The application of SVR model in the improvement of QbD: a case study of the extraction of podophyllotoxin
| Autor: | Hailiu Fan, Jian-Lan Jiang, Chun-Hui Zhai, Teng-Fei Zhao, Jianbang Xuan |
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| Rok vydání: | 2018 |
| Předmět: |
Pharmacology
0209 industrial biotechnology Mathematical optimization Models Statistical Generalization Organic Chemistry Stability (learning theory) Pharmaceutical Science Process design 02 engineering and technology Quadratic function Quality by Design Field (computer science) Support vector machine 020901 industrial engineering & automation Drug Design Drug Discovery 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Design space Algorithms Podophyllotoxin Mathematics |
| Zdroj: | Drug Development and Industrial Pharmacy. 44:1506-1511 |
| ISSN: | 1520-5762 0363-9045 |
| DOI: | 10.1080/03639045.2018.1467924 |
| Popis: | In order to make a further optimization of process design via increasing the stability of design space, we brought in the model of Support Vector Regression (SVR). In this work, the extraction of podophyllotoxin was researched as a case study based on Quality by Design (QbD). We compared the fitting effect of SVR and the most used quadratic polynomial model (QPM) in QbD, and an analysis was made between the two design spaces obtained by SVR and QPM. As a result, the SVR stayed ahead of QPM in prediction accuracy, the stability of model and the generalization ability. The introduction of SVR into QbD made the extraction process of podophyllotoxin well designed and easier to control. The better fitting effect of SVR improved the application effect of QbD and the universal applicability of SVR, especially for non-linear, complicated and weak-regularity problems, widened the application field of QbD. |
| Databáze: | OpenAIRE |
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