PMID- 34311055
OWN - NLM
STAT- MEDLINE
DCOM- 20220107
LR  - 20220107
IS  - 1096-0295 (Electronic)
IS  - 0273-2300 (Linking)
VI  - 125
DP  - 2021 Oct
TI  - Development of quantitative model of a local lymph node assay for evaluating skin 
      sensitization potency applying machine learning CatBoost.
PG  - 105019
LID - S0273-2300(21)00160-4 [pii]
LID - 10.1016/j.yrtph.2021.105019 [doi]
AB  - The estimated concentrations for a stimulation index of 3 (EC3) in murine local 
      lymph node assay (LLNA) is an important quantitative value for determining the 
      strength of skin sensitization to chemicals, including cosmetic ingredients. 
      However, animal testing bans on cosmetics in Europe necessitate the development 
      of alternative testing methods to LLNA. A machine learning-based prediction 
      method can predict complex toxicity risks from multiple variables. Therefore, we 
      developed an LLNA EC3 regression model using CatBoost, a new gradient boosting 
      decision tree, based on the reliable Cosmetics Europe database which included 
      data for 119 substances. We found that a model using in chemico/in vitro tests, 
      physical properties, and chemical information associated with key events of skin 
      sensitization adverse outcome pathway as variables showed the best performance 
      with a coefficient of determination (R(2)) of 0.75. In addition, this model can 
      indicate the variable importance as the interpretation of the model, and the most 
      important variable was associated with the human cell line activation test that 
      evaluate dendritic cell activation. The good performance and interpretability of 
      our LLNA EC3 predictable regression model suggests that it could serve as a 
      useful approach for quantitative assessment of skin sensitization.
CI  - Copyright (c) 2021 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Ambe, Kaori
AU  - Ambe K
AD  - Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, 
      Nagoya City University, Nagoya, Japan. Electronic address: 
      ambek@phar.nagoya-cu.ac.jp.
FAU - Suzuki, Masaharu
AU  - Suzuki M
AD  - Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, 
      Nagoya City University, Nagoya, Japan. Electronic address: 
      c202712@ed.nagoya-cu.ac.jp.
FAU - Ashikaga, Takao
AU  - Ashikaga T
AD  - Division of Risk Assessment, National Institute of Health Sciences, Kawasaki, 
      Japan. Electronic address: takao.ashikaga@nihs.go.jp.
FAU - Tohkin, Masahiro
AU  - Tohkin M
AD  - Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, 
      Nagoya City University, Nagoya, Japan. Electronic address: 
      tohkin@phar.nagoya-cu.ac.jp.
LA  - eng
PT  - Journal Article
DEP - 20210724
PL  - Netherlands
TA  - Regul Toxicol Pharmacol
JT  - Regulatory toxicology and pharmacology : RTP
JID - 8214983
SB  - IM
MH  - Animal Testing Alternatives
MH  - Animals
MH  - Cell Line
MH  - Databases, Factual
MH  - Dendritic Cells/drug effects
MH  - Dermatitis, Allergic Contact/*diagnosis
MH  - Europe
MH  - Humans
MH  - Keratinocytes/drug effects
MH  - *Local Lymph Node Assay
MH  - *Machine Learning
MH  - Mice
MH  - T-Lymphocytes/drug effects
MH  - United Nations/standards
OTO - NOTNLM
OT  - Adverse outcome pathway
OT  - CatBoost
OT  - Integrated approaches to testing and assessment
OT  - Machine learning
OT  - Murine local lymph node assay
OT  - Quantitative model
OT  - Skin sensitization
EDAT- 2021/07/27 06:00
MHDA- 2022/01/08 06:00
CRDT- 2021/07/26 20:12
PHST- 2020/11/15 00:00 [received]
PHST- 2021/06/13 00:00 [revised]
PHST- 2021/07/21 00:00 [accepted]
PHST- 2021/07/27 06:00 [pubmed]
PHST- 2022/01/08 06:00 [medline]
PHST- 2021/07/26 20:12 [entrez]
AID - S0273-2300(21)00160-4 [pii]
AID - 10.1016/j.yrtph.2021.105019 [doi]
PST - ppublish
SO  - Regul Toxicol Pharmacol. 2021 Oct;125:105019. doi: 10.1016/j.yrtph.2021.105019. 
      Epub 2021 Jul 24.