Common Feature Pharmacophore Model Research Articles

Structure based identification of novel inhibitors against ATP synthase of Mycobacterium tuberculosis: A combined in silico and in vitro study

The shortcomings of conventional tuberculosis treatments resulting from the development of drug resistance in Mycobacterium tuberculosis drive a need for the formulation of novel therapeutic agents. The diarylquinoline class of drugs such as bedaquiline was recently approved for the treatment of multidrug-resistant strains of tuberculosis, primarily targeting c and ε subunits of the ATP synthases. Yet resistance to bedaquiline has already been reported. Therefore, Rv1311 was used as the target for the identification of possible inhibitors against the M. tuberculosis. The structure of Rv1311 was predicted and common feature pharmacophore models were generated which facilitated the identification of potential inhibitors in the ZINC database. The activities of the selected molecules were compared with known inhibitors of the ATP synthase using quantitative structure–activity relationship. The ZINC classified inhibitors showed comparable predicted activities with that of known inhibitors. Furthermore, the inhibitory behavior of the studied drug molecules was experimentally determined using in vitro techniques and showed the minimum inhibitory concentration as low as 25 μM. The resulted outcomes provide a deeper insight into the structural basis of Rv1311 inhibitions and can facilitate the process of drug design against tuberculosis.

Multicomplex-based pharmacophore modeling in conjunction with multi-target docking and molecular dynamics simulations for the identification of PfDHFR inhibitors

Plasmodium falciparum dihydrofolate reductase enzyme (PfDHFR) is counted as one of the attractive and validated antimalarial drug targets. However, the point mutations in the active site of wild-type PfDHFR have developed resistance against the well-known antifolates. Therefore, there is a dire need for the development of inhibitors that can inhibit both wild-type and mutant-type DHFR enzyme. In the present contribution, we have constructed the common feature pharmacophore models from the available PfDHFR. A representative hypothesis was prioritized and then employed for the screening of natural product library to search for the molecules with complementary features responsible for the inhibition. The screened candidates were processed via drug-likeness filters and molecular docking studies. The docking was carried out on the wild-type PfDHFR (3QGT); double-mutant PfDHFR (3UM5 and 1J3J) and quadruple-mutant PfDHFR (1J3K) enzymes. A total of eight common hits were obtained from the docking calculations that could be the potential inhibitors for both wild and mutant type DHFR enzymes. Eventually, the stability of these candidates with the selected proteins was evaluated via molecular dynamics simulations. Except for SPECS14, all the prioritized candidates were found to be stable throughout the simulation run. Overall, the strategy employed in the present work resulted in the retrieval of seven candidates that may show inhibitory activity against PfDHFR and could be further exploited as a scaffold to develop novel antimalarials.Communicated by Ramaswamy H. Sarma

An Integrated Computational Approach for Plant-Based Protein Tyrosine Phosphatase Non-Receptor Type 1 Inhibitors.

Background: Protein tyrosine phosphatase non-receptor type 1 is a therapeutic target for the type 2 diabetes mellitus. According to the International Diabetes Federation 2015 report, one out of 11 adults suffers from diabetes mellitus globally.Objective: Current anti-diabetic drugs can cause life-threatening side-effects. The present study proposes a pipeline for the development of effective and plant-derived anti-diabetic drugs that may be safer and better tolerated.Methods: Plant-derived protein tyrosine phosphatase non-receptor type 1 inhibitors possessing antidiabetic activity less than 10μM were used as a training set. A common feature pharmacophore model was generated. Pharmacophore-based screening of plant-derived compounds of the ZINC database was conducted using ZINCpharmer. Screened hits were assessed to evaluate their drug-likeness, pharmacokinetics, detailed binding behavior, and aggregator possibility based on their physiochemical properties and chemical similarity with reported aggregators.Results: Through virtual screening and in silico pharmacology protocol isosilybin (ZINC30731533) was identified as a lead compound with optimal properties. This compound can be recommended for laboratory tests and further analyses to confirm its activity as protein tyrosine phosphatase non-receptor type 1 inhibitor.Conclusion: The present study has identified plant-derived anti-diabetic virtual lead compound with the potential to inhibit protein tyrosine phosphatase non-receptor type 1, which may be helpful to enhance insulin production. This computer-aided study could facilitate the development of novel pharmacological inhibitors for diabetes treatment.

