A QSAR for Baseline Toxicity:  Validation, Domain of Application, and Prediction

Abstract

The interest in modeling and application of structure-activity relationships has steadily increased in recent decades. It is generally acknowledged that these empirical relationships are valid only within the same domain for which they were developed. However, model validation is sometimes neglected, and the application domain is not always well-defined. The purpose of this paper is to outline how validation and domain definition can facilitate the modeling and prediction of baseline toxicity for a large database. A large number of theoretical descriptors (867) were generated from two-dimensional molecular structures for compounds present in the U.S. EPA's Fathead Minnow Database (611) and the Syracuse Research Corporation's PhysProp Database (25,000+). A quantitative structure-activity relationship model was developed for baseline toxicity (narcosis) toward the fathead minnow (Pimephales promelas) using a projection-based regression technique, PLSR (partial least squares regression). The PLSR model was subsequently validated with an external test set. The main factors of variation were related to size/shape and polar interactions. The prediction error was comparable to, or slightly better than, the ECOSAR procedures. A set of 16,805 compounds, drawn from the PhysProp Database, was projected onto the PLSR model. More than 90% (15,597) of the compounds fall within the valid model domain, defined by the residual standard deviation and the leverage. The predicted baseline toxicity indicates an acute hazard for two-thirds of these compounds, classes I-III in the OECD Globally Harmonized Classification System (LC50 < or = 100 mg L(-1)). Finally, the mode of action assigned in the U.S. EPA Fathead Minnow Database was investigated. Reclassification to narcosis as the mode of action is suggested for 92 compounds, mostly from the groups "unsure" and "mixed". The present classification into specific modes of action seems to be further strengthened by the findings in this investigation.

Statistical external validation and consensus modeling: A QSPR case study for Koc prediction

“…In fact we, like other authors [33][34][35][36][37][38], are strongly convinced, from personal experience, that models with high estimated ''predictivity'', highlighted only by internal validation methods, can be less predictive and even unpredictive when verified on new chemicals not used in developing the model [4].…”

Identifying and designing chemicals with minimal acute aquatic toxicity

“…Others applied the concept of narcosis to aquatic toxicology, establishing the relationship between octanol-water partition coefficient (logP o/w ) and acute toxicity by narcosis (51)(52)(53). McFarland later showed that aquatic toxicity can be considered the result of penetration of toxicant into biophases and its interaction with one or more biochemical sites of action (54). Bioavailability of chemicals in aquatic organisms was shown to relate to absorption; for example, chemical partitioning across fish gills (55) represents an important route of exposure.…”

Is there a risk for the aquatic environment due to the existence of emerging organic contaminants in treated domestic wastewater? Greece as a case-study

“…According to the funnel hypothesis [20], in mixtures containing a large number of chemicals, the compounds are more possible to exhibit a baseline mechanism of action, thus the risk assessment should be based on the hypothesis of concentration addition. For this reason, baseline toxicity from ECOSAR program (also known as narcosis or nonspecific toxicity) was used for PNEC calculations [11,21,22]. It is necessary to be noted that the groups of perfluorinated compounds and siloxanes were not taken into account for the calculation of RQ mix , since, as it has been mentioned before, the toxicity of these chemicals cannot be predicted by ECOSAR model.…”