A strategy for ranking environmentally occurring chemicals. Part VI. QSARs for the mutagenic effects of halogenated aliphatics.

A strategy for the systematic analysis and priority ranking of environmental chemicals has been applied to a class of 58 halogenated aliphatic hydrocarbons. A training set of ten compounds representing this class, was selected by statistical design. The training set compounds were then subjected to biological testing in the Salmonella typhimurium reverse mutation assay (Ames test). The measured biological data, recorded as dose-response curves, were analyzed to determine the mutagenic potency (slope of the initial portion) and the mutagen dose (MD 50) required to increase the number of revertants above the background by 50%. For each compound, four mutagenic potency estimates and four MD 50 values were determined, all originating from the tester strains TA 100 and TA 1535 with and without metabolic activation. The obtained responses were analyzed with multivariate techniques to give QSAR models relating the mutagenic potency data to the physico-chemical properties of the compounds. Finally, the derived QSARs were used to predict the mutagenic potencies and the MD 50S for the non-tested compounds in the class.     

QSAR-based toxicity classification and prediction for single and mixed aromatic compounds

Quantitative structure-activity relationships (QSARs) based on the octanol/water partition coefficient were employed to predict acute toxicities of 36 substituted aromatic compounds and their mixtures. In this study, the model developed by Verhaar et al. was modified and used to calculate octano/water partition coefficients of chemical mixtures. To validate the model, acute toxicities of these chemicals were measured to Vibrio fischeri in terms of EC50. The results indicated that the obtained QSAR models could be used to predict toxicities of samples consi sting of these substituted aromatic compounds, individually or in combinations. The obtained equations were proved to be robust enough by using the leave-one-out test method. By classifying these chemicals into two groups, polar and non-polar, the toxicities of chemical mixtures within each group can be predicted accurately from their calculated partition coefficients.     

On further application of r as a metric for validation of QSAR models

Validation is a crucial aspect for quantitative structure–activity relationship (QSAR) model development. External validation is considered, in general, as the most conclusive proof of predictive capacity of a QSAR model. In the absence of truly external data set, external validation is usually performed on test set compounds, which are members of the original data set but not used in model development exercise. In the case of small data sets, QSAR researchers experience problem in model development due to the fact that the developed models may be less reliable on account of the small number of training set compounds and such models may also show poor external predictability because the models may not have captured all necessary features required for the particular structure–activity relationships. The present paper attempts to show that ‘true r_(LOO)’ statistic calculated based on the model derived from the undivided data set with application of variable selection strategy at each cycle of leave‐one‐out (LOO) validation may reflect external validation characteristics of the developed model thus obviating the requirement of splitting of the data set into training and test sets. This approach may be helpful in the case of small data sets as it uses all available data for model development and validation thus making the resulting model more reliable. Copyright © 2009 John Wiley & Sons, Ltd.     

Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.