PMID- 31932558
OWN - NLM
STAT- MEDLINE
DCOM- 20200828
LR  - 20200828
IS  - 1880-3989 (Electronic)
IS  - 0388-1350 (Linking)
VI  - 45
IP  - 1
DP  - 2020
TI  - Adjustment of a no expected sensitization induction level derived from Bayesian 
      network integrated testing strategy for skin sensitization risk assessment.
PG  - 57-67
LID - 10.2131/jts.45.57 [doi]
AB  - Skin sensitization is a key adverse effect to be addressed during hazard 
      identification and risk assessment of chemicals, because it is the first step in 
      the development of allergic contact dermatitis. Multiple non-animal testing 
      strategies incorporating in vitro tests and in silico tools have achieved good 
      predictivities when compared with murine local lymph node assay (LLNA). The 
      binary test battery of KeratinoSens(TM) and h-CLAT could be used to classify 
      non-sensitizers as the first part of bottom-up approach. However, the 
      quantitative risk assessment for sensitizing chemicals requires a No Expected 
      Sensitization Induction Level (NESIL), the dose not expected to induce skin 
      sensitization in humans. We used Bayesian network integrated testing strategy (BN 
      ITS-3) for chemical potency classification. BN ITS-3 predictions were performed 
      without a pre-processing step (selecting data from their physic-chemical 
      applicability domains) or post-processing step (Michael acceptor chemistry 
      correction), neither of which necessarily improve prediction accuracy. For 
      chemicals within newly defined applicability domain, all under-predictions fell 
      within one potency class when compared with LLNA results, indicating no chemicals 
      that were incorrectly classified by more than one class. Considering the 
      potential under-prediction by one class, a worst case value to each class from BN 
      ITS-3 was used to derive a NESIL. When in vivo and human data from suitable 
      analogs cannot be used to estimate the uncertainty, adjusting the NESIL derived 
      from BN ITS-3 may help perform skin sensitization risk assessment. The overall 
      workflow for risk assessment was demonstrated by incorporating the binary test 
      battery of KeratinoSens(TM) and h-CLAT.
FAU - Otsubo, Yuki
AU  - Otsubo Y
AD  - Safety Science Research Laboratories, Kao Corporation.
FAU - Nishijo, Taku
AU  - Nishijo T
AD  - Safety Science Research Laboratories, Kao Corporation.
FAU - Mizumachi, Hideyuki
AU  - Mizumachi H
AD  - Safety Science Research Laboratories, Kao Corporation.
FAU - Saito, Kazutoshi
AU  - Saito K
AD  - Safety Science Research Laboratories, Kao Corporation.
FAU - Miyazawa, Masaaki
AU  - Miyazawa M
AD  - Safety Science Research Laboratories, Kao Corporation.
FAU - Sakaguchi, Hitoshi
AU  - Sakaguchi H
AD  - Safety Science Research Laboratories, Kao Corporation.
LA  - eng
PT  - Journal Article
PL  - Japan
TA  - J Toxicol Sci
JT  - The Journal of toxicological sciences
JID - 7805798
SB  - IM
MH  - Bayes Theorem
MH  - Humans
MH  - In Vitro Techniques
MH  - Risk Assessment/*methods
MH  - Skin Tests/*methods
OTO - NOTNLM
OT  - Bayesian network
OT  - Integrated testing strategy
OT  - Potency prediction
OT  - Risk assessment
OT  - Skin sensitization
EDAT- 2020/01/15 06:00
MHDA- 2020/08/29 06:00
CRDT- 2020/01/15 06:00
PHST- 2020/01/15 06:00 [entrez]
PHST- 2020/01/15 06:00 [pubmed]
PHST- 2020/08/29 06:00 [medline]
AID - 10.2131/jts.45.57 [doi]
PST - ppublish
SO  - J Toxicol Sci. 2020;45(1):57-67. doi: 10.2131/jts.45.57.