Integrated in silico approaches for the prediction of Ames test mutagenicity

Modi, Sandeep; Li, Jin; Malcomber, Sophie; Moore, Claire; Scott, Andrew D.; White, Andrew; Carmichael, Paul L.

doi:10.1007/s10822-012-9595-5

Cited by 21 publications

(17 citation statements)

References 49 publications

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“…Combinations of models were explored also by Modi et al (Modi et al, 2012). Three in house QSAR models were built using three different modelling techniques: (1) an in-house alert model; (2) a kNN approach (k-Nearest Neighbours); (3) a naïve Bayesian model (NB) using chemical features (e.g., physico-chemical, structural descriptors).…”

Section: Evaluations Combomentioning

confidence: 99%

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Benigni¹,

Battistelli²,

Bossa³

et al. 2019

EFS3

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To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including analysis of the impact on genotoxicity of the metabolic structural changes. The prediction ability of (Q)SARs for in vitro and in vivo tests were evaluated. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test; instead, the reliability of the (Q)SAR models for assays / endpoints different from in vitro bacterial mutagenicity appears to be quite far from optimality. Secondly, two new Read Across approaches were applied to predict Ames mutagenicity and in vitro Chromosomal Aberrations: Read Across was largely successful for predicting the Ames test results, but much less for in vitro Chromosomal Aberrations. The worse results for endpoints different from Ames may be attributable to the several revisions of experimental protocols and evaluation criteria of results that have made the databases qualitatively non‐homogeneous, and poorly suitable for modelling. A third dimension of this research is the evaluation of the impact of the structural changes‐in result of metabolic or degradation processes‐on the genotoxic potential of the substances. Parent/Metabolite structural differences (beyond the known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. In addition, Structural Alerts analysis applied under human expert supervision permitted the rationalization of the large majority of the changes of patterns of genotoxicity. The findings from this work are suitable for being integrated into Weight‐of‐Evidence and Tiered evaluation schemes. The importance of the human expert knowledge is particularly emphasized.

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Section: Evaluations Combomentioning

confidence: 99%

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Benigni¹,

Battistelli²,

Bossa³

et al. 2019

EFS3

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“…PubChem 2D fingerprints were used to generate descriptors for the compounds in our data set. 25 In the PubChem fingerprints database, there are 881 bits of descriptors related to element counts, aromatic or nonaromatic ring counts, atom pairs, atom neighborhoods, and specific fragments.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

Rule-Based Classification Models of Molecular Autofluorescence

Lin

et al. 2015

J. Chem. Inf. Model.

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Fluorescence-based detection has been commonly used in high-throughput screening (HTS) assays. Autofluorescent compounds, which can emit light in the absence of artificial fluorescent markers, often interfere with the detection of fluorophores and result in false positive signals in these assays. This interference presents a major issue in fluorescence-based screening techniques. In an effort to reduce the time and cost that will be spent on prescreening of autofluorescent compounds, in silico autofluorescence prediction models were developed for selected fluorescence-based assays in this study. Five prediction models were developed based on the respective fluorophores used in these HTS assays, which absorb and emit light at specific wavelengths (excitation/emission): Alexa Fluor 350 (A350) (340 nm/450 nm), 7-amino-4-trifluoromethyl-coumarin (AFC) (405 nm/520 nm), Alexa Fluor 488 (A488) (480 nm/540 nm), Rhodamine (547 nm/598 nm), and Texas Red (547 nm/618 nm). The C5.0 rule-based classification algorithm and PubChem 2D chemical structure fingerprints were used to develop prediction models. To optimize the accuracies of these prediction models despite the highly imbalanced ratio of fluorescent versus nonfluorescent compounds presented in the collected data sets, oversampling and undersampling strategies were applied. The average final accuracy achieved for the training set was 97%, and that for the testing set was 92%. In addition, five external data sets were used to further validate the models. Ultimately, 14 representative structural features (or rules) were determined to efficiently predict autofluorescence in data sets containing both fluorescent and nonfluorescent compounds. Several cases were illustrated in this study to demonstrate the applicability of these rules.

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“…Many useful computational models have been developed with varying success. Models ranging from studies on aquatic toxicity that utilise multiple linear regression and neural networks through to predictive hepatotoxicity (Cruz-Monteagudo et al 2008;Low et al 2011 and mutagenicity (Bakhtyari et al 2013;Xu et al 2012;Modi et al 2012) that use k-nearest neighbour, support vector machines and random forests are frequently reported in literature.…”

Section: Introductionmentioning

confidence: 99%

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

et al. 2015

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Drug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug's toxicity. Using data acquired from the National Institute of Health's (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce Communicated by D. Neagu. Electronic supplementary materialThe online version of this article (doi:10.1007/s00500-015-1925-9) contains supplementary material, which is available to authorized users.

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Integrated in silico approaches for the prediction of Ames test mutagenicity

Cited by 21 publications

References 49 publications

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

Rule-Based Classification Models of Molecular Autofluorescence

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

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