Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards <i>Daphnia magna</i>

Cassotti, Matteo; Consonni, Viviana; Mauri, Andrea; Ballabio, Davide

doi:10.1080/1062936x.2014.977818

Cited by 19 publications

(8 citation statements)

References 56 publications

(62 reference statements)

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“…The consensus approach is well-known in the field of QSAR [ 58 , 59 , 60 , 61 ] and consists in combining the predictions of several models to maximize their advantages and minimize their drawbacks, aiming to increase the global prediction accuracy. In this work, the selected classification models, which are based on very different descriptors and classification techniques, were merged in two types of consensus approaches ( Table 5 ): Consensus 1: a molecule was classified if and only if the following conditions were met: (1) all the model predictions agreed in its predicted class; (2) the molecule was inside the AD of all of the models.…”

Section: Resultsmentioning

confidence: 99%

In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

Nembri

Grisoni

Consonni

et al. 2016

IJMS

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Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of the most important CYP isoforms, namely 2C9 and 3A4. The presented models are calibrated on 9122 drug-like compounds, using three different modelling approaches and two types of molecular description (classical molecular descriptors and binary fingerprints). For each isoform, three classification models are presented, based on a different approach and with different advantages: (1) a very simple and interpretable classification tree; (2) a local (k-Nearest Neighbor) model based classical descriptors and; (3) a model based on a recently proposed local classifier (N-Nearest Neighbor) on binary fingerprints. The salient features of the work are (1) the thorough model validation and the applicability domain assessment; (2) the descriptor interpretation, which highlighted the crucial aspects of P450-drug interaction; and (3) the consensus aggregation of models, which largely increased the prediction accuracy.

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Section: Resultsmentioning

confidence: 99%

In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

Nembri

Grisoni

Consonni

et al. 2016

IJMS

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“…CS1: QSARs on short‐term toxicity . The first case study targets the validation of two QSARs for the prediction of short‐term toxicity on Daphnia Magna : (1) the MICHEM model, which is a K ‐Nearest Neighbor model based on 8 Dragon descriptors, and (2) VEGA‐Demetra, which is a neuro‐fuzzy model based on multiple linear regression and 16 molecular descriptors. The test set includes 18 compounds that are external to the training sets of both models and were specifically selected for the purpose of this study.…”

Section: Figurementioning

confidence: 99%

On the Misleading Use of for QSAR Model Comparison

Consonni

Todeschini

Ballabio

et al. 2018

Molecular Informatics

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Quantitative Structure - Activity Relationship (QSAR) models play a central role in medicinal chemistry, toxicology and computer-assisted molecular design, as well as a support for regulatory decisions and animal testing reduction. Thus, assessing their predictive ability becomes an essential step for any prospective application. Many metrics have been proposed to estimate the model predictive ability of QSARs, which have created confusion on how models should be evaluated and properly compared. Recently, we showed that the metric QF32 is particularly well-suited for comparing the external predictivity of different models developed on the same training dataset. However, when comparing models developed on different training data, this function becomes inadequate and only dispersion measures like the root-mean-square error (RMSE) should be used. The intent of this work is to provide clarity on the correct and incorrect uses of QF32 , discussing its behavior towards the training data distribution and illustrating some cases in which QF32 estimates may be misleading. Hereby, we encourage the usage of measures of dispersions when models trained on different datasets have to be compared and evaluated.

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“…and two models implemented in VEGA. Cassotti et.al [33]. evaluated the accuracy, stability and reliability of two acute toxicity models (MICHEM and ChemProp) to daphnia.…”

Section: Introductionmentioning

confidence: 99%

Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

et al. 2021

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Background A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset. Results In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools. Conclusion QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

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Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna

Cited by 19 publications

References 56 publications

In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

On the Misleading Use of for QSAR Model Comparison

Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

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