Research on Drug Release Prediction of Salbutamol Sulfate Osmotic Pump Tablet Based on Artificial Neural Network and Genetic Algorithms
LI Xin-cheng1，WANG Ze1，2，ZHU Wei-xing3，GUO Fei1，ZHU Bin-jie4
Author information+
1.School of Mechanical Engineering，Jiangsu University，Zhenjiang 212013，China；2.Altitude Vocation School， Chinese Pharmaceutical University，Zhenjiang 212003，China；3.School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013，China；4.Department of Electronic Engineering， Tsinghua University， Beijing 100084，China
{{custom_zuoZheDiZhi}}
{{custom_authorNodes}}
{{custom_bio.content}}
{{custom_bio.content}}
{{custom_authorNodes}}
Collapse
History+
Received
Published
2005-03-08
2006-01-10
Issue Date
2006-01-10
Abstract
OBJECTIVE To pridict salbutamol sulfate osmotic pump tablet drug release by BP-GA neural network. A genetic-algorithm-based system using Artificial Neural Network for pridicting salbutamol sulfale osmotic pump tablet drug release. METHODS To realize the algorithm, a BP-GA neural network model was established.The data from the salbutamol sulfate osmotic pump tablet were analyzed,the effects of PEG1500 content η,the ratio of thickness δ to drug release coefficient γ and the 8 h drug release scale F8 into were investigated. RESULTS Comparing the experiment results with that of simulations and analysis based on the BP-GA neural network, the precision of drug release coefficient γ and the precision of 8 h drug release scale F8 were at 97.23% and 94.68% , respectively. CONCLUSION This model is based on the GA optimized the learning and weight of BP-NN. With the BP-GA neural network system, the weakness, such as the slow training speed of BP-NN and the vulnerability to local area pole smallness, are overcomed.It can be used in the prediction of salbutamol sulfate osmotic pump tablet drug release.
LI Xin-cheng;WNG Ze;ZHU Wei-xing;GUO Fei;ZHU in-jie.
Research on Drug Release Prediction of Salbutamol Sulfate Osmotic Pump Tablet Based on Artificial Neural Network and Genetic Algorithms [J]. Chinese Pharmaceutical Journal, 2006, 41(02): 115-118
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] AGTONOVIC-KUSTRIN S，BERESFORD R. Bsic concepts of artificial net works(ANNS) modeling and its application in pharmaceutical research[J] .J Pharm Anal，2000，22(4)：717-727.
[2] JUNICHI T. Multi objective simultaneous optimization technique based on an artificial neural network in sustained release formulations [J] .J Controlled Release， 1997， 49(1)：11-22.
[3] ROCKSLOH K. Optimization of crushing strength and disintegration time of a high-dose plant extract tablet by neural networks [J] .Drug Dev Indus Pharm，1999，25(9)：1010-1025.
