PMID- 24444449
OWN - NLM
STAT- MEDLINE
DCOM- 20150629
LR  - 20220311
IS  - 1879-3177 (Electronic)
IS  - 0887-2333 (Linking)
VI  - 28
IP  - 4
DP  - 2014 Jun
TI  - Skin sensitization risk assessment model using artificial neural network analysis 
      of data from multiple in vitro assays.
PG  - 626-39
LID - S0887-2333(14)00006-X [pii]
LID - 10.1016/j.tiv.2014.01.003 [doi]
AB  - The sensitizing potential of chemicals is usually identified and characterized 
      using in vivo methods such as the murine local lymph node assay (LLNA). Due to 
      regulatory constraints and ethical concerns, alternatives to animal testing are 
      needed to predict skin sensitization potential of chemicals. For this purpose, 
      combined evaluation using multiple in vitro and in silico parameters that reflect 
      different aspects of the sensitization process seems promising. We previously 
      reported that LLNA thresholds could be well predicted by using an artificial 
      neural network (ANN) model, designated iSENS ver.1 (integrating in vitro 
      sensitization tests version 1), to analyze data obtained from two in vitro tests: 
      the human Cell Line Activation Test (h-CLAT) and the SH test. Here, we present a 
      more advanced ANN model, iSENS ver.2, which additionally utilizes the results of 
      antioxidant response element (ARE) assay and the octanol-water partition 
      coefficient (LogP, reflecting lipid solubility and skin absorption). We found a 
      good correlation between predicted LLNA thresholds calculated by iSENS ver.2 and 
      reported values. The predictive performance of iSENS ver.2 was superior to that 
      of iSENS ver.1. We conclude that ANN analysis of data from multiple in vitro 
      assays is a useful approach for risk assessment of chemicals for skin 
      sensitization.
CI  - Copyright (c) 2014 Elsevier Ltd. All rights reserved.
FAU - Tsujita-Inoue, Kyoko
AU  - Tsujita-Inoue K
AD  - Shiseido Research Center, Shiseido Co. Ltd., 2-2-1 Hayabuchi, Tsuzuki-ku, 
      Yokohama-shi, Kanagawa 224-8558, Japan.
FAU - Hirota, Morihiko
AU  - Hirota M
AD  - Shiseido Research Center, Shiseido Co. Ltd., 2-2-1 Hayabuchi, Tsuzuki-ku, 
      Yokohama-shi, Kanagawa 224-8558, Japan. Electronic address: 
      morihiko.hirota@to.shiseido.co.jp.
FAU - Ashikaga, Takao
AU  - Ashikaga T
AD  - Shiseido Research Center, Shiseido Co. Ltd., 2-2-1 Hayabuchi, Tsuzuki-ku, 
      Yokohama-shi, Kanagawa 224-8558, Japan.
FAU - Atobe, Tomomi
AU  - Atobe T
AD  - Shiseido Research Center, Shiseido Co. Ltd., 2-2-1 Hayabuchi, Tsuzuki-ku, 
      Yokohama-shi, Kanagawa 224-8558, Japan.
FAU - Kouzuki, Hirokazu
AU  - Kouzuki H
AD  - Shiseido Research Center, Shiseido Co. Ltd., 2-2-1 Hayabuchi, Tsuzuki-ku, 
      Yokohama-shi, Kanagawa 224-8558, Japan.
FAU - Aiba, Setsuya
AU  - Aiba S
AD  - Department of Dermatology, Tohoku University Graduate School of Medicine, 1-1 
      Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8574, Japan.
LA  - eng
PT  - Journal Article
DEP - 20140118
PL  - England
TA  - Toxicol In Vitro
JT  - Toxicology in vitro : an international journal published in association with 
      BIBRA
JID - 8712158
RN  - 0 (Allergens)
SB  - IM
MH  - Allergens/*toxicity
MH  - Antioxidant Response Elements
MH  - Biological Assay
MH  - Dermatitis, Contact/*immunology
MH  - Humans
MH  - *Neural Networks, Computer
MH  - Risk Assessment
OTO - NOTNLM
OT  - Antioxidant response element, ARE
OT  - Artificial neural network
OT  - Risk assessment
OT  - SH test
OT  - Skin sensitization
OT  - h-CLAT
EDAT- 2014/01/22 06:00
MHDA- 2015/06/30 06:00
CRDT- 2014/01/22 06:00
PHST- 2013/09/15 00:00 [received]
PHST- 2013/12/21 00:00 [revised]
PHST- 2014/01/08 00:00 [accepted]
PHST- 2014/01/22 06:00 [entrez]
PHST- 2014/01/22 06:00 [pubmed]
PHST- 2015/06/30 06:00 [medline]
AID - S0887-2333(14)00006-X [pii]
AID - 10.1016/j.tiv.2014.01.003 [doi]
PST - ppublish
SO  - Toxicol In Vitro. 2014 Jun;28(4):626-39. doi: 10.1016/j.tiv.2014.01.003. Epub 
      2014 Jan 18.