Structure-activity relationships in toxicology and ecotoxicology: An assessment

Turner, Loren; Choplin, F.; Dugard, Paul H.; Hermens, Joop L. M.; Jaeckh, Rudolf; Marsmann, M.; Roberts, David W.

doi:10.1016/0887-2333(87)90015-4

Cited by 53 publications

(14 citation statements)

References 46 publications

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“…However, a QSAR will be a poor model with limited predictive capability if it is based on an improperly selected set of chemicals used to calibrate (train) the model, the so-called training set [2-41. It is essential that the training set consists of representative chemical compounds that are distributed in a balanced way over the structural factor domain of the chemicals studied IS]. For example, recent reviews [ 1,7] do not even mention that statistical design of the training set is essential for a successful QSAR modeling. We note that the problem of how to select a representative training set for QSAR development in toxicology *To whom correspondence may be addressed.…”

Section: General Considerationsmentioning

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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Section: General Considerationsmentioning

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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show abstract

“…For example, recent reviews [10,11] do not even mention that the statistical design of the training set is essential for QSAR modeling. For example, recent reviews [10,11] do not even mention that the statistical design of the training set is essential for QSAR modeling.…”

Section: Statistical Design In the Structural Factorsmentioning

confidence: 99%

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

Tosato

Marchini

Passerini

et al. 1990

Enviro Toxic and Chemistry

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The paper provides an overview of how quantitative structure‐activity relationship (QSAR) studies may represent a tool for predictions of toxic/ecotoxic effects of chemicals and identification of potentially hazardous ones. The paper is divided into two parts: theory and application. In the first part, the issues discussed are: the philosophy of QSAR, the conditions that must be fulfilled for constructing sound models and a strategy to establish priorities for further toxicological testing. In the second part, illustrations are reported regarding how a number of environmentally significant chemicals is distributed into chemical classes that meet QSAR criteria for modeling and how QSAR studies are carried out. For this latter purpose, four classes of chemicals are considered and emphasis is placed on the selection of the training sets and the validation of the models.

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“…One set of a few methods, such as a twocompartment system with an interposed monolayer of cells to mimic chemical transport as well as tests for binding to cells or proteins, plus physicochemical data (see Turner et al, 1987) might predict the distribution of varied blood concentrations of a chemical in man as different concentrations in the extracellular water of various compartments of the body (brain, kidney tubuli, biliary canaliculi, thyroid follicles, gastric mucosal linings, etc.). Further, this modeling is thought to be accomplished in three steps: 1) The testing of the chemical for its toxicity in several relevant in vitro systems, such as cell lines, human hepatocytes, heart-, kidney-, and nerve cells, in vitro systems representing vitally important human neurotransmission systems, and so forth.…”

Section: Basic Assumptions and Hypothesesmentioning

confidence: 99%

MEIC—A new international multicenter project to evaluate the relevance to human toxicity of in vitro cytotoxicity tests

et al. 1989

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A new international project to evaluate the relevance for human systemic and local toxicity of in vitro tests of general toxicity of chemicals has been organized by the Scandinavian Society of Cell Toxicology under the title Multicenter Evaluation of In Vitro Cytotoxicity (MEIC). The basic assumptions underlying the project, as well as the practical goals and the design of the program are outlined. The list of the first 50 reference chemicals is presented. The chemicals are an otherwise unbiased selection of compounds with known human acutely lethal dosage and blood concentrations, including LD50-values in the rat or mouse. Most agents also have other data on human toxicity and toxicokinetics, including more extensive animal toxicity data. International laboratories already using or developing in vitro tests of various partial aspects of general toxicity are invited to test the substances, the results of which will be evaluated by us. The predictivity of the in vitro results for both partial and gross human toxicity data will be determined with combined use of univariate regression analysis and soft multivariate modeling. The predictivity of the in vitro results will be compared with the predictivity of conventional animal tests for the same chemicals. Finally, batteries of tests with optimal prediction power for various types of human toxicity will be selected. The need for and possible uses of such batteries are discussed.

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Structure-activity relationships in toxicology and ecotoxicology: An assessment

Cited by 53 publications

References 46 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

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

MEIC—A new international multicenter project to evaluate the relevance to human toxicity of in vitro cytotoxicity tests

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