Original articleDiscovery of new cholesteryl ester transfer protein inhibitors via ligand-based pharmacophore modeling and QSAR analysis followed by synthetic exploration
Graphical abstract
Introduction
Atherosclerosis describes the principal progression in arterial dysfunction and remodeling that restricts blood flow to vessels in the peripheral vasculature and is ultimately manifested as coronary artery disease (CAD) [1]. Several epidemiological studies have demonstrated an inverse relationship between serum high-density lipoprotein cholesterol (HDLc) levels and the incidence of ischemic heart disease [2]. HDL mediates the reverse cholesterol transport pathway which removes excess cholesterol from peripheral tissues to the liver for biliary elimination [3].
CETP, a 476-residue glycoprotein, is involved in trafficking lipoprotein particles and neutral lipids, including cholesteryl esters (CE), phospholipids and triglycerides between HDL and low-density lipoproteins (LDL). CETP, as revealed by X-ray crystallography (PDB code: 2OBD, resolution 2.2 Å), has a large highly hydrophobic binding site capable of simultaneously binding up to four lipid molecules [4]. In human plasma, CETP plays a potentially proatherogenic role by moving CE from HDL to very-low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) particles, thereby lowering atheroprotective HDLc and raising proatherogenic VLDLc and LDLc. Apparently, the risk of CAD is proportional to the plasma levels of CETP [5]. In fact, It is quite common within the CAD population to have elevated CETP plasma protein levels that are 2- to 3-fold higher than concentrations typically found in the plasma of normal subjects (1–3 μg/mL) [6].
Evidence exists that the consequences of CETP activity may depend on the metabolic setting, particularly on triglyceride levels. Accordingly, pharmacological CETP inhibition may reduce the risk of CAD in humans, but only in those with high triglyceride levels [5].
The unavailability of satisfactory high resolution crystallographic structures for CETP combined with its prohibitively large binding pocket confined most modeling-related discovery projects to ligand-based approaches particularly quantitative structure–activity relationship analysis (QSAR) [7], [8], [9].
Despite the excellent predictive potential of 3D-QSAR methodologies (e.g., CoMFA and CoMSIA), they generally lack the ability to act as effective search queries to mine virtual three-dimensional (3D) databases for new hits [10], [11].
The continued interest in the development of new CETP inhibitors combined with the lack of adequate CETP crystallographic structures and adequate computer-aided drug discovery efforts in this area, prompted us to explore the possibility of developing ligand-based three-dimensional (3D) pharmacophore(s) integrated within self-consistent QSAR model for CETP inhibitors. The pharmacophore model(s) can be used as 3D search query(ies) to mine 3D libraries for new CETP inhibitors, while the QSAR model helps to predict the biological activities of the captured compounds and therefore prioritize them for in vitro evaluation. We previously reported the use of this innovative approach towards the discovery of new inhibitory leads against glycogen synthase kinase 3β (GSK-3β) [12], dipeptidyl peptidase [13], hormone sensitive lipase (HSL) [14], bacterial MurF [15], protein tyrosine phosphatase 1B (PTP 1B) [16] and influenza neuraminidase [17].
We employed the HYPOGEN module from the CATALYST software package to construct numerous plausible binding hypotheses for CETP inhibitors [18]. Subsequently, genetic function algorithm (GFA) and multiple linear regression (MLR) analysis were employed to search for an optimal QSAR that combines high-quality binding pharmacophores with other molecular descriptors and capable of explaining bioactivity variation across a collection of diverse CETP inhibitors. The optimal pharmacophores were further validated by evaluating their ability to successfully classify a list of compounds as actives or inactives by assessing their receiver-operating characteristic (ROC) curves. Subsequently, the optimal pharmacophores were complemented with tight shape constraints to enhance their ROC profiles. Thereafter, the resulting shape-complemented pharmacophores were used as 3D search queries to screen several available virtual molecular databases for new CETP inhibitors. Active hits were employed as guides to synthesize new series of active CETP inhibitors.
CATALYST models drug–receptor interactions using information derived from the ligand structures [18], [19], [20], [21], [22], [23], [24], [25], [26]. HYPOGEN identifies a 3D array of a maximum of five chemical features common to active training ligands that provides relative alignment for each input molecule consistent with binding to a proposed common receptor site. The conformational flexibility of training ligands is modeled by creating multiple conformers that cover a specified energy range for each input molecule [16], [21], [22], [23], [27], [28], [29], [30], [31].
The SHAPE module in CATALYST is a shape-based similarity searching method. The Van der Waals surface of a molecule (in a certain conformation) is calculated and represented as a set of points of uniform average density on a grid. The surface points enclose a volume on the grid. The geometric center of the set of points is computed along with the three principal component vectors passing through the center. The maximum extents along each principal axis and the total volume are calculated. These provide shape indices that can be compared with the query and used in an initial screening step to eliminate poor matches from further consideration [32]. CATALYST pharmacophores, with or without shape constraints, have been used as 3D queries for database searching and in 3D-QSAR studies [21], [23], [27], [32].
Section snippets
Pharmacophore modeling
The literature was extensively surveyed to collect diverse CETP inhibitors. A dataset of 118 N,N-disubstituted-3-amino-2-propanol derivatives (1–118, Table A under Supplementary material) was used for pharmacophore modeling and subsequent QSAR analysis [33], [34], [35], [36]. The conformational space of each inhibitor was sampled utilizing the poling algorithm implemented within CATALYST [21], [22], [23], [37].
The pharmacophoric space of CETP inhibitors was explored employing three structurally
Conclusions
This work includes elaborate pharmacophore exploration of CETP inhibitors utilizing CATALYST-HYPOGEN. QSAR analysis was employed to select the best combination of molecular descriptors and pharmacophore models capable of explaining bioactivity variation across an informative list of training compounds. The successful pharmacophores were complemented with strict shape constraints to optimize their receiver-operating characteristic (ROC) curve profiles. The best binding hypotheses were used as 3D
Software and hardware
The following software packages were utilized in the present research.
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CATALYST (Version 4.11), Accelrys Inc. (www.accelrys.com), USA.
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CERIUS2 (Version 4.10), Accelrys Inc. (www.accelrys.com), USA.
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CS ChemDraw Ultra 7.01, Cambridge Soft Corp. (http://www.cambridgesoft.com), USA.
Pharmacophore modeling and QSAR analysis were performed using CATALYST (HYPOGEN module) and CERIUS2 software suites from Accelrys Inc. (San Diego, California, www.accelrys.com) installed on a Silicon Graphics Octane2
Acknowledgments
This project was partially sponsored by the Faculty of Graduate Studies (Ph.D. Thesis of Reema Abu Khalaf). The authors wish to thank the Deanship of Scientific Research and Hamdi-Mango Center for Scientific Research at the University of Jordan for their generous funds. The authors are also indebted to national cancer institute for freely providing hit molecules for evaluation.
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