Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. α-β unsaturated aliphatic aldehydes

Devillers, James; Mombelli, Enrico

doi:10.1080/1062936x.2010.528961

Cited by 14 publications

(7 citation statements)

References 43 publications

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“…Since among the list of 197 pesticides and biocides listed in Table 1, only this chemical included the structural feature corresponding to SA #30, we tried to see whether this lack of detection was confirmed with other chemicals including the structural feature associated with SA #30. This is the case for the 21 chemicals listed in Table 2, two of them being classified as 92 J. Devillers et al It is worth noting that a problem of detection of SA was recently stressed in a study focusing on the prediction of the mutagenicity of the -unsaturated aliphatic aldehydes [26]. A logical hypothesis explaining this misdetection of SAs should be that the algorithm for detecting the structural features is not efficient enough, or includes bugs.…”

Section: Resultsmentioning

confidence: 99%

“…The same type of external validation was also performed on the other SAR model implemented in both software tools for predicting the mutagenicity of the -unsaturated aliphatic aldehydes [24,25]. The results were totally different because important statistical failures were detected in the model, which showed limited and unreliable prediction performances [26]. The OECD QSAR Application Toolbox and Toxtree also include a system of structural alerts (SAs) for predicting the carcinogenicity potential of chemicals [27,28]; its prediction results were compared with experimental data collected in databases and original publications for more 500 structurally diverse chemicals [29].…”

Section: Introductionmentioning

confidence: 99%

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Structural alerts for estimating the carcinogenicity of pesticides and biocides

Devillers¹,

Mombelli

Samsera

2011

SAR and QSAR in Environmental Research

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More than 20 years ago, Ashby and Tennant showed the interest of structural alerts for the prediction of the carcinogenicity of chemicals. These structural alerts are functional groups or structural features of various sizes that are linked to the level of carcinogenicity of chemicals. Since this pioneering work it has been possible to refine the alerts over time, as more experimental results have become available and additional mechanistic insights have been gained. To date, one of the most advanced lists of structural alerts for evaluating the carcinogenic potential of chemicals is the list proposed by Benigni and Bossa and that is implemented as a rule-based system in Toxtree and in the OECD QSAR Application Toolbox. In order to gain insight into the applicability of this system to the detection of potential carcinogens we screened about 200 pesticides and biocides showing a high structural diversity. Prediction results were compared with experimental data retrieved from an extensive bibliographical review. The prediction correctness was only equal to 60.14%. Attempts were made to analyse the sources of mispredictions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural alerts for estimating the carcinogenicity of pesticides and biocides

Devillers¹,

Mombelli

Samsera

2011

SAR and QSAR in Environmental Research

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“…Craig et al 2014), sulphur-containing compounds(Richarz et al 2014), pesticide/biocide carcinogenicity(Devillers et al 2011), unsaturated aliphatic aldehydes(Devillers and Mombelli 2010b), aromatic amines (Devillers and Mombelli 2010a), and chemical carcinogens(Mombelli and Devillers 2010).…”

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confidence: 99%

Ethics of animal research in human disease remediation, its institutional teaching; and alternatives to animal experimentation

Cheluvappa

Scowen

Eri

2017

Pharmacology Res & Perspec

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Animals have been used in research and teaching for a long time. However, clear ethical guidelines and pertinent legislation were instated only in the past few decades, even in developed countries with Judeo‐Christian ethical roots. We compactly cover the basics of animal research ethics, ethical reviewing and compliance guidelines for animal experimentation across the developed world, “our” fundamentals of institutional animal research ethics teaching, and emerging alternatives to animal research. This treatise was meticulously constructed for scientists interested/involved in animal research. Herein, we discuss key animal ethics principles – Replacement/Reduction/Refinement. Despite similar undergirding principles across developed countries, ethical reviewing and compliance guidelines for animal experimentation vary. The chronology and evolution of mandatory institutional ethical reviewing of animal experimentation (in its pioneering nations) are summarised. This is followed by a concise rendition of the fundamentals of teaching animal research ethics in institutions. With the advent of newer methodologies in human cell‐culturing, novel/emerging methods aim to minimise, if not avoid the usage of animals in experimentation. Relevant to this, we discuss key extant/emerging alternatives to animal use in research; including organs on chips, human‐derived three‐dimensional tissue models, human blood derivates, microdosing, and computer modelling of various hues.

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“…A particular focus is put on models that could provide information indicating whether the substance could be classified as carcinogenic, mutagenic or toxic to reproduction (CMR) according to Dangerous Substances Directive [9] or CLP Regulation [10]. To our knowledge, previous studies on the utility of non-experimental approaches to predicting CMR properties focused on comparing the results of specific assays to those obtained using selected in silico models rather than the use of in silico results for CLP classification and the prioritisation of industrial compounds for further testing according to the REACH legislation [11][12][13][14][15][16][17][18][19][20][21].…”

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confidence: 99%

On the Use of In Silico Tools for Prioritising Toxicity Testing of the Low‐Volume Industrial Chemicals in REACH

Rybacka

Rudén

Andersson

2014

Basic Clin Pharma Tox

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This study was conducted to evaluate the utility of a selection of commercially and freely available non-testing tools and to analyse how REACH registrants can apply these as prioritisation tool for low-volume chemicals. The analysis was performed on a set of organic industrial chemicals and pesticides with extensive peer-reviewed risk assessment data. Analysed in silico model systems included Derek Nexus, Toxtree, QSAR Toolbox, LAZAR, TEST and VEGA, and results from these were compared with expert-judged risk classification according to the classifying, labelling and packaging (CLP) regulation. The most reliable results were obtained for carcinogenicity; however, less reliable predictions were derived for mutagenicity and reproductive toxicity. A group of compounds frequently predicted as false negatives was identified. These were relatively small molecules with low structural complexity, for example benzene derivatives with hydroxyl-, amino-or aniline-substituents. A rat liver S9 metabolite simulator was applied to illustrate the importance of considering metabolism in the risk assessment procedure. We also discuss outcome of combining predictions from multiple model systems and advise how to apply in silico tools. These models are proposed to be used to prioritise low-volume chemicals for testing within the REACH legislation, and we conclude that further guidance is needed so that industry can select and apply models in a reliable, systematic and transparent way.

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Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. α-β unsaturated aliphatic aldehydes

Cited by 14 publications

References 43 publications

Structural alerts for estimating the carcinogenicity of pesticides and biocides

Structural alerts for estimating the carcinogenicity of pesticides and biocides

Ethics of animal research in human disease remediation, its institutional teaching; and alternatives to animal experimentation

On the Use of In Silico Tools for Prioritising Toxicity Testing of the Low‐Volume Industrial Chemicals in REACH

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