The structural basis of the mutagenicity of chemicals in Salmonella typhimurium: the National Toxicology Program Data Base

A portion of the U.S. National Toxicology Program (NTP) Salmonella typhimurium mutagenicity data base was analyzed by CASE, an artificial intelligence SAR system. CASE identified 13 structural determinants which, with a high probability (p less than or equal to 0.05) predicted the likelihood of mutagenicity of the 243 chemicals in the data base (sensitivity = 0.989; specificity = 0.950) as well as of chemicals not included in the data base. CASE also identified an additional set of structures which were highly predictive of mutagenic potency (sensitivity = 0.949; specificity = 1.00). Even though there is little overlap among the chemicals included in the NTP and Gene-Tox Salmonella data bases, CASE found significant similarities between the structural determinants of the mutagenicity in the two data bases, thereby validating the analyses and indicating a commonality in the structural basis of mutagenicity.     

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.     

Predicting chemical carcinogenesis in rodents: The state of the art in light of a comparative exercise

Within a recent comparative exercise, different approaches to the prediction of rodent carcinogenicity were challenged on a common set of chemicals bioassayed by the U.S. National Toxicology Program. The approaches were of very different natures. Some prediction systems looked for relationships between carcinogenicity and other, more quickly detectable biological events (activity-activity relationships, AAR). Some approaches tended to find structure-activity relationships (SAR). To give an objective evaluation of the results of the exercise, we have analyzed the rodent results and the predictions with the multivariate data analysis methods. The calculated performances varied according to the adopted carcinogenicity classification of the chemicals. When the four rodent results were summarized into a final + or − call, the Tennant approach (AAR method) showed the best performance (about 75% accuracy), whereas the best SAR systems had 60–65% accuracy. A common limitation of almost all the systems was the lack of specificity (too many false positives). Based on these results, better concordance was obtained when the input information was the very costly (and closer to the final endpoint) biological data, rather than the inexpensive (and farther from the endpoint) knowledge of the chemical structure. However, when the rodent results were summarized into a carcinogenicity classification that maintained, to some extent, the gradation intrinsic to the original experimental data, the performance of the AAR systems declined, and the SAR approaches showed a better performance. The difficulty in evaluating the various approaches was further complicated because of a fundamental difference in the approaches themselves: some approaches were ‘pure’ prediction methods (i.e. their predictions were rigorously based on information not inclusive of carcinogenicity); other approaches (e.g. Tennant, Weisburger) used ‘mixed’ information, inclusive of known carcinogenicity results from experiments performed before the NTP bioassays. As far as the SAR systems are concerned, their sets of predictions showed a fundamental similarity. This happened in spite of the extremely different procedures adopted to treat the chemical formula (initial information): very simple calculations (Benigni), intuition of the experts (Weisburger and Lijinsky), sophisticated computer programs (TOPKAT and CASE). The results of the Bakale Ke method, based on the experimental measurement of the chemical electrophilicity, and of the Salmonella typhimurium mutagenicity assay were similar to the patterns of predictions of the SAR methods.     

SAR modelling of complex phenomena: probing methodological limitations

The increased acceptance of the use of structure-activity relationship (SAR) approaches to toxicity modelling has necessitated an evaluation of the limitations of the methodology. In this study, the limit of the capacity of the MULTICASE SAR program to model complex biological and toxicological phenomena was assessed. It was estimated that, provided the data set consists of at least 300 chemicals, divided equally between active and inactive compounds, the program is capable of handling phenomena that are even more "complex" than those modelled up to now (for example, allergic contact dermatitis, Salmonella mutagenicity, biodegradability, inhibition of tubulin polymerisation). However, within the data sets currently used to generate SAR models, there are limits to the complexity that can be handled. This may be the situation with regard to the modelling of systemic toxicity (for example, the LD50).     

Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 42 chemicals tested for carcinogenicity by the U.S. National Toxicology Program

This paper is an extension and update of an earlier review published in this journal (Ashby and Tennant, 1988). A summary of the rodent carcinogenicity bioassay data on a further 42 chemicals tested by the U.S. National Toxicology Program (NTP) is presented. An evaluation of each chemical for structural alerts to DNA-reactivity is also provided, together with a summary of its mutagenicity to Salmonella. The 42 chemicals were numbered and evaluated as an extension of the earlier analysis of 222 NTP chemicals. The activity patterns and conclusions derived from the earlier study remain unchanged for the larger group of 264 chemicals. Based on the extended database of 264 NTP chemicals, the sensitivity of the Salmonella assay for rodent carcinogens is 58% and the specificity for the non-carcinogens is 73%. A total of 32 chemicals were defined as equivocal for carcinogenicity and, of these, 11 (34%) are mutagenic to Salmonella. An evaluation is made of instances where predictions of carcinogenicity, based on structural alerts, disagree with the Salmonella mutagenicity result (12% of the database). The majority of the disagreements are for structural alerts on non-mutagens, and that places these alerts as a sensitive primary screen with a specificity lower than that of the Salmonella assay. That analysis indicates some need for assays complementary to the Salmonella test when screening for potential genotoxic carcinogens. It also reveals that the correlation between structural alerts and mutagenicity to Salmonella is probably greater than 90%. Chemicals predicted to show Michael-type alkylating activity (i.e., CH2 = CHX; where X = an electron-withdrawing group, e.g. acrylamide) have been confirmed as a structural alert, and the halomethanes (624 are possible) have been classified as structurally-alerting. To this end an extended carcinogen-alert model structure is presented. Among the 138 NTP carcinogens now reviewed, 45 (33%) are non-mutagenic to Salmonella and possess a chemical structure that does not alert to DNA-reactivity. These carcinogens therefore either illustrate the need for complementary genetic screening tests to the Salmonella assay, or they represent the group of non-genotoxic carcinogens referred to most specifically by Weisburger and Williams (1981); the latter concept is favoured.     

What indication is common to different genotoxicity data bases?

This paper studies the relationships among 4 in vitro assays: Salmonella mutation (STY), mouse lymphoma L5178Y cell mutation (MLY), chromosomal aberrations in CHO cells (CHA), and sister-chromatid exchanges in CHO cells (SCE), in 3 different data bases: U.S. National Toxicology Program (NTP), International Program for the Evaluation of Short-Term Tests for Carcinogens (IPESTTC), and International Program on Chemical Safety (IPCS). The analysis is performed by modeling each data base with factor analysis. With this tool, it has been possible to separate the different elements (or components) which play a role in each data base. It has also been possible to demonstrate that--together with some specificities of the data bases--there is a common effect which is independent of the data bases, and which typically represents the 'true' relationships among the assays. This element explains 69% of the information contained in NTP, 50% of that of IPESTTC, and 30% of that of IPCS. This common evidence indicates that the responses of STY and CHA to the 'universe' of chemicals are relatively similar, although STY is a bacterial mutation system and CHA is a mammalian cell test for chromosomal damage. The other similarity apparent from this analysis is the one between MLY (mutation in mouse cells) and SCE (cytogenetic evidence in hamster cells). The implication of this result is 2-fold. On the one hand, it is extremely reassuring that the 3 most important comparative studies agree and show common evidence, and this can be recognized rationally. On the other hand, this evidence implies that the scientists involved in mutagenicity research must face the task of exploring and explaining such relationships.     

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.     

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.     

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.     

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.     

Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 39 chemicals tested for carcinogenicity by the U.S. National Toxicology Program

This paper is an extension of compilations published previously in this journal. (Ashby and Tennant, 1988; Ashby et al., 1989). A summary of the rodent carcinogenicity bioassay data on a further 39 chemicals tested by the U.S. National Toxicology Program (NTP) is presented. An evaluation of each chemical for structural alerts to DNA-reactivity is also provided, together with a summary of its mutagenicity to Salmonella. Chemicals with an aliphatic nitro group (-C-NO2) have been added to the composite structure of DNA-reactive sub-groups. The 39 chemicals were numbered and evaluated as an extension of the earlier analysis of 264 NTP chemicals. The activity patterns and conclusions derived from the earlier studies are followed by these 39 chemicals, albeit a detailed analysis of the total database of 301 chemicals is reserved for the succeeding paper.     

Structural requirements for the induction of the SOS repair in bacteria by nitrated polycyclic aromatic hydrocarbons and related chemicals.

