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Special Issue "Advances in Computational Toxicology"

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Toxicology".

Deadline for manuscript submissions: closed (31 August 2011)

Special Issue Editor

Guest Editor
Prof. Dr. Dale Johnson (Website)

1 Emiliem, Inc., 6027 Christie Avenue, Emeryville, CA 94608, USA
2
University of California, Berkeley, Morgan Hall, Berkeley, CA 94720, USA
Phone: +1 (510) 642-7870
Fax: +1 (510) 642-0535
Interests: nephrotoxicity mechanisms; renal bioactivation of toxicants; structure-toxicity relationships

Special Issue Information

Dear Colleagues,

Computational toxicology is an expanding research area that is becoming a multi-disciplinary fusion of bioinformatics and computational sciences with molecular biology and chemistry. The goal is to create more predictive power in the field of toxicology as it applies to both environmental and therapeutic issues.  The field relies on the application of computer technology and mathematical / computational methods to analyze, model, and predict potential toxicological effects from chemical structures, exposure characteristics, and networks of biological pathways affected by chemicals. The field is progressing rapidly due to increased availability of larger and better curated public databases and open-source predictive tools. Newer technologies for large scale data acquisition and the prediction of biological effects using systems biology methodology are expected to expand the scale and complexity of inquiry to a point where data gaps can be filled with predicted values with a high level of confidence.

Prof. Dr. Dale Johnson
Guest Editor

Keywords

  • structural alerts
  • analog identification
  • categorization
  • SAR
  • QSAR
  • biological pathway perturbations
  • systems biology
  • bioaccumulation
  • persistence
  • high-throughput screening
  • high-content screening
  • toxicogenomics
  • metabolomics
  • biomonitoring

Related Special Issue

Published Papers (4 papers)

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Research

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Open AccessArticle Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database
Int. J. Mol. Sci. 2011, 12(10), 6668-6684; doi:10.3390/ijms12106668
Received: 3 August 2011 / Revised: 11 September 2011 / Accepted: 20 September 2011 / Published: 11 October 2011
Cited by 15 | PDF Full-text (187 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN) and are designed to predict if an unknown compound [...] Read more.
In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN) and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen), or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry) and GC-MS (gas chromatography-mass spectrometry) spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA). The scores of the forensic compounds on the main principal components (PCs) were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network) with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%), as well as a good selectivity (a rate of true negatives TN = 92.77%). A comparative analysis of the validation results of all expert systems is presented, and the input variables with the highest discrimination power are discussed. Full article
(This article belongs to the Special Issue Advances in Computational Toxicology)

Review

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Open AccessReview Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project
Int. J. Mol. Sci. 2012, 13(3), 3820-3846; doi:10.3390/ijms13033820
Received: 11 October 2011 / Revised: 30 January 2012 / Accepted: 14 March 2012 / Published: 21 March 2012
Cited by 34 | PDF Full-text (1051 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Computational Toxicology)
Open AccessReview Adaptation of High-Throughput Screening in Drug Discovery—Toxicological Screening Tests
Int. J. Mol. Sci. 2012, 13(1), 427-452; doi:10.3390/ijms13010427
Received: 16 September 2011 / Revised: 11 December 2011 / Accepted: 19 December 2011 / Published: 29 December 2011
Cited by 15 | PDF Full-text (214 KB) | HTML Full-text | XML Full-text
Abstract
High-throughput screening (HTS) is one of the newest techniques used in drug design and may be applied in biological and chemical sciences. This method, due to utilization of robots, detectors and software that regulate the whole process, enables a series of analyses [...] Read more.
High-throughput screening (HTS) is one of the newest techniques used in drug design and may be applied in biological and chemical sciences. This method, due to utilization of robots, detectors and software that regulate the whole process, enables a series of analyses of chemical compounds to be conducted in a short time and the affinity of biological structures which is often related to toxicity to be defined. Since 2008 we have implemented the automation of this technique and as a consequence, the possibility to examine 100,000 compounds per day. The HTS method is more frequently utilized in conjunction with analytical techniques such as NMR or coupled methods e.g., LC-MS/MS. Series of studies enable the establishment of the rate of affinity for targets or the level of toxicity. Moreover, researches are conducted concerning conjugation of nanoparticles with drugs and the determination of the toxicity of such structures. For these purposes there are frequently used cell lines. Due to the miniaturization of all systems, it is possible to examine the compound’s toxicity having only 1–3 mg of this compound. Determination of cytotoxicity in this way leads to a significant decrease in the expenditure and to a reduction in the length of the study. Full article
(This article belongs to the Special Issue Advances in Computational Toxicology)
Open AccessReview Development of a Human Physiologically Based Pharmacokinetic (PBPK) Toolkit for Environmental Pollutants
Int. J. Mol. Sci. 2011, 12(11), 7469-7480; doi:10.3390/ijms12117469
Received: 20 September 2011 / Revised: 13 October 2011 / Accepted: 24 October 2011 / Published: 31 October 2011
Cited by 4 | PDF Full-text (255 KB) | HTML Full-text | XML Full-text
Abstract
Physiologically Based Pharmacokinetic (PBPK) models can be used to determine the internal dose and strengthen exposure assessment. Many PBPK models are available, but they are not easily accessible for field use. The Agency for Toxic Substances and Disease Registry (ATSDR) has conducted [...] Read more.
Physiologically Based Pharmacokinetic (PBPK) models can be used to determine the internal dose and strengthen exposure assessment. Many PBPK models are available, but they are not easily accessible for field use. The Agency for Toxic Substances and Disease Registry (ATSDR) has conducted translational research to develop a human PBPK model toolkit by recoding published PBPK models. This toolkit, when fully developed, will provide a platform that consists of a series of priority PBPK models of environmental pollutants. Presented here is work on recoded PBPK models for volatile organic compounds (VOCs) and metals. Good agreement was generally obtained between the original and the recoded models. This toolkit will be available for ATSDR scientists and public health assessors to perform simulations of exposures from contaminated environmental media at sites of concern and to help interpret biomonitoring data. It can be used as screening tools that can provide useful information for the protection of the public. Full article
(This article belongs to the Special Issue Advances in Computational Toxicology)

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