PMID- 31552475
OWN - NLM
STAT- MEDLINE
DCOM- 20200827
LR  - 20200827
IS  - 1432-0738 (Electronic)
IS  - 0340-5761 (Linking)
VI  - 93
IP  - 11
DP  - 2019 Nov
TI  - In silico toxicity evaluation of dioxins using structure-activity relationship 
      (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR).
PG  - 3207-3218
LID - 10.1007/s00204-019-02580-w [doi]
AB  - Prediction of pEC(50) values of dioxins binding with the aryl hydrocarbon 
      receptor (AhR) is of great significance for exploring how dioxins induce toxicity 
      in human body and evaluating their environmental behaviors and risks. To reveal 
      the factors that influence the toxicity of dioxins, provide more accurate 
      mathematical models for predicting the pEC(50) values of dioxins, and supplement 
      the toxicity database of persistent organic pollutants, qualitative 
      structure-activity relationship (SAR) and two-dimensional quantitative 
      structure-activity relationship (2D-QSAR) were used in this study. The research 
      objects in this study were 60 organic compounds with pEC(50) values and 162 
      compounds without pEC(50) values, which included polychlorinated dibenzofurans 
      (PCDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated 
      dibenzo-p-dioxins (PBDDs). The qualitative structure-activity relationship (SAR) 
      was performed first and concluded that halogen substitutions at any of the 2, 3, 
      7, and 8 sites increased the pEC(50) value of the compound. Moreover, 
      two-dimensional quantitative structure-activity relationship (2D-QSAR) models 
      were established by employing multiple linear regression (MLR) method and 
      artificial neural network (ANN) algorithm to investigate the factors affecting 
      the pEC(50) values of dioxins molecules. MLR was used to establish the 
      well-understood linear model and ANN was used to establish a more accurate 
      non-linear model. Both models have good fitting, robustness, and predictive 
      ability. Importantly, the ability of dioxins binding to AhR is mainly determined 
      by molecular descriptors including E1m, SM09_AEA (dm), RDF065u, F05 [Cl-Cl], and 
      Neoplastic-80. In addition, the pEC(50) values of the 162 dioxins without 
      toxicity data were predicted by MLR and ANN models, respectively.
FAU - Yang, Hong
AU  - Yang H
AD  - Department of Chemistry, Lanzhou University, Lanzhou, 730000, People's Republic 
      of China.
FAU - Du, Zhe
AU  - Du Z
AD  - Department of Chemistry, Lanzhou University, Lanzhou, 730000, People's Republic 
      of China.
FAU - Lv, Wen-Juan
AU  - Lv WJ
AD  - Department of Chemistry, Lanzhou University, Lanzhou, 730000, People's Republic 
      of China.
FAU - Zhang, Xiao-Yun
AU  - Zhang XY
AD  - Department of Chemistry, Lanzhou University, Lanzhou, 730000, People's Republic 
      of China. xyzhang@lzu.edu.cn.
FAU - Zhai, Hong-Lin
AU  - Zhai HL
AD  - Department of Chemistry, Lanzhou University, Lanzhou, 730000, People's Republic 
      of China.
LA  - eng
GR  - 21705064/National Natural Science Foundation of China/International
GR  - 21275067/National Natural Science Foundation of China/International
PT  - Journal Article
DEP - 20190924
PL  - Germany
TA  - Arch Toxicol
JT  - Archives of toxicology
JID - 0417615
RN  - 0 (Dioxins)
RN  - 0 (Environmental Pollutants)
RN  - 0 (Receptors, Aryl Hydrocarbon)
SB  - IM
MH  - Algorithms
MH  - *Dioxins/chemistry/toxicity
MH  - *Environmental Pollutants/chemistry/toxicity
MH  - Linear Models
MH  - *Models, Theoretical
MH  - Neural Networks, Computer
MH  - Protein Binding
MH  - *Quantitative Structure-Activity Relationship
MH  - Receptors, Aryl Hydrocarbon/chemistry
OTO - NOTNLM
OT  - 2D-QSAR
OT  - Dioxins
OT  - Heuristics
OT  - POPs
OT  - SAR
OT  - pEC50
EDAT- 2019/09/26 06:00
MHDA- 2020/08/28 06:00
CRDT- 2019/09/26 06:00
PHST- 2019/07/25 00:00 [received]
PHST- 2019/09/17 00:00 [accepted]
PHST- 2019/09/26 06:00 [pubmed]
PHST- 2020/08/28 06:00 [medline]
PHST- 2019/09/26 06:00 [entrez]
AID - 10.1007/s00204-019-02580-w [pii]
AID - 10.1007/s00204-019-02580-w [doi]
PST - ppublish
SO  - Arch Toxicol. 2019 Nov;93(11):3207-3218. doi: 10.1007/s00204-019-02580-w. Epub 
      2019 Sep 24.