The CASE (computer-automated structure evaluation) methodology was used to investigate the structural basis of the SOS-inducing activity of 56 nitrated polycyclic aromatic hydrocarbons (nitroarenes, nPAH) and the unsubstituted parent PAH molecules. Based upon the presence and/or absence of structural features, CASE identified 5 activating (biophores) and 4 inactivating (biophobes) fragments responsible for the SOS-inducing activity. Based upon these fragments, CASE correctly calculated the genotoxicity of 94.6% of the molecules in the training set (sensitivity = 0.85, specificity = 1.0). Disregarding the questionable experimental results of the unexpected very weak direct-acting activity of the unsubstituted benzo[a]pyrene, dibenzo[a,h]anthracene and 7,12-dimethylbenz[a]anthracene, the concordance of the prediction was 100%, i.e., sensitivity = 1.0, specificity = 1.0. Additionally, the quantitative analysis of the SOS-inducing potency showed a good correlation between the experimental and predicted results. The present analyses indicate an identity in the structural determinants responsible for SOS induction in E. coli PQ37 (SOS chromotest) and mutagenicity in Salmonella typhimurium.     

Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP.

An analysis is presented in which are evaluated correlations among chemical structure, mutagenicity to Salmonella, and carcinogenicity to rats and mice among 301 chemicals tested by the U.S. NTP. Overall, there was a high correlation between structural alerts to DNA reactivity and mutagenicity, but the correlation of either property with carcinogenicity was low. If rodent carcinogenicity is regarded as a singular property of chemicals, then neither structural alerts nor mutagenicity to Salmonella are effective in its prediction. Given this, the database was fragmented and new correlations sought between the derived sub-groups. First, the 301 chemicals were segregated into six broad chemical groupings. Second, the rodent cancer data were partially segregated by target tissue. Using the previously assigned structural alerts to DNA reactivity (electrophilicity), the chemicals were split into 154 alerting chemicals and 147 non-alerting chemicals. The alerting chemicals were split into three chemical groups; aromatic amino/nitro-types, alkylating agents and miscellaneous structurally-alerting groups. The non-alerting chemicals were subjectively split into three broad categories; non-alerting, non-alerting containing a non-reactive halogen group, and non-alerting chemical with minor concerns about a possible structural alert. The tumor data for all 301 chemicals are re-presented according to these six chemical groupings. The most significant findings to emerge from comparisons among these six groups of chemicals were as follows: (a) Most of the rodent carcinogens, including most of the 2-species and/or multiple site carcinogens, were among the structurally alerting chemicals. (b) Most of the structurally alerting chemicals were mutagenic; 84% of the carcinogens and 66% of the non-carcinogens. 100% of the 33 aromatic amino/nitro-type 2-species carcinogens were mutagenic. Thus, for structurally alerting chemicals, the Salmonella assay showed high sensitivity and low specificity (0.84 and 0.33, respectively). (c) Among the 147 non-alerting chemicals less than 5% were mutagenic, whether they were carcinogens or non-carcinogens (sensitivity 0.04).     

Structural basis of carcinogenicity in rodents of genotoxicants and non-genotoxicants

A set of 189 chemicals tested in the National Toxicology Program Cancer Bioassay was subjected to analysis by CASE, the Computer-Automated Structure Evaluation system. In the data set, 63% of the chemicals were carcinogens, approx. 40% of the carcinogens were non-genotoxic, i.e., they possessed neither "structural alerts" for DNA reactivity as defined by Ashby and Tennant, 1988, nor were they mutagenic for Salmonella. The data base can be characterized as a "combined rodent" compilation as chemicals were characterized as "carcinogenic" if they were carcinogenic in either rats or mice or both. CASE identified 23 fragments which accounted for the carcinogenicity, or lack thereof, of most of the chemicals. The sensitivity and specificity were unexpectedly high: 1.00 and 0.86, respectively. Based upon the identified biophores and biophobes, CASE performed exceedingly well in predicting the activity of chemicals not included among the 189 in the original set. CASE predicted correctly the carcinogenicity of non-genotoxic carcinogens thereby suggesting a structural commonality in the action of this group of carcinogens. As a matter of fact biophores restricted to non-genotoxic carcinogens were identified as were "non-electrophilic" biophores shared by genotoxic and non-genotoxic carcinogens. The findings suggest that the CASE program may help in the elucidation of the basis of the action of non-genotoxic carcinogens.     

