Targeting malaria with specific CDK inhibitors

Cyclin-dependent protein kinases (CDKs) are attractive targets for drug discovery and efforts have led to the identification of novel CDK selective inhibitors in the development of treatments for cancers, neurological disorders, and infectious diseases. More recently, they have become the focus of rational drug design programs for the development of new antimalarial agents. CDKs are valid targets as they function as essential regulators of cell growth and differentiation. To date, several CDKs have been characterized from the genome of the malaria-causing protozoan Plasmodium falciparum. Our approach employs experimental and virtual screening methodologies to identify and refine chemical inhibitors of the parasite CDK Pfmrk, a sequence homologue of human CDK7. Chemotypes of Pfmrk inhibitors include the purines, quinolinones, oxindoles, and chalcones, which have sub-micromolar IC50 values against the parasite enzyme, but not the human CDKs. Additionally, we have developed and validated a pharmacophore, based on Pfmrk inhibitors, which contains two hydrogen bond acceptor functions and two hydrophobic sites, including one aromatic ring hydrophobic site. This pharmacophore has been exploited to identify additional compounds that demonstrate significant inhibitory activity against Pfmrk. A molecular model of Pfmrk designed using the crystal structure of human CDK7 highlights key amino acid substitutions in the ATP binding pocket. Molecular modeling and docking of the active site pocket with selective inhibitors has identified possible receptor-ligand interactions that may be responsible for inhibitor specificity. Overall, the unique biochemical characteristics associated with this protein, to include distinctive active site amino acid residues and variable inhibitor profiles, distinguishes the Pfmrk drug screen as a paradigm for CDK inhibitor analysis in the parasite.     

Construction of a three-dimensional pharmacophore for Bcl-2 inhibitors by flexible docking and the multiple copy simultaneous search method

B-Cell lymphoma-2 (Bcl-2) protein is a new promising target for anticancer drugs. A number of anticancer Bcl-2 inhibitors with diverse chemical structures have been discovered in recent years. In this paper, the flexible docking was performed to determine the binding modes of the representative inhibitors from different structural types. Subsequently, the binding modes of inhibitor were used to construct a primary three- dimensional (3D) pharmacophore model. It proved that this model can effectively disrupt the binding of the BH3 domain of proapoptotic Bcl-2 family members to Bcl-2, and match the structural requirement of a new type of Bcl-2 inhibitors. However, these distances between pharmacophoric points are not optimal due to the fact that not all of individual functional groups are located in the ideal position when inhibitors bind to its receptor. In this paper, we proposed a new idea to improve the quality of the pharmacophore model: the multiple copy simultaneous search (MCSS) method was performed to determine the energetically favorable distribution of functional groups with similar features to these pharmacophoric points in the active site of Bcl-2 first. Then their most energetically favorable minima in the positions near the pharmacophoric points were used to optimize the distances between pharmacophoric points. By examining the binding modes of several inhibitors from the same structural type, it was found that the more potent the inhibitor was, the closer it was to the optimized distances between pharmacophoric points. The optimized 3D pharmacophore model obtained in this paper may provide a good starting point for further rational design of Bcl-2 inhibitors.     

The discovery of inhibitors of Fas-mediated cell death pathway using the combined computational method

In this study, pharmacophore and 3D-QSAR models were developed for analogues of 3-substituted-benzofuran-2-carboxylate as inhibitors of Fas-mediated cell death pathways. Our pharmacophore model has good correspondence with experimental results and can explain the variance in biological activities coherently with respect to the structure of the data set compounds. The predictive power for our synthesized compounds were 0.96 for the pharmacophore model, 0.58 for the comparative molecular field analysis (CoMFA) model, and 0.57 for the comparative molecular similarity analysis (CoMSIA) model.     

Design, synthesis and biological activity of novel non-peptidyl endothelin converting enzyme inhibitors, 1-phenyl-tetrazole-formazan analogues

A novel non-peptidyl endothelin converting enzyme inhibitor was obtained through a pharmacophore analysis of known inhibitors and three-dimensional structure database search. Analogues of the new inhibitor were designed using the structure-activity relationship of known inhibitors and synthesized. In anesthetized rats, intraperitoneal administration of the analogues suppressed the pressor responses induced by big endothelin-1.     

