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

Similarities in the Mechanisms of Antibacterial Activity (Microtox™ Assay) and Toxicity to Vertebrates

The structural determinants associated with inhibition of bacterial growth (Microtox assay), as well as those associated with other toxicological activities, were identified using the CASE/Multi-CASE structure-activity relational system. CASE/Multi-CASE also possesses the ability to compare structural determinants associated with different biological phenomena. The extent of the homology between structural determinants can be used to evaluate common mechanisms of action. Using this approach, significant commonalities between structural determinants associated with the Microtox assay and those associated with toxicity to mammals and fish were found, suggesting that there may be a mechanistic relationship between these toxicological activities. The findings indicate that, within these structural similarities, the Microtox assay can be used as an indicator of potential toxicity to mammals and fish.

Structure-activity relationship models for rat carcinogenesis and assessing the role mutagens play in model predictivity

We previously demonstrated that fragment based cat-SAR carcinogenesis models consisting solely of mutagenic or non-mutagenic carcinogens varied greatly in terms of their predictive accuracy. This led us to investigate how well the rat cancer cat-SAR model predicted mutagens and non-mutagens in their learning set. Four rat cancer cat-SAR models were developed: Complete Rat, Transgender Rat, Male Rat and Female Rat, with leave-one-out (LOO) validation concordance values of 69%, 74%, 67% and 73%, respectively. The mutagenic carcinogens produced concordance values in the range 69-76% compared with only 47-53% for non-mutagenic carcinogens. As a surrogate for mutagenicity, comparisons between single site and multiple site carcinogen SAR models were analysed. The LOO concordance values for models consisting of 1-site, 2-site and 4+-site carcinogens were 66%, 71% and 79%, respectively. As expected, the proportion of mutagens to non-mutagens also increased, rising from 54% for 1-site to 80% for 4+-site carcinogens. This study demonstrates that mutagenic chemicals, in both SAR learning sets and test sets, are influential in assessing model accuracy. This suggests that SAR models for carcinogens may require a two-step process in which mutagenicity is first determined before carcinogenicity can be accurately predicted.

Mammary carcinogen-protein binding potentials: novel and biologically relevant structure-activity relationship model descriptors

Previously, SAR models for carcinogenesis used descriptors that are essentially chemical descriptors. Herein we report the development of models with the cat-SAR expert system using biological descriptors (i.e., ligand-receptor interactions) rat mammary carcinogens. These new descriptors are derived from the virtual screening for ligand-receptor interactions of carcinogens, non-carcinogens, and mammary carcinogens to a set of 5494 target proteins. Leave-one-out validations of the ligand mammary carcinogen-non-carcinogen model had a concordance between experimental and predicted results of 71%, and the mammary carcinogen-non-mammary carcinogen model was 72% concordant. The development of a hybrid fragment-ligand model improved the concordances to 85 and 83%, respectively. In a separate external validation exercise, hybrid fragment-ligand models had concordances of 81 and 76%. Analyses of example rat mammary carcinogens including the food mutagen and oestrogenic compound PhIP, the herbicide atrazine, and the drug indomethacin; the ligand model identified a number of proteins associated with each compound that had previously been referenced in Medline in conjunction with the test chemical and separately with association to breast cancer. This new modelling approach can enhance model predictivity and help bridge the gap between chemical structure and carcinogenic activity by descriptors that are related to biological targets.

A topological substructural approach applied to the computational prediction of rodent carcinogenicity

The carcinogenic activity has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A discriminant model was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 189 compounds. The percentage of correct classification was 76.32%. The predictive power of the model was validated by three test: an external test set (compounds not used in the develop of the model, with a 72.97% of good classification), a leave-group-out cross-validation procedure (4-fold full cross-validation, removing 20% of compounds in each cycle, with a good prediction of 76.31%) and two external prediction sets (the first and second exercises of the National Toxicology Program). This methodology evidenced that the hydrophobicity increase the carcinogenic activity and the dipole moment of the molecule decrease it; suggesting the capacity of the TOPS-MODE descriptors to estimate this property for new drug candidates. Finally, the positive and negative fragment contributions to the carcinogenic activity were identified (structural alerts) and their potentialities in the lead generation process and in the design of 'safer' chemicals were evaluated.

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.

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.

Use of electron-electron repulsion energy as a molecular descriptor in QSAR and QSPR studies

Electron-electron repulsion energy (<Vee>) is presented as a new molecular descriptor to be employed in QSAR and QSPR studies. Here it is shown that this electronic energy parameter is connected to molecular quantum similarity measures (MQSM), and as a consequence can be considered as a complement to steric and electronic parameters in description of molecular properties and biological responses of organic compounds. The present strategy considers the molecule as a whole, thus there is no need to employ contributions of isolated fragments as in many calculations of molecular descriptors, like log P or the Free-Wilson analysis. The procedure has been tested in a widespread set of molecules: alcohols, alkanamides, indole derivatives and 1-alkylimidazoles. Molecular properties, as well as toxicity, are correlated using <Vee> as a parameter, and extensions to the method are given for handling difficult systems. In almost all studied cases, satisfactory linear relationships were finally obtained.

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.

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.

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.

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.

Induction by nitriles of sex chromosome aneuploidy: tests of mechanism

Genetic and biochemical assays were conducted to determine if nitrile induced adult paralysis and germline aneuploidy in female Drosophila melanogaster requires a biochemical activation mechanism which results in the release of free cyanide. Two nitriles predicted to differ substantially in their susceptibility to enzymatic cyanide release were found to be equally effective inducers of aneuploidy. Regardless of differences in chemical structure, nitriles seem to be affecting a common cellular target as judged by the lack of synergistic effects when two nitriles are presented simultaneously. Mitochondrial respiration was not inhibited by acetonitrile under conditions in which sodium cyanide completely blocked respiration. A sensitive luciferase enzyme inhibition assay suggests that some, but not all, nitriles may affect hydrophobic protein interactions. These results suggest that there is no single biochemical mechanism by which all nitriles induce aneuploidy, although the cellular target disrupted is probably the same for each chemical. The implications of these findings for structural alert based pre-screening of mutagens are discussed.

Identification of 'genotoxic' and 'non-genotoxic' alerts for cancer in mice: the carcinogenic potency database

A set of chemicals tested for carcinogenicity in mice that have been analyzed by Gold et al. [L.S. Gold, C.B. Sawyer, R. Magaw, G.M. Backman, M. deVeciana, R. Levinson, N.K. Hooper, W.R. Havender, L. Bernstein, R. Peto, M.C. Pike, B.N. Ames, Environ. Health Perspect. 58 (1984) 9-319; L.S. Gold, M. deVeciana, G.M. Backman, M. Lopipero, M. Smith, R. Blumenthal, R. Levinson, L. Bernstein, B.N. Ames, Environ. Health Perspect. 67 (1986) 161-200; L.S. Gold, T.H. Slone, G.M. Backman, R. Magaw, M. DaCosta, P. Lopipero, M. Blumenthal, B.N. Ames, Environ. Health Perspect. 74 (1987) 237-329; L.S. Gold, T.H. Slone, G.M. Backman, S. Eisenberg, M. DaCosta, M. Wong, N.B. Manley, L. Rohrbach, B.N. Ames, Environ. Health Perspect. 84 (1990) 215-286; L.S. Gold, N.B. Manley, T.H. Slone, T.H. Garfinkle, L. Rohrbach, B.N. Ames, Environ. Health Perspect. 100 (1993) 65-135] in the first five plots of the carcinogenic potency database (CPDB) was subjected to CASE/MULTICASE analyses. Briefly, CASE/MULTICASE is a computer-automated structure evaluation system that is capable of identifying structural features of chemicals associated with a specified biological activity (e.g., carcinogenicity or mutagenicity). These features are then incorporated into a structure-activity relationship (SAR) model for the analyzed database. The mouse CPDB used in this study consists of 627 chemicals, 289 of which are carcinogens, 11 marginal or weak carcinogens (i.e., chemicals requiring high doses to induce cancer) and 327 non-carcinogens. In an internal prediction analysis where the CASE/MULTICASE SAR model was used to predict the carcinogenicity of chemicals used to create the model, a concordance between experimental and predicted results of 96% was obtained. This indicates that the model is able to satisfactorily explain the chemicals in the learning set. In a drop-one cross-validation study where chemicals were removed one at a time and the remaining n - 1 chemicals were used in an iterative method to create a model to predict the removed chemical, CASE/MULTICASE was able to achieve a concordance between experimental and predicted results of 70%. Using a modified validation process designed to investigate the predictivity of a more focused SAR model, the system achieved a 78% concordance between experimental and predicted results. Among the major biophores identified by CASE/MULTICASE associated with cancer causation in mice several are derived from electrophilic or potentially electrophilic compounds (e.g., hydrazines, N-mustards, N-nitrosamines, aromatic amines, reactive halogens, and quinones). Other biophores however are derived from chemicals seemingly devoid of actual or potential DNA-reactivity and as such may represent structural feature of non-genotoxic carcinogens.

