Predicting Fate-Related Physicochemical Properties

Schüürmann, Gerrit; Ebert, Ralf‐Uwe; Nendza, Monika; Dearden, John C.; Paschke, Albrecht; Kühne, Ralph

doi:10.1007/978-1-4020-6102-8_9

Cited by 30 publications

(23 citation statements)

References 213 publications

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“…Although for both A and B increment methods to predict their values from molecular structure have been developed, 22,23 the prediction performances of the versions currently available appear to be limited. 24,25 Our goal was to develop a method for predicting the H-bond donor strength in terms of the A parameter directly from the molecular structure of the individual compounds. To this end, local molecular parameters introduced earlier 26,27 were explored for their scope to quantify relevant aspects of the H-bond donor sites.…”

mentioning

confidence: 99%

Modeling the H bond donor strength of OH, NH, and CH sites by local molecular parameters

et al. 2008

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A quantum chemical model is introduced to predict the H-bond donor strength of monofunctional organic compounds from their ground-state electronic properties. The model covers -OH, -NH, and -CH as H-bond donor sites and was calibrated with experimental values for the Abraham H-bond donor strength parameter A using the ab initio and density functional theory levels HF/6-31G** and B3LYP/6-31G**. Starting with the Morokuma analysis of hydrogen bonding, the electrostatic (ES), polarizability (PL), and charge transfer (CT) components were quantified employing local molecular parameters. With hydrogen net atomic charges calculated from both natural population analysis and the ES potential scheme, the ES term turned out to provide only marginal contributions to the Abraham parameter A, except for weak hydrogen bonds associated with acidic -CH sites. Accordingly, A is governed by PL and CT contributions. The PL component was characterized through a new measure of the local molecular hardness at hydrogen, eta(H), which in turn was quantified through empirically defined site-specific effective donor and acceptor energies, EE(occ) and EE(vac). The latter parameter was also used to address the CT contribution to A. With an initial training set of 77 compounds, HF/6-31G** yielded a squared correlation coefficient, r(2), of 0.91. Essentially identical statistics were achieved for a separate test set of 429 compounds and for the recalibrated model when using all 506 compounds. B3LYP/6-31G** yielded slightly inferior statistics. The discussion includes subset statistics for compounds containing -OH, -NH, and active -CH sites and a nonlinear model extension with slightly improved statistics (r(2) = 0.92).

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confidence: 99%

Modeling the H bond donor strength of OH, NH, and CH sites by local molecular parameters

et al. 2008

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“…This will help to unravel the combined information content, and to possibly augment the resulting evaluation with a certain level of probability. Regarding the prediction of physicochemical properties from chemical structure, the reader is referred to an earlier review (Schüürmann et al, 2007), keeping in mind that some of these properties also trigger the need for animal testing (e.g. bioconcentration testing becomes relevant if the logarithmic octanol/water partition coefficient, log K ow , is above 3, because this indicates that the substance may bioaccumulate to a significant degree (ECHA, 2012)).…”

Section: Integrated Testing Strategiesmentioning

confidence: 99%

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis

Péry

Schüürmann

Ciffroy³

et al. 2013

Science of The Total Environment

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For more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA.

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“…Due to the large heterogeneous dataset, the application of the method covers a broad range of organic compound classes. The model has been run automatically as implemented in the in-house software system ChemProp (Schüürmann et al 1997(Schüürmann et al , 2007.…”

Section: Mutagenicity Predictionmentioning

confidence: 99%

Application of preparative capillary gas chromatography (pcGC), automated structure generation and mutagenicity prediction to improve effect-directed analysis of genotoxicants in a contaminated groundwater

Meinert

Schymanski

Küster

et al. 2010

Environ Sci Pollut Res

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The results suggest pcGC as a high-resolution fractionation tool and MOLGEN-MS to improve structure elucidation, whilst mutagenicity prediction failed in our study to predict identified genotoxicants. Genotoxicity, mutagenicity and carcinogenicity caused by chemicals are complex processes, and prediction from chemical structure still appears to be quite difficult. Progress in this field would significantly support EDA and risk assessment of environmental mixtures.

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Predicting Fate-Related Physicochemical Properties

Cited by 30 publications

References 213 publications

Modeling the H bond donor strength of OH, NH, and CH sites by local molecular parameters

Modeling the H bond donor strength of OH, NH, and CH sites by local molecular parameters

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis

Application of preparative capillary gas chromatography (pcGC), automated structure generation and mutagenicity prediction to improve effect-directed analysis of genotoxicants in a contaminated groundwater

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