RESEARCH ARTICLE


Development of Improved Models for Phosphodiesterase-4 Inhibitors with a Multi-Conformational Structure-Based QSAR Method



Adetokunbo Adekoya, Xialan Dong, Jerry Ebalunode, Weifan Zheng*
Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central University, 1801 Fayetteville Street, Durham, NC 27707, USA


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© Adekoya et al.; Licensee Bentham Open.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/), which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.

* Address correspondence to this author at the Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central University, 1801 Fayetteville Street, Durham, NC 27707, USA; Tel: 919 530 6752; Fax: 919 530 6600; E-mail: wzheng@nccu.edu


Abstract

Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.