Kurt Enslein

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 HDis 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.

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%.

Assessment of developmental toxicity potential of chemicals by quantitative structure-toxicity relationship models

Statistically significant quantitative structure-toxicity relationship (QSTR) models have been developed for assessing developmental toxicity potential (DTP) of chemicals. Three submodels, one each for aliphatic, heteroaromatic and carboaromatic compounds, have been cross-validated to ascertain their robustness. The specificities of the models range from 86% to 97%, and their sensitivities between 86% and 89%. For convenient computer-assisted application, the models are installed in a toxicity assessment software package, TOPKAT, which has been recently enhanced with algorithms to identify whether or not a query structure is inside the optimum prediction space (OPS) of a QSTR model. Different functionalities of the TOPKAT program have been explained by assessing the DTP of a number of compounds not used in the model training sets. The DTP of 18 existing drugs was assessed using these models; the DT assay results were available for 5 of these. Three of these 5 molecules were identified to be inside the OPS and their TOPKAT assessment matched their experimental assignment.

Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL)

With the multitude of new chemicals being synthesized and the paucity of long-term test data on chemicals that could be introduced into the environment, innovative approaches must be developed to determine the health and environmental effects of chemicals. Research was conducted to employ quantitative structure-activity relationship (QSAR) techniques to study the feasibility of developing models to estimate the noncarcinogenic toxicity of chemicals that are not addressed in the literature by relevant studies. A database of lowest-observed-adverse effect level (LOAEL) was assembled by extracting toxicity information from 104 U.S. EPA documents, 124 National Cancer Institute/National Toxicology Program (NCI/NTP) reports, and 6 current reports from the literature. A regression model, based on 234 chemicals of diverse structures and chemical classes including both alicyclic and aromatic compounds, was developed to assess the chronic oral LOAELs in rats. The model was incorporated into an automated computer package. Initial testing of this model indicates it has application to a wide range of chemicals. For about 55% of the compounds in the data set, the estimated LOAELs are within a factor of 2 of the observed LOAELs. For over 93%, they are within a factor of 5. Because of the paucity or absence of long-term toxicity data, the public health and risk assessment community could utilize such QSAR models to determine initial estimates of toxicity for the ever-increasing numbers of chemicals that lack complete pertinent data. However, this and other such models should be used only by expert toxicologists who must objectively look at the estimates thus generated in light of the overall weight of evidence of the available toxicologic information of the subject chemical(s).

An Overview of Structure-Activity Relationships as an Alternative To Testing in Animals for Carcinogenicity, Mutagenicity, Dermal and Eye Irritation, and Acute Oral Toxicity

The use of structure-activity relationships (SAR) has proven practical for the development of equations which can be used to estimate the above-listed endpoints for a large variety of chemicals. The SAR models predict these endpoints correctly in 85 to 97% of the cases and often surpass in their predictive ability the results obtainable from the equivalent biological assays. These SAR models are being used at several levels: drug, or more generally, chemical discovery; prioritization for testing; regulatory affairs; investigation of detoxification mechanisms; and risk estimation. In the new compound (discovery) use, potential toxic effects of a set of related compounds are investigated before synthesis to select those chemicals with the lesser probabilities of producing toxic effects for further investigation, at considerable savings in research expenditure since fewer compounds need to be synthesized, and the avoidance of blind alleys. Prioritization for testing is used in numerous instances, such as selecting those chemicals in an environment which are most likely to have toxic effects for priority attention. SAR models are used by regulatory agencies to determine the possible toxic effects of chemicals for which data insufficient to render decisions have been submitted, and to gain insight into possible toxicity problems. SAR models are also used to investigate possible metabolites, and toxicity mechanisms due to the ability of making computer-based structural modifications and observing the effects on the modelled toxic endpoints. Risk analysis is a natural outgrowth of several of the above applications, and is particularly useful for SAR models of carcinogenicity. SAR models as alternatives to animal bioassays should be used in the context of other information for the chemicals of concern. Just as bioassays and in vitro methods have their limitations, so do SAR models. These include the sometimes limited data base on which to base an SAR model, the temptation to extrapolate beyond the confines of the model, and the noise inherent in the bioassays on which the models are based. Within these constraints SAR models have a considerable potential in reducing the number of animals used in toxicity testing.

Augmented stepwise discriminant analysis applied to two classification problems in the biomedical field

Abstract Stepwise discriminant analysis is applied to the machine karyotyping of chromosomes using arm-length measurements and functions of these as the basis for classification, and machine differential diagnosis of gynecological diseases based on a set of twenty-six signs and symptoms. When the karyo-typing procedure is augmented by a reordering scheme which takes advantage of the required number of objects per class, a reproduction of human performance with approximately 6% error is achieved. For the gynecological diseases, under certain circumstances, an error rate of 15% is achieved. This is an error rate of least equivalent and probably lower than that realized by humans.

Teratogenesis: A statistical structure-activity model

This structure-activity model of teratogenicity was developed to provide the ability to rank untested compounds by their probability of teratogenicity. The model is based on 430 compounds collected from various sources in the literature and scored from zero to one as to evidence of teratogenicity. A discriminant equation then separates those compounds in the extremes of this distribution. The false positive classification rate based on the compounds in the equation is approximately 8% and the false negative rate approximately 10%. Approximately 22% of the compounds are not classifiable as either teratogens or nonteratogens with this equation.

Mutagenicity (Ames): A structure-activity model

A statistical structure-activity model of the Salmonella typhimurium (Ames) test has been devised based on 472 chemicals for which this endpoint has been measured. The model uses substructural fragments as the independent parameters to explain the difference in mutagenicity of the different chemicals. The model is able to classify 86% of the chemicals into their correct categories; the false-positive rate is 4.7%, and the false-negative rate 5.3%. Approximately 10% of the chemicals cannot be classified by the existing equation. This structure-activity model can be used as a preliminary screen prior to other testing as well as for setting priorities for more detailed investigations.

A Structure-Biodegradability Relationship Model by Discriminant Analysis

Multivariate analysis, in the strictest sense, is the study of systems of correlated random variables or random samples from such systems (Gifi 1990). However, practically,multivariate methods deal with the problem of linear representation of relationships among a set of measurements on a number of objects. When objects are chemical structures and measurements are molecular descriptors and rate of aerobic biodegradation, for instance, the techniques of multivariate analysis are well suited for studying structure-biodegradability relationships.

A structure-activity prediction model of carcinogenicity based on NCI/NTP assays and food additives.

Structure-activity relationships (SARs) in chemistry represent a set of techniques by which biological effects and physical-chemical properties can be modelled for a set of chemicals. These methods have been applied to the design of pharmaceuticals, pesticides, and herbicides, among other desired endpoints. SAR applications for such endpoints have been mostly popularized by Hansch et al. (Hansch, 1979) in the US. It was not until recently that SAR methods have been applied to toxicity endpoints. As far as carcinogenicity endpoints are concerned, the efforts have involved but a few investigators, notably Wishnok (Wishnok, 1976; Wishnok, 1978), Jurs and his associates (Jurs, 1979; Chou, 1979; Yuan, 1980; Yuta, 1981 ), and the present author and his collaborators (Enslein, 1982; Enslein, 1983; Enslein, 1984a). In the following sections, we will describe the most recent models of carcinogenicity that we have developed. Estimates from these models can be used for decision-making for carcinogenic risk assessment, setting testing priorities, and aiding in the development of new chemical entities. These estimates should not be used in a vacuum, but in the context of other information available on the specific chemicals.