Binding affinity and efficacy-based pharmacophore modeling studies of retinoic acid receptor alpha agonists and virtual screening for potential agonists from NCI

Retinoic acid receptor alpha (RARα) has been considered as one of the most important targets for the treatment of acute promyelocytic leukemia. To discover more novel lead compounds, ligand-based pharmacophore modeling of a series of structurally diverse RARα agonists was applied to acquire the binding model (KI pharmacophore model) and the efficacy model (EC₅₀ pharmacophore model) of RARα. In this paper, a three-dimensional quantitative structure–activity relationship (3D-QSAR) in Discovery Studio 2.5 was used to generate pharmacophore models. Via Fischer’s randomization validation and maximum unbiased validation, the best pharmacophore model for KI pharmacophore model was Hypo1K and for EC₅₀ pharmacophore model was Hypo7E. Virtual screening of National Cancer Institute database using Hypo1K and Hypo7E was performed, respectively. Six potent compounds in the retrieved hits with a CAS number were confirmed to be effective on leukemia cell lines and other tumors in the literatures. As evident from the validation and the biological screening results, it can be concluded that the Hypo1K and Hypo7E were reliable and useful tools for lead optimization of novel RARα agonists.     

3D QSAR pharmacophore-based virtual screening and molecular docking studies for the discovery of potential PDK1 inhibitors

Many researches discover that 3-phosphoinositide-dependent protein kinase-1 (PDK1) is overexpressed in many cancer cells and a promising target for developing novel anticancer drugs. The aim of this study is to identify novel scaffolds and utilize them in designing potent PDK1 inhibitors. 3D pharmacophore models were established based on the known PDK1 inhibitors. The best pharmacophore model, Hypo1, was selected, validated, and used in virtual screening. The obtained compounds were subjected to Lipinski’s rule of five, ADMET filtrations, and molecular docking studies. Finally, five molecules with high Genetic Optimization for Ligand Docking fitness scores and interactions with critical active site amino acids were identified. These hit compounds may act as novel leads for PDK1 inhibitors design.     

The discovery of potential cyclin A/CDK2 inhibitors: a combination of 3D QSAR pharmacophore modeling, virtual screening, and molecular docking studies

Cyclin-dependent kinases are a family of enzymes that regulates the cell cycle process. They have been found to be novel targets for potential anti-cancer drugs. In the present study, a 3D pharmacophore model has been developed for cyclin A/CDK2 from its known inhibitors. The most reliable quantitative HypoGen model (Hypo1) consists of two hydrogen bond acceptors, one hydrogen bond donor and one hydrophobic feature. Hypo1, with a correlation coefficient of 0.98, a root mean square deviation of 0.84, a configuration cost of 16.25 and a cost difference of 102.93, showed a remarkable predictive power and has >90 % probability of representing a true correlation in the activity data. The model was validated using Fisher’s test at 95 % confidence level and test set prediction (r = 0.96). Hypo1 was then employed for virtual screening of Life Chemicals and NCI2003 databases of which multiple conformations were generated for each compound (596,030 compounds, 45,603,414 conformers). Hits were filtered according to the Lipinski, Ghose, and Veber’s rules. Following docking simulations, consensus scoring was used to determine the ligand poses that interact best with the protein binding site and to reduce number of false positives. 11 hits were ultimately selected as potent candidate leads. This work may help in the identification or design of novel anti-cancer drugs based on hits determined. The pharmacophore model obtained and validated in this study can be used as a three-dimensional query in searches for CDK2 inhibitors in additional compound databases.     

