Development of an in silico evaluation system that quantitatively predicts skin sensitization using OECD Guideline...

The Integrated Testing Strategy version 2 (ITSv2) Defined Approach, which is a reliable skin sensitization hazard and multi-step risk assessment method, does not support quantitative risk assessment such as local lymph node assay EC3 values. In this study, we developed a high-performance in silico evaluation system that quantitatively predicts the EC3 values of chemical substances by combining the ITSv2 Defined Approach for hazard identification (ITSv2 HI) with machine learning models. This system uses in chemico/in vitro test data, molecular descriptors, and distance information based on read-across concepts as explanatory variables. The system achieves an R² value of 0.617 on external-validation data. Substances misclassified in ITSv2 HI are considered to have properties that do not match the correspondence between tests expressing the adverse outcome pathway assumed in the ITSv2 Defined Approach and skin sensitization. Therefore, ITSv2 HI is assumed to be correct within the applicability domains of this system. When using only substances within the applicability domains to reconstruct CatBoost models, the R² value reached 0.824 on the external-validation data, representing an improvement in system performance. The results demonstrate the utility of explanatory variables that reflect the read-across concept and the advantages of integrating multiple prediction methods.