Quantitative structure-activity toxicity modeling

Wang YW, Liu HX, Zhao CY, Liu HX, Cai ZW, Jiang GB. Quantitative structure-activity relationship models for prediction of the toxicity of polybrominated diphenyl ether congeners. Environ Sci Technol 2005 39 4961-6. 

Benigni, R., Andreoli, C., Conti, L., Tafani, P., Cotta-Ramusino, M., Carere, A., Crebelli, R. Quantitative structure-activity relationship models correctly predict the toxic and aneuploidizing properties of halogenated methanes in Aspergillus nidulans. Mutagenesis 1993, 8, 301-305. 

Burden, F. R., and Winkler, D. A. (2000) A quantitative structure-activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. Chem. Res. Toxicol. 13,436-440. 

Lampi, M.A., Gurska, J., Huang, X.D., Dixon, D.G. and Greenberg, B.M. (2007) A predictive quantitative structure-activity relationship model for the photoinduced toxicity of polycyclic aromatic hydrocarbons to Daphnia magna with the use of factors for photosensitization and photomodification. Environmental Toxicology and Chemistry/SETAC, 26, 406-415. 

In particular for human toxicity, information shall be generated whenever possible by means other than vertebrate animal tests, through the use of alternative methods, for example, in vitro methods or qualitative or quantitative structure-activity relationship models or from information from structurally related substances (grouping or read-across). 

In this chapter we provide a historical perspective of the development of the field of computational toxicology. Beginning from the similarity-based grouping of elements into the periodic table, the chapter presents a chronology of developments from the simple observations of qualitative relations between structure and toxicity through LFER (linear free energy related) and QSAR (quantitative structure activity relationship) models, to the current... 

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. 

Yuan, H., Wang, Y, Cheng, Y. Local and global quantitative structure-activity relationship modeling and prediction for the baseline toxicity. J. Chem. Inf. Model. 2007, 47, 159-169. 

Feng, L., Han, S., Zhao, Y.-H., Wang, L.-S. and Chen, J. (1996b) Toxicity of organic chemicals to fathead mirmow a united quantitative structure-activity relationship model and its application. Chem. Res. Toxicol., 9, 610-613. 

S.N. Krylov, X.-D Huang, L.F. Zeiler, G.D. Dixon, B.M. Greenburg (1997). Mechanistic quantitative structure-activity relationship model for the photoinduced toxicity of polycyclic aromatic hydrocarbons I. Physical model based on chemical kinetics in a two-compartment system. Environ. Toxicol. Chem., 16,2283-2295. 

In principle, the required toxic information need not to be tested in animal studies. Qualitative or quantitative structure-activity relationship models or data from structurally related substances are also accepted. 

In this chapter, we have developed predictive models based on two well es-tabhshed methods (1) hierarchical quantitative structure-activity (HiQSAR) modeling, and (2) quantitative molecular similarity analysis (QMSA). We have reviewed published work in both of the above areas for important classes of heterocyclic compounds that have therapeutic and toxic effects. Predictive models can be developed based on experimental properties, substituent constants derived from such properties, and also theoretical descriptors which can be calculated directly from molecular structure, hi view of the fact that most potential therapeutic agents and the majority of known drugs and toxicants do not have experimental data available for their evaluation, theoretical descriptors are very useful in the initial screening of compound libraries. 

Zhu, H-, Ye, L., Richard, A., Golbraikh, A., Wright, R A., Rusyn, I., et al. (2009). A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environmental Health Perspect, 117,1257. 

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). 

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. 

Quantitative Structure-Activity Relationship studies search for a relationship between the activity/toxicity of chemicals and the numerical representation of their structure and/or features. The overall task is not easy. For instance, several environmental properties are relatively easy to model, but some toxicity endpoints are quite difficult, because the toxicity is the result of many processes, involving different mechanisms. Toxicity data are also affected by experimental errors and their availability is limited because experiments are expensive. A 3D-QSAR model reflects the characteristics of... 

Accordingly, sorption has received a tremendous amount of attention and any method or modeling technique which can reliably predict the sorption of a solute will be of great importance to scientists, environmental engineers, and decision makers (references herein and in Chaps. 2 and 3). The present chapter is an attempt to introduce an advanced modeling approach which combines the physical and chemical properties of pollutants, quantitative structure-activity, and structure-property relationships (i. e., QSARs and QSPRs, respectively), and the multicomponent joint toxic effect in order to predict the sorption/desorp-tion coefficients, and to determine the bioavailable fraction and the action of various organic pollutants at the aqueous-solid phase interface. 

Altenburger R, Nendza M, Schuurmann G (2003) Mixture toxicity and its modeling by quantitative structure-activity relationships. Environ Toxicol Chem 22 1900-1915... 

For halogenated aromatic hydrocarbons like polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and polychlorinated dibenzo-p-dioxins (PCDDs) the binding to the aryl hydrocarbon (Ah) receptor regulates their toxicity [89]. The Ah receptor controls the induction of one of the cytochrome P450 enzymes in the liver. Toxic responses such as thymic atrophy, iveight loss, immu-notoxicity and acute lethality are associated ivith the relative affinity of PCBs, PCDFs and PCDDs for the Ah receptor [89]. The quantitative structure-activity relationship (QSAR) models predicting the affinity of the halogenated aromatic hydrocarbons ivith the Ah receptor describe the electron acceptor capability as well as the hydrophobicity and polarizability of the chemicals [89[. 

A relatively recent development in QSAR research is molecular reference (MOLREF). This molecular modelling technique is a method that compares the structures of any number of test molecules with a reference molecule, in a quantitative structure-activity relationship study (27). Partial least squares regression analysis was used in molecular reference to analyse the relation between X- and Y-matrices. In this paper, forty-two disubstituted benzene compounds were tested for toxicity to Daphnia... 

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. 

Moore, D.R.J., Breton, R.L. and MacDonald, D.B. (2003) A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish. Environ Toxicol Chem, 22, 1799-1809. 

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