Prediction of the carcinogenicity of a second group of organic chemicals undergoing carcinogenicity testing

Extended Analysis and Evidence Integration of Chloroprene as a Human Carcinogen

β-Chloroprene is used in the production of polychloroprene, a synthetic rubber. In 2010, Environmental Protection Agency (EPA) published the Integrated Risk Information System "Toxicological Review of Chloroprene," concluding that chloroprene was "likely to be carcinogenic to humans." This was based on findings from a 1998 National Toxicology Program (NTP) study showing multiple tumors within and across animal species; results from occupational epidemiological studies; a proposed mutagenic mode of action; and structural similarities with 1,3-butadiene and vinyl chloride. Using mouse data from the NTP study and assuming a mutagenic mode of action, EPA calculated an inhalation unit risk (IUR) for chloroprene of 5 × 10^-4 per µg/m³ . This is among the highest IURs for chemicals classified by IARC or EPA as known or probable human carcinogens and orders of magnitude higher than the IURs for carcinogens such as vinyl chloride, benzene, and 1,3-butadiene. Due to differences in pharmacokinetics, mice appear to be uniquely responsive to chloroprene exposure compared to other animals, including humans, which is consistent with the lack of evidence of carcinogenicity in robust occupational epidemiological studies. We evaluated and integrated all lines of evidence for chloroprene carcinogenicity to assess whether the 2010 EPA IUR could be scientifically substantiated. Due to clear interspecies differences in carcinogenic response to chloroprene, we applied a physiologically based pharmacokinetic model for chloroprene to calculate a species-specific internal dose (amount metabolized/gram of lung tissue) and derived an IUR that is over 100-fold lower than the 2010 EPA IUR. Therefore, we recommend that EPA's IUR be updated.

QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project

The ChemScreen project aimed to develop a screening system for reproductive toxicity based on alternative methods. QSARs can, if adequate, contribute to the evaluation of chemical substances under REACH and may in some cases be applied instead of experimental testing to fill data gaps for information requirements. As no testing for reproductive effects should be performed in REACH on known genotoxic carcinogens or germ cell mutagens with appropriate risk management measures implemented, a QSAR pre-screen for 70,983 REACH substances was performed. Sixteen models and three decision algorithms were used to reach overall predictions of substances with potential effects with the following result: 6.5% genotoxic carcinogens, 16.3% mutagens, 11.5% developmental toxicants. These results are similar to findings in earlier QSAR and experimental studies of chemical inventories, and illustrate how QSAR predictions may be used to identify potential genotoxic carcinogens, mutagens and developmental toxicants by high-throughput virtual screening.

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.

Computer-aided rodent carcinogenicity prediction

The potential of the computer program PASS (Prediction Activity Spectra for Substances) to predict rodent carcinogenicity for chemical compounds was studied. PASS predicts carcinogenicity of chemical compounds on the basis of their structural formula and of structure-activity relationship analysis of known carcinogens and non-carcinogens. The data on structures and experimental results of 2-year carcinogenicity assays for 412 chemicals from the NTP (National Toxicological Program) and 1190 chemicals from the CPDB (Carcinogenic Potency Database) were used in our study. The predictions take into consideration information about species and sex of animals. For evaluation of the predictive accuracy we used two procedures: leave-one-out cross-validation (LOO CV) and leave-20%-out cross-validation. In the last case we randomly divided the studied data set 20 times into two subsets. The data from the first subset, containing 80% of the compounds, were added to the PASS training set (which includes about 46,000 compounds with about 1500 biological activity types collected during the last 20 years to predict biological activity spectra), the second subset with 20% of the compounds was used as an evaluation set. The mean accuracy of prediction calculated by LOO CV is about 73% for NTP compounds in the 'equivocal' category of carcinogenic activity and 80% for NTP compounds in the 'evidence' category of carcinogenicity. The mean accuracy of prediction for the CPDB database is 89.9% calculated by LOO CV and 63.4% calculated by leave-20%-out cross-validation. Influence of incorporation of species and sex data on the accuracy of carcinogenicity prediction was also investigated. It was shown that the accuracy was increased only for data on male animals.

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.