Identification of novel inhibitors of HIV-1 integrase using pharmacophore-based virtual screening combined with molecular docking strategies

Human immunodeficiency virus type-1 (HIV-1) integrase is one of three enzymes encoded by the HIV genome for effective viral replication, and therefore an attractive target for chemotherapeutic interventions in the development of AIDS treatment. In this study, chemical feature-based pharmacophore models of different classes of integrase strand transfer inhibitors have been developed. The best HypoRefine pharmacophore models, Hypo1, which have the best correlation coefficients (0.92) and the lowest RMSs (0.78), contain two hydrogen bond acceptor lipids, one hydrogen bond donor, and one hydrophobic aromatic with four excluded volumes. After filtering by Lipinski’s rule of five, the best pharmacophore model was utilized as a 3D search query to perform a virtual screening to retrieve potential inhibitors. The hit compounds were subsequently subjected to docking studies by GOLD program to refine the retrieved hits. Finally, 4 top ranked compounds based on GOLD score fitness function and rescoring by X-score were investigated for compliance with the standard ranges through in silico ADME studies.     

Discovery of high-affinity ligands of sigma1 receptor, ERG2, and emopamil binding protein by pharmacophore modeling and virtual screening

ERG2, emopamil binding protein (EBP), and sigma-1 receptor (sigma(1)) are enzymes of sterol metabolism and an enzyme-related protein, respectively, that share high affinity for various structurally diverse compounds. To discover novel high-affinity ligands, pharmacophore models were built with Catalyst based upon a series of 23 structurally diverse chemicals exhibiting K(i) values from 10 pM to 100 microM for all three proteins. In virtual screening experiments, we retrieved drugs that were previously reported to bind to one or several of these proteins and also tested 11 new hits experimentally, of which three, among them raloxifene, had affinities for sigma(1) or EBP of <60 nM. When used to search a database of 3525 biochemicals of intermediary metabolism, a slightly modified ERG2 pharmacophore model successfully retrieved 10 substrate candidates among the top 28 hits. Our results indicate that inhibitor-based pharmacophore models for sigma(1), ERG2, and EBP can be used to screen drug and metabolite databases for chemically diverse compounds and putative endogenous ligands.     

Pharmacophore‐Based Drug Design and Biological Evaluation of Novel ABCB1 Inhibitors

Overexpression of ABCB1 is one of major barriers for multidrug resistance in chemotherapy and limits drug oral bioavailability. Inhibition of ABCB1 would sensitize multidrug resistance in clinical cancer chemotherapy. With this aim, a 3D pharmacophore model was created based on known ABCB1 inhibitors with correlation coefficient of 0.94, comprising three hydrophobic features and one hydrogen bond acceptor. It was further validated and used to search our in‐house 3D database for potential ABCB1 inhibitors. The inhibitory activities of the best hits were evaluated by several biological assays, such as rhodamine 123 accumulation assay, chemosensitization assay, multidrug resistance 1‐Madin‐Darby canine kidney cells/Madin‐Darby canine kidney cells permeability assay. Finally, compounds YZ‐3 and YZ‐16 were identified as potential leads to be developed in the designing of novel potent ABCB1 inhibitors.     

Exploring structural requirement,pharmacophore modeling,and de novo design of LRRK2 inhibitors using homology modeling approach

A mutation in the gene, encoding leucine rich repeat kinase 2 (LRRK2), is a genetic cause of Parkinson’s disease (PD). LRRK2 is a dimeric multidomain protein, largely regulates guanosine triphosphate (GTP). G2019S and I2020T, the mutation encodes in the kinase domain of LRRK2 increase the GTPase activity, are the important regulators in pathogenesis of PD. To design potent LRRK2 inhibitors, pharmacophore modeling approach was employed with a wide chemical diversity of compound’s database. The best hypothesis consists of hydrogen-bond acceptor and donor as well as hydrophobic aliphatic and ring aromatic features. The model was validated by the test and decoy sets followed by Fischer’s randomization test. The validated model was used to screen the database of compounds, which were designed through de novo approach. Homology model of the kinase domain of LRRK2 was built initially using the crystal structure of Janus kinase 3. The designed molecules were further screened for ADMET properties, and ligand–receptor interaction of top hits was analyzed by molecular docking studies to explore potent LRRK2 inhibitors.     

