For prediction of aquatic toxicity

Verhaar HJM, vanLeeuwen CJ, Hermens JLM (1992) Classifying environmental pollutants. 1. Structure-activity relationships for prediction of aquatic toxicity. Chemosphere 25 471—491... 

Verhaar, H.J., Vanleeuwen, C.J. and Hermens, J.L. (1992). Classif5dng Environmental Pollutants. 1. Structure-Activity Relationships for Prediction of Aquatic Toxicity. Chemosphere, 25, 471-491. 

An Expert System for Prediction of Aquatic Toxicity of Contaminants... 

Medicinal chemists have, for many years, used QSARs as a tool for drug design. The EPA has used QSARs since 1981 to predict the aquatic toxicity of new, untested commercial chemical substances in the absence of test data. Chemists who are interested in designing safer chemicals will find QSARs very helpful, as they enable one to assess rapidly the toxicity of substances without having to synthesize and test the substances. 

The ECOSAR program is used to predict the aquatic toxicity of chemicals based on their similarity of structure to chemicals for which the aquatic toxicity has been previously measured. Since 1981, the USEPA has used (Q)SARs to predict the aquatic toxicity of all new industrial chemicals (Nabholz et al. 1993 Zeeman et al. 1995). The acute toxicity of a chemical to fish (both fresh- and saltwater), water fleas... 

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. 

It can be imagined that a QSAR for acute aquatic toxicity was selected by an independent body during a literature review of available QSARs, including the final report of a Directorate General (DG) Research Project (European Economic Community, 1995). In particular, the following QSAR for predicting the acute toxicity of organic chemicals to the fathead minnow (Pimephales promelas) was reported ... 

Substances that are carcinogenic, mutagenic, or reproductively toxic (i.e., CMRs), for example, some endocrine disrupters, may pose special problems for derivation of aquatic EQSs (e.g., lack of internationally agreed tests in some cases difficulties with prediction of safe concentrations), but use of special tests for these properties is only justified for a small subset of chemicals that meet clear criteria. Furthermore, EQSs for these substances should not be derived directly from in vitro data or from biomarkers of exposure but from in vivo tests alone. 

Walter et al. (2002) assessed the effect of a mixture of 11 aquatic priority pollutants on algal reproduction. The chemicals were selected for structural diversity by using chemometric methods. In this study, statistical estimates of effect concentrations down to effect levels of 1% were derived by regression analysis of concentration-response data. Based on these estimates of low effects, IA yielded quite accurate predictions of mixture toxicity. 

A9.6.4.7 The Nordic Council of Ministers issued a report (Pederson et al, 1995) entitled Environmental Hazard Classification, that includes information on data collection and interpretation, as well as a section (5.2.8) entitled QSAR estimates of water solubility and acute aquatic toxicity . This section also discusses the estimation of physicochemical properties, including log Kow For the sake of classification purposes, estimation methods are recommended for prediction of minimum acute aquatic toxicity, for ...neutral, organic, non-reactive and non-ionizable compounds such as alcohols, ketones, ethers, alkyl, and aryl halides, and can also be used for aromatic hydrocarbons, halogenated aromatic and aliphatic hydrocarbons as well as sulphides and disulphides, as cited in an earlier OECD Guidance Document (OECD, 1995). The Nordic document also includes diskettes for a computerized application of some of these methods. 

Multiple linear regression (MLR) is one of the most common and simplest method for QSAR modeling. MLR has been used for the prediction of geno-toxicity [12], Daphnia magna toxicity [13], photobacterium phosphoreum toxicity [14], aquatic toxicity [15], eye irritation [16], mutagenicity, and carcinogenicity [17], An MLR model assumes that there is a linear relationship between the molecular descriptors of a compound, which is usually expressed as a feature vector x (with each descriptor as a component of this vector), and... 

The use of models for the prediction of metal toxicity using acid-volatile sulfide (DiTorro et al., 1990, 1992) and equilibrium partitioning for hydrophobic compounds (DiTorro et al., 1991) has been very useful for the prediction of bioavailability and associated toxicity of contaminants in sediments. Recently the Biotic Ligand model has been developed to relate metal bioavailability (and its potential toxicity) using the most recent chemical and physiological effects information on metals in aquatic environments (Paquin et al., 1999 DiTorro et al., 2001 Santore et al., 2001). 

ECOSAR is a freely available software system which matches the structure of a query molecule to one (or more) of its defined chemical class(es). For most classes, aquatic ecotoxicity values are then predicted using available hn-ear correlations between toxicity and hydrophobicity. Row is estimated for the query molecule using KOWWIN (discussed in the chapter by Howard). The most recent version of ECOSAR (used in this study) contains over 150 relationships for approximately 50 chemical classes. For the purposes of assessing ECOSAR for predicting, transformation product toxicity, the structures of each of the chemicals in the data set were entered into the software system and in instances where the query compound was matched to one or more chemical classes, the most potent ecotoxicity estimate for daphnids was selected for comparative purposes. 

ECOSAR Ecological Structure Activity Relationships (ECOSAR)—a computer prediction system for assessing the aquatic toxicity of industrial compounds. It was developed in 1979 by US EPA. The program is based on the SAR calculates acute and chronic toxicity for aqueous organisms (fish, aquatic invertebrates and plants) http //www.epa.gov/oppt/newchems/tools/21ecosar.htm... 

As computing capabiUty has improved, the need for automated methods of determining connectivity indexes, as well as group compositions and other stmctural parameters, for existing databases of chemical species has increased in importance. New naming techniques, such as SMILES, have been proposed which can be easily translated to these indexes and parameters by computer algorithms. Discussions of the more recent work in this area are available (281,282). SMILES has been used to input Contaminant stmctures into an expert system for aquatic toxicity prediction by generating LSER parameter values (243,258). 

EPA has developed an evaluation tool, the PBT Profiler, which predicts PBT potential of chemicals. The PBT Profiler estimates environmental persistence (P), bioconcentration potential (B), and aquatic toxicity (T) of discrete chemicals based on their molecular structure. It is Internet-based and there is no cost for use. 

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. 

The evaluation for aquatic toxicity on daphnids and fish is reported in Tables 12 and 13. Bold values indicate that compounds are out of the model applicability domain (ECOSAR) or that the prediction is not reliable. ECOSAR and ToxSuite are able to predict all the selected compounds while T.E.S.T. fails in prediction for the daphnia toxicity of perfluorinated compounds (PFOS and PFOA). Tables 12 and 13 include also a limited number of experimental results provided by the model training dataset (some data are extracted from USEPA Ecotox database). Predicted results are in agreement for five compounds only (2, 3, 5, 13 and 14) for both endpoints while the predictions for the other compounds are highly variable. 

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