NDLI: Prediction of immobilized artificial membrane-liquid chromatography retention of some drugs from their molecular structure descriptors and LFER parameters.

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Prediction of immobilized artificial membrane-liquid chromatography retention of some drugs from their molecular structure descriptors and LFER parameters.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Fatemi, Mohammad Hossein Shamseddin, Hoda
Description	Country affiliation: Iran Author Affiliation: Fatemi MH ( Department of Chemistry, University of Mazandran, Babolsar, Iran. mhfatemi@umz.ac.ir)
Abstract	In this work multiple linear regression (MLR) was carried out for the prediction of immobilized artificial membrane (IAM) retention factors of 40 basic and neutral drugs in two mobile phase compositions. We developed some MLR models by using linear free energy relationships (LFER) parameters and also theoretically derived molecular descriptor. Root mean square error of MLR model in prediction of log k(wPBS)(IAM) and k(wMOPS)(IAM) are 0.332 and 0.351, respectively, while these values are 0.371 and 0.500 for LFER models. Inspections to these values indicate that the statistical parameters of MLR models are better than LFER models. The credibility of MLR models was evaluated by using leave-many-out cross-validation and y-scrambling procedures. The results of these tests indicate the applicability of theoretically derived molecular descriptors and LFER parameters prediction of IAM retention of drugs.
File Format	HTM / HTML
ISSN	16159306
Issue Number	20
Volume Number	32
e-ISSN	16159314
Journal	Journal of Separation Science
Language	English
Publisher	Wiley-VCH
Publisher Date	2009-10-01
Publisher Place	Germany
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Analytical Discipline Chemistry Chromatography, Liquid Linear Models Membranes, Artificial Models, Chemical Pharmaceutical Preparations Chemistry Instrumentation Methods Molecular Structure Reproducibility Of Results Journal Article
Content Type	Text
Resource Type	Article
Subject	Analytical Chemistry Filtration and Separation

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