A development of a graph‐based ensemble machine learning model for skin sensitization hazard and potency assessment. Issue 11 (20th July 2022)

• Record Type: Journal Article
• Authors: Jeon, Byoungjun; Lim, Min Hyuk; Choi, Tae Hyun; Kang, Byeong‐Cheol; Kim, Sungwan
• Is Part Of: Journal of applied toxicology. Volume 42:Issue 11(2022)
• Page Start: 1832
• Page End: 1842
• Publication Date: 2022-07-20
• Subjects: defined approach -- direct peptide reactivity assay -- graph neural network -- human cell line activation test -- integrated testing strategy -- KeratinoSens™ -- machine learning -- risk assessment -- skin sensitization; Toxicology -- Periodicals; Industrial toxicology -- Periodicals; Environmentally induced diseases -- Periodicals; Toxicology -- Periodicals; 615.9005
• DOI: 10.1002/jat.4361
• Languages: English
• ISSNs: 0260-437X

Abstract

Abstract: Many defined approaches (DAs) for skin sensitization assessment based on the adverse outcome pathway (AOP) have been developed to replace animal testing because the European Union has banned animal testing for cosmetic ingredients. Several DAs have demonstrated that machine learning models are beneficial. In this study, we have developed an ensemble prediction model utilizing the graph convolutional network (GCN) and machine learning approach to assess skin sensitization. The model integrates in silico parameters and data from alternatives to animal testing of well‐defined AOP to improve DA predictivity. Multiple ensemble models were created using the probability produced by the GCN with six physicochemical properties, direct peptide reactivity assay, KeratinoSens™, and human cell line activation test (h‐CLAT), using a multilayer perceptron approach. Models were evaluated by predicting the testing set's human hazard class and three potency classes (strong, weak, and non‐sensitizer). When the GCN feature was used, 11 models out of 16 candidates showed the same or improved accuracy in the testing set. The ensemble model with the feature set of GCN, KeratinoSens™, and h‐CLAT produced the best results with an accuracy of 88% for assessing human hazards. The best three‐class potency model was created with the feature set of GCN and all three assays, resulting in 64% accuracy. These results from the ensemble approach indicate that the addition of the GCN feature could provide an improved predictivity of skin sensitization hazard and potency assessment. Abstract : Several machine learning‐based approaches have been proposed to replace animal testing and improve skin sensitization predictability. This study developed and evaluated an ensemble model of a GCN and multilayer perceptron for skin sensitization hazard and potency. The testing accuracy of ensemble models suggests that the addition of GCN can improve skin sensitization predictability.