Synergy between systemic toxicity and genotoxicity: relevance to human cancer risk

The health risk manager and policy analyst must frequently make recommendations based upon incomplete toxicity data. This is a situation which is encountered in the evaluation of human carcinogenic risks as animal cancer bioassay results are often not available. In this study, in order to assess the relevance of other possible indicators of carcinogenic risks, we used the "chemical diversity approach" to estimate the magnitude of the human carcinogenic risk based upon Salmonella mutagenicity and systemic toxicity data of the "universe of chemicals" to which humans have the potential to be exposed. Analyses of the properties of 10,000 agents representative of the "universe of chemicals" suggest that chemicals that have genotoxic potentials as well as exhibiting greater systemic toxicity are more likely to be carcinogens than non-genotoxicants or agents that exhibit lesser toxicity. Since "genotoxic" carcinogenicity is a hallmark of recognized human carcinogens, these findings are relevant to human cancer risk assessment.

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.

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.

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.

Estimating the extent of the health hazard posed by high-production volume chemicals

We used structure-activity relationship modeling to estimate the number of toxic chemicals among the high-production volume (HPV) group. We selected 200 chemicals from among the HPV chemical list and predicted the potential of each for its ability to induce a variety of adverse effects including genotoxicity, carcinogenicity, developmental, and systemic toxicity. We found a significantly less than expected proportion of toxic chemicals among the HPV sample when compared to a reference set of 10,000 chemicals representative of the universe of chemicals.

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.

SAR modeling of unbalanced data sets

The increased acceptance of SAR approaches to hazard identification has led us to investigate methods to improve the predictive performance of SAR models. In the present study we demonstrate that although on theoretical grounds the ratio of active to inactive chemicals in the learning set should be unity, SAR models can "tolerate" an unbalanced range in ratios from 3:1 (i.e., 75% actives) to 1:2 (i.e., 33% actives) and still perform adequately. On the other hand SAR models derived from learning sets with ratios in excess of 4:1 (80% actives), even when corrected for the initial ratio do not perform satisfactorily.

Knowledge discovery and data mining in toxicology

Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.

The high production volume chemical challenge program: the relevance of the in vivo micronucleus assay

The in vivo rodent bone marrow micronucleus assay (Mnt) has assumed a pivotal role in screening strategies for the identification of substances potentially carcinogenic to humans. The analysis of the results of the current international 5-year effort to provide toxicological data for high production volume chemicals will play a crucial role in developing future strategies for identifying health hazards. As part of that program, consideration is being given to accepting either in vitro genotoxicity data or results of the Mnt. The present analyses indicate that for hazard identification purposes that, in fact, in vitro genotoxicity test results, such as those derived from the Salmonella mutagenicity assay, may be an acceptable alternative.

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.

Chemical carcinogenicity: can it be predicted from knowledge of mutagenicity and allergic contact dermatitis?

We investigated the suggestion [R.E. Albert, Environ. Health Perspect. 105 (1997) 940-948.] that results of mutagenicity testing in Salmonella combined with allergic contact dermatitis (ACD) testing in humans would be predictive of carcinogenicity in rodents. Using the cancer bioassay results of the US National Toxicology Program (NTP), Salmonella mutagenicity tests and a highly predictive structure-activity relational model of ACD, we conclude that the combination is not more predictive than the results of the Salmonella mutagenicity assay alone.

Chemical diversity approach for evaluating mechanistic relatedness among toxicological phenomena

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

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.

Free radical formation and erythrocyte membrane alterations during MetHb formation induced by the BHA metabolite, tert-butylhydroquinone

Erythrocyte membranes are altered as a consequence of oxidative stress following the incubation of intact erythrocytes with one of the major metabolites of the antioxidant butylated hydroxyanisole (BHA), tertbutylhydroquinone(tBHQ). Arather persistent semiquinone radical was observed by electron spin resonance (ESR) spectroscopy when tBHQ was incubated with either homogeneous oxyhemoglobin solutions or suspensions of intact erythrocytes. Erythrocyte ghosts prepared from fresh control erythrocytes and ghosts from erythrocytes preincubated with BHA and its metabolite, tBHQ, were subjected to polyacrylamide gel electrophoresis (SDS-PAGE). Only minor changes of the electrophoresis pattern relative to the control was observed in the BHA incubations whereas tBHQ significantly increased the amount of high molecular weight degradation products of erythrocyte membrane constituents. These changes were only observed when incubations were performed in the presence of oxygen. In control experiments where heme oxygen was replaced by carbon monoxide, no membrane degradation products appeared. These observations can be interpreted in terms of metabolic activation of the antioxidant BHAvia tBHQ to the tert-butylsemiquinone free radical and finally to the corresponding quinone, thereby leading to harmful effects on erythrocyte membrane structures. Moreover, deleterious effects on other biological membranes are also likely to occur.

