In silico toxicology in drug discovery - concepts based on three-dimensional models.
01 Nov 2009-Atla-alternatives To Laboratory Animals (Fund for the Replacement of Animals in Medical Experiments)-Vol. 37, Iss: 5, pp 477-496
TL;DR: The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals, based on three-dimensional models of small molecules binding to such entities.
Abstract: Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities Computer-assisted (in silico) technologies are considered to be efficient alterna
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Federal Institute for Risk Assessment1, University of Oulu2, Centre for Health Protection3, University of Southern Denmark4, Mario Negri Institute for Pharmacological Research5, Imperial College London6, Merck KGaA7, Liverpool John Moores University8, Procter & Gamble9, University of Würzburg10, Charité11, University of Tampere12, University of Manchester13, Maastricht University14, National Institutes of Health15, Clariant16, Nestlé17, Pablo de Olavide University18, Vrije Universiteit Brussel19, University of Antwerp20, University of Tübingen21, Istituto Superiore di Sanità22
TL;DR: In this paper, Adler et al. present a survey of the authors' work in the field of bioinformatics, including the following authors:Sarah AdlerDavid BasketterStuart CretonOlavi PelkonenJan van BenthemValerie Zuang • Klaus Ejner AndersenAlexandre Angers-LoustauAynur AptulaAnna Bal-PriceEmilio Benfenati • Ulrike BernauerJos BessemsFrederic Y. BoisAlan BoobisEsther BrandonSusanne Bremer • Thomas
Abstract: Sarah AdlerDavid BasketterStuart CretonOlavi PelkonenJan van BenthemValerie Zuang • Klaus Ejner AndersenAlexandre Angers-LoustauAynur AptulaAnna Bal-PriceEmilio Benfenati • Ulrike BernauerJos BessemsFrederic Y. BoisAlan BoobisEsther BrandonSusanne Bremer • Thomas BroschardSilvia CasatiSandra CoeckeRaffaella CorviMark CroninGeorge Daston • Wolfgang DekantSusan FelterElise GrignardUrsula Gundert-RemyTuula HeinonenIan Kimber • Jos KleinjansHannu KomulainenReinhard KreilingJoachim KreysaSofia Batista LeiteGeorge Loizou • Gavin MaxwellPaolo MazzatortaSharon MunnStefan PfuhlerPascal PhrakonkhamAldert Piersma • Albrecht PothPilar PrietoGuillermo RepettoVera RogiersGreet SchoetersMichael Schwarz • Rositsa SerafimovaHanna TahtiEmanuela TestaiJoost van DelftHenk van LoverenMathieu Vinken • Andrew WorthJose ´-Manuel Zaldivar
482 citations
TL;DR: This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes and uses data from the publicly available PHYSPROP database, a set of 13 common physicochemical and environmental fate properties.
Abstract: The collection of chemical structure information and associated experimental data for quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.
271 citations
Research Triangle Park1, Umeå University2, Mario Negri Institute for Pharmacological Research3, University of North Carolina at Chapel Hill4, University of Strasbourg5, National Institutes of Health6, United States Environmental Protection Agency7, University of Bari8, Technical University of Denmark9, University of Milano-Bicocca10, Food and Drug Administration11, Lockheed Martin Corporation12, Research Institute for Fragrance Materials13, University of Milan14, United States Department of Agriculture15
TL;DR: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches and the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.
Abstract: BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most ...
252 citations
Cites background from "In silico toxicology in drug discov..."
...These structure-based methods are particularly appealing for their ability to predict toxicologically relevant end points quickly and at low cost (Muster et al. 2008; Vedani and Smiesko 2009)....
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TL;DR: This Guidance describes how to perform hazard identification for endocrine‐disrupting properties by following the scientific criteria which are outlined in Commission Delegated Regulation (EU) 2017/2100 and Commission Regulation 2018/605 for biocidal products and plant protection products, respectively.
Abstract: This Guidance describes how to perform hazard identification for endocrine-disrupting properties by following the scientific criteria which are outlined in Commission Delegated Regulation (EU) 2017/2100 and Commission Regulation (EU) 2018/605 for biocidal products and plant protection products, respectively.
239 citations
TL;DR: It is suggested that structure-based ADMET profiling will probably join the mainstream during the coming years following current trends in the field and results suggested that.
