Data analysis and alternative modelling of MITI-I aerobic biodegradation.

An alternate representation of the biodegradation of chemicals (biochemical oxygen demand (BOD) amount instead of BOD-based degradation percentage) allows the derivation of an improved QSAR model. The structural descriptors for this model are related to biodegradation mechanisms. A linear group contribution model of 99 variables was derived from a training set of 1190 chemicals. The squared correlation coefficients for self-prediction and for 20%-off cross-validation were 0.83 and 0.69, respectively. Additional external validation with 62 reported in the literature chemicals resulted in 91% overall correct classification, although ready biodegradable molecules were too few and predicted poorly (two out of five correct).

Searching for an enhanced predictive tool for mutagenicity.

The Multiple Computer Automated Structure Evaluation (MCASE) program was used to evaluate the mutagenic potential of organic compounds. The experimental Ames test mutagenic activities for 2513 chemicals were collected from various literature sources. All chemicals have experimental results in one or more Salmonella tester strains. A general mutagenicity data set and fifteen individual Salmonella test strain data sets were compiled. Analysis of the learning sets by the MCASE program resulted in the derivation of good correlations between chemical structure and mutagenic activity. Significant improvement was obtained as more data was added to the learning databases when compared with the results of our previous reports. Several biophores were identified as being responsible for the mutagenic activity of the majority of active chemicals in each individual mutagenicity module. It was shown that the multiple-database mutagenicity model showed a clear advantage over normally used single-database models. The expertise produced by this analysis can be used to predict the mutagenic potential of new compounds.

In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system.

Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked indescending order according to their predicted mutagenic potential and the first 100 chemicals with highest mutagenicity scores are presented. The screening result offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.

META V. A model of photodegradation for the prediction of photoproducts of chemicals under natural-like conditions.

Our goal was to create a photodegradation model based on the META expert system [G. Klopman, M. Dimayuga, J. Talafous, J. Chem. Inf. Comput. Sci. 34 (1994a) 1320-1325]. This requires the development of a dictionary of photodegradation pathways. Equipped with such a dictionary, we found that META successfully predicts degradation pathways of organic compounds under UV light. Our model was tested on a wide range of industrial compounds for which literature data exists. The results were excellent as the hit/miss ratio was better than 92%. This work complements our previous elaboration of equivalent mammal metabolism, aerobic and anaerobic biodegradation models.

Estimation of the aqueous solubility of organic molecules by the group contribution approach.

Several group contribution methods to estimate the aqueous solubility of organic molecules are proposed and evaluated for their ability to predict the water solubility of new molecules. The learning set consisted of 1168 organic compounds with experimental data taken from the literature after critical evaluation. The best method, based on a new fragment atom scheme, leads to a squared correlation coefficient of 0.95 and an average absolute calculation error of 0.50 log unit, which is superior to other group contribution methods currently available. One of the advantages of this model is that it has upper and lower limits so that the predicted solubilities cannot be unrealistily high or low.

Structure-specificity relationships for haloalkane dehalogenases.

A structural analysis of the substrate specificity of hydrolytic dehalogenases originating from three different bacterial isolates has been performed using the multiple computer-automated structure evaluation methodology. This methodology identifies structural fragments in substrate molecules that either activate or deactivate biological processes. The analysis presented in this contribution is based on newly measured dehalogenation data combined with data from the literature (91 substrates). The enzymes under study represent different specificity classes of haloalkane dehalogenases (haloalkane dehalogenase from Xanthobacter autotrophicus GJ10, Rhodococcus erythropolis Y2, and Sphingomonas paucimobilis UT26). Three sets of structural rules have been identified to explain their substrate specificity and to predict activity for untested substrates. Predictions of activity and inactivity based on the structural rules from this analysis were provided for those compounds that were not yet tested experimentally. Predictions were also made for the compounds with available experimental data not used for the model construction (i.e., the external validation set). Correct predictions were obtained for 28 of 30 compounds in the validation set. Incorrect predictions were noted for two substrates outside the chemical domain of the set of compounds for which the structural rules were generated. A mechanistic interpretation of the structural rules generated provided a fundamental understanding of the structure-specificity relationships for the family of haloalkane dehalogenases.

