The structural basis of the carcinogenic and mutagenic potentials of phytoalexins

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.

Environmental odors and health hazards

Using the recently developed and validated 'chemical diversity approach', the potential of chemicals, to be detected by the human olfactory system and to cause adverse health effects, was investigated. The analyses found no significant association between odor perceptibility and potential for inducing health effects.

A data mining approach for the elucidation of the action of putative etiological agents: application to the non-genotoxic carcinogenicity of genistein

A procedure designated "the virtual similarity index" (VSI) is described to determine the probability that two or more toxicants are related mechanistically. The approach is structure-activity relationship (SAR) based and generates the virtual toxicological profiles of the chemicals under investigation. It also determines the similarities between them. That commonality is compared to the frequency with which it is found among a population of 10,000 chemicals representing the "universe of chemicals". The similarities between the candidate chemicals and chemicals known to act by other recognized mechanisms are also determined. If the similarities between the candidate chemicals are significantly greater than for the non-related ones, the chemicals are assumed to act by a common mechanism. In that context, the putative non-genotoxic mechanism responsible for the carcinogenicity of genistein (GEN) and its relationship to the action of diethylstilbestrol is examined.

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.

A paradigm for determining the relevance of short-term assays: application to oxidative mutagenesis

A simple substructure-based approach was developed to determine whether a short-term assay under development is related mechanistically to the endpoint it seeks to predict. Thus, substructures associated with mutagenicity in Salmonella are also present in carcinogens and agents active in other mutagenicity and genotoxicity assays. When applied to test results obtained with an Escherichia coli strain designed to identify oxidative mutagens, there was no significant association with either carcinogens or mutagens and genotoxicants detected by other systems. There was, however, a significant association between alerts for oxidative mutagenesis and chemicals capable of inducing allergic contact dermatitis (ACD) in humans.

Allergic contact dermatitis and its relationship to carcinogenicity

The relationship between allergic contact dermatitis (ACD) and carcinogenicity was investigated using a recently developed and validated simulation approach. The analyses indicated that while there are electrophilic and non-electrophilic components to ACD, these were not identical to those operating in carcinogenicity. Accordingly, with respect to carcinogenicity prediction, the results of ACD do not improve the results based upon mutagenicity testing alone, the latter being a surrogate for potential electrophilicity.

Computational toxicology and the generation of mechanistic hypotheses: gamma-butyrolactone

In this study, we use SAR approaches in an attempt to elucidate the action of gamma-butyrolactone (GBL), an illicit drug and a dietary supplement, that can cause coma and deaths in humans while exhibiting low systemic toxicity towards rodents. The lack of systemic toxicity of GBL and of its metabolite(s) was also predicted by validated SAR models. In fact using diverse SAR models, the only significant SAR prediction was that GBL had the potential for inhibiting human cytochrome P4502D6 (CYP2D6). However, inhibition of that isozyme is not necessarily associated with toxicity. It is suggested that GBL users also abuse other substances. When GBL inhibits CYP2D6 this may prevent the CYP2D6-mediated detoxification of other toxicants simultaneously consumed by the GBL user.

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.

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.

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.

Chlorothalonil: lack of genotoxic potential

Based upon analyses using a number of validated structure-activity relationship models, it is concluded that the carcinogenicity in rodents of chlorothalonil is not due to a genotoxic mechanism.

What do animal cancer tests tell us about human cancer risk?: Overview of analyses of the carcinogenic potency database

Many important issues in carcinogenesis can be addressed using our Carcinogenic Potency Database, which analyzes and standardizes the literature of chronic carcinogenicity tests in laboratory animals. This review is an update and overview of our analyses during the past 15 years, using the current database that includes results of 5152 experiments on 1298 chemicals. We address the following: 1. More than half the 1298 chemicals tested in long-term experiments have been evaluated as carcinogens. We describe this positivity rate for several subsets of the data (including naturally occurring and synthetic chemicals), and we hypothesize and important role in the interpretation of results for increased cell division due to administration of high doses. 2. Methodological issues in the interpretation of animal cancer tests: constraints on the estimation of carcinogenic potency and validity problems associated with using the limited data from bioassays to estimate human risk, reproducibility of results in carcinogenesis bioassays, comparison of lifetable and summary methods of analysis, and summarizing carcinogenic potency when multiple experiments on a chemical are positive. 3. Positivity is compared in bioassays for two closely related species, rats and mice, tested under similar experimental conditions. We assess what information such a comparison can provide about interspecies extrapolation. 4. Rodent carcinogens induce tumors in 35 different target organs. We describe the frequency of chemicals that induce tumors in rats or mice at each target site, and we compare target sites of mutagenic and nonmutagenic rodent carcinogens. 5. A broad perspective on evaluation of possible cancer hazards from rodent carcinogens is given, by ranking 74 human exposures (natural and synthetic) on the HERP indes.