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.

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.

QSARS of mutagens and carcinogens: two case studies illustrating problems in the construction of models for noncongeneric chemicals

There is a strong motivation to develop QSAR models for toxicity prediction for use in screening, for setting testing priorities, and for reducing reliance on animal testing. Decisions must be made daily by toxicologists in governments and industry to direct limited testing to the most urgent public health problems, and to direct the types of chemical synthesis and product development efforts undertaken. This need has motivated attempts to construct general QSAR models (e.g., for rodent carcinogenicity), not tailored to congeneric series of chemicals. These various attempts have provided interesting and important scientific evidence; however, they have also shared a limited overall performance. The goal of this paper is to illustrate, by two unrelated actual examples of QSARs for mutagens and carcinogens, some fundamental problems relative to the application of general QSAR approaches to noncongeneric chemicals. Both examples consider data sets that are noncongeneric in a chemical structure and mechanism of action sense: in the first case, a mean mutagenic potency defined as an average over multiple genetic toxicity endpoints, and, in the second case, the NTP two-sexes, two species rodent carcinogenicity bioassay results for 280 carcinogens and noncarcinogens. The problems encountered with the QSAR analyses of these two cases indicate that a successful approach to the problem of QSAR modeling of noncongeneric data will need to consider the multidimensional nature of the problem in both a chemical and a biological sense. Since different chemical classes represent largely independent action mechanisms, some means for extracting local QSARs for constituent classes will be necessary. Alternatively, a general QSAR derived for a noncongeneric data set will need to be scrutinized and decomposed along chemical class lines in order to establish boundaries for application and confidence levels for prediction.

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.

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.

The structure-toxicity relationship challenge at hazardous waste sites

It is shown that the use of a combination of two programs, MULTICASE and META can help assess the carcinogenic risk factor posed by the disposal of industrial organic materials in the ecosystem. MULTICASE is a knowledge-based computer system that had been trained to identify molecular substructures believed to be conducive to carcinogenic potential and META is an expert system trained to predict the aerobic biodegradation products of organic molecules. The programs can be used to assess the health hazard of the discarded chemicals by evaluating their chemical structure, their biodegradability and the structures of the predicted biodegradation products. Several examples of the application of the methodology are described in this paper.

Molecular fragments associated with non-genotoxic carcinogens, as detected using a software program based on graph theory: their usefulness to predict carcinogenicity

We assembled 390 chemicals with a structure non-alerting to DNA-reactivity (145 carcinogens and 245 non-carcinogens) for which rodent carcinogenicity data were available. These non-alerting chemicals were defined by the absence in their molecules of DNA-reactive (directly or after metabolic activation) alerting structures, as described by Ashby and coworkers (Mutat. Res., 204 (1988) 17-115; Mutat. Res., 223 (1989) 73-103; Mutat. Res., 257 (1991) 209-227; Mutat. Res., 286 (1993) 3-74). Using our software program based on graph theory we analyzed the compounds in order to estimate the program's ability to predict nonalerting carcinogens. Our software fragmented the structural formula of the chemicals into all possible fragments of contiguous atoms with size between 2 and 8 (non-hydrogen) atoms and learned about statistically significant fragments from a training set of chemicals. These fragments were used to predict carcinogenicity or lack thereof in a verification set of compounds. For 390 runs of the software program we used (n - 1) of the chemicals as a training set, to predict the excluded chemical at each run (as a test set). Using two different probability thresholds to select significant fragments (P = 0.05 and P = 0.125 1-tailed according to binomial distribution), we performed two analyses: in the better one (P = 0.05) 19% of the molecules tested lacked significant fragments, for the remaining 81% the observed level of accuracy of the prediction was 66.0% against an expected level of accuracy of 51.7%. The difference was highly significant (P < 0.0001). We also examined the more significant activating fragments (biophores) and discussed at length both their biological plausibility and the working hypothesis that additional alerting structures for carcinogenicity (not only those related to genotoxicity) can be detected using this type of SAR approach. This new class of alerting structures could identify subfamilies of congeneric analogs active through mechanisms of receptor mediated carcinogenesis.

