QSARs for identifying and prioritizing substances with persistence and bioconcentration potential

From the 8511 chemicals with 1998 production volumes reported to the U.S. Environmental Protection Agency (U.S. EPA), the TSCA Interagency Testing Committee's (ITC's) Degradation Effects Bioconcentration Information Testing Strategies (DEBITS) was used to identify 56 chemicals. The DEBITS Quantitative Structure-Activity Relationships (QSARs) and the U.S. EPA's PBT profiler QSARs were used to predict the persistence and bioconcentration factors of these 56 chemicals. Partial order ranking was used to prioritise the chemicals based on persistence and bioconcentration potential.     

Comparison of three fish bioaccumulation models for ecological and human risk assessment and validation with field data

This article compares two bioconcentration Quantitative Structure Activity Relationships (QSARs) for fish applied in human risk assessments with the mechanistic bioaccumulation model OMEGA and field data. It was found that all models are virtually similar up to a Kow of 10(6). For substances with a Kow higher than 10(6), the fish bioconcentration curve in the risk assessment model EUSES decreases parabolically. In contrast, OMEGA bioaccumulation outcomes approximately show a linear increase, based on mechanistic bioconcentration and biomagnification properties of chemicals. The OMEGA-outcomes are close to the fish bioconcentration outcomes of the risk assessment model CalTOX. For very hydrophobic substances, field accumulation data in freshwater and marine fish species are closer to OMEGA- and CalTOX-outcomes compared to EUSES. The results also show that it is important to include biomagnification in fish and lipid content of fish in human exposure models.     

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.     

Predictivity of QSAR

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.     

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.     

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.     

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.     

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.     

Assessment of QSARS for Predicting Fate and Effects of Chemicals in the Environment: An International European Project

Abstract In 1993, an international project on QSAR has been started with funding from the Commission of the European Union. The first part of the project is focused on preparing an overview of existing models for the prediction of environmental parameters such as bioconcentration, sorption, degradation and ecotoxicity. Emphasis will be given to defining the limitations of the models. Since all models, including QSARs, have their limitations, it is important that these limitations are known in case QSARs are actually used and applied within the risk assessment context. The second part of the project is directed towards experimental research on new developments with emphasis on the use of multivariate techniques and quantum chemical properties. In this short paper, a general outline of the project will be given, as well as some first results. Results of experimental work within this project will be published in the proceedings of the 6th International Workshop on QSAR in Environmental Sciences and will appear in this same journal.     

QSAR-based toxicity classification and prediction for single and mixed aromatic compounds

Quantitative structure-activity relationships (QSARs) based on the octanol/water partition coefficient were employed to predict acute toxicities of 36 substituted aromatic compounds and their mixtures. In this study, the model developed by Verhaar et al. was modified and used to calculate octano/water partition coefficients of chemical mixtures. To validate the model, acute toxicities of these chemicals were measured to Vibrio fischeri in terms of EC50. The results indicated that the obtained QSAR models could be used to predict toxicities of samples consi sting of these substituted aromatic compounds, individually or in combinations. The obtained equations were proved to be robust enough by using the leave-one-out test method. By classifying these chemicals into two groups, polar and non-polar, the toxicities of chemical mixtures within each group can be predicted accurately from their calculated partition coefficients.     

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF?) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r ²?=?0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(log?BSF? ^Obs???log?BCF? ^Calc)?≤?0.75)) for 59 chemicals included within the model applicability domain was 80%.     

Uses and limitations of quantitative structure-activity relationships (QSARs) to categorize substances on the Canadian domestic substance list as persistent and/or bioaccumulative,and inherently toxic to non-human organisms

Under sections 73 and 74 of the revised Canadian Environmental Protection Act (CEPA 1999), Environment Canada and Health Canada must "categorize" and "screen" about 23,000 substances on the Domestic Substances List (DSL) for persistence (P), bioaccumulation (B), and inherently toxic (iT) properties. Since experimental data for P, B and iT are only available for a few DSL substances, a workshop was held to address issues associated with the use of Quantitative Structure-Activity Relationships (QSARs) to categorize these substances. This paper describes the results of an 11-12 November 1999 International Workshop sponsored by Environment Canada to discuss potential uses and limitations of QSARs to categorize DSL substances as either persistent or bioaccumulative and iT to non-human organisms and to recommend future research needed to develop methods for predicting the P, B and iT of difficult-to-model substances.     

QSAR-based toxicity classification and prediction for single and mixed aromatic compounds

Quantitative structure-activity relationships (QSARs) based on the octanol/water partition coefficient were employed to predict acute toxicities of 36 substituted aromatic compounds and their mixtures. In this study, the model developed by Verhaar et al. was modified and used to calculate octanol/water partition coefficients of chemical mixtures. To validate the model, acute toxicities of these chemicals were measured to Vibrio fischeri in terms of EC⁵⁰. The results indicated that the obtained QSAR models could be used to predict toxicities of samples consisting of these substituted aromatic compounds, individually or in combinations. The obtained equations were proved to be robust enough by using the leave-one-out test method. By classifying these chemicals into two groups, polar and non-polar, the toxicities of chemical mixtures within each group can be predicted accurately from their calculated partition coefficients.     

Uses and limitations of quantitative structure-activity relationships (QSARs) to categorize substances on the Canadian domestic substance list as persistent and/or bioaccumulative,and inherently toxic to non-human organisms

Under sections 73 and 74 of the revised Canadian Environmental Protection Act (CEPA 1999) , Environment Canada and Health Canada must "categorize" and "screen" about 23,000 substances on the Domestic Substances List (DSL) for persistence (P), bioaccumulation (B), and inherently toxic (iT) properties. Since experimental data for P, B and iT are only available for a few DSL substances, a workshop was held to address issues associated with the use of Quantitative Structure-Activity Relationships (QSARs) to categorize these substances. This paper describes the results of an 11-12 November 1999 International Workshop sponsored by Environment Canada to discuss potential uses and limitations of QSARs to categorize DSL substances as either persistent or bioaccumulative and iT to non-human organisms and to recommend future research needed to develop methods for predicting the P, B and iT of difficult-to-model substances.     

CATALOGIC 301C model – validation and improvement

In Europe, REACH legislation encourages the use of alternative in silico methods such as (Q)SAR models. According to the recent progress of Chemical Substances Control Law (CSCL) in Japan, (Q)SAR predictions are also utilized as supporting evidence for the assessment of bioaccumulation potential of chemicals along with read across. Currently, the effective use of read across and QSARs is examined for other hazards, including biodegradability. This paper describes the results of external validation and improvement of CATALOGIC 301C model based on more than 1000 tested new chemical substances of the publication schedule under CSCL. CATALOGIC 301C model meets all REACH requirements to be used for biodegradability assessment. The model formalism built on scientific understanding for the microbial degradation of chemicals has a well-defined and transparent applicability domain. The model predictions are adequate for the evaluation of the ready degradability of chemicals.