Evaluation of the combination mode of azoles antifungal inhibitors with CACYP51 and the influence of Site-directed mutation

14α-demethylase (CYP51) is an essential metabolic enzyme for fungal survival and has been considered as an interesting target for the development of new antifungal inhibitors. Azoles antifungal inhibitors in the treatment of fungal diseases are good candidates via the interaction with the target enzyme CYP51 of fungus. In the study, we constructed the homology model for Candida albicans CYP51 (CACYP51) and analyzed the active site. In order to better understand the structural characteristics of azoles inhibitors and combination mode, the common feature pharmacophore model and the molecular docking were performed. The results suggest that the azoles inhibitors consist of three chemical features: the aromatic groups, phenyl groups and the azoles groups. The aromatic groups of inhibitors occupy the upper of active pocket, the phenyl groups and azoles groups occupy the bottom of active pocket. Further validation studies found these amino acid residues Tyr118, His310 and Ser378 play an important role in the substrate binding, and these amino acid residues with site-directed mutation will weaken the combining ability of the inhibitors.

A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors

Histone deacetylase 3 (HDAC3) has been recently identified as a potential target for the treatment of cancer and other diseases, such as chronic inflammation, neurodegenerative diseases, and diabetes. Virtual screening (VS) is currently a routine technique for hit identification, but its success depends on rational development of VS strategies. To facilitate this process, we applied our previously released benchmarking dataset, i.e., MUBD-HDAC3 to the evaluation of structure-based VS (SBVS) and ligand-based VS (LBVS) combinatorial approaches. We have identified FRED (Chemgauss4) docking against a structural model of HDAC3, i.e., SAHA-3 generated by a computationally inexpensive “flexible docking”, as the best SBVS approach and a common feature pharmacophore model, i.e., Hypo1 generated by Catalyst/HipHop as the optimal model for LBVS. We then developed a pipeline that was composed of Hypo1, FRED (Chemgauss4), and SAHA-3 sequentially, and demonstrated that it was superior to other combinations in terms of ligand enrichment. In summary, we present the first highly-validated, rationally-designed VS strategy specific to HDAC3 inhibitor discovery. The constructed pipeline is publicly accessible for the scientific community to identify novel HDAC3 inhibitors in a time-efficient and cost-effective way.

Development of Pharmacophore Model for Indeno[1,2-b]indoles as Human Protein Kinase CK2 Inhibitors and Database Mining.

Protein kinase CK2, initially designated as casein kinase 2, is an ubiquitously expressed serine/threonine kinase. This enzyme, implicated in many cellular processes, is highly expressed and active in many tumor cells. A large number of compounds has been developed as inhibitors comprising different backbones. Beside others, structures with an indeno[1,2-b]indole scaffold turned out to be potent new leads. With the aim of developing new inhibitors of human protein kinase CK2, we report here on the generation of common feature pharmacophore model to further explain the binding requirements for human CK2 inhibitors. Nine common chemical features of indeno[1,2-b]indole-type CK2 inhibitors were determined using MOE software (Chemical Computing Group, Montreal, Canada). This pharmacophore model was used for database mining with the aim to identify novel scaffolds for developing new potent and selective CK2 inhibitors. Using this strategy several structures were selected by searching inside the ZINC compound database. One of the selected compounds was bikaverin (6,11-dihydroxy-3,8-dimethoxy-1-methylbenzo[b]xanthene-7,10,12-trione), a natural compound which is produced by several kinds of fungi. This compound was tested on human recombinant CK2 and turned out to be an active inhibitor with an IC50 value of 1.24 µM.

Identification of novel VP35 inhibitors: Virtual screening driven new scaffolds.

Ebola virus is a single-stranded, negative-sense RNA virus that causes acute and serious life-threatening illness. In recent years the Ebola virus has spread through several countries in Africa, highlighting the need to develop new treatments for this disease and boosting a new research effort on this subject. However, so far there is no valid treatment for disease created by this pathogen. The Ebola virus Viral Protein 35 (VP35) is a multifunctional protein which is critical for virus replication and infection, and it is considered as a future target for drug development. In this study, we collected 144 VP35 inhibitors which shared the same core scaffold, and a common feature pharmacophore model HypoA was built based on inhibitor-receptor complexes. All 141 compounds were aligned based on the common feature pharmacophore model HypoA (three compounds could not map onto HypoA). The pharmacophore model HypoA was further optimized according to the actual interactions between inhibitors and VP35 protein, resulting in a new pharmacophore model HypoB which was applied for virtual screening. A 3D QSAR model was established by applying the 141 aligned compounds. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.897, for the test set, the correlation coefficient r2 was 0.757. Then a virtual screening was carried out, which comprehensively employing the common feature pharmacophore model, 3D QSAR model and docking study, their combination in a hybrid protocol could help to mutually compensate for their limitations and capitalized on their mutual strengths. After the above three virtual screening methods orderly filtering, seven potential inhibitors with novel scaffolds were identified as new VP35 inhibitors. The mapping results of hit compounds onto pharmacophore model and 3D QSAR model, and the molecular interactions of the potential inhibitors with the active site residues have been discussed in detail.

