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| Preparation of Compound Huoxue Huayu spraying-film (CHH-SF) preparation based on central composite design and artificial neural network modeling |
| Hits 1459 Download times 1187 Received:May 24, 2018 |
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| DOI
10.11656/j.issn.1672-1519.2018.12.21 |
| Key Words
artificial neural network;central composite design;Compound Huoxue Huayu herbs;spraying-film;formulation optimization |
| Author Name | Affiliation | E-mail | | LAO Ruijuan | School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China | | | ZHAO Fang | School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China | | | JIN Xin | Military Medicine Section, Logistics University of Chinese People's Armed Police Force, Tianjin 300309, China | | | LU Jia | School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China | | | LIU Rui | School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China | lr_8000@163.com |
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| Abstract
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| [Objective] To Optimize Compound Huoxue Huayu spraying-film (CHH-SF) and provide reference for its clinical application.[Methods] Based on the single factor test, the amount of polyvinyl pyrrolidone (PVPK30), polyvinyl alcohol (PVA-124) and hypromellose (HPMC) were selected as independent variables, and the film formation time was dependent variable. The prescription is optimized according to the principle of central composite design (CCD). In addition, CCD data was used to train、test and validate the artificial neural network model, further optimize prescriptions. Then compare the effect of star point effect surface and neural network simulation.[Results] The optimal formulation of CHH-SF was PVPK30 2.24 g, PVA-124 0.75 g, HPMC 0.07 g, and the film formation time was 4.63 min.[Conclusion] The CHH-SF is viscosity, easy to use, short film formation time and good homogeneity. The model established by the artificial neural network method is more predictable and can be used for the formulation optimization of the preparation. |
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