Biomedical subjects

Grace Patlewicz

Publications and source records attributed to Grace Patlewicz.

6 recordsLinked to original sources

PubMed2004

Creating molecular diversity from antioxidants in Brazilian propolis. Combination of TOPS-MODE QSAR and virtual structure generation.

A QSAR model for antioxidative activity based on the Sub-Structural Molecular Design (TOPS-MODE) approach is developed for a series of compounds present in Brazilian propolis. This approach permitted the structural interpretation of the antioxidative activity of these compounds in terms of bond contributions. By these means we have identified the structural groups and regions that contribute to the antioxidative activity of the cinnamic acid and flavonoid derivatives present in the propolis. These results were then used to identify the positions and substituents to be used in a virtual compound generation experiment. Using this approach a total of 327 compounds were generated from which more than 70 are predicted to be more active than the most powerful antioxidants in the Brazilian propolis. From these 70 compounds less than 20 have been reported in the literature. Consequently, a high proportion of novel compounds with potential antioxidative activity has been identified by the current approach. This contributes to enhance the molecular diversity of the analogues of Brazilian propolis compounds with antioxidative properties.

Antioxidants↗

PubMed2003

Computer-aided knowledge generation for understanding skin sensitization mechanisms: the TOPS-MODE approach.

The TOPS-MODE (topological substructural molecular descriptors) approach is used to derive models for understanding the molecular structural contribution to skin sensitization. A data set of 93 compounds was used in the development of the models; 29 new skin sensitization values (EC3) are reported here for the first time. The models developed possess high predictivity and have been validated through the use of cross-validation and external validation sets. The models have enabled the formulation of potential new structural alerts far faster and using less data than typically required by traditional approaches. Structural contributions to skin sensitization for various classes of chemicals are presented on the basis of bond contributions. The models have also been able to identify potential structural alerts for chemicals requiring metabolic activation.

Animals↗

PubMed2003

Quantitative structure-activity relationships for predicting skin and respiratory sensitization.

Quantitative structure-activity relationships (QSARs) for predicting skin and respiratory sensitization are reviewed. Overall, progress has been hampered by the sparseness of good quality experimental data, a fact that makes it difficult, at this time, to recommend one or two QSARs for predicting skin and respiratory sensitization. Creation of appropriate data sets for uninvestigated classes of chemicals by experimentation should facilitate the development of more robust QSARs for predicting skin and respiratory sensitization. Such QSARs will be valuable in the evaluation of identifiable toxic hazards where dose responses are relevant, as is the case for skin and respiratory sensitization.

Administration, Cutaneous↗

PubMed2003

Quantitative structure-activity relationships for predicting skin and eye irritation.

Recent quantitative structure-activity relationships (QSARs) for the prediction of skin and eye irritation were reviewed. The QSARs in these areas are hindered by the lack of quality in vivo data and by a lack of understanding of the mechanisms of action. Creation of appropriate data sets for experimentation would facilitate the development of robust QSARs for predicting skin and eye irritation.

Animals↗

PubMed2003

Quantitative structure-activity relationships for predicting percutaneous absorption rates.

Quantitative structure-activity relationships (QSARs) for predicting percutaneous absorption rates were reviewed. Overall progress has been hampered by the sparseness of good quality experimental data. A number of researchers have used the same data set to develop QSARs for predicting percutaneous absorption rates, a fact that makes it difficult, at this time, to recommend one or two QSARs for predicting percutaneous absorption rates. Identification of chemicals within domains of large chemical universes that should be tested to improve QSARs and the subsequent development of experimental percutaneous absorption rates for those chemicals will facilitate the development of more robust QSARs for predicting percutaneous absorption rates.

Absorption↗

PubMed2003

Quantitative structure-activity relationships for predicting mutagenicity and carcinogenicity.

Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed. The QSARs for predicting mutagenicity and carcinogenicity have been mostly limited to specific classes of chemicals (e.g., aromatic amines and heteroaromatic nitro chemicals). The motivation to develop QSARs for predicting mutagenicity and carcinogenicity to screen inventories of chemicals has produced four major commercially available computerized systems that are able to predict these endpoints: Deductive estimation of risk from existing knowledge (DEREK) toxicity prediction by komputer assisted technology (TOPKAT), computer automated structure evaluation (CASE), and multiple computer automated structure evaluation (Multicase). A brief overview of these and some other expert systems for predicting mutagenicity and carcinogenicity is provided. The other expert systems for predicting mutagenicity and carcinogenicity include automatic data analysis using pattern recognition techniques (ADAPT), QSAR Expert System (QSAR-ES), OncoLogic computer optimized molecular parametric analysis of chemical toxicity system (COMPACT), and common reactivity pattern (COREPA).

Animals↗