TIMES-SS—A Mechanistic Evaluation of an External Validation Study Using Reaction Chemistry Principles

Roberts, David W.; Patlewicz, Grace; Dimitrov, S.; Low, Lawrence K.; Aptula, Aynur O.; Kern, Petra; Dimitrova, Gergana; Comber, Mike; Phillips, Richard D.; Niemelä, Jay Russell; Madsen, Charlotte Bernhard; Wedebye, Eva Bay; Bailey, Paul; Mekenyan, Ovanes G.

doi:10.1021/tx700169w

Cited by 53 publications

(26 citation statements)

References 34 publications

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“…MAXDN and MATS1e characterize the molecular electronic structure, which may influence the electrostatic interactions between the chemical and protein. The binding of a chemical with skin proteins is considered as the rate-determing step for skin sensitization induction, where the chemical behaves as an electrophile and the protein as a nucleophile [28]. MATS2m and BELm1 are descriptors concerning molecular size, which may influence the skin penetration of compounds.…”

Section: Resultsmentioning

confidence: 99%

“…On the contrary, if the dataset contains many more non-sensitizers than sensitizers, the QSPR model will also give biased classification results. For example, Roberts et al [28] have validated the TIMES-SS ( TI mes ME tabolism S imulator) expert system platform used for predicting skin sensitization with 40 chemicals, consisting of 24 non-sensitizers and 16 sensitizers. TIMES-SS was able to predict non-sensitizers reasonably well (the prediction accuracy of 87.5%), while it predicted sensitizers very pooly (the prediction accuracy of 56.0%).…”

Section: Resultsmentioning

confidence: 99%

“…As seen from Tables 9 and 10, the prediction performances of the method in this paper are far superior to those in the literature, especially for the specificity. However, expert systems such as DEREK and TIMES-SS have been recently improved by modifing the alerts describing the skin sensitization potential or considering more mechanisitic knowledge and new rules for chemicals [28,32]. Therefore, better results than those of previously reported in the literature may be achieved if the prediction is conducted with the improved expert systems.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

Yuan

Huang

Cao

2009

IJMS

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Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

Yuan

Huang

Cao

2009

IJMS

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“…For example, 40 compounds were tested in the LLNA assay [349] and previous determined results, in conjunction with a read-across approach, led to a good classification of the newly tested compounds. The prediction concordance was found to be 83%.…”

Section: Modeling Studiesmentioning

confidence: 99%

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Gleeson¹,

Modi²,

Bender³

et al. 2012

CPD

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The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a 'good' model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.

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“…One study described an external validation procedure that was carried out on Tissue Metabolism Simulator for Skin Sensitization (TIMES-SS) (Roberts et al, 2007b). The authors experimentally tested 40 chemicals in the LLNA assay and then compared the results with computationally-derived predictions made by TIMES-SS.…”

Section: Introductionmentioning

confidence: 99%

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

Alves

Muratov

Fourches

et al. 2015

Toxicology and Applied Pharmacology

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Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation.

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TIMES-SS—A Mechanistic Evaluation of an External Validation Study Using Reaction Chemistry Principles

Cited by 53 publications

References 34 publications

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

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