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.
shortcoming, we have developed an approach that does not reflect the restricted bias of the chemicals that make up the Department of Environmental and Occupational Health, Graduate School of database from which the SAR model is derived, but allows Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA extrapolation to the 'universe of chemicals' (23). Essentially, 1 To whom correspondence should be addressed.rather than looking for structural overlaps among SAR models, Email: rsnkranz@pitt.edu we chose 10 000 chemical representatives of the 'universe of The mechanistic relationship of the inhibition of gap juncchemicals' (24). The various biological/toxicological properties tional intercellular communication (GJIC) to other toxicoof these chemicals are predicted using validated SAR models. logical phenomena was explored using a recently developedThe prevalence of chemicals predicted to possess two toxicolomethod that models the properties of a large population of gical properties simultaneously is then determined and commolecules chosen to represent the 'universe of chemicals'.pared with that expected. The rationale of the approach as The analyses indicate that inhibition of GJIC is strongly well as the interpretation of the results are described below. linked to the carcinogenic process in rodents, to cellular We applied this approach to the phenomenon of inhibition but not systemic toxicity, to biological phenomena that may of GJIC to determine whether it demonstrated the expected involve inflammatory processes and to development effects.interactions and/or whether in addition it generated new testable The inhibition of GJIC appears not to be associated with mechanistic hypotheses. genotoxic mechanisms. With respect to cancer causation, integration of the analyses suggests that inhibition of GJIC Materials and methods is involved in non-genotoxic cancer induction or in the SAR methodology
non-genotoxic phases of the carcinogenic process (such asFor these studies we used the CASE/MULTICASE SAR expert systems inflammation, cell toxicity, cell proliferation, inhibition of described previously (25,26). Application of this methodology results in the cell differentiation and apoptosis).development of four submodels, each of which are derived from different algorithms and useful for investigating different aspects of the biological phenomena under consideration. The projections of the four individual submodels were integrated into a single prediction based upon Bayes' theorem
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.
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.
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.
The choice of therapeutic strategies for hyperthyroidism during pregnancy is limited. Surgery and radioiodine are typically avoided, leaving propylthiouracil and methimazole in the US. Carbimazole, a metabolic precursor of methimazole, is available in some countries outside of the US. In the US propylthiouracil is recommended because of concern about developmental toxicity from methimazole and carbimazole. Despite this recommendation, the data on developmental toxicity of all three agents are extremely limited and insufficient to support a policy given the broad use of methimazole and carbimazole around the world. In the absence of new human or animal data we describe the development of a new structure-activity relationship (SAR) model for developmental toxicity using the cat-SAR expert system. The SAR model was developed from data for 323 compounds evaluated for human developmental toxicity with 130 categorized as developmental toxicants and 193 as nontoxicants. Model cross-validation yielded a concordance between observed and predicted results between 79% to 81%. Based on this model, propylthiouracil, methimazole, and carbimazole were observed to share some structural features relating to human developmental toxicity. Thus given the need to treat women with Graves's disease during pregnancy, new molecules with minimized risk for developmental toxicity are needed. To help meet this challenge, the cat-SAR method would be a useful in screening new drug candidates for developmental toxicity as well as for investigating their mechanism of action.
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