A Specific Pharmacophore Model of Aurora B Kinase Inhibitors and Virtual Screening Studies Based on it

In this study, 3D‐pharmacophore models of Aurora B kinase inhibitors have been developed by using HipHop and HypoGen modules in Catalyst software package. The best pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9911), consists of one hydrogen‐bond acceptor, one hydrogen‐bond donor, one hydrophobic aliphatic moiety and one ring aromatic feature. Hypo1 was validated by test set and cross‐validation methods. And the specificity of Hypo1 to Aurora B inhibitors was examined with the use of selective inhibitors against Aurora B and its paralogue Aurora A. The results clearly indicate that Hypo1 can differentiate selective inhibitors of Aurora B from those of Aurora A, and the ring aromatic feature likely plays some important roles for the specificity of Hypo1. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD) for identifying new inhibitors of Aurora B. The hit compounds were subsequently subjected to filtering by Lipinski’s rule of five and docking studies to refine the retrieved hits, and some compounds selected from the top ranked hits have been suggested for further experimental assay studies.     

Discovery of potential integrin VLA-4 antagonists using pharmacophore modeling, virtual screening and molecular docking studies

Very late antigen-4 (VLA-4) is an integrin protein, and its antagonists are useful as anti-inflammatory drugs. The aim of this study is to discover novel virtual lead compounds to use them in designing potent VLA-4 antagonists. A best pharmacophore model was generated with correlation coefficient of 0.935, large cost difference of 114.078, comprising two hydrogen bond acceptors and three hydrophobic features. It was further validated and used in database screening for potential VLA-4 antagonists. A homology model of VLA-4 was built and employed in molecular docking of screened hit compounds. Finally, two compounds were identified as potential virtual leads to be deployed in the designing of novel potent VLA-4 antagonists.     

3D-pharmacophore model based virtual screening to identify dual-binding site and selective acetylcholinesterase inhibitors

The formation of β-amyloid plaques in the brain is a key neurodegenerative event in Alzheimer’s disease (AD). Interestingly, research on acetylcholinesterase (AChE) enzyme has increased due to findings supporting this enzyme involvement in the β-amyloid peptide fibril formation during AD pathogenesis. In this investigation, chemical features based 3D pharmacophore models were developed from structurally diverse xanthostigmine derivatives, known inhibitors of AChE enzyme, using 3D-QSAR pharmacophore generation module in Discovery Studio2.5 (DS2.5). The constructed pharmacophore models for AChE inhibitors was further cross-validated using test set and Cat-Scramble methodology. The best quantitative pharmacophore model Hypo1, was used for screening the chemical databases of small compounds including Specs, NCI, and IBScreen, to identify the new compounds that are presumably able to act as dual-binding site AChE inhibitors. The screened virtual hits were then subjected to the Lipinski’s rule of five, blood–brain barrier (BBB), PSA, LogS, percent human oral absorption, and toxicity analysis. Finally, 32 compounds were identified as potential leads against AChE enzyme, showing good estimated activities and promising ADMET properties. Molecular docking of these compounds using FlexX software showed catalytic and peripheral anionic binding site interactions, so called dual binding of the AChE enzyme. Docking study was also performed on butyrylcholinesterase in order to understand the compound selectivity. This study may assist in the discovery and design of novel dual binding site and selective AChE inhibitors with potent inhibitory activity.     

Theoretical approaches to identify the potent scaffold for human sirtuin1 activator: Bayesian modeling and density functional theory