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.

A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software

This report describes in detail a new quantitative structure-activity relational expert system (QSAR-ES) method for predicting the carcinogenic potential of pharmaceuticals and other organic chemicals in rodents, and a beta-test evaluation of its performance. The method employs an optimized, computer-automated structure evaluation (MCASE) software program and new database modules which were developed under a Cooperative Research and Development Agreement (CRADA) between FDA and Multicase, Inc. The beta-test utilized 126 compounds with carcinogenicity studies not included in control database modules and three sets of modules, including: A07-9 (Multicase, Inc.), AF1-4 (FDA-OTR/Multicase, Inc.), and AF5-8 (FDA-OTR/proprietary). The investigation demonstrated that the standard MCASE(A07-9) system which had a small data-set (n = 319), detected few structure alerts (SA) for carcinogenicity (n = 17), and had poor coverage for beta-test compounds (51%). Conversely, the new, optimized FDA-OTR/MCASE(AF5-8) system had a large data-set (n = 934), detected many SA (n = 58) and had good coverage (94%). In addition, the study showed the standard MCASE(A07-9) software had poor predictive value for carcinogens and specificity for noncarcinogens (50 and 42%), detected many false positives (58%), and exhibited poor concordance (46%). Conversely, the new, FDA-OTR/MCASE(AF5-8) system demonstrated excellent predictive value for carcinogens and specificity for non-carcinogens (97%, 98%), detected only one false positive (2%), and exhibited good concordance (75%). The dramatic improvements in the performance of the MCASE were due to numerous modifications, including: (a) enhancement of the size of the control database modules, (b) optimization of MCASE SAR assay evaluation criteria, (c) incorporation of a carcinogenic potency scale for control compound activity and MCASE biophores, (d) construction of individual rodent gender- and species-specific modules, and (e) defining assay acceptance criteria for query and control database compounds.

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.

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.

Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity

Quantitative modeling methods, relating aspects of chemical structure to biological activity, have long been applied to the prediction and characterization of chemical toxicity. The early linear free-energy approaches of Hansch and Free Wilson provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure-activity relationships (QSARs). In addition to modeling of chemical toxicity, these methods have been extensively applied to modeling of medicinal properties of chemicals. However, there are important differences in the nature and objectives of these two applications, which have led to the evolution of different modeling approaches (namely, the need for treating sets of noncongeneric toxic compounds). In this paper are discussed those approaches to chemical toxicity that have taken a more "personalized" configuration and have undergone implementation into software programs able to perform the various steps of the assessment of the hazard posed by the chemicals. These models focus both on a variety of toxicological endpoints and on key elements of toxicity mechanisms, such as metabolism.

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.

Structure-activity model of chemicals that cause human respiratory sensitization

We report a structure-activity model of chemicals with the potential to cause respiratory allergy developed through the CASE/MultiCASE systems. Chemicals documented to elicit a decrease in FEV1 of > or = 20% within 24 h of inhalation provocation challenge were used to form a learning set. Additional requirements for inclusion in the learning set were that chemicals had at least two contiguous nonhydrogen atoms and were nonmetallic. Forty chemicals met these criteria. The model identified several "structural alerts" including the isocyanate functionality (N = C = O), primary and secondary amines, substituted aromatic moieties, and distance descriptors. An external data-withholding exercise used to validate the model yielded a sensitivity of 0.95 and a specificity of 0.95. This model is applicable to initial prediction of the sensitizing ability of untested chemicals and may provide mechanistic insight into the process(es) of respiratory sensitization.

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.

Prioritizing testing of organic compounds detected as gas phase air pollutants: structure-activity study for human contact allergens

Organic compounds that are used or generated anthropogenically in large quantities in cities can be identified through their presence in the urban atmosphere and in air pollutant source emissions. Compounds identified by this method were screened to evaluate their potential to act as contact allergens. The CASE and MULTICASE computer programs, which are based on the detection of structure-activity relationships (SAR), were used to evaluate this potential. These relationships first are determined by comparing chemical structures to biological activity within a learning set comprised of 458 compounds, each of which had been tested experimentally in human trials for its sensitization potential. Using the information contained in this learning set, CASE and MULTICASE predicted the activity of 238 compounds found in the atmosphere for their ability to act as contact allergens. The analysis finds that 21 of 238 compounds are predicted to be active contact allergens (probability >0.5), with potencies ranging from mild to very strong. The compounds come from chemical classes that include chlorinated aromatics and chlorinated hydrocarbons, N-containing compounds, phenols, alkenes, and an S-containing compound. Using the measured airborne concentrations or emission rates of these compounds as an indication of the extent of their use, together with their predicted potencies, provides an efficient method to prioritize the experimental assessment of contact sensitization of untested organic compounds that can be detected as air pollutants.

