BruceantarinCAS# 41451-76-7 |
Quality Control & MSDS
Number of papers citing our products
Chemical structure
| Cas No. | 41451-76-7 | SDF | Download SDF |
| PubChem ID | N/A | Appearance | Powder |
| Formula | C28H30O11 | M.Wt | 542.53 |
| Type of Compound | Diterpenoids | Storage | Desiccate at -20°C |
| Solubility | Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc. | ||
| 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. We recommend that you prepare and use the solution on the same day. However, if the test schedule requires, the stock solutions can be prepared in advance, and the stock solution must be sealed and stored below -20℃. In general, the stock solution can be kept for several months. Before use, we recommend that you leave the vial at room temperature for at least an hour before opening it. |
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| About Packaging | 1. The packaging of the product may be reversed during transportation, cause the high purity compounds to adhere to the neck or cap of the vial.Take the vail out of its packaging and shake gently until the compounds fall to the bottom of the vial. 2. For liquid products, please centrifuge at 500xg to gather the liquid to the bottom of the vial. 3. Try to avoid loss or contamination during the experiment. |
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| Shipping Condition | Packaging according to customer requirements(5mg, 10mg, 20mg and more). Ship via FedEx, DHL, UPS, EMS or other couriers with RT, or blue ice upon request. | ||
Bruceantarin Dilution Calculator
Bruceantarin Molarity Calculator
| 1 mg | 5 mg | 10 mg | 20 mg | 25 mg | |
| 1 mM | 1.8432 mL | 9.2161 mL | 18.4322 mL | 36.8643 mL | 46.0804 mL |
| 5 mM | 0.3686 mL | 1.8432 mL | 3.6864 mL | 7.3729 mL | 9.2161 mL |
| 10 mM | 0.1843 mL | 0.9216 mL | 1.8432 mL | 3.6864 mL | 4.608 mL |
| 50 mM | 0.0369 mL | 0.1843 mL | 0.3686 mL | 0.7373 mL | 0.9216 mL |
| 100 mM | 0.0184 mL | 0.0922 mL | 0.1843 mL | 0.3686 mL | 0.4608 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. | |||||
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Combining empirical knowledge, in silico molecular docking and ADMET profiling to identify therapeutic phytochemicals from Brucea antidysentrica for acute myeloid leukemia.[Pubmed:35895695]
PLoS One. 2022 Jul 27;17(7):e0270050.
Acute myeloid leukemia (AML) is one of the deadly cancers. Chemotherapy is the first-line treatment and the only curative intervention is stem cell transplantation which are intolerable for aged and comorbid patients. Therefore, finding complementary treatment is still an active research area. For this, empirical knowledge driven search for therapeutic agents have been carried out by long and arduous wet lab processes. Nonetheless, currently there is an accumulated bioinformatics data about natural products that enabled the use of efficient and cost effective in silico methods to find drug candidates. In this work, therefore, we set out to computationally investigate the phytochemicals from Brucea antidysentrica to identify therapeutic phytochemicals for AML. We performed in silico molecular docking of compounds against AML receptors IDH2, MCL1, FLT3 and BCL2. Phytochemicals were docked to AML receptors at the same site where small molecule drugs were bound and their binding affinities were examined. In addition, random compounds from PubChem were docked with AML targets and their docking score was compared with that of phytochemicals using statistical analysis. Then, non-covalent interactions between phytochemicals and receptors were identified and visualized using discovery studio and Protein-Ligand Interaction Profiler web tool (PLIP). From the statistical analysis, most of the phytochemicals exhibited significantly lower (p-value Bruceantarin, bruceantin, canthin-6-one, hydnocarpin, and yadanzioside P) can be considered as candidate compounds to prevent and manage AML. However, the phytochemicals should be further studied using in vivo & in vitro experiments on AML models. Therefore, this study concludes that combination of empirical knowledge, in silico molecular docking and ADMET profiling is useful to find natural product-based drug candidates. This technique can be applied to other natural products with known empirical efficacy.
