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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

Flowchart for ligand pharmacophore key generation.
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Figure 2: Flowchart for ligand pharmacophore key generation.

Mentions: The pharmacophore feature pairs for a given conformer of a ligand can be generated according to our previous work [7] and outlined in Fig. (2). Instead of using the LigandScout program, the atom typing program Patty (OE Scientific, NM, USA) was employed in this work to label each atom of an inhibitor molecule. Similar pharmacophore types are defined: positively charged (P), negatively charged (N), hydrophobic (H), hydrogen bond donor (D) and hydrogen bond acceptor (A). Once each atom is typed with a proper pharmacophore type, pharmacophore feature pairs can be generated in a way similar to how the reference (target binding site) pharmacophore feature pairs are generated. If a conformer had the following pharmacophore centers: A, P, A, D, then 6 possible pharmacophore feature pairs could be generated: AP6.9, AA5, AD6, PA9, PD4.8, AD7, where the two letters indicate the feature pair key and the number being the distance between the two feature centers.


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)

Flowchart for ligand pharmacophore key generation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Flowchart for ligand pharmacophore key generation.
Mentions: The pharmacophore feature pairs for a given conformer of a ligand can be generated according to our previous work [7] and outlined in Fig. (2). Instead of using the LigandScout program, the atom typing program Patty (OE Scientific, NM, USA) was employed in this work to label each atom of an inhibitor molecule. Similar pharmacophore types are defined: positively charged (P), negatively charged (N), hydrophobic (H), hydrogen bond donor (D) and hydrogen bond acceptor (A). Once each atom is typed with a proper pharmacophore type, pharmacophore feature pairs can be generated in a way similar to how the reference (target binding site) pharmacophore feature pairs are generated. If a conformer had the following pharmacophore centers: A, P, A, D, then 6 possible pharmacophore feature pairs could be generated: AP6.9, AA5, AD6, PA9, PD4.8, AD7, where the two letters indicate the feature pair key and the number being the distance between the two feature centers.

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