Optimized parameters for the preparation of silk fibroin drug‐loaded microspheres based on the response surface method and a genetic algorithm–backpropagation neural network model

Using silk fibroin as the base material, the drug‐loaded microspheres are prepared by an emulsification method. In order to determine the drug‐loading and drug‐release performance parameters of the microspheres, the central composite design method is used to design and investigate the effects of the...

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Bibliographic Details
Published in:Journal of biomedical materials research. Part B, Applied biomaterials Vol. 109; no. 1; pp. 6 - 18
Main Authors: Zhang, Xujing, Zhou, Jianping, Xu, Yan
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2021
Wiley Subscription Services, Inc
Subjects:
Algorithms
Back propagation
Back propagation networks
Biomedical materials
BP–GA neural network model
central composite design method
Drug Carriers - chemistry
Drug Compounding
Drug delivery systems
Drug development
Drug Liberation
drug‐loaded microspheres
Emulsification
Fibroins - chemistry
Genetic algorithms
In Vitro Techniques
Isoniazid - chemistry
Isoniazid - pharmacology
Load distribution
Loading rate
Materials research
Materials science
Mathematical models
Microspheres
Model accuracy
Models, Chemical
Neural networks
Neural Networks, Computer
Optimization
Parameters
Particle Size
response surface method
Response surface methodology
Silk
Silk fibroin
Stirring
Temperature
response surface method
central composite design method
drug-loaded microspheres
BP-GA neural network model
silk fibroin
ISSN:1552-4973, 1552-4981, 1552-4981
Online Access:Get full text
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Summary:Using silk fibroin as the base material, the drug‐loaded microspheres are prepared by an emulsification method. In order to determine the drug‐loading and drug‐release performance parameters of the microspheres, the central composite design method is used to design and investigate the effects of the parameters of the microsphere preparation process, such as the oil–water ratio, stirring temperature, and stirring rate, on the microsphere particle size, drug‐loading rate, and drug release rate. The “overall desirability” is taken as a comprehensive evaluation index, and the response surface method (RSM) and genetic algorithm–backpropagation (GA–BP) neural network GA–BP model are used to predict and evaluate the parameters of the drug‐loaded microsphere preparation process. The root‐mean‐square error values obtained from the RSM and BP–GA model experiments are 0.000325 and 0.00022, respectively. The results show that the BP–GA model has better prediction accuracy and optimization ability than the RSM. The optimal microsphere preparation process conditions were determined to be as follows: a water–oil ratio of 10:1, at a temperature of 45°C with stirring at a speed of 400 rpm, the particle size of the microspheres is 1.392 μm, the drug‐loading rate is 3.218%, and the drug release rate is 51.991%. The results of this study indicate that this approach is an effective method for the optimization of the parameters of the drug‐loaded microsphere preparation process.
Bibliography:Funding information
Department of Education, Xinjiang Uygur Autonomous Region, Grant/Award Numbers: XJ2019G029, XJEDU2016I016; National Natural Science Foundation of China, Grant/Award Number: 51665055; Xinjiang Universtiy, Grant/Award Number: XJUBSCX‐2017019
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ISSN:1552-4973
1552-4981
1552-4981
DOI:10.1002/jbm.b.34676