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Chemical Structure| 84852-53-9 Chemical Structure| 84852-53-9

Structure of Decabromodiphenyl ethane
CAS No.: 84852-53-9

Chemical Structure| 84852-53-9

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Product Citations

Product Citations

Choi, Soocheol ; Cho, Hyunki ; Karim, Md Adnan ; Park, Chang Gyun ; Yoon, Juyoung ; Kim, Young Jun , et al.

Abstract: Flame retardants are integral components in numerous consumer and industrial products. Accumulating research has shown that retardants disrupt the endocrine system via the modulation of thyroid hormone receptors (THRs). To investigate the mechanisms underlying this effect, we established a luciferase reporter assay system using HEK293 cells expressing the human THR isomers THRα and THRβ, and screened six flame-retardant compounds with agonistic or antagonistic activity. We examined THR agonism or antagonism in these compounds, which included organophosphate (tris(3-chloropropyl) phosphate), bisphenol-type (tetrabromobisphenol A), and brominated compounds (decabromodiphenyl ethane [DBDPEthane], decabromodiphenyl ether [DBDPEther], 1,2-bis(2,4,6-tribromophenoxy) ethane, and DC604). Among these, DBDPEthane, a widely used flame retardant, has emerged as a potential endocrine-disrupting chemical. The structurally related compounds DBDPEther and DBDPEthane were found to exert antagonistic effects on both THRα and THRβ. To elucidate the molecular basis of this antagonism, molecular docking analysis was performed using the ligand-binding domains of THRα and THRβ. The results indicated binding of DBDPEthane within ligand-binding pockets of both THRα and THRβ, forming specific hydrogen bonds and hydrophobic interactions that may support its antagonistic effects. To further characterize the dynamic interactions between DBDPEthane and THRα or THRβ, we conducted molecular dynamics simulations, using the root mean square deviation (RMSD), root mean square fluctuation (RMSF), and solvent-accessible surface area (SASA) as metrics. The results revealed distinct binding stability and conformational flexibility between DBDPEthane and THRβ, supported by RMSD, RMSF and SASA. These findings highlight the potential of DBDPEthane to antagonize both THRα and THRβ, providing functional and structural insights into its thyroid-disrupting properties in the context of receptor subtype selectivity.

Keywords: ; Flame retardant ; Molecular docking ; Reporter gene assay ; Thyroid receptor

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Product Details of [ 84852-53-9 ]

CAS No. :84852-53-9
Formula : C14H4Br10
M.W : 971.22
SMILES Code : BrC1=C(Br)C(Br)=C(Br)C(Br)=C1CCC2=C(Br)C(Br)=C(Br)C(Br)=C2Br
MDL No. :MFCD06407713
InChI Key :BZQKBFHEWDPQHD-UHFFFAOYSA-N
Pubchem ID :10985889

Safety of [ 84852-53-9 ]

GHS Pictogram:
Signal Word:Warning
Hazard Statements:H302-H315-H319-H335
Precautionary Statements:P261-P305+P351+P338

Computational Chemistry of [ 84852-53-9 ] Show Less

Physicochemical Properties

Num. heavy atoms 24
Num. arom. heavy atoms 12
Fraction Csp3 0.14
Num. rotatable bonds 3
Num. H-bond acceptors 0.0
Num. H-bond donors 0.0
Molar Refractivity 137.7
TPSA ?

Topological Polar Surface Area: Calculated from
Ertl P. et al. 2000 J. Med. Chem.

0.0 Ų

Lipophilicity

Log Po/w (iLOGP)?

iLOGP: in-house physics-based method implemented from
Daina A et al. 2014 J. Chem. Inf. Model.

4.77
Log Po/w (XLOGP3)?

XLOGP3: Atomistic and knowledge-based method calculated by
XLOGP program, version 3.2.2, courtesy of CCBG, Shanghai Institute of Organic Chemistry

11.05
Log Po/w (WLOGP)?

WLOGP: Atomistic method implemented from
Wildman SA and Crippen GM. 1999 J. Chem. Inf. Model.

11.1
Log Po/w (MLOGP)?

MLOGP: Topological method implemented from
Moriguchi I. et al. 1992 Chem. Pharm. Bull.
Moriguchi I. et al. 1994 Chem. Pharm. Bull.
Lipinski PA. et al. 2001 Adv. Drug. Deliv. Rev.

10.41
Log Po/w (SILICOS-IT)?

SILICOS-IT: Hybrid fragmental/topological method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

11.01
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

9.67

Water Solubility

Log S (ESOL):?

ESOL: Topological method implemented from
Delaney JS. 2004 J. Chem. Inf. Model.

-13.0
Solubility 0.0000000001 mg/ml ; 0.0 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Insoluble
Log S (Ali)?