3D-QSAR based pharmacophore modeling and virtual screening for identification of novel pteridine reductase inhibitors

Pteridine reductase is a promising target for development of novel therapeutic agents against Trypanosomatid parasites. A 3D-QSAR pharmacophore hypothesis has been generated for a series of L. major pteridine reductase inhibitors using Catalyst/HypoGen algorithm for identification of the chemical features that are responsible for the inhibitory activity. Four pharmacophore features, namely: two H-bond donors (D), one Hydrophobic aromatic (H) and one Ring aromatic (R) have been identified as key features involved in inhibitor-PTR1 interaction. These features are able to predict the activity of external test set of pteridine reductase inhibitors with a correlation coefficient (r) of 0.80. Based on the analysis of the best hypotheses, some potent Pteridine reductase inhibitors were screened out and predicted with anti-PTR1 activity. It turned out that the newly identified inhibitory molecules are at least 300 fold more potent than the current crop of existing inhibitors. Overall the current SAR study is an effort for elucidating quantitative structure-activity relationship for the PTR1 inhibitors. The results from the combined 3D-QSAR modeling and molecular docking approach have led to the prediction of new potent inhibitory scaffolds.     

Discovery of novel fatty acid synthase (FAS) inhibitors based on the structure of ketoaceyl synthase (KS) domain

Development of fatty acid synthase (FAS) inhibitors has increasingly attracted much attention in recent years due to their potential therapeutic use in obesity and cancers. In this investigation, pharmacophore modeling based on the first crystal structure of human KS domain of FAS was carried out. The established pharmacophore model was taken as a 3D query for retrieving potent FAS inhibitors from the chemical database Specs. Docking study was further carried out to refine the obtained hit compounds. Finally, a total of 28 compounds were selected based on the ranking order and visual examination, which were first evaluated by a cell line-based assay. Seven compounds that have good inhibition activity against two FAS overexpressing cancer cell lines were further evaluated by an enzyme-based assay. One compound with a new chemical scaffold was found to have low micromolar inhibition potency against FAS, which has been subjected to further chemical structural modification.     

Application of docking-based comparative intermolecular contacts analysis to validate Hsp90α docking studies and subsequent in silico screening for inhibitors

Heat shock protein (Hsp90α) has been recently implicated in cancer, prompting several attempts to discover and optimize new Hsp90α inhibitors. Towards this end, we docked 83 diverse Hsp90α inhibitors into the ATP-binding site of this chaperone using several docking-scoring settings. Subsequently, we applied our newly developed computational tool-docking-based comparative intramolecular contacts analysis (dbCICA)-to assess the different docking conditions and select the best settings. dbCICA is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration based on its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands, and vice versa. The optimal dbCICA models were translated into valid pharmacophore models that were used as 3D search queries to mine the National Cancer Institute's structural database for new inhibitors of Hsp90α that could potentially be used as anticancer agents. The process culminated in 15 micromolar Hsp90α ATPase inhibitors.     

Identification of novel urease inhibitors: pharmacophore modeling,virtual screening and molecular docking studies

Abstract

Pharmacophore modeling and atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) have been developed on N-acylglycino- and hippurohydroxamic acid derivatives, which are known potential inhibitors of urease. This is followed by virtual screening and ADMET (absorption, distribution, metabolism, excretion and toxicity) studies on a large library of known drugs in order to get lead molecules as Helicobacter pylori urease inhibitors. A suitable three-featured pharmacophore model comprising one H-bond acceptor and two H-bond donor features (ADD.10) has been found to be the best QSAR model. An external library of compounds (～3000 molecules), pre-filtered using Lipinski’s rule of five, has been further screened using the pharmacophore model ADD.10. By analyzing the fitness of the hits with respect to the pharmacophore model and their binding interaction inside the urease active site, four molecules have been predicted to be extremely good urease inhibitors. Two of these have significant potential and should be taken up for further drug-designing process.     

The use of oxadiazole and triazole substituted naphthyridines as HIV-1 integrase inhibitors. Part 1: Establishing the pharmacophore

A series of HIV-1 integrase inhibitors containing a novel metal binding motif consisting of the 8-hydroxy-1,6-naphthyridine core and either an oxadiazole or triazole has been identified. The design of the key structural components was based on a two-metal coordination pharmacophore. This report presents initial structure–activity data that shows the new chelation architecture delivers potent inhibition in both enzymatic and antiviral assays.     

