Study on the Medication Rule of the National Patent of Traditional Chinese Medicine Compound Formula in the Treatment of Depression
10.13288/j.11-2166/r.2023.19.014
- VernacularTitle:国家中药复方专利治疗抑郁症用药规律研究
- Author:
Hongxi LIU
1
;
Jingzi SHI
1
;
Xiao LIANG
2
;
Wei SHEN
2
;
Jingjing WEI
2
;
Yue LIU
2
;
Guojing FU
2
;
Yunling ZHANG
2
Author Information
1. Beijing University of Chinese Medicine， Beijing， 100029
2. Xiyuan Hospital， China Academy of Chinese Medical Sciences
- Publication Type:Journal Article
- Keywords:
depression;
data mining;
medication rules;
traditional Chinese medicine compound patent
- From:
Journal of Traditional Chinese Medicine
2023;64(19):2027-2032
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo explore the medication rules of traditional Chinese medicine compounds for depression in the National Patent Database using data mining， and to provide ideas for the clinical treatment and the development of new drugs for depression. MethodsThe patent data of traditional Chinese medicine compounds for the treatment of depression were searched from inception to July 1st， 2022 on the Patent Publication Announcement website of China National Intellectual Property Administration. The selected traditional Chinese medicine compounds were analyzed by using the data mining section of the ancient and modern medical record cloud platform （V2.3.5） for drug frequency， and based on this， the nature， flavor， channel entry and function of the medicinals were analyzed. Representative high-frequency herbal combinations were obtained through correlation analysis， while the classification of Chinese medicine compounds for depression was analyzed by cluster analysis， and the core combinations of herbs for the treatment of depression were screened out using complex network analysis. ResultsA total of 325 Chinese medicine compounds were included， involving 452 herbs， with a total frequency of 3532 times. The top 10 mostly used herbs were Yujin （Radix Curcumae， 122 times）， Chaihu （Radix Bupleuri， 122 times）， Baishao （Radix Paeoniae Alba， 109 times）， Suanzaoren （Spina Date Seed， 95 times）， Fuling （Poria， 94 times）， Danggui （Radix Angelicae Sinensis， 94 times）， Yuanzhi （Radix Polygalae， 84 times）， Baizhu （Rhizoma Atractylodis Macrocephalae， 72 times）， Shichangpu （Rhizoma Acori Graminei， 71 times）， and Danshen （Salvia Miltiorrhiza， 61 times）. The natures of the herbs were mainly warm （998 times）， neutral （944 times）， slightly cold （596 times） and cold （497 times）； the flavors were mainly sweet （1648 times）， acrid （1392 times）， and bitter （1337 times）； the channels of entry were mainly liver （1695 times）， heart （1521 times）， spleen （1326 times） and lung （1268 times）. The medicinals with the function of soothing liver to relieve constraint， moistening the intestines to promote defecation， calming the heart and the mind， moving qi to relieve constraint were used more frequently. The high frequency herbal combinations by association analysis included “Chaihu （Radix Bupleuri）→ Baishao （Radix Paeoniae Alba）”， “Danggui （Radix Angelicae Sinensis）→Chaihu （Radix Bupleuri）” and “Baishao （Radix Paeoniae Alba）→ Yujin （Radix Curcumae）”. The 22 high frequency medicinals used more than 40 times could be clustered into six categories. Complex network analysis found the core herbal combination for the treatment of depression was the formula of Chaihu （Radix Bupleuri）， Yujin （Radix Curcumae）， Baishao （Radix Paeoniae Alba）， Danggui （Radix Angelicae Sinensis）， Fuling （Poria）， Suanzaoren （Spina Date Seed）， and Xiangfu （Cyperi Rhizoma）. ConclusionTraditional Chinese medicine compounds for the treatment of depression is mainly based on the pathogenesis of constraint， stasis and deficiency， focusing on the liver， heart， spleen and lung， commonly using medicinals with the function of soothing liver to relieve constraint， fortifying spleen and nourishing heart， regulating qi and invigorating blood， and moistening the intestines to promote defecation， which can provide a reference for the clinical treatment and new drug research and development for depression.
