Fragrance allergens: Classification and ranking by QSAR

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

Sesquiterpene Variation in West Australian Sandalwood (Santalum spicatum)

West Australian sandalwood (Santalum spicatum) has long been exploited for its fragrant, sesquiterpene-rich heartwood; however sandalwood fragrance qualities vary substantially, which is of interest to the sandalwood industry. We investigated metabolite profiles of trees from the arid northern and southeastern and semi-arid southwestern regions of West Australia for patterns in composition and co-occurrence of sesquiterpenes. Total sesquiterpene content was similar across the entire sample collection; however sesquiterpene composition was highly variable. Northern populations contained the highest levels of desirable fragrance compounds, α- and β-santalol, as did individuals from the southwest. Southeastern populations were higher in E,E-farnesol, an undesired allergenic constituent, and low in santalols. These trees generally also contained higher levels of α-bisabolol. E,E-farnesol co-occurred with dendrolasin. Contrasting α-santalol and E,E-farnesol chemotypes revealed potential for future genetic tree improvement. Although chemical variation was evident both within and among regions, variation was generally lower within regions. Our results showed distinct patterns in chemical diversity of S. spicatum across its natural distribution, consistent with earlier investigations into sandalwood population genetics. These results are relevant for plantation tree improvement and conservation efforts.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71-88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation.

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R²=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q²_ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential.

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity end-points associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

Prediction of Acute in vivo Toxicity of Some Amine and Amide Drugs to Rats by Multiple Linear Regression, Partial Least Squares and an Artificial Neural Network

The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively.