In silico prediction of novel phosphodiesterase type-5 inhibitors derived from Sildenafil, Vardenafil and Tadalafil

A series of drug-like compounds derived from Sildenafil, Vardenafil and Tadalafil analogues were modelled through the MIA-QSAR (multivariate image analysis applied to quantitative structure-activity relationships) ligand-based approach. A highly predictive model was achieved and novel compounds, miscellany of substructures of these three representative phosphodiesterase type-5 (PDE-5) inhibitors were predicted using the calibration parameters obtained through partial least squares (PLS) regression. The high bioactivities of eight promising compounds were corroborated by docking evaluation. Calculated ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) profiles for such compounds suggest advantages of some of them over the currently available, most common drugs used for the treatment of erectile dysfunction.     

Key Features for Designing Phosphodiesterase-5 Inhibitors

Abstract

Phosphodiesterase superfamily is the key regulator of 3′,5′-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q², and non cross-validated coefficient, r², values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate.

Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

Key features for designing phosphodiesterase-5 inhibitors

Phosphodiesterase superfamily is the key regulator of 3',5'-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q(2), and non cross-validated coefficient, r(2), values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate. Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

A photoaffinity probe covalently modifies the catalytic site of the cGMP-binding cGMP-specific phosphodiesterase (PDE-5)

The cGMP-binding cGMP-specific phosphodiesterase (PDE-5) contains distinct catalytic and allosteric binding sites, and each is cGMP-specific. Cyclic nucleotide phosphodiesterase inhibitors, such as 3-isobutyl-1-methylxanthine (IBMX), are believed to compete with cyclic nucleotides at the catalytic sites of these enzymes, but the portion of PDE-5 that accounts for interaction of either of these inhibitors or the substrates themselves with the catalytic domain of the enzymes has not been identified. IBMX was derivatized to yield the photoaffinity probe 8([3-¹²⁵I,-4-azido]-benzyl)-IBMX, which is referred to as 8(¹²⁵IAB)-IBMX. This probe was incubated with partially purified recombinant bovine PDE-5. After UV irradiation and SDS-PAGE, a single radiolabeled band that coincided with the position of PDE-5 was visualized on the gel, and the photoaffinity labeling of PDE-5 was linear with increasing concentration of the 8(¹²⁵IAB)-IBMX. Prominent Coomassie blue-stained bands other than PDE-5 were not labeled significantly. The photo-affinity labeling was progressively blocked by cGMP at concentrations higher than 10 μM, whereas cAMP or 5′-GMP exhibited only weak inhibitory effects. Other compounds that are believed to interact with the PDE-5 catalytic site, including IBMX, clMP, and β-phenyl-1,N ²-etheno-cGMP (PET-cGMP), also inhibited the photoaffinity labeling in a concentration-dependent manner. The IC₅₀ of PET-cGMP for inhibition of photoaffinity labeling was 10 μM, which compared favorably with an IC₅₀ of 5 μM for inhibition of PDE-5 catalytic activity by this compound. It is concluded that the interaction of this photoaffinity probe with PDE-5 is highly specific for the catalytic site over the allosteric binding sites of PDE-5 and could prove useful in studies to map the catalytic site of PDE-5.     

Virtual Screening and Drug Design for PDE-5 Receptor from Traditional Chinese Medicine Database

Abstract

Erectile dysfunction (ED) is a sexual disorder mainly caused by decrease in cellular concentration of cyclic guanosine monophosphate (cGMP), which is degraded by phosphodiesterase type-5 (PDE-5). As a potent therapeutic target, inhibitors such as Viagra®, Cialis®, and Levitra® have already been developed to target PDE-5 for treating ED; traditional Chinese medicine, Epimedium sagittatum, also has shown prominent results as well. To developed new PDE-5 inhibitors, we performed a virtual screening of traditional Chinese medicine (TCM) database and docking analyses to identify candidates. Known PDE-5 inhibitors were used to construct a three dimensional quantitative structure-activity relationship (3D QSAR) model by HypoGen program. From docking analyses, isochlorogenic acid b was identified as the most potential inhibitory compound. De novo evolution designed 47 derivatives. Of the 47 derivatives, seven were able to map into the pharmacophore model, and these seven compounds were suggested to be the most promising leads for inhibiting PDE-5. An analysis of the hydrogen bond interactions formed between the docked ligands and PDE-5 identified ASN662, SER663 and GLN817 as the most frequently interacting residues. A total of eight novel leading compounds were identified to have favorable interaction with PDE-5. These compounds all had hydrogen bond interactions with three key residues that could be further investigated for understanding of PDE-5 and ligands interaction.     