Toxic effects of griseofulvin: disease models, mechanisms, and risk assessment

Griseofulvin (GF) has been in use for more than 30 years as a pharmaceutical drug in humans for the treatment of dermatomycoses. Animal studies give clear evidence that it causes a variety of acute and chronic toxic effects, including liver and thyroid cancer in rodents, abnormal germ cell maturation, teratogenicity, and embroyotoxicity in various species. No sufficient data from human studies are available at present to exclude a risk in humans: therefore, attempts were made to elucidate the mechanisms responsible for the toxic effects of GF and to address the question whether such effects might occur in humans undergoing GF therapy. It is well documented that GF acts as a spindle poison and its reproductive toxicity as well as the induction of numerical chromosome aberrations and of micronuclei in somatic cells possibly may result from disturbance of microtubuli formation. Likewise, a causal relationship between aneuploidy and cancer has been repeatedly postulated. However, a critical survey of the data available on aneuploidogenic chemicals revealed insufficient evidence for such an association. Conceivably, other mechanisms may be responsible for the carcinogenic effects of the drug. The induction of thyroid tumors in rats by GF is apparently a consequence of the decrease of thyroxin levels and it is unlikely that such effects occur in GF-exposed humans. The appearance of hepatocellular carcinomas (HCC) in mice on GF-supplemented diet is preceded by various biochemical and morphological changes in the liver. Among these, hepatic porphyria is prominent, it may result from inhibition of ferrochelatase and (compensatory) induction of ALA synthetase. GF-induced accumulation of porphyrins in mouse liver is followed by cell damage and necrotic and inflammatory processes. Similar changes are known from certain human porphyrias which are also associated with an increased risk for HCC. However, the porphyrogenic effect of GF therapy in humans is moderate compared with that in the mouse model, although more detailed studies should be performed in order to clarify this relationship on a quantitative basis. A further important effect of GF-feeding in mice is the formation of Mallory bodies (MBs) in hepatocytes. These cytoskeletal abnormalities occur also in humans, although under different conditions; their appearance is associated with the induction of liver disease and HCC. Chronic liver damage associated with porphyria and MB formation, enhanced cell proliferation, liver enlargement, and enzyme induction all may contribute to the hepatocarcinogenic effect of GF in mice. In conclusion, further investigation is required for adequate assessment of health risks to humans under GF therapy.

The NIEHS Predictive-Toxicology Evaluation Project

The Predictive-Toxicology Evaluation (PTE) project conducts collaborative experiments that subject the performance of predictive-toxicology (PT) methods to rigorous, objective evaluation in a uniquely informative manner. Sponsored by the National Institute of Environmental Health Sciences, it takes advantage of the ongoing testing conducted by the U.S. National Toxicology Program (NTP) to estimate the true error of models that have been applied to make prospective predictions on previously untested, noncongeneric-chemical substances. The PTE project first identifies a group of standardized NTP chemical bioassays either scheduled to be conducted or are ongoing, but not yet complete. The project then announces and advertises the evaluation experiment, disseminates information about the chemical bioassays, and encourages researchers from a wide variety of disciplines to publish their predictions in peer-reviewed journals, using whatever approaches and methods they feel are best. A collection of such papers is published in this Environmental Health Perspectives Supplement, providing readers the opportunity to compare and contrast PT approaches and models, within the context of their prospective application to an actual-use situation. This introduction to this collection of papers on predictive toxicology summarizes the predictions made and the final results obtained for the 44 chemical carcinogenesis bioassays of the first PTE experiment (PTE-1) and presents information that identifies the 30 chemical carcinogenesis bioassays of PTE-2, along with a table of prediction sets that have been published to date. It also provides background about the origin and goals of the PTE project, outlines the special challenge associated with estimating the true error of models that aspire to predict open-system behavior, and summarizes what has been learned to date.