Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors

被引：49

作者：

Ali, Hany S. M. ^{[1
,2
]}

Blagden, Nicholas ^{[1
]}

York, Peter ^{[1
]}

Amani, Amir ^{[1
,3
]}

Brook, Toni ^{[1
]}

机构：

[1] Univ Bradford, Inst Pharmaceut Innovat, Sch Pharm, Bradford BD7 1DP, W Yorkshire, England

[2] Assiut Univ, Fac Pharm, Dept Pharmaceut, Assiut, Egypt

[3] Univ Tehran Med Sci, Dept Med Nanotechnol, Sch Adv Med Technol, Tehran 1417614411, Iran

来源：

EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES | 2009年 / 37卷 / 3-4期

关键词：

Artificial neural networks; Modelling; Microfluidics; Nanoprecipitation; Prednisolone; CEFUROXIME AXETIL NANOPARTICLES; DRUG-DELIVERY; PARTICLE-SIZE; CRYSTALLIZATION; NANOCRYSTALS; NANOSUSPENSIONS; TECHNOLOGY;

D O I：

10.1016/j.ejps.2009.04.007

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flow rates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：514 / 522

页数：9

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