Identifying the novel inhibitors of lysine-specific demethylase 1 (LSD1) combining pharmacophore-based and structure-based virtual screening

Abstract

Lysine-specific demethylase 1 (LSD1) has been reported to connect with a range of solid tumors. Thus, the exploration of LSD1 inhibitors has emerged as an effective strategy for cancer treatment. In this study, we constructed a pharmacophore model based on a series of flavin adenine dinucleotide (FAD)-competing inhibitors bearing triazole???dithiocarbamate scaffold combining docking, structure–activity relationship (SAR) study, and molecular dynamic (MD) simulation. Meanwhile, another pharmacophore model was also constructed manually, relying on several speculated substrate-competing inhibitors and reported putative vital interactions with LSD1. On the basis of the two pharmacophore models, multi-step virtual screenings (VSs) were performed against substrate-binding pocket and FAD-binding pocket, respectively, combining pharmacophore-based and structure-based strategy to exploit novel LSD1 inhibitors. After bioassay evaluation, four compounds among 21 hits with diverse and novel scaffolds exhibited inhibition activity at the range of 3.63–101.43?μM. Furthermore, substructure-based enrichment was performed, and four compounds with a more potent activity were identified. After that, the time-dependent assay proved that the most potent compound with IC₅₀ 2.21?μM inhibits LSD1 activity in a manner of time-independent. In addition, the compound exhibited a cellular inhibitory effect against LSD1 in MGC-803 cells and may inhibit cell migration and invasion by reversing EMT in cultured gastric cancer cells. Considering the binding mode and SAR of the series of compounds, we could roughly deem that these compounds containing 3-methylxanthine scaffold act through occupying substrate-binding pocket competitively. This study presented a new starting point to develop novel LSD1 inhibitors.     

Pharmacophore models generation by catalyst and phase consensus-based virtual screening protocol against PI3Kα inhibitors

Phosphoinositide 3-kinases (PI3Ks) family has emerged as promising targets for novel therapeutic agents against neoplastic diseases. Pharmacophore and 3D-quantitative structure–activity relationship modelling were applied to study the structure–activity relationship of PI3K inhibitors. The best HypoGen pharmacophore hypothesis Hypo1 with a correlation coefficient of 0.961 consists of one hydrogen-bond acceptor, one hydrogen-bond donor and two hydrophobic features, whereas the best phase hypothesis AADRRR.378 with favourable statistics (q² = 0.7368, r² = 0.9863) has two hydrogen-bond acceptors, one hydrogen-bond donor and three ring aromatic features. Multiple methods, such as Fischer validation, molecular docking and mapping of test set molecules, were carried out to validate these pharmacophore models. Furthermore, a comparative molecular similarity indices analysis candidate hypothesis model was generated as a supplement of pharmacophore hypothesis. Detailed protein–ligand binding information obtained by Glide was utilised in compound optimisation and virtual screening. A molecular database of 133 known inhibitors and 6179 decoys was built for a screening test to quantitatively analyse various hypotheses and scoring parameters. Finally, we designed a workflow integrating HypoGen pharmacophore searching, phase pharmacophore searching and molecular docking for screening the database. With an improved criterion of enrichment factor (EF = 17.43) and ROC curve (AUC = 0.946), this workflow would provide us an original method for novel PI3K inhibitors.     

Structure-based and shape-complemented pharmacophore modeling for the discovery of novel checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1), a member of the serine/threonine kinase family, is an attractive therapeutic target for anticancer combination therapy. A structure-based modeling approach complemented with shape components was pursued to develop a reliable pharmacophore model for ATP-competitive Chk1 inhibitors. Common chemical features of the pharmacophore model were derived by clustering multiple structure-based pharmacophore features from different Chk1-ligand complexes in comparable binding modes. The final model consisted of one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD), two hydrophobic (HY) features, several excluded volumes and shape constraints. In the validation study, this feature-shape query yielded an enrichment factor of 9.196 and performed fairly well at distinguishing active from inactive compounds, suggesting that the pharmacophore model can serve as a reliable tool for virtual screening to facilitate the discovery of novel Chk1 inhibitors. Besides, these pharmacophore features were assumed to be essential for Chk1 inhibitors, which might be useful for the identification of potential Chk1 inhibitors.     