Ali: Topological method implemented from
Ali J. et al. 2012 J. Chem. Inf. Model.

-11.02
Solubility 0.0000000093 mg/ml ; 0.0 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Insoluble
Log S (SILICOS-IT)?

SILICOS-IT: Fragmental method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

-13.35
Solubility 0.0 mg/ml ; 0.0 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Insoluble

Pharmacokinetics

GI absorption?

Gatrointestinal absorption: according to the white of the BOILED-Egg

Low
BBB permeant?

BBB permeation: according to the yolk of the BOILED-Egg

No
P-gp substrate?

P-glycoprotein substrate: SVM model built on 1033 molecules (training set)
and tested on 415 molecules (test set)
10-fold CV: ACC=0.72 / AUC=0.77
External: ACC=0.88 / AUC=0.94

Yes
CYP1A2 inhibitor?

Cytochrome P450 1A2 inhibitor: SVM model built on 9145 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.83 / AUC=0.90
External: ACC=0.84 / AUC=0.91

No
CYP2C19 inhibitor?

Cytochrome P450 2C19 inhibitor: SVM model built on 9272 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.80 / AUC=0.86
External: ACC=0.80 / AUC=0.87

No
CYP2C9 inhibitor?

Cytochrome P450 2C9 inhibitor: SVM model built on 5940 molecules (training set)
and tested on 2075 molecules (test set)
10-fold CV: ACC=0.78 / AUC=0.85
External: ACC=0.71 / AUC=0.81

No
CYP2D6 inhibitor?

Cytochrome P450 2D6 inhibitor: SVM model built on 3664 molecules (training set)
and tested on 1068 molecules (test set)
10-fold CV: ACC=0.79 / AUC=0.85
External: ACC=0.81 / AUC=0.87

No
CYP3A4 inhibitor?

Cytochrome P450 3A4 inhibitor: SVM model built on 7518 molecules (training set)
and tested on 2579 molecules (test set)
10-fold CV: ACC=0.77 / AUC=0.85
External: ACC=0.78 / AUC=0.86

No
Log Kp (skin permeation)?

Skin permeation: QSPR model implemented from
Potts RO and Guy RH. 1992 Pharm. Res.

-4.38 cm/s

Druglikeness

Lipinski?

Lipinski (Pfizer) filter: implemented from
Lipinski CA. et al. 2001 Adv. Drug Deliv. Rev.
MW ≤ 500
MLOGP ≤ 4.15
N or O ≤ 10
NH or OH ≤ 5

2.0
Ghose?

Ghose filter: implemented from
Ghose AK. et al. 1999 J. Comb. Chem.
160 ≤ MW ≤ 480
-0.4 ≤ WLOGP ≤ 5.6
40 ≤ MR ≤ 130
20 ≤ atoms ≤ 70

None
Veber?

Veber (GSK) filter: implemented from
Veber DF. et al. 2002 J. Med. Chem.
Rotatable bonds ≤ 10
TPSA ≤ 140

0.0
Egan?

Egan (Pharmacia) filter: implemented from
Egan WJ. et al. 2000 J. Med. Chem.
WLOGP ≤ 5.88
TPSA ≤ 131.6

1.0
Muegge?

Muegge (Bayer) filter: implemented from
Muegge I. et al. 2001 J. Med. Chem.
200 ≤ MW ≤ 600
-2 ≤ XLOGP ≤ 5
TPSA ≤ 150
Num. rings ≤ 7
Num. carbon > 4
Num. heteroatoms > 1
Num. rotatable bonds ≤ 15
H-bond acc. ≤ 10
H-bond don. ≤ 5

3.0
Bioavailability Score?

Abbott Bioavailability Score: Probability of F > 10% in rat
implemented from
Martin YC. 2005 J. Med. Chem.

0.17

Medicinal Chemistry

PAINS?

Pan Assay Interference Structures: implemented from
Baell JB. & Holloway GA. 2010 J. Med. Chem.

0.0 alert
Brenk?

Structural Alert: implemented from
Brenk R. et al. 2008 ChemMedChem

2.0 alert: heavy_metal
Leadlikeness?

Leadlikeness: implemented from
Teague SJ. 1999 Angew. Chem. Int. Ed.
250 ≤ MW ≤ 350
XLOGP ≤ 3.5
Num. rotatable bonds ≤ 7

No; 1 violation:MW<2.0
Synthetic accessibility?

Synthetic accessibility score: from 1 (very easy) to 10 (very difficult)
based on 1024 fragmental contributions (FP2) modulated by size and complexity penaties,
trained on 12'782'590 molecules and tested on 40 external molecules (r2 = 0.94)

2.49
 

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