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Influence of parameters distribution on the onset of a threshold effect into model food web stability
No full textPubblicato online: http://www.dsa.unipr.it/SITE/pubblicazioni/attiXII/content/c5.pd
New QSAR Models to Predict Human Transthyretin Disruption by Per- and Polyfluoroalkyl Substances (PFAS): Development and Application
No full text: Per- and polyfluoroalkyl substances (PFAS) are of concern because of their potential thyroid hormone system disruption by binding to human transthyretin (hTTR). However, the amount of experimental data is scarce. In this work, new classification and regression QSARs were developed to predict the hTTR disruption based on experimental data measured for 134 PFAS. Bootstrapping, randomization procedures, and external validation were used to check for overfitting, to avoid random correlations, and to evaluate the predictivity of the QSARs, respectively. The best QSARs were characterized by good performances (e.g., training and test accuracies in classification of 0.89 and 0.85, respectively; R2, Q2loo, and Q2F3 in regression of 0.81, 0.77, and 0.82, respectively) and significantly broader domains compared to the few existing similar models. The application of QSARs application to the OECD List of PFAS allowed for the identification of structural categories of major concern, such as per- and polyfluoroalkyl ether-based, perfluoroalkyl carbonyl, and perfluoroalkane sulfonyl compounds. Forty-nine PFAS showed a stronger binding affinity to hTTR than the natural ligand T4. Uncertainty quantification for each model and prediction further enhanced the reliability assessment of predictions. The implementation of the new QSARs in non-commercial software facilitates their application to support future research efforts and regulatory actions
Externally predictive QSAR models: thresholds of acceptance by various external validation criteria and critical inspection of scatter plots
No full textInfluence of parameters distribution on the onset of a threshold effect into model food webs stability
No full textPredicting the bioconcentration factor in fish from molecular structures
Full text linkThe bioconcentration factor (BCF) is one of the metrics used to evaluate the potential of a substance to bioaccumulate into aquatic organisms. In this work, linear and non-linear regression QSARs were developed for the prediction of log BCF using different computational approaches, and starting from a large and structurally heterogeneous dataset. The new MLR-OLS and ANN regression models have good fitting with R-2 values of 0.62 and 0.70, respectively, and comparable external predictivity with R-ext(2) 0.64 and 0.65 (RMSEext of 0.78 and 0.76), respectively. Furthermore, linear and non-linear classification models were developed using the regulatory threshold BCF >2000. A class balanced subset was used to develop classification models which were applied to chemicals not used to create the QSARs. These classification models are characterized by external and internal accuracy up to 84% and 90%, respectively, and sensitivity and specificity up to 90% and 80%, respectively. QSARs presented in this work are validated according to regulatory requirements and their quality is in line with other tools available for the same endpoint and dataset, with the advantage of low complexity and easy application through the software QSAR-ME Profiler. These QSARs can be used as alternatives for, or in combination with, existing models to support bioaccumulation assessment procedures
Real external predictivity of QSAR models: How to evaluate It? Comparison of different validation criteria and proposal of using the concordance correlation coefficient
No full textThe main utility of QSAR models is their ability to predict
activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient
Q2 F1 (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r2
m (Roy), Q2 F2 (Sch€u€urmann et al.), Q2 F3 (Consonni et al.). In QSAR studies these measures are usually in accordance,
though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient
was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive
In silico approaches for the prediction of the breakthrough of organic contaminants in wastewater treatment plants
No full textThe removal efficiency (RE) of organic contaminants in wastewater treatment plants (WWTPs) is a major determinant of the environmental impact of chemicals which are discharged to wastewater. In a recent study, non-target screening analysis was applied to quantify the percentage removal efficiency (RE%) of more than 300 polar contaminants, by analyzing influent and effluent samples from a Swedish WWTP with direct injection UHPLC-Orbitrap-MS/MS. Based on subsets extracted from these data, we developed quantitative structure-property relationships (QSPRs) for the prediction of WWTP breakthrough (BT) to the effluent water. QSPRs were developed by means of multiple linear regression (MLR) and were selected after checking for overfitting and chance relationships by means of bootstrap and randomization procedures. A first model provided good fitting performance, showing that the proposed approach for the development of QSPRs for the prediction of BT is reasonable. By further populating the dataset with similar chemicals using a Tanimoto index approach based on substructure count fingerprints, a second QSPR indicated that the prediction of BT is also applicable to new chemicals sufficiently similar to the training set. Finally, a class-specific QSPR for PEGs and PPGs showed BT prediction trends consistent with known degradation pathways
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