Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs
We developed the quantative structure-property relationships (QSPRs) models to correlate the molecular structures of surfactant, cosurfactant, oil, and drug with the solubility of poorly water-soluble 2-aryl propionic acid nonsteroidal anti-inflammatory drugs (2-APA-NSAIDs) in self-emulsifying drug...
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doaj-b5cd836431f44faf82dfbda5bfde5c982020-11-24T21:34:35ZengHindawi LimitedJournal of Nanomaterials1687-41101687-41292011-01-01201110.1155/2011/206320206320Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDsChen-Wen Li0Sheng-Yong Yang1Rui He2Wan-Jun Tao3Zong-Ning Yin4Department of Pharmaceutics, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan 610041, ChinaState Key Laboratory of Biotherapy and Cancer Center, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, ChinaDepartment of Pharmaceutics, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan 610041, ChinaDepartment of Pharmacy, Chengdu Family Planning Guidance Institute, Chengdu, Sichuan 610041, ChinaDepartment of Pharmaceutics, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan 610041, ChinaWe developed the quantative structure-property relationships (QSPRs) models to correlate the molecular structures of surfactant, cosurfactant, oil, and drug with the solubility of poorly water-soluble 2-aryl propionic acid nonsteroidal anti-inflammatory drugs (2-APA-NSAIDs) in self-emulsifying drug delivery systems (SEDDSs). The compositions were encoded with electronic, geometrical, topological, and quantum chemical descriptors. To obtain reliable predictions, we used multiple linear regression (MLR) and artificial neural network (ANN) methods for model development. The obtained equations were validated using a test set of 42 formulations and showed a great predictive power, and linear models were found to be better than nonlinear ones. The obtained QSPR models would greatly facilitate fast screening for the optimal formulations of SEDDS at the early stage of drug development and minimize experimental effort.http://dx.doi.org/10.1155/2011/206320 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chen-Wen Li Sheng-Yong Yang Rui He Wan-Jun Tao Zong-Ning Yin |
spellingShingle |
Chen-Wen Li Sheng-Yong Yang Rui He Wan-Jun Tao Zong-Ning Yin Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs Journal of Nanomaterials |
author_facet |
Chen-Wen Li Sheng-Yong Yang Rui He Wan-Jun Tao Zong-Ning Yin |
author_sort |
Chen-Wen Li |
title |
Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs |
title_short |
Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs |
title_full |
Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs |
title_fullStr |
Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs |
title_full_unstemmed |
Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs |
title_sort |
development of quantitative structure-property relationship models for self-emulsifying drug delivery system of 2-aryl propionic acid nsaids |
publisher |
Hindawi Limited |
series |
Journal of Nanomaterials |
issn |
1687-4110 1687-4129 |
publishDate |
2011-01-01 |
description |
We developed the quantative structure-property relationships (QSPRs) models to correlate the molecular structures of surfactant, cosurfactant, oil, and drug with the solubility of poorly water-soluble 2-aryl propionic acid nonsteroidal anti-inflammatory drugs (2-APA-NSAIDs) in self-emulsifying drug delivery systems (SEDDSs). The compositions were encoded with electronic, geometrical, topological, and quantum chemical descriptors. To obtain reliable predictions, we used multiple linear regression (MLR) and artificial neural network (ANN) methods for model development. The obtained equations were validated using a test set of 42 formulations and showed a great predictive power, and linear models were found to be better than nonlinear ones. The obtained QSPR models would greatly facilitate fast screening for the optimal formulations of SEDDS at the early stage of drug development and minimize experimental effort. |
url |
http://dx.doi.org/10.1155/2011/206320 |
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