A Combination of Receptor-Based Pharmacophore Modeling & QM Techniques for Identification of Human Chymase Inhibitors

Inhibition of chymase is likely to divulge therapeutic ways for the treatment of cardiovascular diseases, and fibrotic disorders. To find novel and potent chymase inhibitors and to provide a new idea for drug design, we used both ligand-based and structure-based methods to perform the virtual screening(VS) of commercially available databases. Different pharmacophore models generated from various crystal structures of enzyme may depict diverse inhibitor binding modes. Therefore, multiple pharmacophore-based approach is applied in this study. X-ray crystallographic data of chymase in complex with different inhibitors were used to generate four structure–based pharmacophore models. One ligand–based pharmacophore model was also developed from experimentally known inhibitors. After successful validation, all pharmacophore models were employed in database screening to retrieve hits with novel chemical scaffolds. Drug-like hit compounds were subjected to molecular docking using GOLD and AutoDock. Finally four structurally diverse compounds with high GOLD score and binding affinity for several crystal structures of chymase were selected as final hits. Identification of final hits by three different pharmacophore models necessitates the use of multiple pharmacophore-based approach in VS process. Quantum mechanical calculation is also conducted for analysis of electrostatic characteristics of compounds which illustrates their significant role in driving the inhibitor to adopt a suitable bioactive conformation oriented in the active site of enzyme. In general, this study is used as example to illustrate how multiple pharmacophore approach can be useful in identifying structurally diverse hits which may bind to all possible bioactive conformations available in the active site of enzyme. The strategy used in the current study could be appropriate to design drugs for other enzymes as well.     

Non-urea functionality as the primary pharmacophore in soluble epoxide hydrolase inhibitors

Inhibition of soluble epoxide hydrolase has been proposed as a promising new pharmaceutical target for diseases involving hypertension and vascular inflammation. The most potent sEH inhibitors reported to date contain a urea or amide moiety as the central or ‘primary’ pharmacophore. We evaluated replacing the urea pharmacophore with other functional groups such as thiourea, sulfonamide, sulfonylurea, aminomethylene amide, hydroxyamide, and ketoamide to identify novel and potent inhibitors. The hydroxyamide moiety was identified as a novel pharmacophore affording potency comparable to urea.     

Structural insights of JAK2 inhibitors: pharmacophore modeling and ligand-based 3D-QSAR studies of pyrido-indole derivatives

Abstract

In this study we have performed pharmacophore modeling and built a 3D QSAR model for pyrido-indole derivatives as Janus Kinase 2 inhibitors. An efficient pharmacophore has been identified from a data set of 51 molecules and the identified pharmacophore hypothesis consisted of one hydrogen bond acceptor, two hydrogen bond donors and three aromatic rings, i.e. ADDRRR. A powerful 3D-QSAR model has also been constructed by employing Partial Least Square regression analysis with a regression coefficient of 0.97 (R²) and Q² of 0.95, and Pearson-R of 0.98.     

Pharmacophore-based discovery of 2-(phenylamino)aceto-hydrazides as potent eosinophil peroxidase (EPO) inhibitors

There is an increasing interest in developing novel eosinophil peroxidase (EPO) inhibitors, in order to provide new treatment strategies against chronic inflammatory and neurodegenerative diseases caused by eosinophilic disorder. Within this study, a ligand-based pharmacophore model for EPO inhibitors was generated and used for in silico screening of large 3?D molecular structure databases, containing more than 4 million compounds. Hits obtained were clustered and a total of 277 compounds were selected for biological assessment. A class of 2-(phenyl)amino-aceto-hydrazides with different substitution pattern on the aromatic ring was found to contain the most potent EPO inhibitors, exhibiting IC₅₀ values down to 10?nM. The generated pharmacophore model therefore, represents a valuable tool for the selection of compounds for biological testing. The compounds identified as potent EPO inhibitors will serve to initiate a hit to lead and lead optimisation program for the development of new therapeutics against eosinophilic disorders.     

Pyridone derivatives as potent and selective VLA-4 integrin antagonists

A novel series of pyridone inhibitors has been identified through pharmacophore analysis, as potent antagonists of VLA-4.     

Neuraminidase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 22 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity, for discovering new potent neuraminidase (NA) inhibitors to fight against avian influenza virus. The best hypothesis (Hypo1), consisting of five features, namely, one positive ionizable group, one negative ionizable group, one hydrophobic point, and two hydrogen-bond donors, has a correlation coefficient of 0.902, a root mean square deviation of 1.392, and a cost difference of 72.88, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 88 known NA inhibitors in our test set with a correlation coefficient of 0.818 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.