A perspective on environmental models and QSARs

A general review is presented of the roles of QSARs and mass balance models as tools for assessing the environmental fate and effects of chemicals of commerce. It is argued that all such chemicals must be assessed using a consistent and transparent methodology that uses chemical property data derived from QSARs, or experimental determinations when possible and applies evaluative or region-specific environmental models. These data and models enable an assessment to be made of the key chemical features of persistence, bioaccumulation, potential for long-range transport and toxicity. The other key feature is quantity used or discharged to the environment. A taxonomy of environmental models is presented in which it is suggested that rather than develop a single comprehensive model, the aim should be to establish a set of coordinated and consistent models treating evaluative and real environmental systems at a variety of scales from local to global and including food web models, organism-specific models and human exposure and pharmacokinetic models. The concentrations derived from these models can then be compared with levels judged to be of toxic significance. A brief account is given of perceived QSAR needs in terms of partitioning, reactivity, transport and toxicity data to support these models.     

Evaluation of Critical Body Residue QSARS for Predicting Organic Chemical Toxicity to Aquatic Organisms

Abstract

The critical body residue (CBR) is the concentration of chemical bioaccumulated in an aquatic organism that corresponds to a defined measure of toxicity (e.g., mortality). The CBR can provide an alternative measure of toxicity to traditional waterborne concentration measurements (e.g., concentration in water causing 50% mortality). The CBR has been suggested as a better estimator of dose than the external water concentration and has been postulated to be constant for chemicals with the same mode of action. CBR QSARs have both theoretical and experimental support, developed primarily from studies on the acute toxicity of narcotic chemicals to small fish. CBR QSARs are less well developed for the aquatic toxicity of non-narcotic chemicals. CBRs vary substantially with the mode of action and toxicity endpoint, and may be affected by genetic, hormonal or environmental variation. CBR QSARs may not be applicable to very hydrophobic chemicals, chemicals with specific modes of action, or those with toxicity controlled by kinetic processes such as biotransformation. CBRs models have not been developed or evaluated for sediment and dietary exposure routes. Application of CBR QSARs to contaminated site assessments will require further research and development.     

Global Government applications of analogues,SAR s and QSAR s to predict aquatic toxicity,chemical or physical properties,environmental fate parameters and health effects of organic chemicals

Faced with the need to predict physical and chemical properties, environmental fate, ecological effects and health effects of organic chemicals in the absence of experimental data, several Government organizations have been applying analogues, Structure Activity Relationships (SARs) and Quantitative Structure Activity Relationships (QSARs) to develop those predictions. To establish some benchmarks for monitoring future increases in applications of analogues, SARs and QSARs by global Government organizations, this paper describes the current applications of analogues, SARs and QSARs by Australian, Canadian, Danish, European, German, Japanese, Netherlands, and United States Government organizations to predict physical and chemical properties, environmental fate, ecological effects and health effects of organic chemicals.     

U.S. EPA Regulatory Perspectives on the Use of QSAR for New and Existing Chemical Evaluations

Abstract

As testing is not required, ecotoxicity or fate data are available for ≈ 5% of the approximately 2,300 new chemicals/year (26,000 + total) submitted to the US-EPA. The EPA's Office of Pollution Prevention and Toxics (OPPT) regulatory program was forced to develop and rely upon QSARs to estimate the ecotoxicity and fate of most of the new chemicals evaluated for hazard and risk assessment. QSAR methods routinely result in ecotoxicity estimations of acute and chronic toxicity to fish, aquatic invertebrates, and algae, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. The EPA's Toxic Substances Control Act (TSCA) Inventory of existing chemicals currently lists over 72,000 chemicals. Most existing chemicals also appear to have little or no ecotoxicity or fate data available and the OPPT new chemical QSAR methods now provide predictions and cross-checks of test data for the regulation of existing chemicals. Examples include the Toxics Release Inventory (TRI), the Design for the Environment (DfE), and the OECD/SIDS/HPV Programs. QSAR screening of the TSCA Inventory has prioritized thousands of existing chemicals for possible regulatory testing of: 1) persistent bioaccumulative chemicals, and 2) the high ecotoxicity of specific discrete organic chemicals.     

A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity

Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible "molecular initiating events" based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.     

A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity

Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible “molecular initiating events” based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.     

