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Pharmacophore Model Refinement for 11β‐Hydroxysteroid Dehydrogenase Inhibitors: Search for Modulators of Intracellular Glucocorticoid Concentrations
Author(s) -
Vuorinen Anna,
Nashev Lyubomir G.,
Odermatt Alex,
Rollinger Judith M.,
Schuster Daniela
Publication year - 2014
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201300063
Subject(s) - pharmacophore , glucocorticoid , intracellular , drug discovery , chemistry , computational biology , computer science , pharmacology , bioinformatics , biochemistry , biology , endocrinology
11β‐Hydroxysteroid dehydrogenases (11β‐HSD) control the intracellular concentrations of glucocorticoids: 11β‐HSD1 converts the inactive cortisone to the active cortisol, and 11β‐HSD2 is responsible for the opposite reaction. Inhibition of 11β‐HSD1 is beneficial in the treatment of metabolic syndrome, whereas 11β‐HSD2 inhibition leads to hypertension. Therefore, 11β‐HSD1 inhibitors should be selective over 11β‐HSD2. To support drug discovery and toxicological studies, we have previously reported pharmacophore models for 11β‐HSD1 and 2 inhibition. These models represent the common chemical features of 11β‐HSD inhibitors, which were used as virtual screening filter. Since new inhibitors are constantly discovered, the quality of the pharmacophore models has to be evaluated in order to maintain a good predictive power. In this study, we report a systematic evaluation and refinement of our pharmacophore model collection. We employed our models for virtual screening, especially focusing on the 11β‐HSD2 inhibition. In total, 42 compounds were biologically evaluated and among these we discovered 17 11β‐HSD inhibitors that decreased the residual enzyme activity to 50% or less at the concentration of 20 µM. The experimental 11β‐HSD1 and 2 readouts from these compounds were used for further model refinement. Evaluation metrics were applied for a quantitative comparison of the old and newly generated models which resulted in a set of improved pharmacophore models offering reliable in silico tools for the identification of novel and selective 11β‐HSD inhibitors.

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