An overview of the use of quantitative structure‐activity relationships for ranking and prioritizing large chemical inventories for environmental risk assessments

Abstract

Abstract-Ecological risk assessments for chemical stressors are used to establish linkages between likely exposure concentrations and adverse effects to ecological receptors. At times, it is useful to conduct screening risk assessments to assist in prioritizing or ranking chemicals on the basis of potential hazard and exposure assessment parameters. Ranking of large chemical inventories can provide evidence for focusing research and/or cleanup efforts on specific chemicals of concern. Because of financial and time constraints, data gaps exist, and the risk assessor is left with decisions on which models to use to estimate the parameter of concern. In this review, several methods are presented for using quantitative structure-activity relationships (QSARs) in conducting hazard screening or screening-level risk assessments. The ranking methods described include those related to current regulatory issues associated with chemical inventories from Canada, Europe, and the United States and an example of a screening-level risk assessment conducted on chemicals associated with a watershed in the midwest region of the United States.

Current standing and future prospects for the technologies proposed to transform toxicity testing in the 21st century

“…In particular, (quantitative) structure-activity relationship or (Q)SAR analysis, which predicts toxicological properties of a compound based on its chemical structure, can be valuable for testing strategies (Cronin et al, 2003;Hartung and Hoffmann, 2009). Although, QSAR analysis alone is generally not enough for risk assessment, it can be used to categorize compounds into different toxicity classes and thereby identify the most appropriate tests to continue the testing strategy (Russom et al, 2003;Walker and Carlsen, 2002). Moreover, QSAR data can be used to prioritize compounds that pose the greatest risk of adversely affecting human health.…”

Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

“…While years of research in physical property modeling and structure activity relationships has resulted in the ability to predict many chemical properties with acceptable reliability from knowledge only of chemical structure, prediction of biodegradability among other properties still needs improvement [13]. Russom et al [14] reported, for example, that for the BIOWIN package [15], the EU recommends only using a slow biodegradation output as confirmation that a substance is not readily biodegradable and recommends against relying on fast biodegradation outputs.…”

“…Gamberger et al [8], for example, created two different rules, each designed to best predict data from one or the other dataset. The commonly used BIOWIN model package recommended by the EU Risk Ranking Method [14] includes separate linear and non-linear models built from the MITI-I and the BIODEG data [11,18]. It has been reported that due to cross correlations, it is possible to develop a model that fits the training set data well but is not reliable as a predictor for chemicals outside the training set [19].…”

The Use of Solvation Models in Gas Chromatography

“…The first approach is based on theoretical descriptors. The advantage of using the QSPR approach based on theoretical descriptors is that all of the necessary parameters for prediction can be calculated purely from the three-dimensional representation of the molecular structure of each of the compounds of the mixtures, including mixtures of chemically diverse compounds [3][4]. The main weakness of this approach is that the selected descriptors may be difficult to understand and the models may lack obvious chemical significance.…”