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Qsar Modeling For Predicting Aquatic Toxicity Of Chemicals

3908 words - 16 pages

The EL-based QSARs (classification and regression) models developed using the algae (P. subcapitata) ecotoxicity data of diverse chemicals were applied to other test species, such as algae (S. obliguue), daphnia, fish and bacteria for predicting toxicity of chemicals. Both the developed QSAR models yielded comparable excellent binary classification (toxic and non-toxic) accuracies of 92.50 % (S. obliguue), 93.05 % (daphnia), 94.26 % (fish), and 94.12 % (bacteria) for DTB-QSAR and 92.50 % (S. obliguue), 92.14 % (daphnia), 93.27 % (fish), and 94.12 % (bacteria) for DTF-QSAR, respectively. The regression QSARs also performed well with the external test data yielding high correlation (R2) between the measured and model predicted values of the response and low MSE values of 0.672, 0.51 (S. obliguue), 0.654, 0.45 (daphnia), 0.575, 0.37 (fish), and 0.661, 0.18 (bacteria) for DTB-QSAR, and 0.689, 0.45 (S. obliguue), 0.656, 0.45 (daphnia), 0.605, 0.34 (fish), and 0.648, 0.20 (bacteria) for DTF-QSAR model. Overall, the results suggest that both the EL-QSAR models are capable of predicting the acute toxicity of a broad range of molecules in different test species belonging to different trophic levels, hence providing effective tools for regulatory toxicology.

3.3 Comparison with other studies
Several QSAR methods for toxicity prediction of chemicals in different test species are reported in literature (Table 4).
Table 4
Since the datasets used in earlier researches are generally small considering particular group based chemicals and differ between various models, a direct comparison of our results with these studies is inappropriate. Also, earlier QSARs are mostly based on univariate or multiple linear regression (MLR) approach using toxicity data determined over different time durations. Moreover, most of these studies considered complex descriptors and in several of these, prediction accuracies were not satisfactory, thus limiting the applicability of these models for acute toxicity prediction purpose. The EL-based QSAR modeling approaches in the present study considering the large dataset of structurally diverse chemicals yielded the best prediction accuracy in complete data of different test species of different trophic levels recommended for toxicity evaluation of chemicals by OECD. This study demonstrated applicability of the EL-QSAR models in toxicity prediction of diverse chemicals in various test species and can be used as effective tools in regulatory decision making and risk assessment. Reliable QSARs models are required for toxicity prediction in multiple test species of various trophic levels to ensure a comprehensive risk assessment of chemicals.

3.4 Applicability domain of QSAR models
Validation of the classification and regression EL-QSAR models for screening new chemicals was performed through analysis of the AD using the methods based on the range of the descriptors in training set and those on leverage values for each of...

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