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Sustained and controlled release preparation is ideal for reducing the side effects of drugs, improving patient compliance and enhancing efficacy, among which oral sustained-release tablets are the most widely used. The in vitro release of the preparation is closely related to the in vivo absorption of the drug. However, current in vitro release experiments are labor-intensive and destructive, and the lack of big data also makes it difficult to establish good in vivo and in vitro correlations. Computer modeling, as a technical means that can transform objective principles into mathematical models, has great prospects in data prediction. This review explores the existing computer modeling methods that can be used to predict in vitro release profiles of oral sustained-release tablets, and further discusses auxiliary technologies that can improve the accuracy of the models, providing new ideas for drug development.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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