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1.
SAR QSAR Environ Res ; 29(8): 591-611, 2018 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-30052064

RESUMEN

Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.


Asunto(s)
Modelos Moleculares , Pruebas de Mutagenicidad , Relación Estructura-Actividad , Simulación por Computador , Relación Estructura-Actividad Cuantitativa
2.
SAR QSAR Environ Res ; 26(12): 977-999, 2015 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-26540526

RESUMEN

We evaluated the performance of eight QSAR in silico modelling packages (ACD/ToxSuite™, ADMET Predictor™, DEMETRA, ECOSAR, TerraQSAR™, Toxicity Estimation Software Tool, TOPKAT™ and VEGA) for acute aquatic toxicity towards two species of fish: Fathead Minnow and Rainbow Trout. For the Fathead Minnow, we compared model predictions for 567 substances with the corresponding experimental values for 96-h median lethal concentrations (LC50). Some models gave good results, with r2 up to 0.85. We also classified the predictions of all the models into four toxicity classes defined by CLP. This permitted us to assess other parameters, such as the percentage of correct predictions for each class. Then we used a set of 351 substances with toxicity data towards Rainbow Trout (96-h LC50). In this case the predictability was unacceptable for all the in silico models. The calculated r2 gave poor correlations (≤0.53). Another analysis was performed according to chemical classes and for mode of action. In the first case, all the classes show a high percentage of correct predictions, in the second case only narcotics and polar narcotics were predicted with good confidence. The results indicate the possibility of using in silico methods to estimate aquatic toxicity within REACH regulation, after careful evaluation.

3.
SAR QSAR Environ Res ; 25(12): 999-1011, 2014.
Artículo en Inglés | MEDLINE | ID: mdl-25511972

RESUMEN

Life sciences, and toxicology in particular, are heavily impacted by the development of methods for data collection and data analysis; they are moving from an analytical approach to a modelling approach. The scarce availability of experimental data is a known bottleneck in assessing the properties of new chemicals. Even when a model is available, the resulting predictions have to be assessed by close scrutiny of the chemicals and the biological properties of the compounds concerned. To avoid unnecessary testing, a read across strategy is often suggested and used. In this paper we discuss how to improve and standardize read across activity using ad hoc visualization and data search methods which use similarity measures and fragment search to organize in a chart a picture of all the relevant information that the expert needs to make an assessment. We show in particular how to apply our system to the case of mutagenicity.


Asunto(s)
Pruebas de Mutagenicidad/métodos , Mutágenos/química , Programas Informáticos , Bases de Datos Factuales , Modelos Químicos , Relación Estructura-Actividad Cuantitativa , Toxicología/métodos
4.
SAR QSAR Environ Res ; 25(8): 673-94, 2014.
Artículo en Inglés | MEDLINE | ID: mdl-24911142

RESUMEN

Eight in silico modelling packages were evaluated and compared for the prediction of Daphnia magna acute toxicity from the viewpoint of the European legislation on chemicals, REACH. We tested the following models: Discovery Studio (DS) TOPKAT, ACD/Tox Suite, ADMET Predictor, ECOSAR (Ecological Structure Activity Relationships), TerraQSAR, T.E.S.T. (Toxicity Estimation Software Tool) and two models implemented in VEGA on 480 industrial compounds for 48-h median lethal concentrations (LC50) to D. magna, matching them with experimental values. The quality of the estimates was compared using a standard statistical review and an additional classification approach in which the hazard predictions were grouped using well-defined regulatory criteria. The regression parameters, correlation coefficient being the most influential, showed that four models (ADMET Predictor, DS TOPKAT, TerraQSAR and VEGA DEMETRA) had similar reliability. These performed better than the others, but the coefficient of determination was still low (r2 around 0.6), considering that at least half the predicted compounds were inside the training sets. Additionally, we grouped the results in four defined toxicity classes. TerraQSAR™ gave 60% of correct classifications, followed by DS TOPKAT, ADMET Predictor™ and VEGA DEMETRA, with 56%, 54% and 48%, respectively. These results highlight the challenges associated with developing reliable and easily applied acceptability criteria for the regulatory use of QSAR models to D. magna acute toxicity.


Asunto(s)
Simulación por Computador , Daphnia/efectos de los fármacos , Pruebas de Toxicidad Aguda/métodos , Contaminantes Químicos del Agua/toxicidad , Animales , Modelos Teóricos , Relación Estructura-Actividad Cuantitativa , Reproducibilidad de los Resultados , Programas Informáticos , Contaminantes Químicos del Agua/química
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