Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes.

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an attractive to conventional medicine. Multi-herb recipes have been routinely used in TCM. These have been formulated by using TCM-defined herbal properties (TCM-HPs), the scientific basis of which is unclear. The usefulness of TCM-HPs was evaluated by analyzing the distribution pattern of TCM-HPs of the constituent herbs in 1161 classical TCM prescriptions, which shows patterns of multi-herb correlation. Two artificial intelligence (AI) methods were used to examine whether TCM-HPs are capable of distinguishing TCM prescriptions from non-TCM recipes. Two AI systems were trained and tested by using 1161 TCM prescriptions, 11,202 non-TCM recipes, and two separate evaluation methods. These systems correctly classified 83.1-97.3% of the TCM prescriptions, 90.8-92.3% of the non-TCM recipes. These results suggest that TCM-HPs are capable of separating TCM prescriptions from non-TCM recipes, which are useful for formulating TCM prescriptions and consistent with the expected correlation between TCM-HPs and the physicochemical properties of herbal ingredients responsible for producing the collective pharmacological and other effects of specific TCM prescriptions.

[1]  E. Graf Chinese Drugs of Plant Origin. Chemistry, Pharmacology, and Use in Traditional and Modern Medicine. Von W. Tang und G. Eisenbrand. Springer‐Verlag Berlin etc. 1992, X, 1056, S., 41 Abb. gebd. DM 248,00 , 1992 .

[2]  R Yuan,et al.  Traditional Chinese medicine: an approach to scientific proof and clinical validation. , 2000, Pharmacology & therapeutics.

[3]  Y. Z. Chen,et al.  A computer method for validating traditional Chinese medicine herbal prescriptions. , 2005, The American journal of Chinese medicine.

[4]  D. Mak,et al.  Pharmacological basis of 'Yang-invigoration' in Chinese medicine. , 2004, Trends in pharmacological sciences.

[5]  K. Ko,et al.  Antioxidant and Immunomodulatory Activities of Chinese Tonifying Herbs , 2002 .

[6]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[7]  A. Dasgupta,et al.  Study on mechanism of action of Chinese medicine Chan Su: dose-dependent biphasic production of nitric oxide in trophoblastic BeWo cells. , 2003, Clinica chimica acta; international journal of clinical chemistry.

[8]  C. Hew,et al.  Superoxide and traditional Chinese medicines. , 1995, Journal of ethnopharmacology.

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  D. Mak,et al.  Pharmacological basis of 'Yin-nourishing' and 'Yang-invigorating' actions of Cordyceps, a Chinese tonifying herb. , 2004, Life sciences.

[11]  M. Lazar East meets West: an herbal tea finds a receptor. , 2004, The Journal of clinical investigation.

[12]  K. Shimamoto,et al.  Chinese medicine, Jiang-Tang-Ke-Li, improves insulin resistance by modulating muscle fiber composition and muscle tumor necrosis factor-alpha in fructose-fed rats. , 2003, Hypertension research : official journal of the Japanese Society of Hypertension.

[13]  Wen-yue Jiang,et al.  Therapeutic wisdom in traditional Chinese medicine: a perspective from modern science. , 2005, Trends in pharmacological sciences.

[14]  J. Moss,et al.  Herbal medicines and perioperative care. , 2001, Anesthesiology.

[15]  Gerhard Eisenbrand,et al.  Chinese Drugs of Plant Origin: Chemistry, Pharmacology, and Use in Traditional and Modern Medicine , 1992 .

[16]  K. Chan,et al.  Progress in traditional Chinese medicine. , 1995, Trends in pharmacological sciences.

[17]  Zhi-Wei Cao,et al.  Effect of Selection of Molecular Descriptors on the Prediction of Blood-Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods , 2005, J. Chem. Inf. Model..