QSAR and Chemometric Approaches for Setting Water Quality Objectives for Dangerous Chemicals

Vighi, Marco; Gramatica, Paola; Consolaro, F.; Todeschini, Roberto

doi:10.1006/eesa.2001.2064

Cited by 45 publications

(26 citation statements)

References 18 publications

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“…Table 7 shows that the best statistical quality along with prediction statistics with ETA descriptors is found in case of GFA-MLR equation. (1, 5, 9, 13, 17, 21, 25, 29, 33, 7, 41), (2, 6, 10, 14, ..., 34, 38), ..., (4,8,12,16,20,24,28,32,36,40). …”

Section: Resultsmentioning

confidence: 98%

“…SAR may consist of nearest analog analysis, member of a toxic chemical class, or quantitative SAR (QSAR) [1]. In order to evaluate environmentally safe levels of dangerous chemicals, there is the need for a set of toxicological data on organisms representative of the ecosystems, which is often unavailable or inadequate [4]. Currently there are ecotoxicological data available for very small number of chemicals; thus, the European Union Commissions Scientific committee on Toxicity, Ecotoxicity, and Environment (CSTEE) recommended use of (Q)SAR models and precaution to prioritize further risk assessment of approximately 4500 compounds and their adjuvants [5].…”

Section: Introductionmentioning

confidence: 99%

“…The risk assessment of substances in various environmental compartments is essential for their proper, safe and environmentally friendly production, handling, use and final deposition or destruction. Hazard identification is an important part of risk assessment [4]. The EPAs 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 [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

QSTR with Extended Topochemical Atom (ETA) Indices 8.^a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

Roy

Ghosh

2006

QSAR Comb. Sci.

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Quantitative structure-toxicity relationship (QSTR) study has proved to be a valuable approach in ecotoxicity estimations of acute and chronic toxicity to various organisms, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. In this background, we have modeled the inhibition of 41 substituted phenols on germination rate of Cucumis sativus with extended topochemical atom (ETA) indices using different chemometric tools and compared the ETA relations with non-ETA models derived from different topological indices (Wiener W, Balaban J, Hosoya Z, Zagreb, molecular connectivity indices, E-state indices and kappa shape indices) and physicochemical parameters (AlogP98, MolRef, H_bond_acceptor, H_bond_donor, MW, logK ow and E lumo ). Different statistical tools used in this communication are stepwise regression analysis, multiple linear regression with factor analysis as the preprocessing step for variable selection (FA-MLR), multiple linear regression with genetic function approximation (GFA-MLR), partial least squares regression with factor analysis as the preprocessing step for variable selection (FA-PLS), genetic partial least squares (G/PLS) and principal component regression analysis (PCRA). The results show that the best statistical quality along with prediction statistics with ETA descriptors is found in case of GFA-MLR equation. In case of non-ETA descriptors, the best equation is obtained from PCRA considering both equation statistics and predictive ability. When combined descriptors were considered, FA-PLS gave the best equation. The best ETA equation (GFA-MLR) has better leave-one-out Q 2 value than the best non-ETA equation (PCRA). The results suggest that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing significantly to the comparative inhibition activity of substituted phenols on germination rate of Cucumis sativus.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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QSTR with Extended Topochemical Atom (ETA) Indices 8.^a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

Roy

Ghosh

2006

QSAR Comb. Sci.

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“…As seen in Figures 10 and 11, all compounds of training and test set are found to be inside of the square area. The predicted HIV-1 RT inhibitory activity data must be considered reliable only for those chemicals that fall within the applicability domain on which the model was constructed for future predictions 48 . It was also stated that the reported QSAR models may not be useful to predict the activity of other type of molecules against HIV-1 RT enzymes.…”

Section: 5273mentioning

confidence: 99%

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

Veerasamy

Subramaniam

Chean

et al. 2011

Journal of Enzyme Inhibition and Medicinal Chemistry

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“…Other approaches to predict toxicity include those for predicting water quality objectives (Vighi et al 2001). This was a multivariate QSAR approach that involved the assessment of water quality objectives for a set of 125 chemicals [derived from the European priority list in compliance with Directive 76/464/EEC (EEC 1976)].…”

Section: Expert Systemsmentioning

confidence: 99%

Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

Cronin

Walker

Jaworska

et al. 2003

Environ Health Perspect

201

108

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This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing.

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QSAR and Chemometric Approaches for Setting Water Quality Objectives for Dangerous Chemicals

Cited by 45 publications

References 18 publications

QSTR with Extended Topochemical Atom (ETA) Indices 8.^a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

QSTR with Extended Topochemical Atom (ETA) Indices 8.^a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

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QSAR and Chemometric Approaches for Setting Water Quality Objectives for Dangerous Chemicals

Cited by 45 publications

References 18 publications

QSTR with Extended Topochemical Atom (ETA) Indices 8.a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

QSTR with Extended Topochemical Atom (ETA) Indices 8.a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

Contact Info

Product

Resources

About

QSTR with Extended Topochemical Atom (ETA) Indices 8.^a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

QSTR with Extended Topochemical Atom (ETA) Indices 8.^a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools