Integrated Decision-tree Testing Strategies for Mutagenicity and Carcinogenicity with Respect to the Requirements of the EU REACH Legislation

Combes, Robert; Grindon, Christina; Cronin, Mark T.D.; Roberts, David W.; Garrod, John F.

doi:10.1177/026119290803601s05

Cited by 8 publications

(4 citation statements)

References 80 publications

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“…Carcinogenic potency for rats was selected as response because such data in risk assessment [53] are often considered to be more suitable for human carcinogenicity prediction. The term "carcinogen" generally refers to an agent, mixture, or exposure that increases the age-specific incidence of cancer.…”

Section: Methodsmentioning

confidence: 99%

New public QSAR model for carcinogenicity

Fjodorova

Vračko

Novič

et al. 2010

Chemistry Central Journal

114

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BackgroundOne of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.ResultsModels for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.ConclusionCarcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

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Section: Methodsmentioning

confidence: 99%

New public QSAR model for carcinogenicity

Fjodorova

Vračko

Novič

et al. 2010

Chemistry Central Journal

114

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“…This principle has been used successfully for the definition of non-genotoxicants. If a compound is found negative in three non-animal tests that cover different types of biological processes relevant to DNA damage, then the chemical is judged to be non-mutagenic (Adler et al 2011;Combes et al 2008). Recently, also a test battery for dermal sensitization has been approved.…”

Section: Introductionmentioning

confidence: 99%

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

et al. 2017

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Many in vitro tests have been developed to screen for potential neurotoxicity. However, only few cell function-based tests have been used for comparative screening, and thus experience is scarce on how to confirm and evaluate screening hits. We addressed these questions for the neural crest cell migration test (cMINC). After an initial screen, a hit follow-up strategy was devised. A library of 75 compounds plus internal controls (NTP80-list), assembled by the National Toxicology Program of the USA (NTP) was used. It contained some known classes of (developmental) neurotoxic compounds. The primary screen yielded 23 confirmed hits, which comprised ten flame retardants, seven pesticides and six drug-like compounds. Comparison of concentration-response curves for migration and viability showed that all hits were specific. The extent to which migration was inhibited was 25-90%, and two organochlorine pesticides (DDT, heptachlor) were most efficient. In the second part of this study, (1) the cMINC assay was repeated under conditions that prevent proliferation; (2) a transwell migration assay was used as a different type of migration assay; (3) cells were traced to assess cell speed. Some toxicants had largely varying effects between assays, but each hit was confirmed in at least one additional test. This comparative study allows an estimate on how confidently the primary hits from a cell function-based screen can be considered as toxicants disturbing a key neurodevelopmental process. Testing of the NTP80-list in more assays will be highly interesting to assemble a test battery and to build prediction models for developmental toxicity.

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“…Evaluation of the ability of animal models to predict human responses and toxicities is critical now that there are increasing pressures to reduce animal testing in favor of in-vitro and computational predictive methods [16]. In this work we compared data for drugs that have matched nonclinical and clinical data presented in FDA and EMEA submissions and analyze the results to measure the concordance between nonclinical and clinical adverse event observations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of clinical risks by analysis of preclinical and clinical adverse events

Clark

2015

Journal of Biomedical Informatics

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This study examines the ability of nonclinical adverse event observations to predict human clinical adverse events observed in drug development programs. In addition it examines the relationship between nonclinical and clinical adverse event observations to drug withdrawal and proposes a model to predict drug withdrawal based on these observations. These analyses provide risk assessments useful for both planning patient safety programs, as well as a statistical framework for assessing the future success of drug programs based on nonclinical and clinical observations. Bayesian analyses were undertaken to investigate the connection between nonclinical adverse event observations and observations of that same event in clinical trial for a large set of approved drugs. We employed the same statistical methods used to evaluate the efficacy of diagnostic tests to evaluate the ability of nonclinical studies to predict adverse events in clinical studies, and adverse events in both to predict drug withdrawal. We find that some nonclinical observations suggest higher risk for observing the same adverse event in clinical studies, particularly arrhythmias, QT prolongation, and abnormal hepatic function. However the lack of these events in nonclinical studies is found to not be a good predictor of safety in humans. Some nonclinical and clinical observations appear to be associated with high risk of drug withdrawal from market, especially arrhythmia and hepatic necrosis. We use the method to estimate the overall risk of drug withdrawal from market using the product of the risks from each nonclinical and clinical observation to create a risk profile.

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Integrated Decision-tree Testing Strategies for Mutagenicity and Carcinogenicity with Respect to the Requirements of the EU REACH Legislation

Cited by 8 publications

References 80 publications

New public QSAR model for carcinogenicity

New public QSAR model for carcinogenicity

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

Prediction of clinical risks by analysis of preclinical and clinical adverse events

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