Aquatic structure-toxicity model

A well-defined endpoint is essential for the establishment of an accurate QSAR. Thus, in deriving an aquatic structure-toxicity model, it is important to select the toxicity data obtained from standardized laboratory tests. For this reason LC50 (or EC50) values (i.e., the concentrations that are lethal, or effective, to 50% of the organisms tested) are preferentially used, since they give... 

Absorption across biological membranes is often necessary for a chemical to manifest toxicity. In many cases several membranes need to be crossed and the structure of both the chemical and the membrane need to be evaluated in the process. The major routes of absorption are ingestion, inhalation, dermal and, in the case of exposures in aquatic systems, gills. Factors that influence absorption have been reviewed recently. Methods to assess absorption include in vivo, in vitro, various cellular cultures as well as modelling approaches. Solubility and permeability are barriers to absorption and guidelines have been developed to estimate the likelihood of candidate molecules being absorbed after oral administration. ... 

The Danish EPA has developed an advisory list for self-classification of dangerous substances including 20 624 substances. The substances have been identified by means of QSAR models (Quantitative Structure-Activity Relationship) as having acute oral toxicity, sensitization, mutagenicity, carcinogenicity, and/or danger to the aquatic environment. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

ACD/Tox Suite is a collection of software modules that predict probabilities for basic toxicity endpoints. Predictions are made from chemical structure and based upon large validated databases and QSAR models, in combination with expert knowledge of organic chemistry and toxicology. ToxSuite modules for Acute Toxicity, Genotoxicity, Skin Irritation, and Aquatic Toxicity have been used. 

Zvinavashe, E. et al. (2006) Quantum chemistry based quantitative structure-activity relationships for modeling the (sub)acute toxicity of substituted mononitrobenzenes in aquatic systems. Environ. Toxicol. Chem., 25 (9), 2313-2321. 

Kaiser, K.L.E., Re QSAR models for predicting the acute toxicity of selected organic chemicals with diverse structures to aquatic non-vertebrates and humans. Calleja, M.C., Geladi, P., and Persoone, G., SAR QSAR Environ. Res. 2, 193-234, SAR QSAR Environ. Res., 3, 151-159, 1995. 

The Canadian Environmental Protection Act, 1999 (CEPA 1999) requires the Ministers of the Environment and Health to categorize the substances on the Canadian Domestic Substances List (DSL). The DSL contains 23 000 substances that are subject to categorization (i.e., prioritization). Generally the data selection process involves a search of the scientific literature and databases for quality experimental data for persistence, bioaccumulation potential and inherent toxicity to humans and nonhuman species. If acceptable data are not found, QSARs or other models are used to estimate the persistence, bioaccumulation, and aquatic toxicity of substances based on structure and physical - chemical properties. 

Dimitrov S, Koleva Y, Schultz TW, Walker JD, Mekenyan O. Interspecies quantitative structure-activity relationship model for aldehydes Aquatic toxicity. Environ Toxicol Chem 2004 23(2) 463-70. 

ECOSAE Ecological Structure Activity Relationships (ECOSAE) (http // www.epa.gov/oppt/newchems/tools/21ecosar.htm) predicts the toxicity of industrial chemicals to aquatic organisms such as fish, invertebrates, and algae, and estimates a compound s acute and chronic toxicity. Its QSAR database contains more than 100 models developed for 42 chemical classes [86]. 

The CASE/M-CASE (computer automated structure evaluation) methodology has been used for modeling different toxicological activities [72-77], including aquatic toxicity endpoints [75-77], Briefly, the fundamental assumption of the M-CASE methodology is that the observed biological activity of a... 

The software now uses structurally intrinsic parameters for only one QSAR model (LSER) and the results are used to predict one property (acute toxicity) to four aquatic species by one mechanism (nonreactive, non-polar narcosis) however, we intend to continue to refine our equations as databases grow, incorporate other models, predict other properties, and include other organisms. We will attempt to differentiate between modes of toxic action and improve our estimates accordingly. For the widely divergent classes of chemicals and types of environmental behavior, no one model will best describe every situation and no one species is the optimal organism to monitor. As the software evolves, the expert system should choose the best model based on the contaminant, the species, and the property to be predicted (e.g., toxicity or bioaccumulation). In addition, we envision an interactive screen system for data entry that will bypass the SMILES notation and allow the user to describe the molecule by posing a series of questions about the compound s backbone and functional groups. The responses will translate directly into values of LSER variables. 

Ecological Structure-Activity Relationships (ECOSAR) This model is used to predict aquatic toxicity, an increasingly important component of the EPA risk assessment program. The EPA has a large information database for chemicals that have been the subject of PMN, but for which the PMN was submitted as confidential business information (CBI). This represents just one example where relying on the model alone will not predict the EPA s response to the submission. 

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