QSARs based on statistical design and their use for identifying chemicals for further biological testing

Tosato, Maria Livia; Marchini, Silvia; Passerini, Laura; Pino, Anna; Eriksson, Lennart; Lindgren, Fredrik; Hellberg, Sven; Jönsson, Jan‐Ingvar; Sjöstróm, Michael; Skagerberg, Bert; Wold, Svante

doi:10.1002/etc.5620090303

Cited by 33 publications

(7 citation statements)

References 13 publications

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“…However, there are some aspects that require special attention to ensure QSARs of good quality. By their theoretical foundation [ 5 ] , QSAR models are locally valid and thus require chemical and biological similarity among the considered chemicals. To accomplish this, chemicals are grouped into classes on the ba-sis of their chemical structure.…”

Section: Discussionmentioning

confidence: 99%

A STRATEGY FOR RANKING ENVIRONMENTALLY OCCURRING CHEMICALS. PART V: THE DEVELOPMENT OF TWO GENOTOXICITY QSARs FOR HALOGENATED ALIPHATICS

Eriksson¹,

Jönsson²,

Hellberg³

et al. 1991

Environ Toxicol Chem

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A s4trategy for systematic analysis and priority ranking of chemicals was applied to a class of 58 saturated halogenated aliphatics. The training set, selected by statistical design, was subjected to biological testing in an assay (the DNA precipitation assay) estimating DNA damage in V79 Chinese hamster (cells. The obtained genotoxicity data were modeled by construction of two multivariate quantitative structure-activity relationships (QSARs). To experimentally validate the two derived QSAR models, a validation set of chemicals was chosen (also according to statistical design). Predictions were made for the validation set compounds, and they were found to compare well with the observed data. Thus, the two QSARs were utilized to predict the genotoxicity of 36 nontested compounds belonging to the examined class. In addition, it was found that the genotoxicity data required a multitude of chemical and structural descriptors in order to be modelable, and that log P in particular was an informative descriptor variable. Keywords-H[alogenated aliphaticsMultivariate QSAR Statistical design PLS Hazard ranking 5-86L. ERIKSSON ET AL.

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Section: Discussionmentioning

confidence: 99%

A STRATEGY FOR RANKING ENVIRONMENTALLY OCCURRING CHEMICALS. PART V: THE DEVELOPMENT OF TWO GENOTOXICITY QSARs FOR HALOGENATED ALIPHATICS

Eriksson¹,

Jönsson²,

Hellberg³

et al. 1991

Environ Toxicol Chem

Self Cite

View full text Add to dashboard Cite

show abstract

“…By their theoretical foundation [ 5 ] , QSAR models are locally valid and thus require chemical and biological similarity among the considered chemicals. However, there are some aspects that require special attention to ensure QSARs of good quality.…”

Section: Discussionmentioning

confidence: 99%

A strategy for ranking environmentally occurring chemicals. Part V: The development of two genotoxicity QSARs for halogenated aliphatics

Eriksson

Jönsson

Hellberg

et al. 1991

Enviro Toxic and Chemistry

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A strategy for systematic analysis and priority ranking of chemicals was applied to a class of 58 saturated halogenated aliphatics. The training set, selected by statistical design, was subjected to biological testing in an assay (the DNA precipitation assay) estimating DNA damage in V79 Chinese hamster cells. The obtained genotoxicity data were modeled by construction of two multivariate quantitative structure‐activity relationships (QSARs). To experimentally validate the two derived QSAR models, a validation set of chemicals was chosen (also according to statistical design). Predictions were made for the validation set compounds, and they were found to compare well with the observed data. Thus, the two QSARs were utilized to predict the genotoxicity of 36 nontested compounds belonging to the examined class. In addition, it was found that the genotoxicity data required a multitude of chemical and structural descriptors in order to be modelable, and that log P in particular was an informative descriptor variable.

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“…When a MIS is to be evaluated methods of multivariate statistics are of primary interest such as, e.g., correlation or regression analyses and clustering techniques. In some studies, just a regression analysis is considered as a method toward an evaluation of a MIS [8]. However, regression analyses need a model concept, e.g., whether a linear model is appropriate or not or whether a nonlinear fitting model appears as a more appropriate choice.…”

Section: Methodology 21 Why Partial Order?mentioning

confidence: 99%

Inequalities in the European Union—A Partial Order Analysis of the Main Indicators

Carlsen¹,

Brüggemann

2021

Sustainability

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The inequality within the 27 European member states has been studied. Six indicators proclaimed by Eurostat to be the main indicators charactere the countries: (i) the relative median at-risk-of-poverty gap, (ii) the income distribution, (iii) the income share of the bottom 40% of the population, (iv) the purchasing power adjusted GDP per capita, (v) the adjusted gross disposable income of households per capita and (vi) the asylum applications by state of procedure. The resulting multi-indicator system was analyzed applying partial ordering methodology, i.e., including all indicators simultaneously without any pretreatment. The degree of inequality was studied for the years 2010, 2015 and 2019. The EU member states were partially ordered and ranked. For all three years Luxembourg, The Netherlands, Austria, and Finland are found to be highly ranked, i.e., having rather low inequality. Bulgaria and Romania are, on the other hand, for all three years ranked low, with the highest degree of inequality. Excluding the asylum indicator, the risk-poverty-gap and the adjusted gross disposable income were found as the most important indicators. If, however, the asylum application is included, this indicator turns out as the most important for the mutual ranking of the countries. A set of additional indicators was studied disclosing the educational aspect as of major importance to achieve equality. Special partial ordering tools were applied to study the role of the single indicators, e.g., in relation to elucidate the incomparability of some countries to all other countries within the union.

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QSARs based on statistical design and their use for identifying chemicals for further biological testing

Cited by 33 publications

References 13 publications

A STRATEGY FOR RANKING ENVIRONMENTALLY OCCURRING CHEMICALS. PART V: THE DEVELOPMENT OF TWO GENOTOXICITY QSARs FOR HALOGENATED ALIPHATICS

A STRATEGY FOR RANKING ENVIRONMENTALLY OCCURRING CHEMICALS. PART V: THE DEVELOPMENT OF TWO GENOTOXICITY QSARs FOR HALOGENATED ALIPHATICS

A strategy for ranking environmentally occurring chemicals. Part V: The development of two genotoxicity QSARs for halogenated aliphatics

Inequalities in the European Union—A Partial Order Analysis of the Main Indicators

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