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Chemical Structure| 719-54-0

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Product Citations

Product Citations

Sheridan, Thomas ; Wedam, Rohan ; Tang, Esther ; Hupp, Joseph ;

Abstract: In practical applications of metal-organic frameworks (MOFs) as catalysts for hydrolytic destruction of chemical warfare agents (CWA), such as those entailing integration of the MOF with fabric for protective equipment, co-incorporation of a basic buffer is essential for sustained catalytic turnover – in part, because the salient nucleophile is the hydroxide ion. These buffers will have different pH values, and it has become clear that pH differences can translate to dramatic effects on MOF-catalyzed rates. Predicting the relative magnitude and even the direction of rate variations, however, is hindered by an incomplete understanding of the rate laws involved in CWA (or agent simulant) hydrolysis and their relationship to the nature of the catalyst active-site. Here, we have experimentally examined, with a simulant, a series of Lewis acidic UiO-66-series MOFs (UiO-66-NO2, UiO-66(-H), and UiO-66-NH2) with varying active-site Lewis acidities as determined by their functional groups, and correlate their catalytic performance at different pH values with their measured relative Lewis acidity and with catalyst characteristics governed by Lewis acidity. This comparison is extended to Lewis basic MOFs Zn- and Cu-MFU-4L, with the conclusion that the Lewis acidity of the active site plays a complex, but decisive role in determining the pH that maximizes the rate of hydrolysis. Furthermore, an explanation for this behavior based on the rate-determining steps for hydrolysis is presented, with the rates for hydrolytic attack and for node aqua ligand displacement by an agent simulant being the source of the correlation between optimal pH values and active-site Lewis acidity. Notably, the observation of a peaked maximum in plots of hydrolysis rate versus pH points to a pH-correlated change in rate-determining step. Finally, these results suggest that the selection of certain MOFs as the “best” for nerve agent hydrolysis is largely based on an arbitrary selection of buffer and pH value.

Keywords: MOFs ; chemical warfare agents ; catalysis ; kinetics

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Product Details of [ 719-54-0 ]

CAS No. :719-54-0
Formula : C14H11NO
M.W : 209.24
SMILES Code : O=C1C2=C(C=CC=C2)N(C)C3=CC=CC=C13
MDL No. :MFCD00005024
InChI Key :XUVKSPPGPPFPQN-UHFFFAOYSA-N
Pubchem ID :69751

Safety of [ 719-54-0 ]

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

Computational Chemistry of [ 719-54-0 ] Show Less

Physicochemical Properties

Num. heavy atoms 16
Num. arom. heavy atoms 14
Fraction Csp3 0.07
Num. rotatable bonds 0
Num. H-bond acceptors 1.0
Num. H-bond donors 0.0
Molar Refractivity 66.98
TPSA ?

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

22.0 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

2.25
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

3.11
Log Po/w (WLOGP)?

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

2.69
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.

2.32
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

3.06
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

2.69

Water Solubility

Log S (ESOL):?

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

-3.74
Solubility 0.0377 mg/ml ; 0.00018 mol/l
Class?

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

Soluble
Log S (Ali)?

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

-3.24
Solubility 0.12 mg/ml ; 0.000575 mol/l
Class?

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

Soluble
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

-4.85
Solubility 0.00295 mg/ml ; 0.0000141 mol/l
Class?

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

Moderately soluble

Pharmacokinetics

GI absorption?

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

High
BBB permeant?

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

Yes
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

No
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

Yes
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

Yes
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.

-5.37 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

0.0
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

0.0
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

0.0
Bioavailability Score?

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

0.55

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

1.0 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<1.0
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)

1.51

Application In Synthesis of [ 719-54-0 ]

* All experimental methods are cited from the reference, please refer to the original source for details. We do not guarantee the accuracy of the content in the reference.

  • Downstream synthetic route of [ 719-54-0 ]

[ 719-54-0 ] Synthesis Path-Downstream   1~3

  • 1
  • [ 26456-05-3 ]
  • [ 2905-56-8 ]
  • [ 4217-54-3 ]
  • [ 719-54-0 ]
  • [ 100-52-7 ]
  • 9-[Benzyl-(5-hydroxy-pentyl)-amino]-10-methyl-acridinium; perchlorate [ No CAS ]
  • 2
  • [ 76041-86-6 ]
  • [ 719-54-0 ]
  • 3
  • [ 26767-16-8 ]
  • [ 719-54-0 ]
 

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