Pharmacophore-based virtual screening, biological evaluation and binding mode analysis of a novel protease-activated receptor 2 antagonist.

Protease-activated receptor 2 (PAR2) is a G protein-coupled receptor, mediating inflammation and pain signaling in neurons, thus it is considered to be a potential therapeutic target for inflammatory diseases. In this study, we performed a ligand-based virtual screening of 1.6 million compounds by employing a common-feature pharmacophore model and two-dimensional similarity search to identify a new PAR2 antagonist. The common-feature pharmacophore model was established based on the biological screening results of our in-house library. The initial virtual screening yielded a total number of 47 hits, and additional biological activity tests including PAR2 antagonism and anti-inflammatory effects resulted in a promising candidate, compound 43, which demonstrated an IC50 value of 8.22µM against PAR2. In next step, a PAR2 homology model was constructed using the crystal structure of the PAR1 as a template to explore the binding mode of the identified ligands. A molecular docking method was optimized by comparing the binding modes of a known PAR2 agonist GB110 and antagonist GB83, and applied to predict the binding mode of our hit compound 43. In-depth docking analyses revealed that the hydrophobic interaction with Phe243(5.39) is crucial for PAR2 ligands to exert antagonistic activity. MD simulation results supported the predicted docking poses that PAR2 antagonist blocked a conformational rearrangement of Na(+) allosteric site in contrast to PAR2 agonist that showed Na(+) relocation upon GPCR activation. In conclusion, we identified new a PAR2 antagonist together with its binding mode, which provides useful insights for the design and development of PAR2 ligands.

A combined in silico/in vitro approach unveils common molecular requirements for efficient BVDV RdRp binding of linear aromatic N-polycyclic systems

In this work, we present and discuss a comprehensive set of both newly and previously synthesized compounds belonging to 5 distinct molecular classes of linear aromatic N-polycyclic systems that efficiently inhibits bovine viral diarrhea virus (BVDV) infection. A coupled in silico/in vitro investigation was employed to formulate a molecular rationale explaining the notable affinity of all molecules to BVDV RNA dependent RNA polymerase (RdRp) NS5B. We initially developed a three-dimensional common-feature pharmacophore model according to which two hydrogen bond acceptors and one hydrophobic aromatic feature are shared by all molecular series in binding the viral polymerase. The pharmacophoric information was used to retrieve a putative binding site on the surface of the BVDV RdRp and to guide compound docking within the protein binding site. The affinity of all compounds towards the enzyme was scored via molecular dynamics-based simulations, showing high correlation with in vitro EC50 data. The determination of the interaction spectra of the protein residues involved in inhibitor binding highlighted amino acids R295 and Y674 as the two fundamental H-bond donors, while two hydrophobic cavities HC1 (residues A221, I261, I287, and Y289) and HC2 (residues V216, Y303, V306, K307, P408, and A412) fulfill the third pharmacophoric requirement. Three RdRp (K263, R295 and Y674) residues critical for drug binding were selected and mutagenized, both in silico and in vitro, into alanine, and the affinity of a set of selected compounds towards the mutant RdRp isoforms was determined accordingly. The agreement between predicted and experimental data confirmed the proposed common molecular rationale shared by molecules characterized by different chemical scaffolds in binding to the BVDV RdRp, ultimately yielding compound 6b (EC50 = 0.3 μM; IC50 = 0.48 μM) as a new, potent inhibitor of this Pestivirus.

Screening and Identification of Inhibitors Against Glutathione Synthetase, A Potential Drug Target of Plasmodium falciparum.

