| At present, the pharmacodynamic standards which are adopted during the research of new drug based on Chinese herb prescriptions is almost the same to that of west medicine. That is, to use a unified index system to explain the pharmacodynamic action of Chinese herb prescription. In addition, the corresponding relationship between Chinese herb functions and pharmacodynamic indexes is not clear. This caused the blindness of the research of new drug based on Chinese herb prescription. What is more, the pharmacodynamic standards can not reflect the pharmacodynamic feature of Chinese herb prescription thoroughly. As a result, the pharmacodynamic action of Chinese herb prescription can not be well understood by other countries except China. The above-mentioned situation has become a blockage of Chinese herbs on their way abroad in the new century.Our research center has carried out the pharmacodynamic research of more than 30 Chinese herb prescriptions which are verified to be effective to ischemic cerebral and cardiac vascular diseases. The functions and herb components of these prescriptions are clear, and the pharmacodynamic data of them are completely filed. The main goal of this research is to make a preliminary exploration on the general dependability between Chinese herb functions and pharmacodynamic indexes on the basis of the above-mentioned pharmacodynamic data. In this way, we established a feasible data analyzing method for the research of the general corresponding relationship between Chinese herb functions and pharmacodynamic indexes.Chapter 1 Data AnalysisThe main destination of this chapter is to find out the relationship between certain Chinese herb funcion and the changes of the pharmacodynamic indexes that had been caused by that function. So that, we could make a preliminary exploration for the method which could be used to research the general dependability between Chinese herb function and pharmacodynamic indexes in bigger scope.This chapter contains 4 parts. Through them, we mainly studied the relationship between Chinese herb function combinations and the changes of the pharmacodynamic indexes that had been caused by them, the relationship between single Chinese herb functions and the changes of the pharmacodynamic indexes that had been caused by them, the sequence of certain single functions or function combinations according to their compositive relieving effectiveness of them to the ischemic cardiac and cerebral vascular diseases, and finally found out the Chinese herb functions which were respectively the most effective for certain pharmacodynamic indexes according to the result of data mining conclusion, and the corresponding herb.Part one: In this part, we researched the dependability between Chinese herb function combinations and pharmacodynamic indexes. Several statistical method were used to find out the aggregation of Chinese herb function combinations, which were effective to each pharmacodynamic index, such as single factor ANOVA, ANOVA of repeatedly measured data, mulriple mean comparison, etc. And then we had pharmacodynamic indexes as research units, and found out the sequence of the ameliorating effect of Chinese herb function combinations to them, from strong to weak (according to the average departure of the experimental measured values of certain pharmacodynamic index between medication administration team and model team).Part two: We researched the dependability between single functions and pharmacodynamic indexes. We had the above-mentioned "the sequence of the ameliorating effect of Chinese herb function combinations to certain pharmacodynamic index" as the basis of our research. Then, by means of Mean-rank algorithm, we found out the sequence of the ameliorating effect of single functions to them (from strong to weak). Because one single function may appear in several different function combinations ( For example, A single function "HuoXue" may appear in the many function combinations, such as "HuoXueYiQi" or "LiQiHuoXue", etc.), if we want to find out the ameliorating effect of single functions to certain pharmacodynamic index from the pharmacodynamic data of prescriptions (function combinations), and then sort them into orderly sequence accordingly, we must use Mean-rank algorithm.Part three: we continued to use Mean-rank algorithm to find out single functions" compositive ameliorating strength sequence for ischemic cerebral or cardiac vascular diseases. Concrete methods can be described as: Firstly ,we used serialnumber (1, 2, 3......) to demonstrate "single functions" orderly sequence of theirameliorating effect to certain pharmacodynamic index "(found out in part two). That is, we assimilated the pharmacodynamic indexes of different nature into "serial number", which could be compared. Secondly, we calculated the Mean rank of the above-mentioned "serial number" to find out single functions" compositive ameliorating strength sequence for ischemic cerebral or cardiac vascular diseases.Part four: In this part, we made a summary of the first 3 parts . we mainly filtrated the single functions (and corresponding herbs) with the best ameliorating effect to respective pharmacodynamic indexes, so that we could found the basis for the herb selection of chapter 3s s experiments. To be followed, we summarized the sensitive pharmacodynamic indexes of the Chinese herb functions which are frequently used in clinic for ischemic cardiac and cerebral vascular diseases. The result can be described by the following form:Frequently used functionsSensitive pharmacodynamic indexesYiQiCO> SO> PVR;rat (30S1) blood viscosity;ADP induced rabbit PA;rat cerebral pia mater MC flow rate., flow speed;MDA.XingQiCVR;LVSP;SOD;rat (200S1, 100 S"1) blood viscosity;collagen induced rabbit PA.HuoXueCO. SCX PVR;MDA;6-keto-PGF,;YangXueCVR;LVSP, CABF^ CAVR;rat (200S1, 100 S"1) blood viscosity;collagen induced rabbit PA.QuFengCerebral infarction area;SOD.SanJieCVR;SOD;collagen induced rabbit PA.Zhi TongCO*. SO> PVR;LVW;Cerebral infarction area;SOD;6~ keto-PGFi a;6 - keto-PGFi a /TXB2;rat (200S"1) blood viscosity.Chapter 2 Empirical StudyThe main destination of this chapter is to check-up the results of data analysis on the basis of experiments.Firstly,according to the herb filtrating results of chapter 2, and our center's new drug research experience, we selected the functions and herbs for the prescriptions which are used in empirical study, and then combined 4 prescriptions accordingly.Secondly, we carried out the experiments of " the comparison of the effect of the prescriptions(which are espected to have ameliorating effect to ischemic cardiac vascular disease) to the myocardial infarction of rats and the related biochemical indexes" and "the comparison of the effect of the prescriptions (which are espected to have ameliorating effect to ischemic cardiac vascular disease) to the myocardial infarction of rats which is cause by ischemia and reperfusion "on Prescription 1 and 2. We then carried out the experiments of " the comparison of the effect of the prescriptions(which are espected to have ameliorating effect to ischemic cerebral vascular disease) to the local cerebral ischemia of rats" and "the comparison of the effect of the prescriptions (which are espected to have ameliorating effect to ischemic cerebral vascular disease) to the microcirculation of rats' cerebral pia mater"on Prescription 3 and 4. We also carried out experiments of "the comparison of the effect of the 4 above-mentioned prescriptions to the vitro thrombopoiesis and blood viscosity of rats".Results of empirical study: Most of the experimental prescriptions could manifest the pharmacodynamic action which had been expected by the data mining results.Some of them had apparent ameliorating effects to certain pharmacodynamic indexes. For example: In the experiment of " the comparison of the effect of the prescriptions(which are espected to have ameliorating effect to ischemic cardiac vascular disease) to the myocardial infarction of rats and the related biochemical indexes", the extractive of prescription 1 had good effect to the pharmacodynamic indexes of myocardial infarction area, weight of myocardial infarction area, and percentage of the myocardial infarction (P<0.01) , if they were compared with the control group. Another case in point: Both the extractive of prescription 4 had good ameliorating effect to cerebral infarction area (P<0.05 ~ O.OOl);the extractive of prescription 3 has similar effect.Chapter 3 correlation analysis on the results of data mining and empirical studyThe main directions of this chapter is to research the dependability between certain pharmacodynamic index and the strength of the single function which has been expected to have the best ameliorating action to that pharmacodynamic index. So that, we could check up the data mining results in chapter 2.Firstly, we took strength(the dosage of corresponding herb) of the single function which had been expected to have the best ameliorating action to certain pharmacodynamic index as X-axis, and took the measured value of that pharmacodynamic index as Y-axis, and drew scatterplot accordingly. Secondly, we set correlation analysis for those two variances, and got coefficient correlation (R), the possibility for R to be zero (P).According to the results of correlation analysis, we could divide the pharmacodynamic indexs into 3 groups:First group: The pharmacodynamic indexes that coincide well with data mining results. This group include: flow rate of cerebral pia mater microcirculation, dry weight of thrombus, percentage of myocardial infarction (myocardial ischemia and reperfusion), serum SOD activity, serum MDA activity.Second group: The pharmacodynamic indexes, the correlating tendency (R value) of which is coincident with data mining results.But the results are not dependable (p>0.05) . This group include: percentage of cerebral infarction, fluid state of cerebral pia mater microcirculation, whole blood viscosity (200S"1) -. whole blood viscosity (100S1) > length of thrombus, wet weight of thrombus, percentage of myocardial infarction(myocardial ischemia).Third group: The pharmacodynamic indexes that does not coincide with data mining results. This group include: whole blood viscosity (5S1) s.It is thus clear that, most of the experimental results of chapter 3 coincide well with the data mining result of chapter 2. However, there is several pharmacodynamic indexes, the correlating results of which are not dependable or just contradict to the data mining results. This phenomenon can mainly attribute to three reasons: Firstly, it is caused by the limit of experimental groups. It make the number of samples in the analysis so small, that a very slight fluctuation of numerical value can affect the analysis results. Secondly, when selecting herbs and dosage for experimental prescriptions, we did not make the dosage of the herbs(which were verified to have good ameliorative effect to certain pharmacodynamic index)a lot different among the experimental prescriptions. Finally, because the scale of the data resource that are used in chapter 2 is very limited, the data mining results themselves may be distorted to some extent. Thus, if we can enlarge the data resource in the future, and improve the experimental design, we may get more clear and accurate results. |
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