Bayesian and pharmacophore modeling approaches were utilized to identify the fragments and critical chemical features of small molecules that enhance sirtuin1 (SIRT1) activity. Initially, 48 Bayesian models (BMs) were developed by exploring 12 different fingerprints (ECFC, ECFP, EPFC, EPFP, FPFC, FPFP, FCFC, FCFP, LCFC, LCFP, LPFC, and LPLP) with diameters of 4, 6, 8, and 10. Among them the BM1 model was selected as the best model based on its good statistical parameters including total accuracy: 0.98 and positive recalls: 0.95. Additionally, BM1 showed good predictive power for the test set (total accuracy: 0.87 and positive recall: 0.87). In addition, 10 qualitative pharmacophore models were generated using 6 well-known SIRT1 activators. Hypothesis2 (Hypo2) was selected as best hypothesis, among 10 Hypos, based on its discriminant ability between the highly active and least/moderately active SIRT1 activators. The best models, BM1 and Hypo2 were used as a query in virtual screens of a drug-like database and the hit molecules were sorted based on Bayesian score and fit value, respectively. In addition, the highest occupied molecular orbital, lowest unoccupied molecular orbital, and energy gap values were calculated for the selected virtual screening hits using density functional theory. Finally, 16 compounds were selected as leads based on their energy gap values, which represent the high reactivity of molecules. Thus, our results indicated that the combination of two-dimensional (2D) and 3D approaches are useful for the discovery and development of specific and potent SIRT1 activators, and will benefit medicinal chemists focused on designing novel lead compounds that activate SIRT1.     

Discovery of non-glycoside sodium-dependent glucose co-transporter 2 (SGLT2) inhibitors by ligand-based virtual screening

A ligand-based virtual screening strategy (a combination of pharmacophore model generation, shape-based scoring, and structure clustering analysis) was developed to discover novel SGLT2 inhibitors. The best pharmacophore model, generated from eight glycoside inhibitors, was utilized to virtually screen three chemical databases that led to the identification of three non-glycoside SGLT2 inhibitors. This is the first report of the generation of a pharmacophore model from glycosides that has then been used to discover novel non-glycosides hits.     

Carbonic anhydrase inhibitors. Inhibition of cytosolic isozymes I and II and transmembrane,tumor-associated isozyme IX with sulfamates including EMATE also acting as steroid sulfatase inhibitors

A series of sulfamates or bis-sulfamates incorporating aliphatic, aromatic, polycyclic (steroidal), and sugar moieties in their molecules has been synthesized and assayed as inhibitors of the zinc enzyme carbonic anhydrase (CA), and more precisely of the cytosolic isozymes CA I andII, and the transmembrane, tumor-associated isozymes CA IX. Some of these compounds were previously reported to act as inhibitors of steroid sulfatases, among which estrone sulfatase (ES) and dehydroepiandrosterone sulfatase (DHEAS) are the key therapeutic targets for estrogen-dependent tumors. Very potent (nanomolar) inhibitors were detected against the three investigated CA isozymes. Best CA I inhibitors were phenylsulfamate and some of its 4-halogeno derivatives, as well as the aliphatic compound n-octyl sulfamate. Against CA II, low nanomolar inhibitors (1.1-5 nM) were phenylsulfamate and some of its 4-halogeno/nitro derivatives, n-octyl sulfamate, and estradiol 3,17beta-disulfamate among others. All the investigated sulfamates showed efficient CA IX inhibitory properties, with inhibition constants in the range of 18-63 nM. The best CA IX inhibitor detected so far was 4-chlorophenylsulfamate. These data are critical for the design of novel antitumor properties, mainly for hypoxic tumors that overexpress CA IX, which are nonresponsive to radiation or chemotherapy. The antitumor properties of the ES/DHEAS inhibitors in clinical trials may on the other hand also be due to their potent inhibitory properties of CA isozymes involved in tumorigenicity, such as CA II and CA IX.     

Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation

Virtual screening of compound libraries is an alternative and complementary approach to high-throughput screening in the lead discovery process. A new strategy is described to search for possible leads of human carbonic anhydrase II, applying a protocol of several consecutive hierarchical filters involving a preselection based on functional group requirements and fast pharmacophore matching. A suitable pharmacophore is derived by a sophisticated "hot spot" analysis of the binding site to detect regions favorable for protein-ligand interactions. In subsequent steps, molecular similarity with known reference ligands is used to rerank the hits from the pharmacophore matching. Finally the best scored candidates are docked flexibly into the protein binding pocket. After examination of the affinity predictions, 13 compounds were selected for experimental testing. Of these 13, three could be shown to be subnanomolar, one is nanomolar, while a further seven are micromolar inhibitors. The binding mode of two hits could be confirmed by crystal structure analysis. The novelty of the discovered leads is best supported by the fact that a search in the patent literature showed the newly discovered subnanomolar compounds to comprise scaffolds not yet covered by existing patents.     