Malaria is the world's most fatal disease - causing up to 2.7 million deaths annually all over the world. The ability of organisms to develop resistance against existing antimalarial drugs exacerbates the problem. There is a clear cut need for more effective, affordable and accessible drugs that act by novel modes of action. Glutathione synthetase (GS) from Plasmodium falciparum represents an important potential drug target due to its defensive role; hence ceasing the respective metabolic step will destroy the parasite. A three dimensional model of Plasmodium GS was constructed by de novo modelling method and potential GS inhibitors were identified from a library of glutathione (GSH) analogues retrieved from Ligand-info database and filtered using Lipinski and ADME rules. Two common feature pharmacophore models were generated from the individual inhibitor clusters to provide insight into the key pharmacophore features that are crucial for the GS inhibition. Molecular docking of selective compounds into the predicted GS binding site revealed that the compound CMBMB was the best GS inhibitor when compared to the standard reference Chloroquine (CQ). This was taken as indicating that CMBMB was the best effective and safest drug against P. falciparum.

Toward Predicting Drug-Induced Liver Injury: Parallel Computational Approaches to Identify Multidrug Resistance Protein 4 and Bile Salt Export Pump Inhibitors

Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of multidrug resistance protein 4 (MRP4), in addition to bile salt export pump (BSEP), might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and noninhibitors in the training set. Models were externally validated and used to predict the affinity of compounds toward BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of nine well characterized MRP4 inhibitors and nine potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/noninhibition were developed with cross-validated receiver operator curve values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements. Similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.

Molecular recognition of CYP26A1 binding pockets and structure–activity relationship studies for design of potent and selective retinoic acid metabolism blocking agents

All-trans-retinoic acid (ATRA), the biologically most active metabolite of vitamin A, plays a major role in the regulation of cellular differentiation and proliferation, and it is also an important pharmacological agent particularly used in the treatment of cancer, skin, neurodegenerative and autoimmune diseases. However, ATRA is very easy to be metabolized into 4-hydroxyl-RA in vivo by CYP26A1, an inducible cytochrome P450 enzyme, eventually into more polar metabolites. Therefore, it is vital to develop specific retinoic acid metabolism blocking agents (RAMBAs) to inhibit the metabolic enzyme CYP26A1 in the treatment of relevant diseases aforementioned.In this study, CYP26A1 and its interactions with retinoic acid-competitive metabolism blocking agents were investigated by a combined ligand- and structure-based approach. First, since the crystal structure of CYP26A1 protein has not been determined, we constructed the 3D structure of CYP26A1 using homology modeling. In order to achieve a deeper insight into the mode of action of RAMBAs in the active site, the molecular superimposition model and the common feature pharmacophore model were constructed, and molecular docking was performed. The molecular superimposition model is composed of three features: the main chain groups, side chain groups, and azole groups. The common feature pharmacophore model consists of five chemical features: four hydrophobic groups and one hydrogen acceptor (HHHHA). The results of molecular docking show that the characteristic groups of RAMBAs were mapped into three different active pockets, respectively.A structure–activity relationship (SAR) was obtained by a combination of the molecular superimposition and docking results with the pharmacophore model. This study gives more insight into the interaction model inside the CYP26A1 active site and provides guidance for the design of more potent and possibly more selective RAMBAs.

Pharmacophore modeling, virtual screening, and in vitro testing reveal haloperidol, eprazinone, and fenbutrazate as neurokinin receptors ligands.

Neurokinin receptors (NKRs) have been shown to be involved in many physiological processes, rendering them promising novel drug targets, but also making them the possible cause for side effects of several drugs. Aiming to answer the question whether the binding to NKRs could have a share in the side effects or even the desired effects of already licensed drugs, we generated a set of ligand-based common feature pharmacophore models based on the structural information about subtype-selective and nonselective NKR antagonists and screened an in-house database mainly composed of licensed drugs. The prospective pharmacological investigations of the virtual hits haloperidol, eprazinone, and fenbutrazate confirmed them to be NKR ligands in vitro. By the identification of licensed drugs as so far unknown NKR ligands, this study contributes to establishing an activity profile of the investigated compounds and confirms the presented pharmacophore models as useful tools for this purpose.

Rohitukine inhibits in vitro adipogenesis arresting mitotic clonal expansion and improves dyslipidemia in vivo