Structural basis for the induction of preneoplastic glutathione S-transferase positive foci by hepatocarcinogens

A data base consisting of 100 chemicals tested for the ability to enhance the formation of glutathione-S-transferase (GST) positive preneoplastic lesions were analyzed by the CASE structure-activity relational system. A number of structural determinants associated with the induction of GST-positive foci were recognized. The majority of these describe non-electrophilic moieties. It is concluded that there is a structural basis for the induction of these neoplastic lesions; interestingly, it was found that this activity is associated with structures that are non-electrophilic. Reconstruction experiments have indicated that the identified structures are meaningful and that their significance could be better understood with the availability of test results on additional chemicals.

Structural evidence for a dichotomy in rodent carcinogenesis: involvement of genetic and cellular toxicity

The structural determinants identified as associated with carcinogenicity in rodents were compared to the structural determinants associated with mutagenicity in Salmonella and toxicity to cultured cells. The analysis revealed that the determinants of carcinogenicity could be separated into two almost mutually exclusive groups: Those associated with mutagenicity (and electrophilicity) and those associated with cell toxicity. These findings provide evidence for two mechanisms of cancer induction: A genotoxic one and a non-genotoxic one associated with cell toxicity.

Carcinogenicity of azathioprine: an S-AR investigation

Besides its use in the treatment of a variety of presumed autoimmune diseases, azathioprine is given as an immunosuppressant to patients who have had renal transplants. Though epidemiological studies have provided "sufficient" evidence of its carcinogenicity in humans, the carcinogenicity tests in rats and mice are considered to be inconclusive because of limitations in the design and results of these tests (IARC, 1981, 1987). Rosenkranz and Klopman (1991) used the CASE program to identify the structural features responsible for its carcinogenicity. They concluded that this genotoxic chemical was a carcinogen due to the presence of the molecular fragment C"-S-C=. The finding was based on the presence of this biophore fragment in five other compounds, namely: 2-amino-5-nitrothiazole, 2-mercaptobenzothiazole, fenthione, 4,4'-thiodianiline and nithiazide. Recently, Ashby (1992) has expressed concern over the validity of their findings. With the aim of contributing to this debate on the mechanism of carcinogenicity of azathioprine, we have analyzed the structural basis of carcinogenicity of azathioprine and the five support compounds using the carcinogenicity predictor of our toxicity prediction program, TOPKAT. The results, more in line with Ashby's concerns, indicate that no molecular fragment involving the S atom is associated with the carcinogenic properties of these molecules. According to the TOPKAT program the carcinogenicity, if any, of azathioprine is due to the NO2 electrophile because its other major structural features are found to be either associated with non-carcinogenicity or do not discriminate carcinogens from non-carcinogens.

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.

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.

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.

Computer prediction of possible toxic action from chemical structure; the DEREK system

1. The development of DEREK, a computer-based expert system (derived from the LHASA chemical synthesis design program) for the qualitative prediction of possible toxic action of compounds on the basis of their chemical structure is described. 2. The system is able to perceive chemical sub-structures within molecules and relate these to a rulebase linking the sub-structures with likely types of toxicity. 3. Structures can be drawn in directly at a computer graphics terminal or retrieved automatically from a suitable in-house database. 4. The system is intended to aid the selection of compounds based on toxicological considerations, or separately to indicate specific toxicological properties to be tested for early in the evaluation of a compound, so saving time, money and some laboratory animals and resources.

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.

An association between mutagenicity and carcinogenic potency

A comparison between mutagenic and non-mutagenic rodent carcinogens studied by the U.S. National Toxicology Program revealed that as a group, rat carcinogens mutagenic in Salmonella typhimurium are more potent than their non-mutagenic counterparts.