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      In silico toxicology: computational methods for the prediction of chemical toxicity

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          Abstract

          Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late‐stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147–172. doi: 10.1002/wcms.1240

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          Most cited references71

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          Mechanisms of nanotoxicity: Generation of reactive oxygen species⋆

          Nanotechnology is a rapidly developing field in the 21 st century, and the commercial use of nanomaterials for novel applications is increasing exponentially. To date, the scientific basis for the cytotoxicity and genotoxicity of most manufactured nanomaterials are not understood. The mechanisms underlying the toxicity of nanomaterials have recently been studied intensively. An important mechanism of nanotoxicity is the generation of reactive oxygen species (ROS). Overproduction of ROS can induce oxidative stress, resulting in cells failing to maintain normal physiological redox-regulated functions. This in turn leads to DNA damage, unregulated cell signaling, change in cell motility, cytotoxicity, apoptosis, and cancer initiation. There are critical determinants that can affect the generation of ROS. These critical determinants, discussed briefly here, include: size, shape, particle surface, surface positive charges, surface-containing groups, particle dissolution, metal ion release from nanometals and nanometal oxides, UV light activation, aggregation, mode of interaction with cells, inflammation, and pH of the medium.
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            Modeling the primary size effects of citrate-coated silver nanoparticles on their ion release kinetics.

            Ion release is an important environmental behavior of silver nanoparticles (AgNPs), and characterization of Ag(+) release is critical for understanding the environmental fate, transport, and biological impacts of AgNPs. The ion release kinetics of AgNPs with three primary diameters (20, 40, and 80 nm) were studied by dispersing them in quarter-strength Hoagland medium at two initial concentrations (300 and 600 μg/L). Ag(+) release rates were found to depend on primary particle size and concentration, when other environmental factors (e.g., dissolved oxygen and protons) were kept constant. A kinetic model was developed to describe the Ag(+) release based on the hard sphere theory using the Arrhenius equation. The model fitted the experimental data well with correlation coefficients of 0.97-0.99, and the model usefully interpreted the dependence of ion release kinetics on the primary particle size and concentration. Moreover, the effects of environmental factors (e.g., dissolved oxygen, pH, temperature, and salinity) potentially can be interpreted as well. This model provides fundamental insight into the ion release kinetics of AgNPs in aqueous environments, allowing us to better understand and predict the nanotoxicity of AgNPs.
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              The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform.

              The US Tox21 collaborative program represents a paradigm shift in toxicity testing of chemical compounds from traditional in vivo tests to less expensive and higher throughput in vitro methods to prioritize compounds for further study, identify mechanisms of action and ultimately develop predictive models for adverse health effects in humans. The NIH Chemical Genomics Center (NCGC) is an integral component of the Tox21 collaboration owing to its quantitative high-throughput screening (qHTS) paradigm, in which titration-based screening is used to profile hundreds of thousands of compounds per week. Here, we describe the Tox21 collaboration, qHTS-based compound testing and the various Tox21 screening assays that have been validated and tested at the NCGC to date. Published by Elsevier Ltd.
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                Author and article information

                Journal
                Wiley Interdiscip Rev Comput Mol Sci
                Wiley Interdiscip Rev Comput Mol Sci
                10.1111/(ISSN)1759-0884
                WCMS
                Wiley Interdisciplinary Reviews. Computational Molecular Science
                Wiley Periodicals, Inc. (Hoboken, USA )
                1759-0876
                1759-0884
                06 January 2016
                March 2016
                : 6
                : 2 ( doiID: 10.1002/wcms.2016.6.issue-2 )
                : 147-172
                Affiliations
                [ 1 ] King Abdullah University of Science and Technology (KAUST)Computational Bioscience Research Centre (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) ThuwalSaudi Arabia
                Author notes
                [*] [* ]Correspondence to: vladimir.bajic@ 123456kaust.edu.sa
                Article
                WCMS1240
                10.1002/wcms.1240
                4785608
                27066112
                c62d0607-e640-4a26-931e-7de8f94d0466
                © 2016 The Authors. WIREs Computational Molecular Science published by John Wiley & Sons, Ltd.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 10 August 2015
                : 27 October 2015
                : 10 November 2015
                Page count
                Pages: 26
                Funding
                Funded by: King Abdullah University of Science and Technology
                Categories
                Chemoinformatics
                Computer Algorithms and Programming
                Databases and Expert Systems
                Advanced Review
                Advanced Reviews
                Custom metadata
                2.0
                wcms1240
                wcms1240-hdr-0001
                March/April 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.8.5 mode:remove_FC converted:10.03.2016

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