The second National Toxicology Program comparative exercise on the prediction of rodent carcinogenicity: definitive results

Chemical carcinogenicity has been the target of a large array of attempts to create alternative predictive models, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models. Among the theoretical models, the application of the science of structure-activity relationships (SAR) has earned special prominence. A crucial element is the independent evaluation of the predictive ability. In the past decade, there have been two fundamental comparative exercises on the prediction of chemical carcinogenicity, held under the aegis to the US National Toxicology Program (NTP). In both exercises, the predictions were published before the animal data were known, thus using a most stringent criterion of predictivity. We analyzed the results of the first comparative exercise in a previous paper [Mutat. Res. 387 (1997) 35]; here, we present the complete results of the second exercise, and we analyze and compare the prediction sets. The range of accuracy values was quite large: the systems that performed best in this prediction exercise were in the range 60-65% accuracy. They included various human experts approaches (e.g. Oncologic) and biologically based approaches (e.g. the experimental transformation assay in Syrian hamster embryo (SHE) cells). The main difficulty for the structure-activity relationship-based approaches was the discrimination between real carcinogens, and non-carcinogens containing structural alerts (SA) for genotoxic carcinogenicity. It is shown that the use of quantitative structure-activity relationship models, when possible, can contribute to overcome the above problem. Overall, given the uncertainty linked to the predictions, the predictions for the individual chemicals cannot be taken at face value; however, the general level of knowledge available today (especially for genotoxic carcinogens) allows qualified human experts to operate a very efficient priority setting of large sets of chemicals.     

Simultaneous evaluation of genotoxicity data from different sources: a multivariate statistical approach.

A great deal of information on short-term mutagenicity assays presently exists, having been generated through individual as well as large comparative programs. The comparative programs have often examined the same tests, but with different sets of chemicals; this then gives rise to the problem of how to identify the information which is common to the different data bases, i.e., the general properties of the assays. This paper continues previous analyses of this subject, and describes a general approach by which different and heterogeneous data bases can be compared to each other. The results relative to 4 assays (Salmonella typhimurium gene mutation, mouse lymphoma L5178Y cell gene mutation, chromosomal aberrations in CHO cells, and SCEs in CHO cells) in 4 different data bases were studied. Factor analysis was used to model the different pieces of information. The analysis demonstrated a concordance between the indications of the U.S. National Toxicology Program and the International Program for the Evaluation of Short-Term Tests for Carcinogens, whereas the results of Gene-Tox and the International Program for Chemical Safety turned out to be biased, to different degrees, by their specific aims and characteristics. Moreover, the general properties--independent of the specific data bases--of the 4 assays were highlighted, and the similarities between the performances of the assays were given a quantitative measure.     

SAR modeling of genotoxic phenomena: the effect of supplementation with physiological chemicals

Structure-activity relationship (SAR) modeling of toxicological phenomena is optimal when the ratio of toxicants to non-toxicants included in the model is unity. Frequently, however, the experimental data available are enriched with toxicants, this appears to be especially true for genotoxicity data sets. It is demonstrated herein, using a Salmonella mutagenicity data set, that when there is a paucity of non-toxicants, the learning set may be augmented with physiological chemicals on the assumption that they are non-genotoxic.     

An illusion of hormesis in the Ames test: statistical significance is not equivalent to biological significance

A recent report (Calabrese et al., Mutat. Res. 726 (2011) 91-97) concluded that an analysis of Ames test mutagenicity data provides evidence of hormesis in mutagenicity dose-response relationships. An examination of the data used in this study and the conclusions regarding hormesis reveal a number of concerns regarding the analyses and possible misinterpretations of the Salmonella data. The claim of hormesis is based on test data from the National Toxicology Program using Salmonella strain TA100. Approximately half of the chemicals regarded as hormetic, and the majority of the specific dose-responses identified as hormetic, were actually nonmutagenic. We conclude that the data provide no evidence of hormetic effects. The Ames test is an excellent measure of bacterial mutagenicity, but the numbers of revertant (mutant) colonies on the plate are the result of a complex interaction between mutagenicity and toxicity, which renders the test inappropriate for demonstrating hormesis in bacterial mutagenicity experiments.     

Structural requirements for the mutagenicity of environmental nitroarenes

The nitroarenes comprise a large group of widely distributed environmental agents some of which are extraordinarily mutagenic while others are devoid of such activity. A newly developed computer program has been used to determine which structural features of these molecules might account for this broad spectrum of activities. Using Salmonella mutagenicity data for 53 nitroarenes, 2 fragments associated with activity and 2 deactivating fragments have been identified. The coexistence of an active and a deactivating fragment on the same molecule results in a nitroarene possessing marginal or no mutagenicity. The activity of 47 of 53 nitroarenes was correctly predicted by this procedure. Most of the discrepancies involved hexa- and heptacyclic nitroarenes which were predicted to be active but reported to be inactive. The ‘CASE’ program can be used to predict the mutagenicity of the many untested nitroarenes identified in the ambient environment.