Stephen H. Bryant, Kathi Canese, Vyacheslav Chetvernin, Deanna M. Church, Michael DiCuccio, Ron Edgar, Scott Federhen, Michael Feolo, Lewis Y. Geer, Wolfgang Helmberg, Yuri Kapustin, David Landsman, David J. Lipman, Thomas L. Madden, Donna R. Maglott, Vadim Miller, Ilene Mizrachi, James Ostell, Kim D. Pruitt, Gregory D. Schuler, Edwin Sequeira, Stephen T. Sherry, Martin Shumway, Karl Sirotkin, Alexandre Souvorov, Grigory Starchenko, Tatiana A. Tatusova, Lukas Wagner, Eugene Yaschenko and Jian Ye. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 2009, Vol. 37, Database issue D5-D15
Fast Prediction of Cytochrome P450 Mediated Drug Metabolism
Patrik Rydberg, Poongavanam Vasanthanathan, Lars Olsen
Department of Medicinal Chemistry, University of Copenhagen
A new method for predicting the site of metabolism in cytochrome P450 mediated drug metabolism is presented. It is based upon the fact that sites with similar neighboring atoms have similar activation energies for the reaction with cytochrome P450 enzymes. The activation energies are given by fragment recognition, and lookup in a fragment table. The energies in the fragment table have been produced by state-of-the-art density functional theory calculations on model systems.
The method is shown to be as accurate as MetaSite for isoform specific metabolism, and as accurate as CypScore for general cytochrome P450 metabolism. The major advantage of our method is that it can be systematically improved and extended without the need for parameterization.
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Global and Local Functional Group Signatures of Kinase Inibitors: Implications for Kinase Inhibitor Searching and Design
Dilip Narayanan (Systems Biology Worldwide)
Abnormal phosphorylation of cellular proteins by kinases leads to dysfunctional signalling pathways and thus to diseases like cancer. After G-protein coupled Receptors, Kinases are one of the most important drug target families in drug discovery. Approaches like kinomics attempt to understand the selectivity and poly pharmacology of kinase inhibitors in terms of genomics. Such chemogenomic approaches towards kinases can also be coded as algorithms in chemoinformatics.
OntomineTM, finds Global and Local positive constraints (conserved Organic functional group counts) and negative constraints (absence of organic functional groups) in biologically or therapeutically related set of small molecules e.g. kinase inhibitors.
The AMBIT bioasassay data (1) can be used to find functional groups patterns or constraints representative of particular kinases in the dataset. For the first part of our study we left out 10% of the small molecule ligands in the bioassay data and compared the predictions with known protein kinase inhibition patterns in the AMBIT assay. The prediction results were comparable to the known inhibition profile.
In the second study the positive and negative constraints were used to search an SDF database to characterize and find novel kinase inhibitors. The results show the predicted kinase inhibitors and their selectivity profile.
Our results demonstate that the in silico assay can capture the key reasons for specific kinase interactions. Thus, this in silico signature can be used to design or search for novel kinase inhibitors. It can be further extended by incorporating position specific information on functional groups.
(1) Karaman, M.W. et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 26, 127-132.
Mechanisms of Apoptosis Induced by Stilbene Derivatives on Human Chronic Myelogenous Leukemic (K562) Cells
Haslan Roslie, Norfadilah Rajab, Chan Kok Meng, Saraswati S. Velu,Syed Illah, Irma Bunyamin, Jean Frederick Faizal Weber, Noel F Thomas, Abu Bakar Abdul Majeed, Salmaan Hussain Inayat-Hussain*
Universiti Kebangsaan and UKM Medical Molecular Biology Institute, Malaysia
Stilbenes, exemplified by resveratrol, pterostilbene and piceatannol, have been extensively studied due to their wide range of biological activities including antileukemic properties. In this study, 23 stilbene derivatives with varied substitution patterns have been synthesized. The cytotoxicity of these compounds towards human chronic myelogenous leukemia, K562 cell lines were evaluated using the MTT assay. The most potent stilbenoids were further investigated to see whether they could cause genotoxicity in K562 cells using Alkaline Comet Assay. The mechanism of apoptosis induced by the most potent compound was also investigated by flow cytometry and immunobloting were employed to measure the status of mitochondrial membrane potential and the processing of caspases, respectively. Among all the stilbenes tested four namely: 8, 12, 19 and 23 showed cytotoxicity against K562 cells with IC50s of 78µM, 38µM, 67µM and 19.5µM, respectively. Following 2, 4 and 24 hour treatment with the most potent compounds (compound 12 and 23) at IC10 and IC25 on K562 cells, no significant DNA strand breakage was observed by the Alkaline Comet Assay suggesting that the cytotoxicities induced by both compounds were independent of DNA damage as a primary signal of cell death. Following 24 hours of treatment with compound 12 and 23, K562 cells underwent apoptosis as assessed by flow cytometry by detecting the phosphatidylserine externalization. Since compound 23 is a better apoptotic inducer compared to compound 12, the involvement of mitochondrial membrane potential (ΔΨm) and the processing of apical and executioner caspases were investigated in compound 23-treated K562 cells. K562 cells stained with the potentiometric dye, tetramethylrhodamine ethyl ester (TMRE) exhibited loss of mitochondrial membrane potential (ΔΨm) with total depolarization observed at 100µM of compound 23. Detection of cleaved caspase using imunoblotting further confirmed the involvement of caspases-9 and -3 activation in compound 23-induced apoptosis in K562 cells. The activation of these caspases occurred in a concentration- and time-dependent manner. In summary, this study provides insight into the possible mechanism of apoptosis induced by the newly synthesized stilbene derivatives especially compound 23. Further studies will provide a better understanding of the potential antileukemic effects of these new stilbene derivatives.
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