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88547-38-0

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88547-38-0 Usage

Chemical Properties

Colourless solid

Check Digit Verification of cas no

The CAS Registry Mumber 88547-38-0 includes 8 digits separated into 3 groups by hyphens. The first part of the number,starting from the left, has 5 digits, 8,8,5,4 and 7 respectively; the second part has 2 digits, 3 and 8 respectively.
Calculate Digit Verification of CAS Registry Number 88547-38:
(7*8)+(6*8)+(5*5)+(4*4)+(3*7)+(2*3)+(1*8)=180
180 % 10 = 0
So 88547-38-0 is a valid CAS Registry Number.

88547-38-0SDS

SAFETY DATA SHEETS

According to Globally Harmonized System of Classification and Labelling of Chemicals (GHS) - Sixth revised edition

Version: 1.0

Creation Date: Aug 14, 2017

Revision Date: Aug 14, 2017

1.Identification

1.1 GHS Product identifier

Product name (S)-N-DESISOPROPYLPROPRANOLOL

1.2 Other means of identification

Product number -
Other names S(-)-1-Amino-3-(1-naphthyloxy)-2-propanol

1.3 Recommended use of the chemical and restrictions on use

Identified uses For industry use only.
Uses advised against no data available

1.4 Supplier's details

1.5 Emergency phone number

Emergency phone number -
Service hours Monday to Friday, 9am-5pm (Standard time zone: UTC/GMT +8 hours).

More Details:88547-38-0 SDS

88547-38-0Relevant articles and documents

Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code

Hartman, Jessica H.,Cothren, Steven D.,Park, Sun-Ha,Yun, Chul-Ho,Darsey, Jerry A.,Miller, Grover P.

, p. 3749 - 3759 (2013)

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k cat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and K m) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.

A new catalyst for the asymmetric Henry reaction: Synthesis of β-nitroethanols in high enantiomeric excess

White, James D.,Shaw, Subrata

, p. 6270 - 6273 (2013/02/23)

A new chiral tetrahydrosalen ligand has been designed and synthesized from cis-2,5-diaminobicyclo[2.2.2]octane. The complex generated in situ by the interaction of the ligand with (CuOTf)2·C6H 5CH3 was an efficient catalyst for the asymmetric Henry reaction, producing nitroaldol products in high yield and good stereoselectivity. Henry reactions catalyzed by this tetrahydrosalen-Cu(I) complex led to syntheses of β-adrenergic blocking agents (S)-toliprolol, (S)-moprolol, and (S)-propanolol.

Synthesis of the Stereoisomers of -Fluoropropranolol, a Tracer for the β-Adrenergic Receptor

Tewson, T. J.,Stekhova, Svetlana

, p. 58 - 59 (2007/10/03)

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