Modeling the mouse lymphoma forward mutational assay: the Gene-Tox program database.

An SAR model of the induction of mutations at the tk(+/-) locus of L5178Y mouse lymphoma cells (MLA, for mouse lymphoma assay) was derived based upon a re-evaluation of experimental results reported by a Gene-Tox (GT) working group [A.D. Mitchell, A.E. Auletta, D. Clive, P.E. Kirby, M.M. Moore, B.C. Myhr, The L5178Y/tk(+/-) mouse lymphoma specific gene and chromosomal mutation assay. A phase III report of the U.S. Environmental Protection Agency Gene-Tox Program, Mutation Res. 394 (1997) 177-303.]. The predictive performance of the GT MLA SAR model was similar to that of a Salmonella mutagenicity model containing the same number of chemicals. However, the structural determinants (biophores) derived from the GT MLA SAR model include both electrophilic as well as non-electrophilic moieties, suggesting that the induction of mutations in the MLA may occur by both direct interaction with DNA and by non-DNA-related mechanisms. This was confirmed by the observation that the set of biophores associated with MLA overlapped significantly with those associated with phenomena related to loss of heterozygosity, chromosomal rearrangements and aneuploidy. The MLA SAR model derived from the GT data evaluation was significantly more predictive than an SAR model previously derived from MLA data reported by the US National Toxicology Program [B. Henry, S.G. Grant, G. Klopman, H.S. Rosenkranz, Induction of forward mutations at the thymidine kinase locus of mouse lymphoma cells: evidence for electrophilic and non-electrophilic mechanisms, Mutation Res. 397 (1998) 331-335.]. Moreover, the latter model appeared to be more complex than the former, suggesting that the GT induction data was both simpler mechanistically and more homogeneous than that of the NTP.

Structural determinants of developmental toxicity in hamsters.

A CASE/MULTICASE structure activity relationship (SAR) model of developmental toxicity of chemicals in hamsters (HaDT) was developed. The model exhibited a predictive performance of 74%. The model's overall predictivity and informational content were similar to those of an SAR model of mutagenicity in Salmonella. However, unlike the Salmonella mutagenicity model, the HaDT model did not identify overtly chemically reactive moieties as associated with activity. Moreover, examination of the number and nature of significant structural determinants suggested that developmental toxicity in hamsters was not the result of a unique mechanism or attack on a specific molecular target. The analysis also indicated that the availability of experimental data on additional chemicals would improve the performance of the SAR model.

The mysterious Mauritian red nectar: a selective toxicant?

Using knowledge-based computational structure-activity relationship models, it is hypothesized that the aurone responsible for the uniquely red nectar of several Mauritian bird-pollinated plants functions as a repellant of nectar-robbing or herbivorous mammalian species.

Applications of the case/multicase SAR method to environmental and public health situations.

The availability of validated and characterized SAR models of toxicological phenomena provides a method to apply SAR technology to a variety of environmental, public health and industrial situations. These include (i) the prioritization of environmental pollutants for control and/or regulation, (ii) the design of multi-action optimized therapeutics from which the potential for unwanted side-effects have been engineered out, (iii) the development of SAR-based computer-driven screening procedure to identify candidate therapeutics based upon combinatorial chemistry or compilations of molecular structures, (iv) the generation of toxicological profiles to be used in the selection of benign chemicals in the early stages of product development.

Development, characterization and application of predictive-toxicology models.

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover, the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.

Diversity analysis of 14 156 molecules tested by the National Cancer Institute for anti-HIV activity using the quantitative structure-activity relational expert system MCASE.