Induction of forward mutations at the thymidine kinase locus of mouse lymphoma cells: evidence for electrophilic and non-electrophilic mechanisms

A database of 209 chemicals tested for induction of forward mutations at the heterozygous thymidine kinase (TK +/-) locus in L5178Y mouse lymphoma cells was analyzed for structure-activity relationships using the MultiCASE expert system. Consistent with evidence of several contributing biological mechanisms, these studies suggest that such mutations may occur by more than one mechanism. As might be expected, there was evidence for a component involving direct electrophilic attack on the cellular DNA, in a manner previously established as causative in the induction of mutations in Salmonella. In addition, however, there was also strong evidence for another mechanism or mechanisms involving chromosome missegregation, cellular toxicity or an alternate site of action, such as the microtubules.

Intercellular communication, tumor promotion and non-genotoxic carcinogenesis: relationships based upon structural considerations

An SAR model for inhibition of metabolic cooperation (iMC) was developed. The structural and physicochemical features associated with the ability to cause iMC are primarily lipophilic moieties consistent with the possibility that they represent receptor-binding ligands. There are also significant parallels between the structural descriptors associated with iMC and those associated with tumor promotion and with carcinogenesis in rodents. Overall, the present study provides structural evidence that iMC is a feature associated with the carcinogenic process.

Computational predictive programs (expert systems) in toxicology

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

Structure-activity and mechanistic relationships: the effect of chemical overlap on structural overlap in data bases of varying size and composition

Similarities between structures in SAR models derived from different data bases, termed 'structural overlap', can be used for determining mechanistic similarities, e.g., mutagenicity vs. carcinogenicity. However, the structural overlap may be affected by the proportion of common chemicals in the data bases, termed 'chemical overlap'. In order to refine our ability to determine mechanistic similarities, we investigated the relationship between chemical and structural overlap as well as the effect of the ratio of active to inactive chemicals and data base size on that relationship, using a data base of Salmonella mutagenicity. A linear relationship between chemical overlap and structural overlap with a slope of 0.332 was found. For data bases of 210, 300 and 390 chemicals, this relationship was consistent. Differences in ratios of active to inactive chemicals, i.e., 1:2, 1:1 and 2:1, did not appear to affect the linear model. We can use this relationship to adjust for chemical overlap when examining the structural overlap of data bases developed for different endpoints to determine the extent of mechanistic similarities.

Estimation of the optimal data base size for structure-activity analyses: the Salmonella mutagenicity data base

In the present study, the effects of data base size on predictivity, informational content and structural overlap of derived Structure-Activity Relationship (SAR) models were investigated. It was found that indices of predictivity (i.e., sensitivity, specificity, and concordance between experimental and predicted results (OCP) increased with increasing size of the data base until the range is 300-400 chemicals, at which point they plateau. The greater the size of the data base, the greater the informational content of the model; however, the rate of this increase is no longer optimal when the size of the data base exceeds 400 chemicals.

Direct tubulin polymerization perturbation contributes significantly to the induction of micronuclei in vivo

The computational analysis data presented indicate a significant mechanistic association between the ability of a chemical to cause tubulin polymerization perturbation (TPP), via direct interaction with the protein, and the in vivo induction of micronuclei (MN). Since it is known that TPP is not a genotoxic event, the analyses suggest that the induction of MN by a non-genotoxic mechanism is a significant alternate pathway.

