A baseline inhalation toxicity model for narcosis in mammals

This paper presents the results of an analysis of the rodent inhalation literature and the development of a quantitative structure–activity relationships (QSAR) model for 4-hour LC50 as baseline toxicity to complement the baseline toxicity model for aquatic animals. We used the same literature review criteria developed for the ECOTOX database which selects only primary references with explicit experimental methods to form a high-quality database. Our literature review focused on the primary references reporting a 4-hour exposure for a single species of rodent in which the chemical had been clearly tested as a vapour and for which the exposure concentrations were not ambiguous. An expert system was used to remove reactive chemicals, receptor-mediated toxicants, and any test that produced symptoms inconsistent with non-polar narcosis. The QSAR model derived for narcosis in rodents was log LC50 = 0.69 × log VP + 1.54 which had an r ² of 0.91, which is significantly better than the baseline toxicity model for aquatic animals. This simple model suggests that there is no intrinsic barrier to estimating baseline toxicity for in vivo endpoints in mammalian or terrestrial toxicology.     

Using chemical categories to fill data gaps in hazard assessment

Hazard assessments of chemicals have been limited by the availability of test data and the time needed to evaluate the test data. While available data may be inadequate for the majority of industrial chemicals, the body of existing knowledge for most hazards is large enough to permit reliable estimates to be made for untested chemicals without additional animal testing. We provide a summary of the growing use by regulatory agencies of the chemical categories approach, which groups chemicals based on their similar toxicological behaviour and fills in the data gaps in animal test data such as genotoxicity and aquatic toxicity. Although the categories approach may be distinguished from the use of quantitative structure–activity relationships (QSARs) for specific hazard endpoints, robust chemical categories are founded on quantifying the chemical structure with parameters that control chemical behaviour in conventional hazard assessment. The dissemination of the QSAR Application Toolbox by the Organisation for Economic Cooperation and Development (OECD) is an effort to facilitate the use of the categories approach and reduce the need for additional animal testing.     

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 KOW 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.     

Prediction of acute aquatic toxicity in Tetrahymena pyriformis – ‘Eco-Derek’, a knowledge-based system approach£

A ‘proof-of-concept’ version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, ‘Eco-Derek’, was derived by adapting a well-established, knowledge-based structure–activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure–activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity–log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.     

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.     

SAR—The U.S. Regulatory Perspective

Abstract

Structure-Activity Relationships (SAR) have been used for over a decade by the U.S. EPA's Office of Pollution Prevention and Toxics (OPPT) in their new chemicals program. The development and use of SAR resulted from the need to make rapid risk-based decisions on thousands of new chemicals per year while seldom receiving data on chemical properties, potential exposures, or hazards to humans or organisms in the environment. Qualitative SAR and quantitative SAR methods (QSAR) have been used to fill some of these data gaps by estimating the potential properties and hazards of such chemicals. SAR has been used to assess chemical hazards, identify testing needs, and set priorities. Validation of these SAR assessment tools is an ongoing process.     

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.     

Computational Chemistry in Environmental Toxicology QSAR

Abstract

Computational chemistry provides a means for the calculation or estimation of three-dimensional chemical structure, organization and analysis of chemical data, classification of industrial chemicals by structure and properties, prediction of toxicity, and identification of chemical structure. The development of the EPA National Environmental Supercomputer Center (NESC) in Bay City, Michigan, makes available to scientists in EPA Headquarters, the ability to perform advanced QSAR modeling. This provides the means to develop and apply QSAR models for chemicals acting by a variety of molecular mechanisms. The work makes possible improved programmatic support to the Office of Pollution Prevention and Toxics under the Toxic Substances Control Act and the Pollution Prevention Act.     

Toward molecular design for hazard reduction—fundamental relationships between chemical properties and toxicity

The relationship of in-silico predicted physical/chemical properties and human toxicity is analyzed for a statistically significant sample size of chemical compounds. Results for compounds with known toxicity endpoints, as designated by EPA's Toxic Release Inventory (TRI), are compared to a series of commercial chemicals that are not regulated under TRI. Physical properties for all compounds are predicted using Schrodinger's QikProp, an established tool for predicting adsorption, distribution, metabolism, and excretion (ADME) characteristics. The results of this analysis indicate that the physical/chemical property distributions of TRI chemicals are statistically significantly different from those of bulk commercial chemicals, particularly related to properties associated with bioavailability. Using a partitioning analysis, several key physical/chemical properties and ranges are identified that can be used to readily differentiate TRI chemical characteristics from those of bulk commercial chemicals.     

In silico-aided prediction of biological properties of chemicals: oestrogen receptor-mediated effects

In silico methods are a valid tool for analysing the properties of chemical compounds and interest in computational modelling techniques to predict the activity of chemicals is constantly growing. Many computational methods can be used to analyse the toxicity or biological activity of chemicals, particularly as regards their interactions with biological macromolecules (e.g. receptors) and other physico-chemical properties. An overview of these methods is provided in this tutorial review, with some examples of their application to predict oestrogen receptor (ER)-mediated effects. Nuclear receptors, particularly ER, have been studied with in silico tools since concern is growing about substances, called endocrine disrupters, that can interfere with hormone regulation. Molecular modelling techniques such as Quantitative Structure-Activity Relationships (QSAR), related methods like 3D-QSAR, and virtual docking have been used to investigate these phenomena and are described here. Implications about regulatory acceptance and use of these methods and the resulting models for identifying hazards and setting priorities are also addressed.     

Quantitative prediction of biodegradability,metabolite distribution and toxicity of stable metabolites

An evaluation of the capability of organic chemicals to mineralize is an important factor to consider when assessing their fate in the environment. Microbial degradation can convert a toxic chemical into an innocuous one, and vice versa, or alter the toxicity of a chemical. Moreover, primary biodegradation can convert chemicals into stable products that can be difficult to mineralize. In this paper, we present some new results obtained on the basis of a recently developed probabilistic approach to modeling biodegradation based on microbial transformation pathways. The metabolic transformations and their hierarchy were calibrated by making use of the ready biodegradability data from the MITI-I test and expert knowledge for the most probable transformation pathways. A model was developed and integrated into an expert software system named CATABOL that is able to predict the probability of biodegradation of organic chemicals directly from their structure. CATABOL simulates the effects of microbial enzyme systems, generates the most plausible transformation pathways, and quantitatively predicts the persistence and toxicity of the biodegradation products. A subset of 300 organic chemicals were selected from Canada's Domestic Substances List and subjected to CATABOL to compare predicted properties of the parent chemicals with their respective first stable metabolite. The results show that most of the stable metabolites have a lower acute toxicity to fish and a lower bioaccumulation potential compared to the parent chemicals. In contrast, the metabolites appear to be generally more estrogenic than the parent chemicals.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.