Using the MCASE program, a procedure to analyze the diversity of the large amount of available HIV-1 antiviral data was proposed. A subset of 1 819 chemicals was logically selected from the original 14 156 chemicals tested by NCI. This subset of chemicals was shown to contain most of the structural and the functional information of the original database. A full analysis of the 1 819 chemicals by the MCASE program produced a correlation between chemical structures and HIV antiviral activity. In our model, 74 fragments were identified as being responsible for all the chemical's HIV antiviral activity. These fragments may be related to different inhibiting mechanisms, some known and some probably still unknown. The expert system resulting from this analysis can be used to predict the activity of new chemicals and to design new agents that can target multiple enzymes. This was shown to be the case by using the model to predict the activity of 10 diverse chemicals whose activities were not known at the time of model development. Of these, 8 were predicted in agreement with experimental observations. As far as we can tell, this is probably the first project ever to attempt to create a quantitative model of activity for such a massive database of diverse chemicals.

Evaluation and application of models for the prediction of ready biodegradability in the MITI-I test.

Three existing models and one newly developed model for the prediction of ready biodegradability of organic compounds are evaluated by comparing the descriptors they use, and the consistency of the models when applied to the set of High Production Volume Chemicals (HPVC) in the European Union. Linear regression models developed for the OECD showed the best performance in the external validation (84.7% correct), although comparison with the other three models is flawed because of the class specificity of these models. With these models 567 of the 894 compounds could be predicted in the validation. The multivariate statistical model showed the best performance in the external validation (82.7% correct) combined with the broadest applicability of the model. The evaluation of the predictions of the models for the HPVC shows that all models are highly consistent in their prediction of not-ready biodegradability, but much less consistency is seen in the prediction of ready biodegradability. This complies with the observation that all 4 models show better performance in their predictions of not-ready biodegradability.

Thalidomide and metabolites: indications of the absence of 'genotoxic' carcinogenic potentials.

Because of the reintroduction into human therapeutics of thalidomide, a recognized developmental toxicant in humans, there has been concern about its potential for inducing other health effects as well. The present study is concerned with the possible mutagenicity and carcinogenicity of this chemical. Using the expert system, META, a series of putative metabolites of thalidomide was generated. In addition to the known or hypothesized metabolites of thalidomide (N=12), a number of additional putative metabolites (N=131) were identified by META. The structures of these chemicals were subjected to structure-activity analyses using predictive CASE/MULTICASE models of developmental toxicity, rodent carcinogenicity and mutagenicity in Salmonella. While thalidomide and some of its putative metabolites were predicted to be developmental toxicants, none of them were predicted to be rodent carcinogens. Putative metabolites containing the hydroxamic acid or hydroxylamine moieties were predicted to be mutagens. None of the 'known' metabolites of thalidomide contained these reactive moieties. Whether such intermediates are indeed generated or whether they are generated and are either unstable in the presence of oxygen or react rapidly with nucleophiles is unknown.

Relationship between allergic contact dermatitis and electrophilicity.

To evaluate the role of electrophilicity in the induction of allergic contact dermatitis (ACD) in humans, we compared the structure-activity relationship (SAR) model of ACD with those of electrophilic and nonelectrophilic subsets of chemicals in the ACD database. For these analyses, electrophilicity was defined as the potential of a chemical to induce mutations in Salmonella. It was found that electrophilicity accounted for approximately 30-40% of ACD-inducing ability, and the remainder was associated with nonelectrophilic structures. The identification of these moieties opens the possibility for studying their role in ACD.

SOS chromotest and mutagenicity in Salmonella: evidence for mechanistic differences.

An examination of the relationship of the experimental results obtained with chemicals tested in the SOS chromotest and for mutagenicity in Salmonella indicates that the two assays respond to different genotoxic stimuli. Furthermore, the relationship between results obtained in these assays and in rodents carcinogenicity bioassays suggests that the short-term assays respond to a different spectrum of carcinogens. The same conclusions were reached based upon an analysis of the structural features associated with these three phenomena. With respect to using these short-term assays to predict carcinogens, the present results suggest that they are not equivalent, but complement one another.

Chemical diversity approach for evaluating mechanistic relatedness among toxicological phenomena.