A study of the structural basis of the carcinogenicity of tamoxifen, toremifene and their metabolites

An analysis of the chemical structure of tamoxifen, toremifene and their metabolites indicates that metabolism to a DNA-reactive hydroxylamine intermediate is possible. The parent compounds and many of their metabolites are predicted to be rodent carcinogens. Moreover, many of these metabolites contain a 6 A or 8.4 A distance descriptor biphore. These geometric descriptors may be related to an ability of these chemicals to bind to an estrogen receptor. The prediction of the carcinogenicity of toremifene is not in accord with studies published thus far. However, the reports available have not excluded this possibility, since the protocols used have not addressed it systematically.

Computer prediction of possible toxic action from chemical structure: an update on the DEREK system

Computer-based assessment of potential toxicity has become increasingly popular in recent years. The knowledge-base system DEREK is developed under the guidance of a multinational Collaborative Group of expert toxicologists and provides a qualitative approach to toxicity prediction. Major developments of the DEREK program and knowledge-base have taken place in the last 3 years. Program developments include improvements in both the user interface and data processing. Work on the knowledge-base has concentrated on the areas of genotoxicity and skin sensitisation. DEREK's predictive capabilities for these toxicological end-points has been demonstrated. In addition to the continued expansion of the knowledge-base, a number of enhancements are planned in the DEREK program. In particular, work is in progress to develop further DEREK's ability to report the reasoning behind its predictions.

Structural determinants of developmental toxicity

Developmental toxicity, an area of public concern, suffers from the lack of accessible, reliable, peer-reviewed compilations of data and substantial gaps in testing. These deficits frequently make it necessary for regulatory agencies to use other toxicological end points to regulate developmental toxicants. We have utilized a database of chemicals identified as developmental toxicants in rats, mice, rabbits, and humans and an expert system which learns the association between molecular structure and biological response (Computer Automated Structure Evaluation; CASE) to explore structure-activity relationships in developmental toxicity. Developmental toxicity was defined as death, growth retardation, or structural or functional malformations. In analyzing the data CASE selects its own molecular descriptors from a learning set of active and inactive molecules. Using randomly constructed learner and tester sets, the concordance of the predictions with the actual data was between 77 and 82%. CASE identified 13 major structural fragments associated with developmental toxicity in mice, 15 in rats, 9 in rabbits, and 7 in humans. These analyses indicate that there is indeed a structural basis for developmental toxicity which may be used to predict the developmental hazard of untested or inadequately tested chemicals.

Structural relationships between mutagenicity, maximum tolerated dose, and carcinogenicity in rodents

The CASE structure-activity relational system was applied to a study of the structural bases of toxicity as expressed in the maximum tolerated dose (MTD) of a group of chemicals for which rodent carcinogenicity and mutagenicity data were also available. All of the results were obtained under the aegis of the U.S. National Toxicology Program. The analyses revealed that there was a structural basis for the MTD in mice and in rats and that these overlapped considerably. There was also some overlap between structural determinants of the MTD and of carcinogenicity in rodents but there was also a significant "antagonism" between such fragments; i.e., fragments associated with high toxicity (low MTD) were associated with lack of carcinogenicity and vice versa. The highest overlaps observed were between the structural determinant for a low MTD (i.e., high toxicity) and mutagenicity in Salmonella.

1,4-Dioxane: prediction of in vivo clastogenicity

1,4-Dioxane was analyzed with the CASE program to determine the structural basis of its potential genotoxicity and carcinogenicity. These investigations led to the prediction that while 1,4-dioxane was not genotoxic in vitro, it was an inducer of micronuclei in the bone marrow of rats and a carcinogen for both rats and mice. If it is assumed that the induction of micronuclei is the result of DNA damage, then this potential and the previous report of the in vivo induction of DNA strand breaks in rat liver raise the possibility of a genotoxic action for 1,4-dioxane. However it is also conceivable that we have identified a structural feature which contributes to the induction of micronuclei by a non-genotoxic mechanism.

Structural basis of the in vivo induction of micronuclei

The structural basis of the in vivo induction of micronuclei was examined with CASE, a structure-activity relational method. The CASE program identified a number of structures associated with this activity. When used to predict the activity of chemicals not included in the learning set, these structural determinants gave a concordance in excess of 83%. The existence of a structural basis for the induction of micronuclei will permit an investigation of the mechanistic basis of this phenomenon.