We developed a common feature pharmacophore model using known antiadipogenic compounds (CFPMA). We identified rohitukine, a reported chromone anticancer alkaloid as a potential hit through in silico mapping of the in-house natural product library on CFPMA. Studies were designed to assess the antiadipogenic potential of rohitukine. Rohitukine was isolated from Dysoxylum binacteriferum Hook. to ⬧95% purity. As predicted by CFPMA, rohitukine was indeed found to be an antiadipogenic molecule. Rohitukine inhibited lipid accumulation and adipogenic differentiation in a concentration- and exposure-time-dependent manner in 3T3-L1 and C3H10T1/2 cells. Rohitukine downregulated expression of PPARγ, CCAAT/enhancer binding protein α, adipocyte protein 2 (aP2), FAS, and glucose transporter 4. It also suppressed mRNA expression of LPL, sterol-regulatory element binding protein (SREBP) 1c, FAS, and aP2, the downstream targets of PPARγ. Rohitukine arrests cells in S phase during mitotic clonal expansion. Rohitukine was bioavailable, and 25.7% of orally administered compound reached systemic circulation. We evaluated the effect of rohitukine on dyslipidemia induced by high-fat diet in the hamster model. Rohitukine increased hepatic expression of liver X receptor α and decreased expression of SREBP-2 and associated targets. Rohitukine decreased hepatic and gonadal lipid accumulation and ameliorated dyslipidemia significantly. In summary, our strategy to identify a novel antiadipogenic molecule using CFPMA successfully resulted in identification of rohitukine, which confirmed antiadipogenic activity and also exhibited in vivo antidyslipidemic activity.

Computational modeling of MRP4 and BSEP substrates and inhibitors (896.10)

Drug‐induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of MRP4, in addition to BSEP, might be a risk factor that is responsible for the development of cholestatic DILI. Recently, we demonstrated that MRP4 inhibition was associated with an increased cholestatic potential of drugs that are BSEP non‐inhibitors, while MRP4 inhibition among BSEP inhibitors did not provide additional benefit to predict cholestasis. The current study aimed to develop in silico models to delineate molecular features underlying MRP4 and BSEP inhibition.Models were developed using 257 BSEP and 88 MRP4 inhibitors and non‐inhibitors in the training set based on various molecular descriptors as well as fingerprints (ECFP and FCFP). Models were validated using an external test set; subsequently, these models were used to predict the affinity towards BSEP and MRP4 of a database (CDD) containing over 2,200 FDA‐approved drugs. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed with the Catalyst software suite using a training set of 20 well‐characterized MRP4 substrates and inhibitors.For MRP4 substrates and inhibitors, common feature pharmacophore models were characterized by hydrogen‐bond acceptor and donor features as possible interaction forces between MRP4 ligands and the transporter protein. Models were further used to predict over 70 potential MRP4/BSEP inhibitors by integrating hits retrieved from the CDD database with a list of 208 compounds known to cause DILI.The Bayesian (BSEP, MRP4) and pharmacophore (MRP4) models demonstrated significant classification accuracy and predictability. Potential novel MRP4 inhibitors were predicted by virtual screening of the database based on Bayesian and pharmacophore models. Subsequent in vitro testing should further our knowledge on MRP4 and BSEP affinity and their role in DILI.Grant Funding Source: NIH R01GM041935, R01DK61425, T32‐ES007126, and Deutsche Forschungsgemeinschaft Ko4185/1‐1

The Veratrum alkaloids jervine, veratramine, and their analogues as prostate cancer migration and proliferation inhibitors: biological evaluation and pharmacophore modeling

Veratrum alkaloids are a group of steroidal alkaloids with penta-, hexa-, or heptacyclic C-nor-D-homosteroidal or regular hexacycliccholestane skeleton. Activation of the Hedgehog (Hh) pathway aberrantly associated with numerous malignancies. Cyclopamine blocked Hh pathway by directly binding Smo and slows down the growth of these tumors in various animal models. To less extent, jervine and the veratrane Veratrum alkaloid veratramine were also reported as possible Hh signaling inhibitors. This encouraged the study of our small library of natural, biocatalytic, and semisynthetic jervane and veratrane alkaloids’ ability to inhibit the growth, proliferation, and migration of the prostate metastatic cancer cell line PC-3 to establish a preliminary structure–activity relationship. Alkaloids possessing C-3-keto functionality with ∆4,5 or ∆1,2 and ∆4,5 exhibited significant enhancement of antiproliferative and antimigratory activities. A common feature pharmacophore model of active Veratrum alkaloids was built and validated using receiver-operating characteristic curve analyses.

