Japanese drug byakusi obt. from Angelica spp. Also from lemon oil and other Citrus spp. and from Heracleum nepalense
Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.
571.5±50.0 °C at 760 mmHg
HS Code Reference
Personal Projective Equipment
For Reference Standard and R&D, Not for Human Use Directly.
provides coniferyl ferulate(CAS#:482-25-7) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate
Background: The elucidation of the biological roles of individual active compounds in terms of their in vivo bio-distribution and bioactivity could provide crucial information to understand how natural compounds work together as treatments for diseases.
Purpose: We examined the functional roles of Byakangelicin (Byn) to improve the brain accumulation of active compounds, e.g., umbelliferone (Umb), curcumin (Cur), and doxorubicin (Dox), and consequently to enhance their biological activities.
Methods: Active compounds were administered intravenously to mice, with or without Byn, after which organs were isolated and visualized for their ex vivo fluorescence imaging to determine the bio-distribution of each active compound in vivo. For the in vivo bioactivity, Cur, either with or without Byn, was administered to a lipopolysaccharide (LPS)-induced neuro-inflammation model for 5 days, and its anti-inflammatory effects were examined by ELISA using a brain homogenate and serum.
Results: We successfully demonstrated that the levels of active compounds (Umb, Cur, and Dox) in the brain, lung, and pancreas were greatly elevated by the addition of Byn via direct ex vivo fluorescence monitoring. In addition, sufficient accumulation of the active compound, Cur, greatly reduced LPS-induced neuro-inflammation in vivo.
Conclusion: Byn could serve as a modulator to allow improved brain accumulation of diverse active compounds (Umb, Cur, and Dox) and enhanced therapeutic effects.
Brain; Byakangelicin; Compound mixture; Ex vivo monitoring; Synergistic biodistribution.
Byakangelicin as a modulator for improved distribution and bioactivity of natural compounds and synthetic drugs in the brain
Yoon Young Kang 1, Jihyeon Song 1, Jun Yeong Kim 1, Heesun Jung 1, Woon-Seok Yeo 1, Yoongho Lim 1, Hyejung Mok 2
Understanding how natural compounds work together for disease treatments can improve their clinical efficacy and therapeutic effects. To elucidate the mechanisms of synergistic biological effects in natural compound mixtures, umbelliferone (UMB, 7-hydroxycoumarin), derived from Angelica (A.) gigas, was selected as active compound with fluorescent characteristic to examine bioactivities in vitro in the presence of other compounds from Angelica gigas. Antioxidant effects of UMB in biochemical assays and cellular reactive oxygen species (ROS) levels in RAW264.7 cells were not significantly improved by addition of other compounds. However, intracellular uptake, inhibition of the efflux pump P-glycoprotein (P-gp), and physiological stability of UMB were greatly enhanced by the addition of other compounds, specifically Angelicin (ANG) and Byakangelicin (BYN). Taken together, enhanced intracellular localization and enzymatic stability in compound mixtures might lead to superior synergistic bioactivity of UMBs in compound mixtures.
Angelica gigas; Compound mixture; Synergistic biological effect; Umbelliferone.
Enhanced intracellular uptake and stability of umbelliferone in compound mixtures from Angelica gigas in vitro
Yoon Young Kang 1, Jun Yeong Kim 1, Jihyeon Song 1, Hyejung Mok 2
Background: The quality evaluation of traditional Chinese medicine (TCM) formulations is needed to guarantee the safety and efficacy. In our laboratory, we established interaction rules between chemical quality control and biological activity evaluations to study Yuanhu Zhitong tablets (YZTs). Moreover, a quality marker (Q-marker) has recently been proposed as a new concept in the quality control of TCM. However, no appropriate methods are available for the identification of Q-markers from the complex TCM systems.
Purpose: We aimed to use an integrative pharmacological (IP) approach to further identify Q-markers from YZTs through the integration of multidisciplinary knowledge. In addition, data mining was used to determine the correlation between multiple constituents of this TCM and its bioactivity to improve quality control.
Methods: The IP approach was used to identify the active constituents of YZTs and elucidate the molecular mechanisms by integrating chemical and biosynthetic analyses, drug metabolism, and network pharmacology. Data mining methods including grey relational analysis (GRA) and least squares support vector machine (LS-SVM) regression techniques, were used to establish the correlations among the constituents and efficacy, and dose efficacy in multiple dimensions.
Results: Seven constituents (tetrahydropalmatine, α-allocryptopine, protopine, corydaline, imperatorin, isoimperatorin, and byakangelicin) were identified as Q-markers of YZT using IP based on their high abundance, specific presence in the individual herbal constituents and the product, appropriate drug-like properties, and critical contribution to the bioactivity of the mixture of YZT constituents. Moreover, three Q-markers (protopine, α-allocryptopine, and corydaline) were highly correlated with the multiple bioactivities of the YZTs, as found using data mining. Finally, three constituents (tetrahydropalmatine, corydaline, and imperatorin) were chosen as minimum combinations that both distinguished the authentic components from false products and indicated the intensity of bioactivity to improve the quality control of YZTs.
Conclusions: Tetrahydropalmatine, imperatorin, and corydaline could be used as minimum combinations to effectively control the quality of YZTs.
Least squares support vector machine; Quality evaluation; Quality markers; Vasorelaxation bioactivity; Yuanhu Zhitong tablet.
Identification of quality markers of Yuanhu Zhitong tablets based on integrative pharmacology and data mining
Ke Li 1, Junfang Li 2, Jin Su 3, Xuefeng Xiao 4, Xiujuan Peng 5, Feng Liu 5, Defeng Li 3, Yi Zhang 3, Tao Chong 6, Haiyu Xu 7, Changxiao Liu 8, Hongjun Yang 3
2018 May 15