Decreased electrophilicity of chemicals carcinogenic only at the maximum tolerated dose

While there was no significant difference between the actual or predicted mutagenicity and clastogenicity of a group of chemicals carcinogenic only at the maximum tolerated dose (MTD) and a group of chemicals carcinogenic below the MTD, as a group, the chemicals carcinogenic below the MTD exhibited a significantly decreased LUMO (Lowest Unoccupied Molecular Orbital) energy, indicative of increased electrophilicity (i.e. DNA reactivity). These findings suggest that chemicals carcinogenic only at the MTD either require increased doses of "weak" electrophiles to be carcinogenic or that they may act by a "non-genotoxic" mechanism.

Quantification of the predictivity of some short-term assays for carcinogenicity in rodents

A statistical procedure is described for assessing the predictive performance of short-term tests for carcinogenicity in which the actual number of chemicals tested is taken into consideration. The method is then applied to several widely used short-term assays.

Implications of newly recognized relationships between mutagenicity, genotoxicity and carcinogenicity of molecules

The CASE structure-activity relational method was used to predict the mutagenicity, cytogenotoxicity, carcinogenicity, sensory irritation, male rat-specific alpha 2 mu-nephrotoxicity and maximum tolerated dose of a population of molecules (N greater than or equal to 1300). These chemicals were then sorted out by their predicted responses to specific tests and sub-populations of molecules with different prevalence with respect to described endpoints were constructed, i.e. 0-100% prevalences of mutagens, rodent carcinogens and SCE inducers. The predicted properties of these populations were analyzed and the overlap among tests was determined. The method also permits the determination of the dependence among assays and the level of false-positive and false-negative predictions.

Methapyrilene: DNA as a possible target

The structural features contributing to the potential carcinogenicity, DNA-reactivity and genotoxicity of methapyrilene and its non-carcinogenic congener pyrilamine were examined. The analyses suggest that the former has the potential for DNA-reactivity, a property which is absent from the latter.

Use of a composite polyfunctional model electrophile as a probe to analyze the performance of an artificial intelligence structure-activity method

The CASE structure-activity relational method was applied to the model polyfunctional electrophile proposed by Ashby and associates. The predicted activities from data bases of 'structural alerts', mutagenicity in Salmonella and rodent carcinogenicity were compared. It was thus found that the predictive efficacy of CASE was increased when it employed a combination of human and artificial intelligence, as exemplified by the CASE analysis of 'structural alerts.

The structural basis of the carcinogenic and mutagenic potentials of phytoalexins

CASE, a structure-activity relational system, was used to predict the proportion of substances to be carcinogenic and mutagenic among plant pesticides (phytoalexins) and other natural products compared to that of randomly selected chemicals. There were no significant differences between phytoalexins and other natural products. On the other hand, the natural products, as a group, were predicted to be less mutagenic and carcinogenic than randomly selected chemicals. 37% of natural products are predicted to be rodent carcinogens.

Novel structural concepts in elucidating the potential genotoxicity and carcinogenicity of tetrandrine, a traditional herbal drug

Tetrandrine, a widely used remedy, is predicted to be a 'genotoxic' carcinogen. This finding suggests that the usage of this substance in non-life-threatening situations should be evaluated.

The carcinogenic potential of cocaine

Analysis of cocaine by CASE, an expert system, results in the prediction that cocaine is a rodent carcinogen. In view of the widespread exposure to cocaine this is cause for alarm, especially as in utero exposure has been widely documented and the developing human fetus is at an increased risk of transplacental cancer induction.

The structural basis of the mutagenicity of chemicals in Salmonella typhimurium: the Gene-Tox data base

The CASE structure-activity methodology has been applied to a Gene-Tox derived Salmonella mutagenicity data base consisting of 808 chemicals. Based upon qualitative structural features, CASE identified 29 activating and 3 inactivating structural determinants which correctly predicted the probability of carcinogenicity of 93.7% of the known mutagens and non-mutagens in the data base (sensitivity = 0.998, and specificity = 0.704). Additionally, based upon a qualitative structure-activity analysis, CASE's performance was even better, leading to a sensitivity of 0.981 and a specificity of 1.000. Using the structural determinants identified in this data base, CASE gave excellent predictions of the mutagenicity of chemicals not included in the data base. The identified biophores and biophobes can also be used to investigate the structural basis of the mutagenicity of various chemical classes.