The discovery of novel vascular endothelial growth factor receptor tyrosine kinases inhibitors: pharmacophore modeling, virtual screening and docking studies

We have applied pharmacophore generation, database searching and docking methodologies to discover new structures for the design of vascular endothelial growth factor receptors, the tyrosine kinase insert domain-containing receptor kinase inhibitors. The chemical function based pharmacophore models were built for kinase insert domain-containing receptor kinase inhibitors from a set of 10 known inhibitors using the algorithm HipHop, which is implemented in the CATALYST software. The highest scoring HipHop model consists of four features: one hydrophobic, one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic function. Using the algorithm CatShape within CATALYST, the bound conformation of 4-amino-furo [2, 3-d] pyrimidine binding to kinase insert domain-containing receptor kinase was used to generate a shape query. A merged shape and hypothesis query that is in an appropriate alignment was then built. The combined shape and hypothesis model was used as a query to search Maybridge database for other potential lead compounds. A total of 39 compounds were retrieved as hits. The hits obtained were docked into kinase insert domain-containing receptor kinase active site. One novel potential lead was proposed based on CATALYST fit value, LigandFit docking scores, and examination of how the hit retain key interactions known to be required for kinase binding. This compound inhibited vascular endothelial growth factor stimulated kinase insert domain-containing receptor phosphorylation in human umbilical vein endothelial cells.     

Discovery of novel acyl coenzyme a: cholesterol acyltransferase inhibitors: pharmacophore-based virtual screening, synthesis and pharmacology

The present study describes ligand‐based pharmacophore modeling of a series of structurally diverse acyl coenzyme A cholesterol acyltransferase inhibitors. Quantitative pharmacophore models were generated using HypoGen module of Discovery Studio 2.1, whereby the best pharmacophore model possessing two hydrophobic, one ring aromatic, and one hydrogen bond acceptor feature for inhibition of acyl coenzyme A cholesterol acyltransferase showed a very good correlation coefficient (r = 0.942) along with satisfactory cost analysis. Hypo1 was also validated by test set and cross‐validation methods. Developed models were found to be predictive as indicated by low error values for test set molecules. Virtual screening against Maybridge database using Hypo1 was performed. The two most potent compounds ( 47 and 48 ; predicted IC₅₀ = 1 nm ) of the retrieved hits were synthesized and biologically evaluated. These compounds showed 86% and 88% inhibition of acyl coenzyme A cholesterol acyltransferase (at 10 μg/mL) with IC₅₀ value of 3.6 and 2.5 nm , respectively. As evident from the close proximity of biological data to the predicted values, it can be concluded that the generated model (Hypo1) is a reliable and useful tool for lead optimization of novel acyl coenzyme A cholesterol acyltransferase inhibitors.     

Combined structure-based pharmacophore, virtual screening, and 3D-QSAR studies of structural diverse dehydrosqualene synthase inhibitors

Dehydrosqualene synthase (CrtM) is a key enzyme in the synthesis of presqualene diphosphate in Staphylococcus aureus. In the current study, a combination of structure-based pharmacophore and 3D-QSAR methods are used to clarify the essential quantitative structure–activity relationship (QSAR) of known CrtM inhibitors; the multicomplex-based pharmacophore (MCBP) guided method has been suggested to generate a comprehensive pharmacophore of CrtM based on twenty crystal structures of CrtM inhibitor complex. Performances of the MCBP-based virtual screening approach were applied to screen specs chemical databases (202, 408 compounds). Thirty-eight compounds were selected from the final hits and should be shifted to experimental studies. The MCBP model has been successfully used to identify the bioactive conformation and align 24 structurally diverse CrtM inhibitors. The QSAR analyses have been performed on these CrtM inhibitors based on MCBP guided alignment. These results may provide important information for further design and discovery of novel CrtM inhibitors.     