Identification of Novel Anaplastic Lymphoma Kinase (ALK) Inhibitors Using a Common Feature Pharmacophore Model Derived from Known Ligands Crystallized with ALK

In this investigation, a common feature pharmacophore model of anaplastic lymphoma kinase inhibitors was developed based on several known anaplastic lymphoma kinase inhibitors that were co-crystallized with anaplastic lymphoma kinase. The established pharmacophore model Hypo1 was carefully validated and then adopted to screen two in silico chemical databases, Specs (202 408 compounds) and Enamine (1 105 894 compounds), for retrieving novel anaplastic lymphoma kinase inhibitors. The hit compounds were further filtered using a fast bumping-check tool and molecular docking. Finally, 25 compounds were selected and purchased from market. The bioactivity of these compounds was firstly measured at the cellular level against a typical anaplastic lymphoma kinase mutant-driven cancer cell line, Karpas299. And six of them showed a good anti-viability activity. The kinase inhibitory potency against the recombinant human anaplastic lymphoma kinase kinase was tested to the most active compound at the cellular level, T0508-5181 (from Specs), which gave a half maximal inhibitory concentration (IC(50)) of 5.3 μM.

Discovery of N6-phenyl-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine derivatives as novel CK1 inhibitors using common-feature pharmacophore model based virtual screening and hit-to-lead optimization

Aberrant activation of casein kinase 1 (CK1) has been demonstrated to be implicated in the pathogenesis of cancer and various central nervous system disorders. Discovery of CK1 inhibitors has thus attracted much attention in recent years. In this account, we describe the discovery of N6-phenyl-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine derivatives as novel CK1 inhibitors. An optimal common-feature pharmacophore hypothesis, termed Hypo2, was firstly generated, followed by virtual screening using Hypo2 against several chemical databases. One of the best hit compounds, N6-(4-chlorophenyl)-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine, was chosen for the subsequent hit-to-lead optimization under the guide of Hypo2, which led to the discovery of a new lead compound (1-(3-(3-amino-1H-pyrazolo[3,4-d]pyrimidin-6-ylamino)phenyl)-3-(3-chloro-4-fluorophenyl)urea) that potently inhibits CK1 with an IC50 value of 78 nM.

DISCOVERY AND EVALUATION OF POTENTIAL SONIC HEDGEHOG SIGNALING PATHWAY INHIBITORS USING PHARMACOPHORE MODELING AND MOLECULAR DYNAMICS SIMULATIONS

Sonic hedgehog (Shh) plays an important role in the activation of Shh signaling pathway that regulates preservation and rebirth of adult tissues. An abnormal activation of this pathway has been identified in hyperplasia and various tumorigenesis. Hence the inhibition of this pathway using a Shh inhibitor might be an efficient way to treat a wide range of malignancies. This study was done in order to develop a lead chemical candidate that has an inhibitory function in the Shh signaling pathway. We have generated common feature pharmacophore models using three-dimensional (3D) structural information of robotnikinin, an inhibitor of the Shh signaling pathway, and its analogs. These models have been validated with fit values of robotnikinin and its analogs, and the best model was used as a 3D structural query to screen chemical databases. The hit compounds resulted from the screening docked into a proposed binding site of the Shh named pseudo-active site. Molecular dynamics (MD) simulations were performed to investigate detailed binding modes and molecular interactions between the hit compounds and functional residues of the pseudo-active site. The results of the MD simulation analyses revealed that the hit compounds can bind the pseudo-active site with high affinity than robotnikinin. As a result of this study, a candidate inhibitor (GK 03795) was selected as a potential lead to be employed in future Shh inhibitor design.

Molecular Docking and Pharmacophore Filtering in the Discovery of Dual-Inhibitors for Human Leukotriene A4 Hydrolase and Leukotriene C4 Synthase

Combination of drugs for multiple targets has been a standard treatment in treating various diseases. A single chemical entity that acts upon multiple targets is emerging nowadays because of their predictable pharmacokinetic and pharmacodynamic properties. We have employed a computer-aided methodology combining molecular docking and pharmacophore filtering to identify chemical compounds that can simultaneously inhibit the human leukotriene hydrolase (hLTA4H) and the human leukotriene C4 synthase (hLTC4S) enzymes. These enzymes are the members of arachidonic acid pathway and act upon the same substrate, LTA4, producing different inflammatory products. A huge set of 4966 druglike compounds from the Maybridge database were docked into the active site of hLTA4H using the GOLD program. Common feature pharmacophore models were developed from the known inhibitors of both the targets using Accelrys Discovery Studio 2.5. The hits from the hLTA4H docking were filtered to match the chemical features of both the pharmacophore models. The compounds that resulted from the pharmacophore filtering were docked into the active site of hLTC4S and the hits those bind well at both the active sites and matched the pharmacophore models were identified as possible dual inhibitors for hLTA4H and hLTC4S enzymes. Reverse validation was performed to ensure the results of the study.