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 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.     

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.     

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.     

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.     

Structural basis of the genotoxicity of nitrosatable phenols and derivatives present in smoked food products

The CASE methodology for studying structure-activity relationships has been applied to investigating the basis of the genotoxicity of phenols and derivatives following exposure to nitrous acid. The structural features identified include availability of positions para or ortho to the hydroxyl groups, that one meta position must remain unoccupied and one ortho or para position must be unsubstituted as well. The analyses revealed that genotoxicity is dependent upon the ease of formation of the active phenyldiazonium intermediate and is influenced only secondarily by the nature of the genotoxicant or its ease of entry into the cell. With this data base, CASE predicts the genotoxicity, following nitrosation, of a number of agents, including serotonin, acetaminophen, and of some naturally-occurring pesticides present in edible plants.     

Testing by artificial intelligence: computational alternatives to the determination of mutagenicity.

In order to develop methods for evaluating the predictive performance of computer-driven structure-activity methods (SAR) as well as to determine the limits of predictivity, we investigated the behavior of two Salmonella mutagenicity data bases: (a) a subset from the Genetox Program and (b) one from the U.S. National Toxicology Program (NTP). For molecules common to the two data bases, the experimental concordance was 76% when "marginals" were included and 81% when they were excluded. Three SAR methods were evaluated: CASE, MULTICASE and CASE/Graph Indices (CASE/GI). The programs "learned" the Genetox data base and used it to predict NTP molecules that were not present in the Genetox compilation. The concordances were 72, 80 and 47% respectively. Obviously, the MULTICASE version is superior and approaches the 85% interlaboratory variability observed for the Salmonella mutagenicity assays when the latter was carried out under carefully controlled conditions.     

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.     

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).     

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.     

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.     

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 2μ-nephrotoxicity and maximum tolerated dose of a population of molecules (N1300). 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 predited properties of these populations were analyzed and the ovelap among tests was determined. The false-positive and false-negative predictions.     

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.     

CASE, the computer-automated structure evaluation method, correctly predicts the low mutagenicity for Salmonella of nitrated cyclopenta-fused polycyclic aromatic hydrocarbons

Recently Goldring et al. [Mutation Res., 187 (1987) 67-77] reported the synthesis and purification of a series of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons. On the basis of expected charge distributions, these chemicals were predicted to be potent mutagens and, yet, contrary to expectation, they were found to be only weakly mutagenic for Salmonella. In their discussion, the authors suggest that application of CASE, an artificial intelligence system recently developed in these laboratories, would also not predict the low mutagenicity of this group of chemicals. In the present report, it is shown that CASE, in fact, correctly predicts the low mutagenicity of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons.     

Mutagenic exposure in the rubber manufacturing industry: an industry wide survey

Mutagenic exposure conditions in several rubber manufacturing companies (n=9) in The Netherlands were studied. Mutagenicity of total suspended particulate matter in air (TSPM) and of wipe samples from possible contact surfaces were measured in the Ames mutagenicity assay with Salmonella typhimurium YG1041 in the presence of a metabolic activation system. Large differences in median mutagenicity of TSPM samples were observed between companies (range 49-1056rev/m(3)) and to a lesser extent between production functions (range 129-402rev/m(3)). The production function curing revealed overall the highest TSPM mutagenicity levels. Forty-one percent of the surface wipe samples revealed mutagenic activity ranging from 26 to 665rev/cm(2). Mixing had the largest proportion of positive samples resulting in a median surface mutagenic contamination of 39rev/cm(2). Surface mutagenic contamination, averaged per department/company combination, showed only a weak correlation with TSPM mutagenicity (r=0.28, P=0.05). Company, production function and total soluble matter (e.g. mass collected upon extraction with organic solvents with different polarity) explained 79 and 81% of the variability in mutagenicity of TSPM and surface contamination levels, respectively. "Company" was identified as the most important exposure determinant for mutagenic activity in TSPM and surface wipe samples. This indicates the importance of company specific determinants like production volume and rubber chemicals used for the encountered mutagenic exposure conditions. Detection of substantial mutagenic activity on possible contact surfaces supports furthermore the potential importance of the dermal route in the uptake of genotoxic compounds of workers in the rubber manufacturing industry.     

Structural basis of the mutagenicity of phenylazoaniline dyes

The CASE (Computer-Automated Structure Evaluation) methodology was used to gain an understanding of the basis of mutagenicity of phenylazoanilines. It was found that the activity of these molecules is dependent upon an intact moiety that spans the azo linkage, i.e., the azo bond must remain intact for mutagenicity. The study also addressed the effect of sulfonation on the activity of these azo dyes. It was revealed that sulfonation at only certain sites resulted in loss of mutagenicity. Sulfonation of the structural moiety responsible for the activity of phenylazoaniline dyes did not necessarily result in complete elimination of activity as this substitution could generate new structural moieties which contribute to the activity of the molecules.     

Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet?

