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K Enslein

Publications and source records attributed to K Enslein.

33 records · Page 2Linked to original sources
PubMedDate not supplied

A quantitative structure-toxicity relationships model for the dermal sensitization guinea pig maximization assay.

We have developed quantitative structure-toxicity relationship (QSTR) models for assessing dermal sensitization using guinea pig maximization test (GPMT) results. The models are derived from 315 carefully evaluated chemicals. There are two models, one for aromatics (excluding one-benzene-ring compounds), and the other for aliphatics and one-benzene-ring compounds. For sensitizers, the models can resolve whether they are weak/moderate or severe sensitizers. The statistical methodology, based on linear discriminant analysis, incorporates an optimum prediction space (OPS) algorithm. This algorithm ensures that the QSTR model will be used only to make predictions on query structures which fall within its domain. Calculation of the similarities between a query structure and the database compounds from which the applicable model was developed are used to validate each skin sensitization assessment. The cross-validated specificity of the equations ranges between 81 and 91%, and the sensitivity between 85 and 95%. For an independent test set, specificity is 79%, and sensitivity 82%.

Administration, Topical↗
PubMedDate not supplied

Assessment of n-octanol/water partition coefficient: when is the assessment reliable?

A model, VLOGP, has been developed for assessment of n-octanol/water partition coefficient, log P, of chemicals from their structures. Unlike group contribution methods, VLOGP is based on linear free energy relationship (LFER) approach and employs information-rich electrotopological structure quantifiers derived solely from molecular topology. VLOGP, a robust and cross-validated model derived from accurately measured experimental log P values of 6675 diverse chemicals, has a coefficient of determination, R2, of 0.986 and a standard error of estimate of 0.20. When applied to the training set, the largest deviation observed between experimental and calculated log P was 0.42. VLOGP is different from other log P predictors in that its application domain, called Optimum Prediction Space (OPS), has been quantitatively defined, i.e., structures to which the model should not be applied for predicting log P can be identified. A computer-assisted implementation of this model within HDi's toxicity assessment software package, TOPKAT 3.0, automatically checks whether the submitted structure is inside the OPS or not. VLOGP was applied to a set of 113 chemicals not included in the training set. It was observed that for the structures inside the OPS the average deviation between experimental and model-calculated log P values is 0.27, whereas the corresponding deviation for structures outside the OPS is 1.35. This demonstrates the necessity of identifying the structures to which a model is not applicable before accepting a model-based predicted log P value. For a set of 47 nucleosides, the performance of VLOGP was compared with that of four published log P predictors; a standard deviation of 0.33 was obtained with VLOGP, whereas the standard deviation from other log P predictors ranged between 0.46 and 1.20.

1-Octanol↗
PubMedDate not supplied

Carcinogenesis: a predictive structure-activity model.

A statistical structure-activity equation has been developed for the estimation of carcinogenic potential for chemicals that have not been subjected to carcinogenesis assays. This discriminant equation is based on substructural fragments and molecular weight obtained for 343 compounds. The data base used for the design of the equation was obtained from the monographs of the International Agency for Research on Cancer. The accuracy of classification for the carcinogens in the model is between 87 and 91%, and for noncarcinogens between 78 and 80% in the presence of between 5.5 and 10.2% of the compounds not being classifiable. The false negative rate ranges between 4 and 5%; the false positive rate is near 11%.

Carcinogens↗
PubMedDate not supplied

A toxicity estimation model.

A statistical model has been developed for estimation of acute toxicity. The model, currently operational for rat oral LD50, permits the estimation of rat oral LD50 for untested chemical compounds. Only the chemical structure, partition coefficient, and molecular weight for a compound are needed for estimation purposes. The chemical structure is partitioned into substructural fragments using the CIDS fragment keys. A regression model is developed on the basis of 425 compounds. A test of the regression equation with 100 compounds not used in its design shows that 56 percent of the compounds are predicted with less than 0.4 log unit deviation between extimated and measured LD50. This toxicity estimation model can be readily adapted to other species and to other measures of toxicity by the use of suitable design data bases. The model also identifies the contribution to toxicity of the fragments and physical characteristics. The use of this model can materially reduce the amount of toxicological testing for new compounds. It also permits the ranking of potentially toxic compounds to allow the most likely candidates to be tested. The method may also prove applicable to the determination of optimum dosages for new drugs.

Animals↗