Molecular modelling of the mouse cytochrome P450 CYP2F2 based on the CYP102 crystal structure template and selective CYP2F2 substrate interactions.

The results of homology modelling of the mouse cytochrome P450, CYP2F2, are reported, based on the CYP102 crystallographic template. It is found that selective CYP2F2 substrates are able to fit the putative active site of the enzyme via aromatic pi-pi stacking and, in some cases, hydrogen-bonded interactions. Two alkylnaphthalenes were investigated via the model to evaluate whether they are likely to act as CYP2F2 substrates and, of these, 2-isopropyl-naphthalene was found to fit the putative active site, whereas 2-(2-hexadecyl)naphthalene was unable to do this, due to its bulky side-chain. Consequently, it is possible to utilize homology modelling to evaluate the likelihood of enzyme-substrate selectivity for CYP2F2 and predict routes of metabolism mediated by this enzyme. This procedure can therefore be used to investigate the potential for activation of xenobiotics via this enzyme, especially those related to known CYP2F substrates, such as naphthalene.

TIMES-SS--a promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity.

The TImes MEtabolism Simulator platform used for predicting Skin Sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a Consortium comprising industry and regulators. The model was developed with the aim of minimizing animal testing and to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. Here, we describe the extent to which the five OECD principles are met and in particular the results from an external evaluation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine local lymph node assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. Further evaluation of these results highlighted certain inconsistencies which were rationalized by a consideration of reaction chemistry principles for sensitization. Improvements for TIMES-SS were proposed where appropriate. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization hazard under legislative programs such as REACH.

TIMES-SS--a mechanistic evaluation of an external validation study using reaction chemistry principles.

The TImes MEtabolism Simulator platform used for predicting skin sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a consortium comprised of industry and regulators. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic three-dimensional quantitative structure-activity relationships. Here, we describe an external validation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine local lymph node assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. The LLNA results were evaluated with respect to reaction chemistry principles for sensitization. Additional testing on a further four chemicals was carried out to explore some of the specific reaction chemistry findings in more detail. Improvements for TIMES-SS, where appropriate, were put forward together with proposals for further research work. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization potential under legislative programs such as REACH.

Skin sensitization: modeling based on skin metabolism simulation and formation of protein conjugates.

A quantitative structure-activity relationship (QSAR) system for estimating skin sensitization potency has been developed that incorporates skin metabolism and considers the potential of parent chemicals and/or their activated metabolites to react with skin proteins. A training set of diverse chemicals was compiled and their skin sensitization potency assigned to one of three classes. These three classes were, significant, weak, or nonsensitizing. Because skin sensitization potential depends upon the ability of chemicals to react with skin proteins either directly or after appropriate metabolism, a metabolic simulator was constructed to mimic the enzyme activation of chemicals in the skin. This simulator contains 203 hierarchically ordered spontaneous and enzyme controlled reactions. Phase I and phase II metabolism were simulated by using 102 and 9 principal transformations, respectively. The covalent interactions of chemicals and their metabolites with skin proteins were described by 83 reactions that fall within 39 alerting groups. The SAR/QSAR system developed was able to correctly classify about 80% of the chemicals with significant sensitizing effect and 72% of nonsensitizing chemicals. For some alerting groups, three-dimensional (3D)-QSARs were developed to describe the multiplicity of physicochemical, steric, and electronic parameters. These 3D-QSARs, so-called pattern recognition-type models, were applied each time a latent alerting group was identified in a parent chemical or its generated metabolite(s). The concept of the mutual influence amongst atoms in a molecule was used to define the structural domain of the skin sensitization model. The utility of the structural model domain and the predictability of the model were evaluated using sensitization potency data for 96 chemicals not used in the model building. The TIssue MEtabolism Simulator (TIMES) software was used to integrate a skin metabolism simulator and 3D-QSARs to evaluate the reactivity of chemicals thus predicting their likely skin sensitization potency.