Structure of 719-54-0
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The BI-3802 was designed by Boehringer Ingelheim and could be obtained free of charge through the Boehringer Ingelheim open innovation portal opnMe.com, associated with its negative control.
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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.
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Keywords: MOFs ; chemical warfare agents ; catalysis ; kinetics
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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 |
GHS Pictogram: |
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Signal Word: | Warning |
Hazard Statements: | H315-H319-H335 |
Precautionary Statements: | P261-P305+P351+P338 |
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 |
22.0 Ų |
Log Po/w (iLOGP)? iLOGP: in-house physics-based method implemented from |
2.25 |
Log Po/w (XLOGP3)? XLOGP3: Atomistic and knowledge-based method calculated by |
3.11 |
Log Po/w (WLOGP)? WLOGP: Atomistic method implemented from |
2.69 |
Log Po/w (MLOGP)? MLOGP: Topological method implemented from |
2.32 |
Log Po/w (SILICOS-IT)? SILICOS-IT: Hybrid fragmental/topological method calculated by |
3.06 |
Consensus Log Po/w? Consensus Log Po/w: Average of all five predictions |
2.69 |
Log S (ESOL):? ESOL: Topological method implemented from |
-3.74 |
Solubility | 0.0377 mg/ml ; 0.00018 mol/l |
Class? Solubility class: Log S scale |
Soluble |
Log S (Ali)? Ali: Topological method implemented from |
-3.24 |
Solubility | 0.12 mg/ml ; 0.000575 mol/l |
Class? Solubility class: Log S scale |
Soluble |
Log S (SILICOS-IT)? SILICOS-IT: Fragmental method calculated by |
-4.85 |
Solubility | 0.00295 mg/ml ; 0.0000141 mol/l |
Class? Solubility class: Log S scale |
Moderately soluble |
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) |
No |
CYP1A2 inhibitor? Cytochrome P450 1A2 inhibitor: SVM model built on 9145 molecules (training set) |
Yes |
CYP2C19 inhibitor? Cytochrome P450 2C19 inhibitor: SVM model built on 9272 molecules (training set) |
Yes |
CYP2C9 inhibitor? Cytochrome P450 2C9 inhibitor: SVM model built on 5940 molecules (training set) |
No |
CYP2D6 inhibitor? Cytochrome P450 2D6 inhibitor: SVM model built on 3664 molecules (training set) |
No |
CYP3A4 inhibitor? Cytochrome P450 3A4 inhibitor: SVM model built on 7518 molecules (training set) |
No |
Log Kp (skin permeation)? Skin permeation: QSPR model implemented from |
-5.37 cm/s |
Lipinski? Lipinski (Pfizer) filter: implemented from |
0.0 |
Ghose? Ghose filter: implemented from |
None |
Veber? Veber (GSK) filter: implemented from |
0.0 |
Egan? Egan (Pharmacia) filter: implemented from |
0.0 |
Muegge? Muegge (Bayer) filter: implemented from |
0.0 |
Bioavailability Score? Abbott Bioavailability Score: Probability of F > 10% in rat |
0.55 |
PAINS? Pan Assay Interference Structures: implemented from |
0.0 alert |
Brenk? Structural Alert: implemented from |
1.0 alert: heavy_metal |
Leadlikeness? Leadlikeness: implemented from |
No; 1 violation:MW<1.0 |
Synthetic accessibility? Synthetic accessibility score: from 1 (very easy) to 10 (very difficult) |
1.51 |
* 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.
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