Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

Anticancer SAR models for MCF-7 and MDA-MB-231 breast cell lines.

The National Cancer Institute's Developmental Therapeutics Program (DTP) maintains the screening results obtained in 60 standardized cancer cell lines for ~43,000 compounds. Here the application of the categorical structure-activity relationship (cat-SAR) program for the identification of the structural attributes of identified compounds that display differential cytostatic or cytotoxic activity to one breast cancer cell line and not another is reported. The goal of this approach is to separate features associated with antiproliferative activity towards many cell lines from those that affect only a specific cell type. To assess this approach, SAR models were developed for cytostatic and cytotoxic activity against the human breast cancer cell lines MCF-7 and MDAMB-231 and three differential activity models for compounds that were potent cytostatic and cytotoxic agents in MCF-7 cells, but relatively inactive against MDA-MB-231 cells. The MCF-7 and MDA-MB-231 models comprised the most potent 200 active and least potent 200 inactive compounds found in the DTP database and the differential activity models comprised 200 compounds potent in one cell line and not the other and 200 compounds equally potent between the cell lines. Leave-one-out validations of the individual MCF-7 and MDA-MB-231 models returned values between 83 and 85% concordance, with values obtained between 66 and 76% concordance for the differential activity models. The cat-SAR approach identified the chemical attributes associated with cytostatic and cytotoxic activity for the MCF-7 and MDA-MB-231 breast cancer cell lines included in the DTP and furthermore, were able to differentiate the selective activity of compounds between the two breast cancer lines. Thus it is conceivable that such cell line-specific mechanisms could be exploited for the discovery of highly specific anti-breast cancer agents and could also potentially facilitate the development of SAR models with sufficient resolution and clarity to identify chemical moieties associated with antiproliferative activity towards selective individual cancer types while being innocuous to other cell types.

A categorical structure-activity relationship analysis of the developmental toxicity of antithyroid drugs.

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.