The CASE/MULTICASE structure-activity relationship (SAR) system was used to assess a new procedure to investigate the mechanistic relatedness of various toxicological endpoints. The method consisted of predicting the activity of 10,000 randomly selected chemicals using validated and characterized SAR models from a variety of biological and toxicological endpoints. The prevalence of chemicals predicted to possess the ability to induce two or more toxicological effects simultaneously should provide a measure of the mechanistic relatedness of these phenomena. Eight toxicological endpoints were predicted and the results were compared to predictions based on an eye irritation SAR model. Allergic contact dermatitis demonstrated a 29.6% greater than expected overlap between expected and observed results (p < 0.001). Similar results were seen for respiratory hypersensitivity (33.1%), sensory irritation (28.9%), cell toxicity (25.9%), and Ah receptor binding (19.8%). A lesser degree of overlap was seen between eye irritation and Salmonella mutagenicity (11.5%) and the inhibition of gap junction intercellular communication (6.7%). Moreover, a negative overlap, suggesting possibly an antagonistic phenomena, was observed between eye irritation and alpha 2 mu-induced nephropathy. These results indicate that this method can provide a useful tool to investigate mechanistic relatedness between diverse toxicological endpoints.

Human developmental toxicity and mutagenesis.

A previously described SAR model of human developmental toxicity was analyzed further. The model shows a number of mechanistic similarities with SAR models of other toxicological phenomena (systemic toxicity, chromosomal and genomic effects). This implies that there are many targets associated with developmental effects. Surprisingly the analyses revealed no significant mechanistic overlap between developmental toxicity in humans and mutagenicity in Salmonella, a surrogate for the occurrence of point mutations. Our study indicates that this lack of similarity is likely the result of the pre-screening strategies which largely eliminate Salmonella mutagens from among the therapeutics introduced into human medicine.

Identification of structural features and associated mechanisms of action for carcinogens in rats.

A set of chemicals tested for carcinogenicity in rats that have been analyzed in the Carcinogenic Potency Database (CPDB) was subjected to CASE/MULTICASE (a computer-automated structure evaluation system) structure-activity relationship (SAR) analyses. This SAR system identifies structural features of chemicals in a learning set that are associated with a predefined activity and produces an SAR model based on these characteristics. The rat CPDB used in this study consisted of 745 chemicals, 383 of which are carcinogens, 14 marginally active carcinogens (i.e., chemicals that require a relatively high dose to induce carcinogenesis) and 348 are non-carcinogens. In an internal prediction analysis where CASE/MULTICASE 'predicted' the activity of chemicals in the learning set, the system was able to achieve a concordance between experimental and predicted results of 95%. This indicates that the program is able to adequately assess the chemicals in the database. In a 10-fold cross-validation study where 10 disjoint sets of 10% of the chemicals were removed from the database and the remaining 90% of the chemicals were used as a learning set, CASE/MULTICASE was able to achieve a concordance between experimental and predicted results of 64%. Using a modified validation process designed to investigate the predictivity of a more focused SAR model, the system was able to achieve a concordance of 71% between experimental and predicted results. Among the major biophores identified by CASE/MULTICASE as associated with cancer causation in rats, several are derived from electrophilic or potentially electrophilic compounds (e.g., aromatic amines, nitrogen mustards, isocyanates, epoxides). Other biophores however are derived from chemicals seemingly devoid of actual or potential DNA-reactivity and as such may represent structural features of non-genotoxic carcinogens.

Studies on the potential for genotoxic carcinogenicity of fragrances and other chemicals.

The potential of fragrances, physiological chemicals, natural products and a group of randomly selected chemicals to induce cancers by a genotoxic mechanism (i.e. "genotoxic" carcinogenesis) was compared using structure-activity relationships (SAR) models. Fragrances are significantly less likely to induce genotoxic carcinogenicity than randomly selected chemicals or natural products. With respect to the latter potential, fragrances were indistinguishable from normal mammalian physiological constituents.