Assessing Applicability Domains of Toxicological QSARs: Definition, Confidence in Predicted Values, and the Role of Mechanisms of Action

Abstract

AbstractThere are many issues relating to the use of Quantitative Structure–Activity Relationships (QSARs) to make predictions for regulatory purposes. Among those issues, characterization of models and the development of suitable tools to determine applicability domains rank as the more important. With regard to aquatic toxicology, QSARs for acute effects (e.g., IGC50−1) often take the form of a hydrophobic [i.e., Logarithm of the 1‐Octanol/Water Partition Coefficient (log P)]‐electrophilic [e.g., Maximum Acceptor Superdelocalizability (Amax)]‐dependent, regression‐based model. In this study, the applicability domain of a model for the toxicity of aromatic compounds to Tetrahymena pyriformis [log (IGC50−1)=0.545(0.015) log P+16.2(0.62) Amax−5.91(0.20); n=384, r2(adj)=0.859, r2(pred)=0.856, s=0.275, F=1163, Pr>F=0.0001] was assessed. The structural and physicochemical domains of the model were characterized using two test sets of toxicity data (one prescreened to be within the descriptor space and structural domain of the training set and the other to be outside the structural domain of the training set). For test set compounds inside the domain of the model, there was no relationship between absolute residue values for predictions and hydrophobicity; however, there was a linear relationship between absolute residue values and electrophilicity. It was concluded that predictivity in the region of the domain associated with higher electrophilicity, greater potency, and derivatives containing both halo‐ and nitro‐groups is poorer than elsewhere in the domain, and therefore less confidence should be given to those values. Compounds in this region of the domain of the model are associated with the soft‐, or pro‐electrophilic mechanisms of toxic action. For the second test set, i.e., derivatives outside the structural domain, an examination of absolute residue values revealed that the observed toxicity is typically in excess of that predicted, especially for compounds in the structural space(s) of well‐known electrophilic mechanisms of reactive toxicity. Caution is therefore urged in using statistical approaches to account for, and apply confidence to predictions from the applicability domain. An appreciation of the mechanism of toxicity appears to be critical to the determination of the most likely applicability domain.