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(R)-(-)-Rolipram

(R)-(-)-Rolipram

Catalog No. BCC5429
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Quality Control of (R)-(-)-Rolipram

Chemical structure

(R)-(-)-Rolipram

Biological Activity of (R)-(-)-Rolipram

More active enantiomer of the PDE4 inhibitor rolipram; 2-10-fold more potent than the S-(+) enantiomer. Also available as part of the Phosphodiesterase Inhibitor.

(R)-(-)-Rolipram Dilution Calculator

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Chemical Properties of (R)-(-)-Rolipram

Cas No. 85416-75-7 SDF Download SDF
Chemical Name (4R)-4-[3-(Cyclopentyloxy)-4-methoxyphenyl]pyrrolidin-2-one
SMILES COc1ccc(cc1OC2CCCC2)C3CNC(=O)C3
Standard InChIKey HJORMJIFDVBMOB-UHFFFAOYSA-N
Standard InChI InChI=1S/C16H21NO3/c1-19-14-7-6-11(12-9-16(18)17-10-12)8-15(14)20-13-4-2-3-5-13/h6-8,12-13H,2-5,9-10H2,1H3,(H,17,18)
Formula C16H21NO3 M.Wt 275.34
Solubility Soluble to 100 mM in ethanol and to 100 mM in DMSO
Storage Store at RT
General tips For obtaining a higher solubility , please warm the tube at 37 ℃ and shake it in the ultrasonic bath for a while.Stock solution can be stored below -20℃ for several months.
Shipping Condition Packaging according to customer requirements(5mg, 10mg, 20mg and more). Ship via FedEx, DHL, UPS, EMS or other courier with RT , or blue ice upon request.

Preparing Stock Solutions of (R)-(-)-Rolipram

1 mg 5 mg 10 mg 20 mg 25 mg
1 mM 3.6319 mL 18.1594 mL 36.3187 mL 72.6375 mL 90.7968 mL
5 mM 0.7264 mL 3.6319 mL 7.2637 mL 14.5275 mL 18.1594 mL
10 mM 0.3632 mL 1.8159 mL 3.6319 mL 7.2637 mL 9.0797 mL
50 mM 0.0726 mL 0.3632 mL 0.7264 mL 1.4527 mL 1.8159 mL
100 mM 0.0363 mL 0.1816 mL 0.3632 mL 0.7264 mL 0.908 mL
* Note: If you are in the process of experiment, it's necessary to make the dilution ratios of the samples. The dilution data above is only for reference. Normally, it's can get a better solubility within lower of Concentrations.

References on (R)-(-)-Rolipram

Multistep continuous-flow synthesis of (R)- and (S)-rolipram using heterogeneous catalysts.[Pubmed: 25877201]


Chemical manufacturing is conducted using either batch systems or continuous-flow systems. Flow systems have several advantages over batch systems, particularly in terms of productivity, heat and mixing efficiency, safety, and reproducibility. However, for over half a century, pharmaceutical manufacturing has used batch systems because the synthesis of complex molecules such as drugs has been difficult to achieve with continuous-flow systems. Here we describe the continuous-flow synthesis of drugs using only columns packed with heterogeneous catalysts. Commercially available starting materials were successively passed through four columns containing achiral and chiral heterogeneous catalysts to produce (R)-rolipram, an anti-inflammatory drug and one of the family of γ-aminobutyric acid (GABA) derivatives. In addition, simply by replacing a column packed with a chiral heterogeneous catalyst with another column packed with the opposing enantiomer, we obtained antipole (S)-rolipram. Similarly, we also synthesized (R)-phenibut, another drug belonging to the GABA family. These flow systems are simple and stable with no leaching of metal catalysts. Our results demonstrate that multistep (eight steps in this case) chemical transformations for drug synthesis can proceed smoothly under flow conditions using only heterogeneous catalysts, without the isolation of any intermediates and without the separation of any catalysts, co-products, by-products, and excess reagents. We anticipate that such syntheses will be useful in pharmaceutical manufacturing.

Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical protocol using [11C](R)-rolipram.[Pubmed: 24586526]


Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [(11)C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [(11)C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (V T/f P) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a V T/f P error of <5%, and only one had error >10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [(11)C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-V T/f P in [(11)C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.

Voxelwise quantification of [(11)C](R)-rolipram PET data: a comparison between model-based and data-driven methods.[Pubmed: 23512132]


This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [(11)C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); and (3) the Logan graphical plot. Nonphysiologic VT estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise VT estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1±1%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y=1.034x-0.013; R(2)=0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation (R(2)>0.96). H-BFM yielded the most reliable voxelwise quantification of [(11)C](R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only VT estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness.

Regio- and enantioselective palladium-catalyzed allylic alkylation of nitromethane with monosubstituted allyl substrates: synthesis of (R)-rolipram and (R)-baclofen.[Pubmed: 22992268]


The Pd-catalyzed asymmetric allylic alkylation (AAA) reaction of nitromethane with monosubstituted allyl substrates was realized for the first time to provide corresponding products in high yields with excellent regio- and enantioselectivities. The protocol was applied to the enantioselective synthesis of (R)-baclofen and (R)-rolipram.

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