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Grace Patlewicz

Publications and source records attributed to Grace Patlewicz.

12 recordsLinked to original sources

Electrophilic chemistry related to skin sensitization. Reaction mechanistic applicability domain classification for a published data set of 106 chemicals tested in the mouse local lymph node assay.

This article presents an overview of electrophilic reaction mechanisms relevant to skin sensitization, with reference to a published skin sensitization test data set for 106 chemicals. Where appropriate to aid the interpretation, additional data on a small number of further compounds are also discussed. It is shown that there is a close correspondence in the way differences and similarities in skin sensitization potency of chemicals relate to differences and similarities in their physical organic chemistry and electrophilic reaction mechanistic chemistry. The 106 chemicals are classified into their reaction mechanistic applicability domains, and reactivity-sensitization trends are analyzed for each domain: the Michael acceptor and pro-Michael acceptor electrophile domain; the SNAr electrophile domain; the SN2 electrophile domain; the Schiff base electrophile domain; the acyl transfer electrophile domain; and the non-electrophilic non-pro-electrophilic domain. The last of these domains should be populated mainly by non-sensitizers. Classification of 87 of the 106 compounds, using these domains, was straightforward. In most of the domains and subdomains where there are sufficient compounds, clear trends can be seen, in conformity with the Relative Alkylation Index (RAI) model, between sensitization potential and reactivity/hydrophobicity. Of the remaining 19 compounds, 7 are alpha-X-methyl-gamma-lactones that on the basis of published organic chemistry studies and guinea pig sensitization data can be classed as pro-Michael acceptors by elimination of HX but that are mostly negative in the LLNA, indicating a difference in bioactivation capabilities between mice and guinea pigs. The other 12 compounds, whose chemistry was not immediately obvious, were found after further analysis and literature research to fit into appropriate mechanistic domains that rationalize their skin sensitizing properties.

Animals↗

Mechanistic applicability domains for non-animal based prediction of toxicological endpoints. QSAR analysis of the schiff base applicability domain for skin sensitization.

Several recent (1999 onward) publications on skin sensitization to aldehydes and ketones, which can sensitize by covalent binding to skin protein via Schiff base formation, present QSARs based on the Taft sigma parameter to model reactivity and log P to model hydrophobicity. Here, all of the data are reanalyzed together in a stepwise self-consistent way using the parameters log P (octanol/water) and Sigmasigma, the latter being the sum of Taft sigma values for the two groups R and R' in RCOR'. A QSAR is derived: pEC3 = 1.12(+/-0.07) Sigmasigma + 0.42(+/-0.04) log P - 0.62(+/-0.13); n = 16 R(2) = 0.952 R(2)(adj) = 0.945 s = 0.12 F = 129.6, based on mouse local lymph node assay (LLNA) data for 11 aliphatic aldehydes, 1 alpha-ketoester and 4 alpha,beta-diketones. In developing this QSAR, an initial regression equation for a training set of 10 aldehydes was found to predict a test set consisting of the other 6 compounds. The QSAR is found to be well predictive for LLNA data on a series of alpha,gamma-diketones and also correctly predicts the nonsensitizing properties of simple dialkylketones. It is shown to meet all of the criteria of the OECD principles for applicability within regulatory practice. In view of the structural diversity within the sets of compounds considered here, the present findings confirm the view that within the mechanistic applicability domain the differences in sensitization potential are dependent solely on differences in chemical reactivity and partitioning.

Benzaldehydes↗

Non-enzymatic glutathione reactivity and in vitro toxicity: a non-animal approach to skin sensitization.

