Coupling between human brain activity and body movements: Insights from non-invasive electromagnetic recordings

info:eu-repo/semantics/publishe

Development of a QSAR model for respiratory irritation

Question The RespiraTox project, funded by NC3R CrackIT, develops a QSAR model, which aims to predict the potential of individual compounds to cause irritation in the respiratory tract. We distinguished two mode of actions, i) "sensory irritation", characterized by a decrease in breathing rates, and ii) "tissue irritation" characterized by primarily histopathological findings. If possible, the QSAR model will lead to a reduction of de novo animal testing. Methods We based the classification "irritating to respiratory tract" on several in vivo studies with inhalation exposure. In a tiered approach, we considered information from studies with acute exposure from the ECHA CHEM database; the Hazardous Substance database; the harmonized classification and labelling inventory from ECHA, and repeated dose studies from the Fraunhofer RepDose database. Prior to model development, the CAS numbers and compound structures were quality controlled and corrected. Multiple features were generated from these structures: a) structural descriptors (ECFPS), and b) physico-chemical properties. We explored several machine learning algorithms including Logistic Regression, Random Forests, and Gradient Boosted Decision Trees to derive a classification model. The internal validation procedure employed stratified k-fold cross-validation (k=5). Results The final dataset includes about 2500 irritating and 800 non-irritating individual compounds. The criteria for success of a given model is the measured Area Under ROC-Curve (AUC). The AUC for Logistic Regression is 0.71 using the combined feature set and 0.78 for both Random Forest and Gradient Boosted Decision Trees. The applicability domain is determined by features with highest impact on the final model. We additionally investigated five read-across groups within the training set to better understand the performance of the model. Each read-across group represents a set of structurally similar compounds, with a common toxicological mode of action. Within these groups, predicted and experimental values are compared. Conclusion The developed QSAR model predicts respiratory irritation with reasonable accuracy. To promote its use and acceptance, the final model can be accessed online via an user-friendly interface. The tool provides, a prediction for untested compound and in addition shows nearest neighbours with their experimental data from the training set. Our current approach will be further refined and improved e.g. by differentiating sensory and tissue irritation

Development of a QSAR model to predict respiratory irritation by individual constituents

The RespiraTox project, funded by NC3R CrackIT, develops a QSAR model, which predicts the potential of individual compounds to cause irritation in the respiratory tract. We distinguished two mode of actions, i) “sensory irritation”, characterized by a decrease in breathing rates, and ii) “tissue irritation” characterized by primarily histopathological findings. QSAR models rely on high quality datasets. We based the classification “irritating to respiratory tract” on several data types from in vivo studies with inhalation exposure. In a tiered approach, we considered information from i) studies with acute exposure from the ECHA CHEM database (DB), ii) the Hazardous Substance DB, iii) the harmonized classification and labelling inventory from ECHA, and iv) repeated dose studies from the Fraunhofer RepDose DB. For later stage validation, we withhold human data from Fraunhofer Breath DB. The final dataset includes about 2500 irritating and 800 non-irritating compounds. Prior to model development, the CAS numbers and compound structures were quality controlled and corrected. Two kinds of information were generated from the compounds structures: i) structural descriptors (ECFPS), and ii) physico-chemical properties. We explored several machine learning algorithms including Logistic Regression (LR), Random Forests (RF), and Gradient Boosted Decision Trees (BT) to derive a classification model. The internal validation procedure employed stratified k-fold cross-validation (k=5). The overall approach adheres to the five OECD principles. The criteria used to measure performance of a given model is the Area Under ROC-Curve (AUC). The AUC for LR using the combined feature set is 0.71. The optimal performance for both RF and BT is 0.78. The applicability domain is determined by features with highest impact on the final model. The current approach will be further refined and improved (e.g. by differentiating sensory and tissue irritation). The final model will be provided online as user-friendly interface to promote its use by toxicologists, regulators, and overall to reduce the testing of animals