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Structure of 636-94-2

Chemical Structure| 636-94-2

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de la Cruz Jr, Ireneo L ; Lee, Cheng-I ; Lin, Kun-Mo ;

Abstract: This work develops a semiempirical 1D numericalmodel with average measured [•OH(g)] (denoted as [•OH(g)]M) asthe boundary condition and measured [•OH(aq)] (denoted as[•OH(aq)]M) to calibrate the accumulated [•OH(aq)] modeled(denoted as [•OH(aq)]S) in the solution treated by a plasma jet.The [•OH(g)]M obtained in the plasma plume is integrated fromthe [•OH(g)] distribution detected in the radial direction atposition 1 mm above the interface. The [•OH(aq)]M in the solutionsis determined from the fluorescence measurements by exciting 2-hydroxyterephthalic acid at 310 nm and detecting the fluorescenceemitted at 425 nm for cases with different plasma treatment times.The developed numerical model considers both the diffusion and convection for the domain covering 1 mm above the interface withthe dominant generation and consumption mechanisms considered in the discharge plume to evaluate the incoming flux of •OH(g)through the interface, which is calibrated with [•OH(aq)]M in the solution treated. The simulated results show that the transportbehavior (i.e., diffusion and convection) plays only a minor role in the contribution of [•OH(aq)]S, while the electron-impactdissociation reactions play significant roles in the generation of •OH(g) in the discharge plume, leading to the high local [•OH(g)] andincoming flux of •OH(g) to the interface. The self-association reactions of •OH(g) contribute to the remarkable consumption of•OH(g). The simulated [•OH(g)] distribution increases from the [•OH(g)]M determined at the upstream boundary to its maximumnear the central region as the density reaches 9.5 × 1019 m−3 and decreases rapidly above the interface.

Purchased from AmBeed: ;

Sol R. Martínez ; Emmanuel Odell ; Luis E. Ibarra ; Arianna Sosa Lochedino ; Ana B. Wendel ; Andrés M. Durantini , et al.

Abstract: Sonodynamic inactivation (SDI) of pathogens has an important advantage when compared to optical excitation-based protocols due to the deeper penetration of ultrasound (US) excitation in biological media or animal tissue. Sonosensitizers (SS) are compounds or systems that upon US stimulation in the therapeutic window (frequency = 0.8–3 MHz and intensity < 3 W/cm2) can induce damage to vital components of pathogenic microorganisms. Herein, we report the synthesis and application of conjugated polymer nanoparticles (CPNs) as an efficient SS in SDI of methicillin-resistant Staphylococcus aureus (MRSA), Klebsiella pneumoniae and Candida tropicalis. A frequent problem in the design and testing of new SS for SDI is the lack of proper sonoreactor characterization which leads to reproducibility concerns. To address this issue, we performed dosimetry experiments in our setup. This enables the validation of our results by other researchers and facilitates meaningful comparisons with different SDI systems in future studies. On a different note, it is generally accepted that the mechanisms of action underlying SS-mediated SDI involve the production of reactive oxygen species (ROS). In an attempt to establish the nature of the cytotoxic species involved in our CPNs-based SDI protocol, we demonstrated that singlet oxygen (1O2) does not play a major role in the observed sonoinduced killing effect. SDI experiments in planktonic cultures of optimally growing pathogens using CPNs result in a germicide effect on the studied pathogenic microorganisms. The implementation of SDI protocols using CPNs was further tested in mature biofilms of a MRSA resulting in ∼40 % reduction of biomass and ∼70 % reduction of cellular viability. Overall, these results highlight the unique and unexplored capacity of CPNs to act as sonosensitizers opening new possibilities in the design and application of novel inactivation protocols against morbific microbes.

Keywords: Sonodynamic inactivation ; Conjugated polymer nanoparticles ; Microbes ; Biofilm ; Drug-resistant microorganism

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Alternative Products

Product Details of [ 636-94-2 ]

CAS No. :636-94-2
Formula : C8H6O5
M.W : 182.13
SMILES Code : C1=CC(=CC(=C1C(=O)O)O)C(=O)O
MDL No. :MFCD09835368
InChI Key :CDOWNLMZVKJRSC-UHFFFAOYSA-N
Pubchem ID :97257

Safety of [ 636-94-2 ]

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

Computational Chemistry of [ 636-94-2 ] Show Less

Physicochemical Properties

Num. heavy atoms 13
Num. arom. heavy atoms 6
Fraction Csp3 0.0
Num. rotatable bonds 2
Num. H-bond acceptors 5.0
Num. H-bond donors 3.0
Molar Refractivity 42.38
TPSA ?

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

94.83 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

0.8
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

1.62
Log Po/w (WLOGP)?

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

0.79
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.

0.63
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

0.15
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

0.8

Water Solubility

Log S (ESOL):?

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

-2.2
Solubility 1.15 mg/ml ; 0.00632 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.

-3.22
Solubility 0.109 mg/ml ; 0.000597 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

-0.57
Solubility 49.0 mg/ml ; 0.269 mol/l
Class?

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

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

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

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

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.

-6.26 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

1.0
Bioavailability Score?

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

0.56

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

0.0 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)

1.21
 

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Technical Information

Categories

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