PMID- 25786524
OWN - NLM
STAT- MEDLINE
DCOM- 20151026
LR  - 20191210
IS  - 1880-3989 (Electronic)
IS  - 0388-1350 (Linking)
VI  - 40
IP  - 2
DP  - 2015 Apr
TI  - In silico risk assessment for skin sensitization using artificial neural network 
      analysis.
PG  - 193-209
LID - 10.2131/jts.40.193 [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 the skin sensitization potential of chemicals. For this 
      purpose, an integrated evaluation system employing 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. 2 (integrating in vitro sensitization tests version 2), to analyze data 
      obtained from in vitro tests focused on different aspects of skin sensitization. 
      Here, we examined whether LLNA thresholds could be predicted by ANN using in 
      silico-calculated descriptors of the three-dimensional structures of chemicals. 
      We obtained a good correlation between predicted LLNA thresholds and reported 
      values. Furthermore, combining the results of the in vitro (iSENS ver. 2) and in 
      silico models reduced the number of chemicals for which the potency category was 
      under-estimated. In conclusion, the ANN model using in silico parameters was 
      shown to be have useful predictive performance. Further, our results indicate 
      that the combination of this model with a predictive model using in vitro data 
      represents a promising approach for integrated risk assessment of skin 
      sensitization potential of chemicals.
FAU - Tsujita-Inoue, Kyoko
AU  - Tsujita-Inoue K
AD  - Shiseido Research Center, Shiseido Co. Ltd.
FAU - Atobe, Tomomi
AU  - Atobe T
FAU - Hirota, Morihiko
AU  - Hirota M
FAU - Ashikaga, Takao
AU  - Ashikaga T
FAU - Kouzuki, Hirokazu
AU  - Kouzuki H
LA  - eng
PT  - Journal Article
PL  - Japan
TA  - J Toxicol Sci
JT  - The Journal of toxicological sciences
JID - 7805798
SB  - IM
MH  - Animals
MH  - *Computer Simulation
MH  - *Local Lymph Node Assay
MH  - Mice
MH  - *Neural Networks, Computer
MH  - Predictive Value of Tests
MH  - Risk Assessment/*methods
MH  - Skin Irritancy Tests/*methods
EDAT- 2015/03/20 06:00
MHDA- 2015/10/27 06:00
CRDT- 2015/03/20 06:00
PHST- 2015/03/20 06:00 [entrez]
PHST- 2015/03/20 06:00 [pubmed]
PHST- 2015/10/27 06:00 [medline]
AID - 10.2131/jts.40.193 [doi]
PST - ppublish
SO  - J Toxicol Sci. 2015 Apr;40(2):193-209. doi: 10.2131/jts.40.193.