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.

Chemical structure determines target organ carcinogenesis in rats.

SAR models were developed for 12 rat tumour sites using data derived from the Carcinogenic Potency Database. Essentially, the models fall into two categories: Target Site Carcinogen-Non-Carcinogen (TSC-NC) and Target Site Carcinogen-Non-Target Site Carcinogen (TSC-NTSC). The TSC-NC models were composed of active chemicals that were carcinogenic to a specific target site and inactive ones that were whole animal non-carcinogens. On the other hand, the TSC-NTSC models used an inactive category also composed of carcinogens but to any/all other sites but the target site. Leave one out (LOO) validations produced an overall average concordance value for all 12 models of 0.77 for the TSC-NC models and 0.73 for the TSC-NTSC models. Overall, these findings suggest that while the TSC-NC models are able to distinguish between carcinogens and non-carcinogens, the TSC-NTSC models are identifying structural attributes that associate carcinogens to specific tumour sites. Since the TSC-NTSC models are composed of active and inactive compounds that are genotoxic and non-genotoxic carcinogens, the TSC-NTSC models may be capable of deciphering non-genotoxic mechanisms of carcinogenesis. Together, models of this type may also prove useful in anticancer drug development since they essentially contain chemical moieties that target a specific tumour site.

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.

Development of an information-intensive structure-activity relationship model and its application to human respiratory chemical sensitizers.

Structure-activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.

Structure-activity approach to the identification of environmental estrogens: the MCASE approach.

A sizable number of environmental contaminants and natural products have been found to possess hormonal activity and have been termed endocrine-disrupting chemicals. Due to the vast number (estimated at about 58,000) of environmental contaminants, their potential to adversely affect the endocrine system, and the paucity of health effects data associated with them, the U.S. Congress was led to mandate testing of these compounds for endocrine-disrupting ability. Here we provide evidence that a computational structure-activity relationship (SAR) approach has the potential to rapidly and cost effectively screen and prioritize these compounds for further testing. Our models were based on data for 122 compounds assayed for estrogenicity in the ESCREEN assay. We produced two models, one for relative proliferative effect (RPE) and one for relative proliferative potency (RPP) for chemicals as compared to the effects and potency of 17beta-estradiol. The RPE and RPP models achieved an 88 and 72% accurate prediction rate, respectively, for compounds not in the learning sets. The good predictive ability of these models and their basis on simple to understand 2-D molecular fragments indicates their potential usefulness in computational screening methods for environmental estrogens.

A substructure-based SAR model for odor perception in humans relevant to health risk assessments.

The ability of human to perceive odors is a very complex phenomenon involving the selective binding of molecules to approximately 1000 olfactory receptors. Accordingly, the derivation of a substructure-based SAR model can be expected to be problematic. Yet, based upon published data on odor thresholds of volatile organic chemicals, we were able to derive such an SAR model. An examination of the structural determinants and related modulators indicates that lipophilicity is a major contributor to olfactory perception. The availability of a substructure-based SAR model permits an examination of the relationship between the presence in the environment of odorous chemicals and public health risks.

Prevalence of mutagens in the environment: experimental data versus simulations.

The recent availability of experimental data on the mutagenicity in Salmonella of a subset of high production volume chemicals allowed a comparison with the estimate derived from computer-based simulations. The prevalence of mutagens in the subsets was not significantly different: 20% versus 19.5% based upon experimental and simulation data, respectively. This provides support for the use of the rapid and low-cost simulation approach.

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.

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.

Chemical categories for health hazard identification: a feasibility study.

The use of chemical categories has been suggested in order to lower the number of chemicals tested in the High Production Volume (HPV) Chemical Challenge Program. In this investigation we examined the reliability of using organic chemical categories to classify chemicals as either toxic or nontoxic for individual toxicological effects as well as for panels of such endpoints. The analyses indicate that chemical categories are unable to consistently identify groups of chemicals with similar toxic responses either for a multiplicity of endpoints or for single effects. Our analyses suggest that if chemical categories are to be used to identify health hazards, that computer-based SAR approaches appear to be superior to arbitrary chemical categories for predicting specific toxicological effects but they are not, at this time, useful for defining the overall toxicity.

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.

A new approach to evaluate mechanistic relationships among genotoxic phenomena: validation.

In order to determine its applicability for the study of genotoxicity, a recently developed method to probe for possible mechanistic relationships among toxicological phenomena was applied to the induction of mutations in Salmonella typhimurium. Since the basis of this phenomenon is understood, this would provide a test of the applicability of the new method to DNA-based mechanisms. The results presented indicate that significant relationships are indeed found among phenomena involving damage to or modification of DNA but not between them and non-genotoxic phenomena. The present results suggest that the newly developed approach could be applied to test mechanistic hypotheses involving genotoxic phenomena.

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.

Exploring the relationship between the inhibition of gap junctional intercellular communication and other biological phenomena.

The mechanistic relationship of the inhibition of gap junctional intercellular communication (GJIC) to other toxicological phenomena was explored using a recently developed method that models the properties of a large population of molecules chosen to represent the 'universe of chemicals'. The analyses indicate that inhibition of GJIC is strongly linked to the carcinogenic process in rodents, to cellular but not systemic toxicity, to biological phenomena that may involve inflammatory processes and to development effects. The inhibition of GJIC appears not to be associated with genotoxic mechanisms. With respect to cancer causation, integration of the analyses suggests that inhibition of GJIC is involved in non-genotoxic cancer induction or in the non-genotoxic phases of the carcinogenic process (such as inflammation, cell toxicity, cell proliferation, inhibition of cell differentiation and apoptosis).

A battery of cell toxicity assays as predictors of eye irritation: a feasibility study.

The newly developed "chemical diversity approach" was used to determine whether or not it is likely that a panel of in vitro cell toxicity assays capable of predicting in vivo eye irritation could be assembled. The analyses, based upon available and validated structure-activity relationship models of toxicity in cultured human HeLa cells and murine Balb/c 3T3 cells, indicate that a battery of cytotoxicity tests could provide a viable alternative to the animal-based procedure.

The High Production Volume Chemical Challenge Program : The Rodent LD50 and its Possible Replacement.

The High Production Volume Chemical Challenge Program provides an opportunity to re-examine the usefulness and informational value of tests currently used to obtain preliminary hazard identification data. With a view to assessing the mechanistic information provided by the rodent LD50 test and to ascertain the possibility of replacing it with other "more acceptable" assays, we used a recently developed approach to determine the relationship of the LD50 assay to other toxicological protocols. Our analyses indicate that, of the assays examined, the LD50 was significantly related to toxicity in cultured cells and to binding at the Ah receptor.

Applications of the case/multicase SAR method to environmental and public health situations.

The availability of validated and characterized SAR models of toxicological phenomena provides a method to apply SAR technology to a variety of environmental, public health and industrial situations. These include (i) the prioritization of environmental pollutants for control and/or regulation, (ii) the design of multi-action optimized therapeutics from which the potential for unwanted side-effects have been engineered out, (iii) the development of SAR-based computer-driven screening procedure to identify candidate therapeutics based upon combinatorial chemistry or compilations of molecular structures, (iv) the generation of toxicological profiles to be used in the selection of benign chemicals in the early stages of product development.

Development, characterization and application of predictive-toxicology models.

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover, the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.

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.