QSARs for Prioritizing PBT Substances to Promote Pollution Prevention

Carlsen, Lars; Walker, John D.

doi:10.1002/qsar.200390004

Cited by 30 publications

(17 citation statements)

References 11 publications

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“…3 show that although the quadratic equation fits better than the linear one, it is still not satisfactory enough. Polynomial equations were also utilized to investigate the relationships between bioconcentration factors (logBCF) and logK ow , and preferable results were gained by using quartic equations [21,22] . As toxicities of narcotic compounds are mainly determined by their bioconcentration factors (BCF), with other kinds of effects omitted, the relationship between toxicities and logK ow could be expressed by quartic equation: A very high value 0.996 is achieved for r 2 adj of eq.…”

Section: Single-compound Toxicitymentioning

confidence: 99%

Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols

Wang

Zhang

et al. 2006

CHINESE SCI BULL

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Alkanols, which are narcotic compounds, were studied. The acute toxicities (15min-EC 50 ) of 15 alkanols, which have a large span of hydrophobicity, to Photobacterium phosphoreum were measured. Quantitative structure-activity relationship (QSAR) analysis of single toxicity of alkanols was conducted by using octanol-water partition coefficient (logK ow ). The polynomial expression was used instead of linear equation to develop QSAR model, and a QSAR model with a good predictive potential was achieved. Furthermore, the mixture toxicity of alkanols was studied. In order to predict joint toxicity of mixtures that contain very hydrophobic alkanols, the adjustment of octanol-water partition coefficient was performed, considering the influence of volume effect. And equivalent octanol-water partition coefficient was introduced. The QSAR model of mixture toxicity was developed by using equivalent mixture octanol-water partition coefficient. The result showed that even a linear model could predict mixture toxicity well by using equivalent mixture octanol-water partition coefficient.

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Section: Single-compound Toxicitymentioning

confidence: 99%

Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols

Wang

Zhang

et al. 2006

CHINESE SCI BULL

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“…Due to shortage of experimental data, QSAR estimates for the selection of persistent, bio-accumulative and toxic (PBT) substances appear as an attractive alternative [4]. It has been argued that all new chemicals should be assessed using a consistent and transparent methodology that uses chemical property data derived from QSARs, or experimental determination when possible and applies evaluative or regio-specific environmental models [5].…”

Section: Introductionmentioning

confidence: 99%

QSTR with Extended Topochemical Atom Indices. 4. Modeling of the Acute Toxicity of Phenylsulfonyl Carboxylates toVibrio fischeriusing Principal Component Factor Analysis and Principal Component Regression Analysis

Roy

Ghosh

2004

QSAR Comb. Sci.

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The present paper deals with modeling of the acute toxicity of 56 phenylsulfonyl carboxylates to Vibrio fischeri. Principal component factor analysis has been used as the data-preprocessing step for the selection of independent variables for the subsequent multiple regression analysis. The statistical quality of the best model using ETA descriptors is as follows: n 56, Q 2 0.726, R a 2 0.837, R 0.923, F 57.4 (df 5, 50), s 0.186, AVRES 0.136. Attempt has also been made to model the data set with different non-ETA parameters (topological indices including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E-State parameters apart from physicochemical parameters like AlogP98, MolRef and H-bond-acceptor) and the best model shows the following quality: n 56, Q 2 0.763, R a 2 0.798, R 0.903,

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“…The theory of partial order ranking is presented elsewhere [9] and its application in relation to QSAR is presented in previous papers [10][11][12][13]. In brief, Partial Order Ranking is a simple principle, which a priori includes "≤" as the only mathematical relation.…”

Section: Partial Order Rankingmentioning

confidence: 99%

Giving Molecules an Identity. On the Interplay Between QSARs and Partial Order Ranking

Carlsen¹

2004

Molecules

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Abstract:The interplay between 'noise-deficient' QSAR and Partial Order Ranking, including analysis of average linear ranks, constitutes an effective tool in giving substances which have not been investigated experimentally an identity by comparison with experimentally well-characterized, structurally similar compounds. It is disclosed that experimentally well-characterized compounds may serve as substitutes for highly toxic compounds in experimental studies without exhibiting the same extreme toxicity, while from an overall viewpoint they exhibit analogous environmental characteristics.

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QSARs for Prioritizing PBT Substances to Promote Pollution Prevention

Cited by 30 publications

References 11 publications

Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols

Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols

QSTR with Extended Topochemical Atom Indices. 4. Modeling of the Acute Toxicity of Phenylsulfonyl Carboxylates toVibrio fischeriusing Principal Component Factor Analysis and Principal Component Regression Analysis

Giving Molecules an Identity. On the Interplay Between QSARs and Partial Order Ranking

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