Global government applications of analogues,SARs and QSARs to predict aquatic toxicity,chemical or physical properties,environmental fate parameters and health effects of organic chemicals

Faced with the need to predict physical and chemical properties, environmental fate, ecological effects and health effects of organic chemicals in the absence of experimental data, several Government organizations have been applying analogues, Structure Activity Relationships (SARs) and Quantitative Structure Activity Relationships (QSARs) to develop those predictions. To establish some benchmarks for monitoring future increases in applications of analogues, SARs and QSARs by global Government organizations, this paper describes the current applications of analogues, SARs and QSARs by Australian, Canadian, Danish, European, German, Japanese, Netherlands, and United States Government organizations to predict physical and chemical properties, environmental fate, ecological effects and health effects of organic chemicals.     

Qsar Models for Predicting the Acute Toxicity of Selected Organic Chemicals with Diverse Structures to Aquatic Non-Vertebrates and Humans

Abstract

The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the organic chemicals from the 50 priority chemicals prescribed by the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. The models used for the evaluations are the best combination of physico-chemical properties that could be obtained so far for each organism, using the partial least squares projection to latent structures (PLS) regression method and backpropagated neural networks (BPN). Non-linear models, whether derived from PLS regression or backpropagated neural networks, appear to be better than linear models for describing the relationship between acute toxicity and molecular structure. BPN models, in turn, outperform non-linear models obtained from PLS regression. The predictive power of BPN models for the crustacean test species are better than the model for humans (based on human lethal concentration). The physico-chemical properties found to be important to predict both human acute toxicity and the toxicity to aquatic non-vertebrates are the n?octanol water partition coefficient (Pow) and heat of formation (HF). Aside from the two former properties, the contribution of parameters that reflect size and electronic properties of the molecule to the model is also high, but the type of physico-chemical properties differs from one model to another. In all of the best BPN models, some of the principal component analysis (PCA) scores of the ¹³C-NMR spectrum, with electron withdrawing/accepting capacity (LUMO, HOMO and IP) are molecular size/volume (VDW or MS1) parameters are relevant. The chemical deviating from the QSAR models include non-pesticides as well as some of the pesticides tested. The latter type of chemical fits in a number of the QSAR models. Outliers for one species may be different from those of other test organisms.     

Decision tree SAR models for developmental toxicity based on an FDA/TERIS database

Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the accuracy of prediction. Also, the model ensemble approach reduced the variability of prediction measures compared to the single model approach. Further research with data derived from animal species- and endpoint-specific components of an extended and refined FDA/TERIS database has the potential to derive SAR models that would be useful in the developmental risk assessment of the thousands of untested chemicals.     

Discriminating toxicant classes by mode of action: 3. Substructure indicators§

Decision support for selecting suitable QSARs for predictive purposes is suggested by a stepwise procedure: The first tier pre-filters the compounds based on substructure indicators for baseline versus excess toxicity. This step, if sufficiently conservative, discriminates chemicals, whose toxicity can be reliably estimated from their log?K _OW from those, that require further classification by biological and chemical domain. A test set of 115 chemicals from 9 different MOA classes was used to compare the discriminatory power of several classification schemes based on substructure indicators. Performance, evaluated by contingency table statistics, is varied and no single scheme provides sufficient applicability and reliability for pre-filtering chemical inventories. Major improvements are feasible with combined use of three classification schemes: assignments of baseline toxicants are protective, recognition of excess toxicants is acceptable and applicability range increases favourably.     

Regulatory use of (Q)SARs in toxicological hazard assessment strategies

In 2001, the European Commission published a policy statement ("White Paper") on future chemicals regulation and risk reduction that proposed the use of non-animal test systems and tailor-made testing approaches, including (Q)SARs, to reduce financial costs and the number of test animals employed. The authors have compiled a database containing data submitted within the EU chemicals notification procedure. From these data, (Q)SARs for the prediction of local irritation/corrosion and/or sensitisation potential were developed and published. These (Q)SARs, together with an expert system supporting their use, will be submitted for official validation and application within regulatory hazard assessment strategies. The main features are: ? two sets of structural alerts for the prediction of skin sensitisation hazard classification as defined by the European risk phrase R43, comprising 15 rules for chemical substructures deemed to be sensitising by direct action with cells or proteins, and three rules for substructures acting indirectly, i.e., requiring biochemical transformation; ? a decision support system (DSS) for the prediction of skin and/or eye lesion potential built from information extracted from our database. This DSS combines SARs defining reactive chemical substructures relevant for local lesions to be classified, and QSARs for the prediction of the absence of such a potential. The role of the BfR database, and (Q)SARs derived from it, in the use of current and future (EU) testing strategies for irritation and sensitisation is discussed.