Development of an in silico evaluation system that quantitatively predicts skin sensitization using OECD Guideline No. 497 ITSv2 defined approach for skin sensitization classification

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.

Peptide reactivity assays for skin sensitisation - scope and limitations

The direct peptide reactivity assay (DPRA) is an OECD test guideline method that aims to determine if a chemical is reactive enough to be a skin sensitiser. It involves incubation of the test chemical at 5 mMolar concentration for 24 h with a cysteine-based peptide at 0.5 mMolar concentration and measurement of the percentage depletion (DP) of the peptide. The kinetic direct peptide reactivity assay (kDPRA) is derived from the DPRA and involves incubating the peptide with the test chemical at a range of concentrations and incubation times to produce a data matrix of DP values, which is analysed to give a reactivity parameter logk_max that assigns chemicals to the 1A potency class (high potency) if logk_max reaches the threshold value of -2. Here the DPRA, with a threshold of 47% DP, is compared against the kDPRA for their abilities to distinguish between the 1A and non-1A potency classes. It is found that they perform very similarly against a dataset of 157 chemicals with known potency, with only marginal differences in predictive performance. The thresholds of -2.0 (kDPRA) and 47% DP (DPRA) to distinguish 1A sensitisers are not scientific absolutes but the best compromises for a heterogenous set of data containing classes of chemicals for which different thresholds would be applicable. It is concluded that although the kDPRA represents a major advance towards predicting skin sensitisation potency on a continuous basis without animal testing, it offers no significant advantage over the DPRA for the purpose of 1A classification.

Validation of Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) approaches as alternatives to skin sensitization risk assessment

The aim of this study was conducted to validate the physicochemical properties of a total of 362 chemicals [305 skin sensitizers (212 in the previous study + 93 additional new chemicals), 57 non-skin sensitizers (38 in the previous study + 19 additional new chemicals)] for skin sensitization risk assessment using quantitative structure-activity relationship (QSAR)/quantitative structure-property relationship (QSPR) approaches. The average melting point (MP), surface tension (ST), and density (DS) of the 305 skin sensitizers and 57 non-sensitizers were used to determine the cutoff values distinguishing positive and negative sensitization, and correlation coefficients were employed to derive effective 3-fold concentration (EC3 (%)) values. QSAR models were also utilized to assess skin sensitization. The sensitivity, specificity, and accuracy were 80, 15, and 70%, respectively, for the Toxtree QSAR model; 88, 46, and 81%, respectively, for Vega; and 56, 61, and 56%, respectively, for Danish EPA QSAR. Surprisingly, the sensitivity, specificity, and accuracy were 60, 80, and 64%, respectively, when MP, ST, and DS (MP+ST+DS) were used in this study. Further, MP+ST+DS exhibited a sensitivity of 77%, specificity 57%, and accuracy 73% when the derived EC3 values were classified into local lymph node assay (LLNA) skin sensitizer and non-sensitizer categories. Thus, MP, ST, and DS may prove useful in predicting EC3 values as not only an alternative approach to animal testing but also for skin sensitization risk assessment.

Quantitative structure-activity and quantitative structure-property relationship approaches as alternative skin sensitization risk assessment methods

This study aimed to predict skin sensitization potency of selected chemicals by quantitatively analyzing their physicochemical properties by employing quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) approaches as alternative risk assessment methods to animal testing. Correlations between effective concentration for a stimulation index of 3 (EC3) (%), the amount of a chemical required to elicit a threefold increase in lymph node cell proliferative activity (stimulation index, ≥3), were calculated using local lymph node assay (LLNA) and physicochemical properties of 212 skin sensitizers and 38 non-sensitizers were investigated. The correlation coefficients between melting point (MP) and EC3 and between surface tension (ST) and EC3 were 0.65 and 0.69, respectively. The correlation coefficient for MP + ST and EC3 was estimated to be 0.72. Thus, correlation coefficients between EC3 and MP, ST, and MP + ST reliably predicted the skin sensitization potential of the chemicals with sensitivities of 72% (126/175), 70% (122/174), and 73% (116/158); specificities of 77% (27/35), 69% (22/32), and 81% (26/32); and accuracies of 73% (153/210), 70% (144/206), and 75% (142/190), respectively. Our findings suggest that the EC3 value may be more accurately predicted using the ST values of chemicals as opposed to MP values. Thus, information on MP and ST parameters of chemicals might be useful for predicting the EC3 values as not only an alternative approach to animal testing, but as a risk assessment method for skin sensitization.