219 citations
References
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TL;DR: VMD is a molecular graphics program designed for the display and analysis of molecular assemblies, in particular biopolymers such as proteins and nucleic acids, which can simultaneously display any number of structures using a wide variety of rendering styles and coloring methods.
46,130 citations
"In silico toxicology in drug discov..." refers methods in this paper
...Figures 2, 4–8 and 12a–e were generated, in part, by using VMD software (84)....
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TL;DR: Mechanisms underlying the disruption of the development of vital systems, such as the endocrine, reproductive, and immune systems, are discussed with reference to wildlife, laboratory animals, and humans.
Abstract: Large numbers and large quantities of endocrine-disrupting chemicals have been released into the environment since World War II. Many of these chemicals can disturb development of the endocrine system and of the organs that respond to endocrine signals in organisms indirectly exposed during prenatal and/or early postnatal life; effects of exposure during development are permanent and irreversible. The risk to the developing organism can also stem from direct exposure of the offspring after birth or hatching. In addition, transgenerational exposure can result from the exposure of the mother to a chemical at any time throughout her life before producing offspring due to persistence of endocrine-disrupting chemicals in body fat, which is mobilized during egg laying or pregnancy and lactation. Mechanisms underlying the disruption of the development of vital systems, such as the endocrine, reproductive, and immune systems, are discussed with reference to wildlife, laboratory animals, and humans.
3,323 citations
TL;DR: The most toxic halogenated aromatic is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and based on in vivo and in vitro studies the relative toxicities have been determined relative to TCDD (i.e., toxic equivalents).
Abstract: Halogenated aromatic compounds, typified by the polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), biphenyls (PCBs), and diphenylethers (PCDEs), are industrial compounds or byproducts which have been widely identified in the environment and in chemical-waste dumpsites. Halogenated aromatics are invariably present in diverse analytes as highly complex mixtures of isomers and congeners and this complicates the hazard and risk assessment of these compounds. Several studies have confirmed the common receptor-mediated mechanism of action of toxic halogenated aromatics and this has resulted in the development of structure-activity relationships for this class of chemicals. The most toxic halogenated aromatic is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and based on in vivo and in vitro studies the relative toxicities of individual halogenated aromatics have been determined relative to TCDD (i.e., toxic equivalents). The derived toxic equivalents can be used for hazard and risk assessment of halogenated aromatic mixtures; moreover, for more complex mixtures containing congeners for which no standards are available (e.g., bromo/chloro mixtures), several in vitro or in vivo assays can be utilized for hazard or risk assessment.
1,756 citations
01 Jan 1990
TL;DR: The most toxic halogenated aromatic is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) as discussed by the authors.
Abstract: Halogenated aromatic compounds, typified by the polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), biphenyls (PCBs), and diphenylethers (PCDEs), are industrial compounds or byproducts which have been widely identified in the environment and in chemical-waste dumpsites. Halogenated aromatics are invariably present in diverse analytes as highly complex mixtures of isomers and congeners and this complicates the hazard and risk assessment of these compounds. Several studies have confirmed the common receptor-mediated mechanism of action of toxic halogenated aromatics and this has resulted in the development of structure-activity relationships for this class of chemicals. The most toxic halogenated aromatic is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and based on in vivo and in vitro studies the relative toxicities of individual halogenated aromatics have been determined relative to TCDD (i.e., toxic equivalents). The derived toxic equivalents can be used for hazard and risk assessment of halogenated aromatic mixtures; moreover, for more complex mixtures containing congeners for which no standards are available (e.g., bromo/chloro mixtures), several in vitro or in vivo assays can be utilized for hazard or risk assessment.
1,730 citations
TL;DR: Three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases.
Abstract: From the historically grown archive of protein−ligand complexes in the Protein Data Bank small organic ligands are extracted and interpreted in terms of their chemical characteristics and features. Subsequently, pharmacophores representing ligand−receptor interaction are derived from each of these small molecules and its surrounding amino acids. Based on a defined set of only six types of chemical features and volume constraints, three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases. The algorithms for ligand extraction and interpretation as well as the pharmacophore creation technique from the automatically interpreted data are presented and applied to a rhinovirus capsid complex as application example.
1,480 citations
"In silico toxicology in drug discov..." refers methods in this paper
...Next, similar approaches such as LigandScout (78) and the VirtualToxLab can be combined to strengthen or discard a prediction....
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