Evaluation of combinations of in vitro sensitization test descriptors for the artificial neural network-based risk assessment model of skin sensitization

被引:44
|
作者
Hirota, Morihiko [1 ]
Fukui, Shiho [2 ]
Okamoto, Kenji [2 ]
Kurotani, Satoru [3 ]
Imai, Noriyasu [3 ]
Fujishiro, Miyuki [4 ]
Kyotani, Daiki [4 ]
Kato, Yoshinao [5 ]
Kasahara, Toshihiko [6 ]
Fujita, Masaharu [6 ]
Toyoda, Akemi [7 ]
Sekiya, Daisuke [8 ]
Watanabe, Shinichi [8 ]
Seto, Hirokazu [9 ]
Takenouchi, Osamu [10 ]
Ashikaga, Takao [1 ]
Miyazawa, Masaaki [10 ]
机构
[1] Shiseido Co Ltd, Shiseido Res Ctr, Tsuzuki Ku, Yokohama, Kanagawa 2248558, Japan
[2] Kanebo Cosmet Inc, Odawara, Kanagawa 2500002, Japan
[3] Kose Corp, Itabashi Ku, Tokyo 1740051, Japan
[4] Cosmos Tech Ctr Co Ltd, Itabashi Ku, Tokyo 1740046, Japan
[5] Nippon Menard Cosmet Co Ltd, Nishi Ku, Nagoya, Aichi 4510071, Japan
[6] Fujifilm Corp, Minamiashigara, Kanagawa 2500193, Japan
[7] Pola Chem Ind Inc, Totsuka Ku, Yokohama, Kanagawa 2440812, Japan
[8] Lion Corp, Odawara, Kanagawa 2560811, Japan
[9] P&G Japan KK, Higashinada Ku, Kobe, Hyogo 6580032, Japan
[10] Kao Corp, Ichikai, Tochigi 3213497, Japan
关键词
skin sensitization; risk assessment; SH test; h-CLAT; ARE; DPRA; artificial neural network; JCIA; LYMPH-NODE ASSAY; TEST H-CLAT; SCREENING CONTACT ALLERGENS; PIG MAXIMIZATION TEST; HUMAN CELL-LINES; PEPTIDE REACTIVITY; DENDRITIC CELLS; TNF-ALPHA; POTENCY; CHEMICALS;
D O I
10.1002/jat.3105
中图分类号
R99 [毒物学(毒理学)];
学科分类号
100405 ;
摘要
The skin sensitization potential of chemicals has been determined with the use of the murine local lymph node assay (LLNA). However, in recent years public concern about animal welfare has led to a requirement for non-animal risk assessment systems for the prediction of skin sensitization potential, to replace LLNA. Selection of an appropriate in vitro test or in silico model descriptors is critical to obtain good predictive performance. Here, we investigated the utility of artificial neural network (ANN) prediction models using various combinations of descriptors from several in vitro sensitization tests. The dataset, collected from published data and from experiments carried out in collaboration with the Japan Cosmetic Industry Association (JCIA), consisted of values from the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA), SH test and antioxidant response element (ARE) assay for chemicals whose LLNA thresholds have been reported. After confirming the relationship between individual in vitro test descriptors and the LLNA threshold (e.g.EC3 value), we used the subsets of chemicals for which the requisite test values were available to evaluate the predictive performance of ANN models using combinations of h-CLAT/DPRA (N = 139 chemicals), the DPRA/ARE assay (N = 69), the SH test/ARE assay (N = 73), the h-CLAT/DPRA/ARE assay (N = 69) and the h-CLAT/SH test/ARE assay (N = 73). The h-CLAT/DPRA, h-CLAT/DPRA/ARE assay and h-CLAT/SH test/ARE assay combinations showed a better predictive performance than the DPRA/ARE assay and the SH test/ARE assay. Our data indicates that the descriptors evaluated in this study were all useful for predicting human skin sensitization potential, although combinations containing h-CLAT (reflecting dendritic cell-activating ability) were most effective for ANN-based prediction. Copyright (c) 2015 John Wiley & Sons, Ltd. The skin sensitization potential of chemicals has been determined with the use of the murine local lymph node assay (LLNA). However, in recent years public concern about animal welfare has led to a requirement for non-animal risk assessment systems for the prediction of skin sensitization potential, to replace LLNA. Selection of an appropriate in vitro test or in silico model descriptors is critical to obtain good predictive performance
引用
收藏
页码:1333 / 1347
页数:15
相关论文
共 50 条
  • [21] HOGPred: artificial neural network-based model for orphan GPCRs
    Kaushik, Aman Chandra
    Sahi, Shakti
    NEURAL COMPUTING & APPLICATIONS, 2018, 29 (04): : 985 - 992
  • [22] HOGPred: artificial neural network-based model for orphan GPCRs
    Aman Chandra Kaushik
    Shakti Sahi
    Neural Computing and Applications, 2018, 29 : 985 - 992
  • [23] Development of an in vitro skin sensitization test based on ROS production in THP-1 cells
    Saito, Kazutoshi
    Miyazawa, Masaaki
    Nukada, Yuko
    Sakaguchi, Hitoshi
    Nishiyama, Naohiro
    TOXICOLOGY IN VITRO, 2013, 27 (02) : 857 - 863
  • [24] Evaluation of a human skin equivalent model as an in vitro system to evaluate the contact sensitization potential of chemicals
    Sikorski, EE
    Gerberick, GF
    Limardi, LC
    JOURNAL OF INVESTIGATIVE DERMATOLOGY, 1996, 106 (04) : 804 - 804
  • [25] Artificial neural network-based evaluation of layer modulus of asphalt pavement
    Key Laboratory of Road and Traffic Engineering, Tongji University, Shanghai 200092, China
    Tongji Daxue Xuebao, 2007, 8 (1044-1047+1148):
  • [26] Evaluation of the Efficacy of Artificial Neural Network-Based Music Therapy for Depression
    Ding, Qian
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [27] Assessment of a defined approach based on a stacking prediction model to identify skin sensitization hazard
    Tourneix, Fleur
    Alepee, Nathalie
    Detroyer, Ann
    Eilstein, Joan
    Teissier, Silvia Martinozzi
    Nardelli, Laurent
    Nocairi, Hicham
    Pauloin, Thierry
    Piroird, Cecile
    Del Bufalo, Aurelia
    TOXICOLOGY IN VITRO, 2019, 60 : 134 - 143
  • [28] Integrated Skin Sensitization Assessment Based on OECD Methods (I): Deriving a Point of Departure for Risk Assessment
    Natsch, Andreas
    Gerberick, George Frank
    ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION, 2022, 39 (04) : 636 - 646
  • [29] An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate
    Huseyin Ince
    Ali Fehim Cebeci
    Salih Zeki Imamoglu
    Computational Economics, 2019, 53 : 817 - 831
  • [30] Artificial Neural Network-Based Algorithm for ARMA Model Order Estimation
    Al-Qawasmi, Khaled E.
    Al-Smadi, Adnan M.
    Al-Hamami, Alaa
    NETWORKED DIGITAL TECHNOLOGIES, PT 2, 2010, 88 : 184 - +