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      Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project

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          Abstract

          There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.

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          Most cited references54

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          Mechanisms of hepatotoxicity.

          H Jaeschke (2002)
          This review addresses recent advances in specific mechanisms of hepatotoxicity. Because of its unique metabolism and relationship to the gastrointestinal tract, the liver is an important target of the toxicity of drugs, xenobiotics, and oxidative stress. In cholestatic disease, endogenously generated bile acids produce hepatocellular apoptosis by stimulating Fas translocation from the cytoplasm to the plasma membrane where self-aggregation occurs to trigger apoptosis. Kupffer cell activation and neutrophil infiltration extend toxic injury. Kupffer cells release reactive oxygen species (ROS), cytokines, and chemokines, which induce neutrophil extravasation and activation. The liver expresses many cytochrome P450 isoforms, including ethanol-induced CYP2E1. CYP2E1 generates ROS, activates many toxicologically important substrates, and may be the central pathway by which ethanol causes oxidative stress. In acetaminophen toxicity, nitric oxide (NO) scavenges superoxide to produce peroxynitrite, which then causes protein nitration and tissue injury. In inducible nitric oxide synthase (iNOS) knockout mice, nitration is prevented, but unscavenged superoxide production then causes toxic lipid peroxidation to occur instead. Microvesicular steatosis, nonalcoholic steatohepatitis (NASH), and cytolytic hepatitis involve mitochondrial dysfunction, including impairment of mitochondrial fatty acid beta-oxidation, inhibition of mitochondrial respiration, and damage to mitochondrial DNA. Induction of the mitochondrial permeability transition (MPT) is another mechanism causing mitochondrial failure, which can lead to necrosis from ATP depletion or caspase-dependent apoptosis if ATP depletion does not occur fully. Because of such diverse mechanisms, hepatotoxicity remains a major reason for drug withdrawal from pharmaceutical development and clinical use.
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            The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME-Tox).

            ATP binding cassette (ABC) drug transporters play an important role in cancer drug resistance, protection against xenobiotics, and in general in the passage of drugs through cellular and tissue barriers. This review explores how human ABC transporters modulate the pharmacological effects of various drugs, and how this predictable ADME-TOX modulation can be used during the process of drug discovery and development. We provide a description of the relevant human ABC drug transporters and review the models and assay systems that can be applied for the analysis of their expected drug interactions. The use of the in vitro, in vivo, in silico models, their combination, and the emerging clinical information are evaluated with respect to their potential application in early drug screening.
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              Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

              Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                Molecular Diversity Preservation International (MDPI)
                1422-0067
                2012
                21 March 2012
                : 13
                : 3
                : 3820-3846
                Affiliations
                [1 ]Lhasa Ltd., 22-23 Blenheim Terrace, Woodhouse Lane, Leeds, LS2 9HD, UK; E-Mails: Katharine.Briggs@ 123456lhasalimited.org (K.B.); David.Watson@ 123456lhasalimited.org (D.K.W.)
                [2 ]Research Programme on Biomedical Informatics (GRIB), Fundació IMIM, Universitat Pompeu Fabra, PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain; E-Mails: mcases@ 123456imim.es (M.C.); manuel.pastor@ 123456upf.edu (M.P.); fsanz@ 123456imim.es (F.S.)
                [3 ]Department of Preclinical Safety, Novartis Institutes for Biomedical Research (NIBR), Postfach CH-4002, Basel, Switzerland; E-Mail: david.heard@ 123456novartis.com
                [4 ]Molecular Networks GmbH, IZMP, Henkestr. 91, 91052 Erlangen, Germany; E-Mail: schwab@ 123456molecular-networks.com
                [5 ]Bayer HealthCare, Investigational Toxicology, Müllerstr. 178, 13353 Berlin, Germany; E-Mails: thomas.steger-hartmann@ 123456bayer.com (T.S.-H.); andreas.sutter@ 123456bayer.com (A.S.); joerg.wichard@ 123456bayer.com (J.D.W.)
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: francois.pognan@ 123456novartis.com ; Tel.: +47-613242921.
                Article
                ijms-13-03820
                10.3390/ijms13033820
                3317745
                22489185
                b034d219-d9ee-4a1d-acb5-28112b52f8b1
                © 2012 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.

                This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 11 October 2011
                : 30 January 2012
                : 14 March 2012
                Categories
                Review

                Molecular biology
                in vitro toxicity,expert systems,computational models,in silico toxicity,data integration,decision support system,ontology,manual curation,predictive toxicology,data sharing,histopathology,in vivo toxicity,knowledge management,qsar

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