OBJECTIVE To optimize the forming process of Yindan huoxue tongyu granules， and evaluate the quality of the granules. METHODS Taking forming rate， angle of repose， moisture， moisture absorption rate and dissolution rate as indexes， single factor experiment combined with Plackett-Burman design was adopted to screen key process parameters； analytic hierarchy process combined with entropy weight method and Box-Behnken response surface method were used to optimize the molding process of Yindan huoxue tongyu granules， and the forming process was verified. The relative homogeneity index， bulk density， vibration density， Hausner ratio， angle of repose， moisture and hygroscopicity were used as secondary physical indexes to establish the physical fingerprints of 10 batches of Yindan huoxue tongyu granules to evaluate particle quality consistency. RESULTS The optimal molding process of Yindan huoxue tongyu granules was as follows： mannitol as the fixed excipient， the drug-assisted ratio was 1∶1（m/m） and the drying time was 1 h； 90% ethanol was used as wetting agent and the amount of it was 32%， the drying temperature was 70 ℃. The results of validation tests showed that the average comprehensive score was 97.45， which was close to the predicted value of 97.18. The similarities between the physical fingerprints of 10 batches of Yindan huoxue tongyu granules prepared by the optimal molding process and the reference physical fingerprint were all higher than 0.99. CONCLUSIONS The molding process is stable and feasible， and the quality of Yindan huoxue tongyu granules produced is stable and controllable.

OBJECTIVE To evaluate the quality of Yindan huoxue tongyu granules. METHODS Taking high performance liquid chromatography with ultraviolet and evaporative light scattering detection as method， the fingerprint of 15 batches of Yindan huoxue tongyu granules was established， and similarity evaluation was performed by Similarity Evaluation System of Chromatographic Fingerprint of TCM （2012 edition） to determine common peaks. The common peaks were identified by comparing with reference substance chromatograms and single decoction piece chromatograms. Network pharmacology was used to screen out core targets and pathways of identified components， construct a “component-target-pathway” network diagram， and predict the pharmacodynamic components of Yindan huoxue tongyu granules， and the content determination of these components was carried out by the same method. RESULTS HPLC fingerprints of 15 batches of Yindan huoxue tongyu granules were characterized with 40 common peaks， and 17 components including salvianolic acid B， astragaloside Ⅳ， notoginsenoside R1， and ginkgolide A were identified. Network pharmacology predicted that 17 components mainly acted on 97 core targets and 137 pathways to exert their pharmacological effect. Average contents of 13 bioactive components in 15 batches of samples were 0.126 8， 0.232 0， 0.073 8， 0.353 2， 3.620 2， 0.191 0， 0.333 3， 0.317 4， 0.785 0， 0.538 2， 0.460 0， 2.475 1 and 0.347 7 mg/g， including calycosin-7-O-β-D-glucoside， rosmarinic acid， formononetin， lithospermic acid， salvianolic acid B， ononin， ginsenoside Rb1， ginsenoside Rd， ginkgolide C， ginkgolide A， ginkgolide B， notoginsenoside R1， and astragaloside Ⅳ. CONCLUSIONS The established fingerprint of Yindan huoxue tongyu granules can reflect the overall characteristics of the preparation. The content determination method for its pharmacodynamic components， developed in combination with network pharmacology， is accurate， reliable， and exhibits good repeatability， making it suitable for evaluating the quality of Yindan huoxue tongyu granules.

OBJECTIVE To optimize the water extraction technology for Kaixin granules. METHODS UPLC-MS/MS method was established for the simultaneous determination of ginsenoside Rg1， ginsenoside Re， ginsenoside Rb1， tenuifolin， polygalaxanthone Ⅲ and 3， 6′-disinapoyl-sucrose. An orthogonal test was designed with extraction times， extraction duration， and the volume of added water as factors. Using the contents of the aforementioned six indicator components and the extract yield as evaluation indexes， analytic hierarchy process-entropy weight method was employed to determine the combined weights of each indicator. Subsequently， process optimization and validation were conducted by integrating grey relational analysis （GRA） and back propagation （BP） neural network. RESULTS The water extraction technology optimized by the orthogonal test and GRA was 10- fold water for the first decoction and 8-fold water for the subsequent two， extracting 3 times，extracting for 1 h each time； the average comprehensive score of the validation experiment was 91.10 （RSD＝0.31%， n＝3）. The water extraction technology optimized by BP neural network was extracting 3 times with 10-fold water added each time， extracting for 1.5 h each time； the average comprehensive score of the validation experiment was 95.89 （RSD＝0.73%， n＝3）. Considering practical production requirements， the optimal water extraction technology was extraction performed three times， with 10-fold water for the first decoction and 8-fold water for the subsequent two extractions， with an extraction time of 1 h each. CONCLUSIONS The optimized water extraction technology for Kaixin granules is stable and feasible.