TY - JOUR
T1 - Mechanism-informed read-across assessment of skin sensitizers based on SkinSensDB
AU - Tung, Chun Wei
AU - Wang, Chia Chi
AU - Wang, Shan Shan
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Integrative testing strategies using adverse outcome pathway (AOP)-based alternative assays for assessing skin sensitizers show the potential for replacing animal testing. However, the application of alternative assays for a large number of chemicals is still time-consuming and expensive. In order to facilitate the assessment of skin sensitizers based on integrative testing strategies, a mechanism-informed read-across assessment method was proposed and evaluated using data from SkinSensDB. First, the prediction performance of two integrated testing strategy models was evaluated giving the highest area under the receiver operating characteristic curve (AUC) values of 0.928 and 0.837 for predicting human and LLNA data, respectively. The proposed read-across prediction method achieves AUC values of 0.957 and 0.802 for predicting human and LLNA data, respectively, with interpretable activation statuses of AOP events. As data grows, a better prediction performance is expected. A user-friendly tool has been constructed and integrated into SkinSensDB that is publicly accessible at http://cwtung.kmu.edu.tw/skinsensdb.
AB - Integrative testing strategies using adverse outcome pathway (AOP)-based alternative assays for assessing skin sensitizers show the potential for replacing animal testing. However, the application of alternative assays for a large number of chemicals is still time-consuming and expensive. In order to facilitate the assessment of skin sensitizers based on integrative testing strategies, a mechanism-informed read-across assessment method was proposed and evaluated using data from SkinSensDB. First, the prediction performance of two integrated testing strategy models was evaluated giving the highest area under the receiver operating characteristic curve (AUC) values of 0.928 and 0.837 for predicting human and LLNA data, respectively. The proposed read-across prediction method achieves AUC values of 0.957 and 0.802 for predicting human and LLNA data, respectively, with interpretable activation statuses of AOP events. As data grows, a better prediction performance is expected. A user-friendly tool has been constructed and integrated into SkinSensDB that is publicly accessible at http://cwtung.kmu.edu.tw/skinsensdb.
KW - Adverse outcome pathway
KW - Read-across
KW - Skin sensitizer
KW - SkinSensDB
UR - http://www.scopus.com/inward/record.url?scp=85042730172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042730172&partnerID=8YFLogxK
U2 - 10.1016/j.yrtph.2018.02.014
DO - 10.1016/j.yrtph.2018.02.014
M3 - Article
C2 - 29486270
AN - SCOPUS:85042730172
VL - 94
SP - 276
EP - 282
JO - Regulatory Toxicology and Pharmacology
JF - Regulatory Toxicology and Pharmacology
SN - 0273-2300
ER -