Albert R. Cunningham

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.

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.

Anacardic acid inhibits estrogen receptor alpha-DNA binding and reduces target gene transcription and breast cancer cell proliferation

Anacardic acid (AnAc; 2-hydroxy-6-alkylbenzoic acid) is a dietary and medicinal phytochemical with established anticancer activity in cell and animal models. The mechanisms by which AnAc inhibits cancer cell proliferation remain undefined. AnAc 24:1ω5 was purified from geranium (Pelargonium × hortorum) and shown to inhibit the proliferation of estrogen receptor α (ERα)–positive MCF-7 and endocrine-resistant LCC9 and LY2 breast cancer cells with greater efficacy than ERα-negative primary human breast epithelial cells, MCF-10A normal breast epithelial cells, and MDA-MB-231 basal-like breast cancer cells. AnAc 24:1ω5 inhibited cell cycle progression and induced apoptosis in a cell-specific manner. AnAc 24:1ω5 inhibited estradiol (E2)–induced estrogen response element (ERE) reporter activity and transcription of the endogenous E2 target genes pS2, cyclin D1, and cathepsin D in MCF-7 cells. AnAc 24:1ω5 did not compete with E2 for ERα or ERβ binding, nor did AnAc 24:1ω5 reduce ERα or ERβ steady-state protein levels in MCF-7 cells; rather, AnAc 24:1ω5 inhibited ER-ERE binding in vitro. Virtual screening with the molecular docking software Surflex evaluated AnAc 24:1ω5 interaction with ERα ligand binding (LBD) and DNA binding (DBD) domains in conjunction with experimental validation. Molecular modeling revealed AnAc 24:1ω5 interaction with the ERα DBD but not the LBD. Chromatin immunoprecipitation experiments revealed that AnAc 24:1ω5 inhibited E2-ERα interaction with the endogenous pS2 gene promoter region containing an ERE. These data indicate that AnAc 24:1ω5 inhibits cell proliferation, cell cycle progression, and apoptosis in an ER-dependent manner by reducing ER-DNA interaction and inhibiting ER-mediated transcriptional responses. Mol Cancer Ther; 9(3); 594–605

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.

Cell and molecular biology of chemical allergy

OBJECTIVE The objective of this review is to provide current approaches to gain increased understanding of the molecular basis of chemical allergenicity. Chemical allergy refers to an allergic reaction to a low molecular weight agent (ie, <1 kD). The symptoms and pathology of chemical asthma resemble those of allergy to larger sized agents, such as pollens, weeds, and danders. The differences relate to mechanisms of disease. To stimulate an immune response, low molecular weight chemicals function as haptens and bind to carrier macromolecules. This article focuses on the chemical reactions and physicochemical characteristics of chemical allergens. DATA SOURCES Data were obtained from published clinical reports and from the Documentation of Threshold Limit Values (1998) published by the American Congress of Governmental Industrial Hygienists. RESULTS In vitro studies indicate the stoichiometric reaction of some chemical allergens with glutathione and the subsequent transfer of the allergen from glutathione to other nucleophiles. Computer-generated structure-activity relationship models have been developed for chemicals that induce respiratory allergy. The models, based on physicochemical properties of the agents, have high sensitivity and specificity. CONCLUSIONS The structure-activity relationship model suggests that chemical binding is the essential feature of chemical allergens. Their in vivo reactions with thiols may result in glutathione deficiency with consequent alteration in cellular reduction-oxidation (redox) status, release of cytokines, and promotion of the T helper cell 2 phenotype. Prevention of permanent disease is dependent on periodic medical surveillance of affected workers. When detected early, the disease can frequently be reversed.

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 carcinogenicity of diethylstilbestrol: structural evidence for a non-genotoxic mechanism

Abstract An analysis of the structure of diethylstilbestrol (DES) indicates that neither DES nor any of its metabolites are potential mutagens. Moreover, the present analyses suggest (a) that the observed carcinogenic spectrum of DES reflects the activity of metabolic intermediates and (b) that the carcinogenicity of DES in mice is due to the presence of a 6 Å geometric descriptor that appears to be related to an estrogen receptor.

Evaluation of In Silico Models for the Identification of Respiratory Sensitizers

Low molecular weight (LMW) respiratory sensitizers can cause occupational asthma but due to a lack of adequate test methods, prospective identification of respiratory sensitizers is currently not possible. This article presents the evaluation of structure-activity relationship (SAR) models as potential methods to prospectively conclude on the sensitization potential of LMW chemicals. The predictive performance of the SARs calculated from their training sets was compared to their performance on a dataset of newly identified respiratory sensitizers and nonsensitizers, derived from literature. The predictivity of the available SARs for new substances was markedly lower than their published predictive performance. For that reason, no single SAR model can be considered sufficiently reliable to conclude on potential LMW respiratory sensitization properties of a substance. The individual applicability domains (ADs) of the models were analyzed for adequacies and deficiencies. Based on these findings, a tiered prediction approach is subsequently proposed. This approach combines the two SARs with the highest positive and negative predictivity taking into account model specific chemical AD issues. The tiered approach provided reliable predictions for one-third of the respiratory sensitizers and nonsensitizers of the external validation set compiled by us. For these chemicals, a positive predictive value of 96% and a negative predictive value of 89% were obtained. The tiered approach was not able to predict the other two-thirds of the chemicals, meaning that additional information is required and that there is an urgent need for other test methods, e.g., in chemico or in vitro, to reach a reliable conclusion.

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.

Sar modeling of unbalanced data sets

Abstract 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.