There is a great deal of current interest in the use of commercial, automated programs for the prediction of mutagenicity and carcinogenicity based on chemical structure. However, the goal of accurate and reliable toxicity prediction for any chemical, based solely on structural information remains elusive. The toxicity prediction challenge is global in its objective, but limited in its solution, to within local domains of chemicals acting according to similar mechanisms of action in the biological system; to predict, we must be able to generalize based on chemical structure, but the biology fundamentally limits our ability to do so. Available commercial systems for mutagenicity and/or carcinogenicity prediction differ in their specifics, yet most fall in two major categories: (1) automated approaches that rely on the use of statistics for extracting correlations between structure and activity; and (2) knowledge-based expert systems that rely on a set of programmed rules distilled from available knowledge and human expert judgement. These two categories of approaches differ in the ways that they represent, process, and generalize chemical-biological activity information. An application of four commercial systems (TOPKAT, CASE/MULTI-CASE, DEREK, and OncoLogic) to mutagenicity and carcinogenicity prediction for a particular class of chemicals—the haloacetic acids (HAs)—is presented to highlight these differences. Some discussion is devoted to the issue of gauging the relative performance of commercial prediction systems, as well as to the role of prospective prediction exercises in this effort. And finally, an alternative approach that stops short of delivering a prediction to a user, involving structure-searching and data base exploration, is briefly considered.     

Study of the causes of direct-acting mutagenicity in coffee and tea using the Ara test in Salmonella typhimurium

The mutagenic activities of 6 of the chemicals identified in coffee solutions were assayed with the Salmonella Ara test, under experimental conditions optimized for coffee mutagenicity. Caffeine was the only non-mutagenic compound. Among the other 5 chemicals, hydrogen peroxide was the strongest mutagen and chlorogenic acid the weakest; methylglyoxal, glyoxal and caffeic acid exhibited intermediate mutagenicities. The minimal mutagenic doses of these components correlated negatively with their relative concentrations in coffee. It was concluded that chlorogenic acid, caffeic acid, glyoxal and methylglyoxal cannot contribute alone to the mutagenicity of coffee in the Ara test, since their minimal mutagenic concentrations were much higher than their respective levels in the coffee samples assayed. By contrast, 40-60% of the mutagenic activity in coffee and also in tea could be attributed to their H2O2 contents. Catalase abolished more than 95% of the mutagenic activity of coffee, as detected by the Ara test. A similar sensitivity to catalase has been reported by other authors in relation to the coffee mutagenicity identified by the Salmonella His test. Nevertheless, the results presented in this paper suggest that the Ara forward and the His reverse mutation tests are sensitive to the mutagenicity of different constituents in coffee solutions. We propose that the His test, sensitive at high coffee doses, mainly recognizes the mutagenicity of methylglyoxal, whilst the Ara test, sensitive at low coffee doses, mainly detects the mutagenic activity of hydrogen peroxide. The data reported also suggest that the direct-acting mutagenicity(ies) detected by the Ara test in tea solutions is (are) based on similar, if not identical, mechanisms.     

In silico assessment of chemical mutagenesis in comparison with results of Salmonella microsome assay on 909 chemicals

Genotoxicity is one of the important endpoints for risk assessment of environmental chemicals. Many short-term assays to evaluate genotoxicity have been developed and some of them are being used routinely. Although these assays can generally be completed within a short period, their throughput is not sufficient to assess the huge number of chemicals, which exist in our living environment without information on their safety. We have evaluated three commercially available in silico systems, i.e., DEREK, MultiCASE, and ADMEWorks, to assess chemical genotoxicity. We applied these systems to the 703 chemicals that had been evaluated by the Salmonella/microsome assay from CGX database published by Kirkland et al. We also applied these systems to the 206 existing chemicals in Japan that were recently evaluated using the Salmonella/microsome assay under GLP compliance (ECJ database). Sensitivity (the proportion of the positive in Salmonella/microsome assay correctly identified by the in silico system), specificity (the proportion of the negative in Salmonella/microsome assay correctly identified) and concordance (the proportion of correct identifications of the positive and the negative in Salmonella/microsome assay) were increased when we combined the three in silico systems to make a final decision in mutagenicity, and accordingly we concluded that in silico evaluation could be optimized by combining the evaluations from different systems. We also investigated whether there was any correlation between the Salmonella/microsome assay result and the molecular weight of the chemicals: high molecular weight (>3000) chemicals tended to give negative results. We propose a decision tree to assess chemical genotoxicity using a combination of the three in silico systems after pre-selection according to their molecular weight.     

Artificial intelligence and Bayesian decision theory in the prediction of chemical carcinogens

Two procedures for predicting the carcinogenicity of chemicals are described. One of these (CASE) is a self-learning artificial intelligence system that automatically recognizes activating and/or deactivating structural subunits of candidate chemicals and uses this to determine the probability that the test chemical is or is not a carcinogen. If the chemical is predicted to be carcinogen, CASE also projects its probable potency.

The second procedure (CPBS) uses Bayesian decision theory to predict the potential carcinogenicity of chemicals based upon the results of batteries of short-term assays. CPBS is useful even if the test results are mixed (i.e. both positive and negative responses are obtained in different genotoxic assays). CPBS can also be used to identify highly predictive as well as cost-effective batteries of assays.

For illustrative purposes the ability of CASE and CPBS to predict the carcinogenicity of a carcinogenic and a non-carcinogenic polycyclic aromatic hydrocarbon is shown. The potential for using the two methods in tandem to increase reliability and decrease cost is presented.