Guidelines for developing and using quantitative structure‐activity relationships

Walker, John D.; Jaworska, Joanna; Comber, Mike; Schultz, T.W.; Dearden, John C.

doi:10.1897/01-627

Cited by 117 publications

(65 citation statements)

References 100 publications

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“…A possible reason for this is the absence of a sufficient amount of experimental data of adequate quality and from the same experimental configuration. This is currently the decisive obstacle to the establishment of models to predict the influence of chemicals on biochemical parameters [39]. Adequate substance-specific experimental tests to verify the postulated effects are currently not available, nor can the relevance of chemicals for the effects considered here be examined by means of QSAR predictions.…”

Section: Application To Findings On "New" End Pointsmentioning

confidence: 99%

Project summary: a critical synopsis of mechanisms of action of low-dose xenobiotics in mammalian organisms as a basis for assessing aggregated effects of chemical mixtures and identifying "new" toxicological end points

2011

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Project summary: a critical synopsis of mechanisms of action of low-dose xenobiotics in mammalian organisms as a basis for assessing aggregated effects of chemical mixtures and identifying "new" toxicological end pointsKarin Heine 1 , Ali Taghavi 2 and Fritz Kalberlah 1* AbstractBackground: European chemicals legislation (registration, evaluation, authorisation and restriction of chemical substances (REACH)) requires a broad assessment of chemicals with respect to, inter alia, their health-relevant properties. Due to the extreme number of substances to be assessed and the limited current toxicological knowledge on their respective properties, REACH implicitly requires a paradigm change: away from knowledge generated mainly from costly animal experiments towards the use of mechanistic findings. Moreover, effect mechanisms at the biochemical or cellular level are essential when conclusions shall be drawn about "new" endpoints and mixtures of xenobiotics. This study (funded by the German Federal Environment Agency) describes examples of biochemical processes in the mammalian organism and how xenobiotics interfere with them. Interference with physiological processes expected to lead to adverse health effects is characterised as "toxicity pathway". The study describes toxicological endpoints not usually covered in routine animal testing and the respective toxicity pathways. Results and conclusions: Screening for chemicals which exert effects via common toxicity pathways and subsequently conducting targeted short-term tests may generate new information about the toxicity of chemicals without performing extensive substance-by-substance animal experiments. Information on common toxicity pathways may also provide input for the assessment of mixture effects. The U.S. Environmental Protection Agency is working intensely on this concept. It involves the use of enormous amounts of data on relevant biochemical and cellular processes, which are generated by "high-throughput screening" methods, and then are combined with substance-specific kinetic data, experimental apical test outcomes and modelling. Current limitations in the regulatory use of this integrated approach on risk assessment will be outlined.

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Section: Application To Findings On "New" End Pointsmentioning

confidence: 99%

Project summary: a critical synopsis of mechanisms of action of low-dose xenobiotics in mammalian organisms as a basis for assessing aggregated effects of chemical mixtures and identifying "new" toxicological end points

2011

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“…All accepted MLR equations have regression coefficients and F ratio significant at 95 and 99% levels, respectively, if not stated otherwise. The generated QSAR equations were validated by leaveone-out statistics using MINITAB software [58] and the calculated parameters are predicted residual sum of squares (PRESS) [60][61], cross validation R 2 (Q 2 ), standard deviation based on PRESS (S PRESS ) [61] and standard deviation of error of prediction (SDEP) [62].…”

Section: Data Treatment and Softwarementioning

confidence: 99%

QSAR modeling for lipid peroxidation inhibition potential of flavonoids using topological and structural parameters

2008

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Abstract:In the present study, Quantitative Structure-Activity Relationship (QSAR) modeling has been carried out for lipid peroxidation (LPO)-inhibition potential of a set of 27 flavonoids, using structural and topological parameters. For the development of models, three methods were used: (1) stepwise regression, (2) factor analysis followed by multiple linear regressions (FA-MLR) and (3) partial least squares (PLS) analysis. The best equation was obtained from stepwise regression analysis (Q 2 = 0.626) considering the leave-oneout prediction statistics. Warsaw and Springer-Verlag Berlin Heidelberg. © Versita

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“…(4),Ȳ means average activity value of the entire data set while Y obs and Y cal represent observed and LOO estimated activity values. Standard deviation of error of prediction (SDEP) [51] is calculated according to the formula…”

Section: Data Treatment and Softwarementioning

confidence: 99%

QSAR modeling of antiradical and antioxidant activities of flavonoids using electrotopological state (E-State) atom parameters

2007

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Abstract:In the present paper QSAR modeling using electrotopological state atom (E-state) parameters has been attempted to determine the antiradical and the antioxidant activities of flavonoids in two model systems reported by Burda et al. (2001). The antiradical property of a methanolic solution of 1, 1-diphenyl-2-picrylhydrazyl (DPPH) and the antioxidant activity of flavonoids in a β-carotenelinoleic acid were the two model systems studied. Different statistical tools used in this communication are stepwise regression analysis, multiple linear regressions with factor analysis as the preprocessing step for variable selection (FA-MLR) and partial least squares analysis (PLS). In both the activities the best equation is obtained from stepwise regression analysis, considering, both equation statistics and predictive ability (antiradical activity: R 2 = 0.927, Q 2 = 0.871 and antioxidant activity: R 2 = 0.901, Q 2 = 0.841).

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Guidelines for developing and using quantitative structure‐activity relationships

Cited by 117 publications

References 100 publications

Project summary: a critical synopsis of mechanisms of action of low-dose xenobiotics in mammalian organisms as a basis for assessing aggregated effects of chemical mixtures and identifying "new" toxicological end points

Project summary: a critical synopsis of mechanisms of action of low-dose xenobiotics in mammalian organisms as a basis for assessing aggregated effects of chemical mixtures and identifying "new" toxicological end points

QSAR modeling for lipid peroxidation inhibition potential of flavonoids using topological and structural parameters

QSAR modeling of antiradical and antioxidant activities of flavonoids using electrotopological state (E-State) atom parameters

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