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Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.

Adekoya A, Dong X, Ebalunode J, Zheng W - Curr Chem Genomics (2009)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central University, 1801 Fayetteville Street, Durham, NC 27707, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Predicted activity vs. experimental activity for the training set (top) and test set (bottom) for model 1.
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Figure 6: Predicted activity vs. experimental activity for the training set (top) and test set (bottom) for model 1.

Mentions: The predictive power of the new models developed with the MC SBPPK method exceeds that of other models developed with more traditional QSAR techniques [12, 18, 19], and that of the model developed with our previous SB-PPK method [7]. For example, a MOE (Chemical Computing Group, Montreal, Canada) based 2D QSAR method afforded models with r2 of 0.66 for the training set, and R2 of 0.58 for the test set [7]. A reported CoMFA model gave an r2 of 0.986, but an R2 of 0.560, indicating overtraining on the training set and underperforming on the test set. A CoMSIA model gave an r2 of 0.967, but an R2 of 0.590 for the test set, again indicating overtraining and underperforming during prediction. The most balanced model developed in our previous work afforded an r2 of 0.75 for the training and R2 of 0.624 for the test set. In the current work, the two best models gave r2 of 0.83 and 0.83 for the training sets, and R2 of 0.74 and 0.67 for the test sets. Figs. (6, 7) show the scatter plots of predicted vs. experimental activities. Figs. (8, 9) show the absolute errors of prediction for both MC SBPPK models. Thus, the MC SBPPK method has successfully incorporated multi-conformers in the QSAR analysis, and afforded more predictive models than all previously reported methods.


Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.

Adekoya A, Dong X, Ebalunode J, Zheng W - Curr Chem Genomics (2009)

Predicted activity vs. experimental activity for the training set (top) and test set (bottom) for model 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2802764&req=5

Figure 6: Predicted activity vs. experimental activity for the training set (top) and test set (bottom) for model 1.
Mentions: The predictive power of the new models developed with the MC SBPPK method exceeds that of other models developed with more traditional QSAR techniques [12, 18, 19], and that of the model developed with our previous SB-PPK method [7]. For example, a MOE (Chemical Computing Group, Montreal, Canada) based 2D QSAR method afforded models with r2 of 0.66 for the training set, and R2 of 0.58 for the test set [7]. A reported CoMFA model gave an r2 of 0.986, but an R2 of 0.560, indicating overtraining on the training set and underperforming on the test set. A CoMSIA model gave an r2 of 0.967, but an R2 of 0.590 for the test set, again indicating overtraining and underperforming during prediction. The most balanced model developed in our previous work afforded an r2 of 0.75 for the training and R2 of 0.624 for the test set. In the current work, the two best models gave r2 of 0.83 and 0.83 for the training sets, and R2 of 0.74 and 0.67 for the test sets. Figs. (6, 7) show the scatter plots of predicted vs. experimental activities. Figs. (8, 9) show the absolute errors of prediction for both MC SBPPK models. Thus, the MC SBPPK method has successfully incorporated multi-conformers in the QSAR analysis, and afforded more predictive models than all previously reported methods.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central University, 1801 Fayetteville Street, Durham, NC 27707, USA.

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.

No MeSH data available.


Related in: MedlinePlus