The development of non-animal methods to predict the potential of chemicals to cause skin sensitization is of great importance. On the basis of many published studies into the underlying chemical mechanisms skin sensitization, the immunological priming which leads to the disease allergic contact dermatitis, is recognized as a reactive chemistry endpoint. Consequently, the combination of chemical assays with in vitro techniques may provide a useful surrogate to animal testing for skin sensitization. This study attempts to investigate the relationship between skin sensitization assessed in the local lymph node assay (LLNA) initially and a thiol reactivity index based on glutathione (GSH), pEC(50) thiol (EC(50) being defined as the concentration of the test substance which gives 50% depletion of free thiol under standard conditions) in combination with a measure of cytotoxicity (pIGC(50)) to Tetrahymena pyriformis (TETRATOX). The pEC(50) thiol values and the pIGC(50) values were determined for twenty-four compounds for which LLNA test data were available. Thiol reactivity was found to discriminate sensitizers from non-sensitizers according to the rule: pEC(50) thiol>-0.55 indicates that the compound will be a skin sensitizer. However, because of metabolic activation a pEC(50) thiol<-0.55 does not necessarily mean that the compound will be a non-sensitizer. Excess toxicity to T. pyriformis (i.e. the extent of toxic potency over that expected by non-polar narcosis) was determined in order to assess biological reactivity. The best discrimination based on excess toxicity in the TETRATOX assay was given by the "rule": excess toxicity>0.50 indicates that the compound will be a skin sensitizer. These approaches become more powerful when combined. When taken together, the thiol and TETRATOX assays predict the sensitization potential of 23 of the 24 compounds correctly. alpha-Hexylcinnamic aldehyde is incorrectly predicted to be a non-sensitizer, whereas LLNA results suggest it may be a weak sensitizer, this inaccuracy being rationalized in terms of its high hydrophobicity. Due to the selectivity of electro(nucleo)philic reactions some sensitizing compounds will not be identified using a single nucleophile such as thiol.

Allergens↗

Skin sensitization: reaction mechanistic applicability domains for structure-activity relationships.

The prediction of skin sensitization potential with minimum animal testing is currently of great importance in light of forthcoming legislation. A number of structure-activity relationships for skin sensitization have been published over the years, but their applicability has often been limited to structural classes. The concept of an applicability domain for a quantitative structure-activity relationship [(Q)SAR] is increasingly being viewed as key for the predictive application of (Q)SARs. This is particularly the case for skin sensitization if more widely applicable SARs are to be developed. In this paper, we analyze a recently published chemical data set for skin sensitization, apply reaction mechanistic criteria to domain classification, and evaluate the structure-activity trends observed within each of these mechanistic domains.

Acylation↗

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↗

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↗

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↗

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↗

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↗

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↗

From knowledge generation to knowledge archive. A general strategy using TOPS-MODE with DEREK to formulate new alerts for skin sensitization.

A general strategy for knowledge flow concerning skin sensitization based on the combined use of TOPS-MODE and DEREK expert system is proposed. TOPS-MODE is used as a knowledge generator, while DEREK represents the knowledge archive. A TOPS-MODE classification model allows the identification of structural fragments and groups responsible for strong/moderate skin sensitization. These structural contributions are sorted, analyzed, and graphically displayed in an appropriate way allowing the identification of several structural alerts for skin sensitization. Nine structural alerts already implemented in DEREK are identified using this strategy. They comprise, among others, alkyl halides, aldehydes, alpha,beta-unsaturated compounds, aromatic amines, phenols, hydroquinone, isothiazolinone, and alkyl sulfonates. Four new hypotheses are generated using TOPS-MODE structural contributions to skin sensitization, which are not recognized as structural alerts by DEREK. They include the reduction of aromatic nitro groups and epoxidation reaction of double bonds as metabolic activation steps that can lead to reactive haptens which can trigger the skin sensitization mechanism. Another new alert is based on 1,2,5-thiadiazole-1,1-dioxide for which we have identified a possible mechanism explaining its strong skin sensitization profile. It is based on the existence of a tautomeric equilibrium and further reaction with nucleophiles, which are both supported by experimental evidence. Finally, we have identified a possible new mechanism for the skin sensitization of nonreactive compounds, which involves the formation of noncovalent complexes with proteins in a processing- and metabolism-independent way.

Journal Article↗

A stepwise approach for defining the applicability domain of SAR and QSAR models.

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

Animals↗