Discovery, synthesis and evaluation of novel cholesterol absorption inhibitors

Chemical-based common feature pharmacophore modelling of Niemann Pick C1 Like 1 inhibitors was performed to provide some insights on the important pharmacophore features essential for Niemann Pick C1 Like 1 inhibition using Discovery Studio V2.5. After in-house database screening, a new series of substituted oxazolidinones, selected from the top ranked hits, have been synthesized and evaluated as novel cholesterol absorption inhibitors. All compounds demonstrated effect of different degrees in lowering the total cholesterol in serum, especially compounds 1a, 2a and 2d, the potency of which was comparable to that of ezetimibe. It was also found that 1a, 1d and 2d could raise high-density lipoprotein cholesterol levels markedly. Interestingly, compounds 2a-2f appeared to have the moderate potential to lower triglyceride levels, which were superior to that of normal cholesterol absorption inhibitors including ezetimibe.     

A novel pharmacophore model to identify leads for simultaneous inhibition of anti-coagulation and anti-inflammatory activities of snake venom phospholipase A(2)

In addition to catalytic action, snake venom phospholipase A(2) induces several pharmacological effects including neurotoxicity, cardiotoxicity as well as anti-coagulant and anti-platelet aggregation effects. Therefore, strategy to identify dual inhibitor for this enzyme will be of much importance in medical research. In this paper, structure-based pharmacophore mapping, molecular docking, protein-ligand interaction fingerprints, binding energy calculations, and binding affinity predictions were employed in a virtual screening strategy to identify new hits for dual inhibition of anti-coagulation and inflammation of phospholipase A(2) . A structure-based pharmacophore map was modeled which comprised of important interactions as observed in co-crystal of phospholipase A(2) and its dual inhibitor indomethacin. The generated model was used to retrieve molecules from ChemBridge, a free database of commercially available compounds. A total of 381 molecules mapped on the developed pharmacophore model from ChemBridge database. The hits retrieved were further screened by molecular docking, protein-ligand interaction fingerprints, binding energy calculations, and binding affinity predictions using Genetic Optimization for Ligand Docking and moe. Based on these results, 32 chemo-types molecules were predicted as potential lead scaffolds for developing novel, potent and structurally diverse dual inhibitor of phospholipase A(2.).     

Computational studies on structurally diverse dipeptidyl peptidase IV inhibitors: an approach for new antidiabetic drug development

Dipeptidyl peptidase IV (DPP-IV) deactivates the natural hypoglycemic incretin hormone GLP-1. Inhibition of this enzyme restores glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, we suggested an in silico work flow for the identification of novel DPP-IV inhibitors. Ligand-based and structure-based pharmacophores were designed using HipHop program provided in catalyst and ligandScout 3.0 software, respectively. Generated models were validated by receiver operating characteristic curve analysis, Guner–Henry scoring method and by pharmacophore-based screening of marketed DPP-IV inhibitors. Ligand-based pharmacophore model A scored 0.8 AUC value, 0.865 Guner–Henry score and gave all marketed DPP-IV inhibitors as hits through screening while structure-based pharmacophore B scored 0.77 AUC value, 0.66 Guner–Henry score and gave four marketed DPP-IV inhibitors as hits (except alogliptin) out of five. These validated pharmacophores have effectively been used in search of three databases, Maybridge hitfinder collection, Chemdiv, and Asinex. Resulting hits were subjected to molecular docking using ligandfit program. Five hit compounds namely Asinex ASN 09417841, AW 00785, ChemDiv 0173-0023, ChemDiv 0276-0112, and ChemDiv 8010-1357 scored high Ligscore1 and ?PLP1 score comparable to standard drug sitagliptin. Good interactions were found with important residues like Glu205, Glu206, Tyr662, Phe357, Arg358, Tyr666 etc. They were reported as novel virtual leads to design potent DPP-IV inhibitors.