Development of a robust QSAR model to predict the affinity of pyrrolidine analogs for dipeptidyl peptidase IV (DPP- IV)

QSAR analysis using multiple linear regression and partial least squares methods were conducted on a data set of 47 pyrrolidine analogs acting as DPP IV inhibitors. The QSAR models generated (both MLR and PLS) were robust with statistically significant s, F, r, r(2) and r(2) (CV) values. The analysis helped to ascertain the role of shape flexibility index, Ipso atom E-state index and electrostatic parameters like dipole moment, in determining the activity of DPP IV inhibitors. In addition to QSAR modeling, Lipinski's rule of five was also employed to check the pharmacokinetic profile of DPP IV inhibitors. Since none of the compounds violated the Lipinski's rule of five indicating that the DPP IV inhibitors reported herein have sound pharmacokinetic profile and can be considered as potential drug candidates for diabetes mellitus Type II.     

A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins

Advances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array.     

QSAR studies on 4-anilino-3-quinolinecarbonitriles as Src kinase inhibitors using robust PCA and both linear and nonlinear models

Quantitative structure-activity relationship (QSAR) studies have been carried out on 4-anilino-3-quinolinecarbonitriles, a set of novel Src kinase inhibitors, with the aim of dissecting the structural requirements for Src inhibitory activities. After outlier identification using robust principal component analysis (robust PCA), linear models based on forward selection combined with multiple linear regression, (FS-MLR), enhanced replacement method followed by multiple linear regression (ERM) and a nonlinear model using support vector regression (SVR) were constructed and compared. All models were rigorously validated using leave-one-out cross-validation (LOOCV), 5-fold cross-validations (5-CV) and shuffling external validation (SEVs). ERM seems to outperform both FS-MLR and SVR evidenced by better prediction performance (n?=?35, R²_training?=?0.918, R²_pred?=?0.928). Robustness and predictive ability of ERM model were also evaluated. The generated QASR model revealed that the Src inhibitory activity of 4-anilino-3-quinolinecarbonitriles could be associated with the size of substituents in the C7 position and the steric hindrance effect. The results of the present study may be of great help in designing novel 4-anilino-3-quinolinecarbonitriles with more potent Src kinase inhibitory activity.     

Quantitative structure-activity relationship of phenoxyphenyl-methanamine compounds with 5HT2A, SERT, and hERG activities

QSAR models have been used to evaluate activities for compounds in the phenoxyphenyl-methanamine (PPMA) class of compounds. These models utilize Hammett-type donating-withdrawing substituent values as well as simple parameters to describe substituent size and elucidate the SAR of the 'A' and 'B' rings. Using this methodology, intuitive QSAR relationships were found for the three biological activities with R(2) values of 0.73, 0.45, and 0.58 for 5HT(2A), SerT, and hERG activities.     

2D- and 3D-QSAR modelling,molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line

Abstract

Triple-negative breast cancers (TNBCs) are one of the most aggressive and complex forms of cancers in women. TNBCs are commonly known for their complex heterogeneity and poor prognosis. The present work aimed to develop a predictive 2D and 3D quantitative structure–activity relationship (QSAR) models against metastatic TNBC cell line. The 2D-QSAR was based on multiple linear regression analysis and validated by Leave-One-Out (LOO) and external test set prediction approach. QSAR model presented regression coefficient values for training set (r²), LOO-based internal regression (q²) and external test set regression (pred_r²) which are 0.84, 0.82 and 0.75, respectively. Five properties, Epsilon4 (electronegativity), ChiV3cluster (valence molecular connectivity index), chi3chain (retention index for three-membered ring), TNN5 (nitrogen atoms separated through 5 bond distance) and nitrogen counts, were identified as important structural features responsible for anticancer activity of MDA-MB-231 inhibitors. Five novel derivatives of glycyrrhetinic acid (GA) named GA-1, GA-2, GA-3, GA-4 and GA-5 were semi-synthesised and screened through the QSAR model. Further, in vitro activities of the derivatives were analysed against human TNBC cell line, MDA-MB-231. The result showed that GA-1 exhibits improved cytotoxic activity to that of parent compound (GA). Further, atomic property field (APF)-based 3D-QSAR and scoring recognise C-30 carboxylic group of GA-1 as major influential factor for its anticancer activity. The significance of C-30 carboxylic group in GA derivatives was also confirmed by molecular docking study against cancer target glyoxalase-I. Finally, the oral bioavailability and toxicity of GA-1 were assessed by computational ADMET studies.

Communicated by Ramaswamy H. Sarma