Showing papers on "Pharmacophore published in 2016"

Textbook of drug design and discovery

Abstract: DRUG DESIGN AND DISCOVERY: AN OVERVIEW Lester A. Mitscher Introduction Historical Perspective What Kinds of Compounds Become Drugs? Preparation and Organization for Drug Seeking Sources of Hits, Leads, and Candidate Drugs Lead Optimization Cell Biology and Genomics as a source of Drug Targets Future Developments ROLE OF MOLECULAR RECOGNITION IN DRUG DESIGN Peter Andrews and Michael Dooley Introduction Thermodynamic Considerations of Drug Binding The Physical Basis of Intermolecular interaction The Total Energy of Intermolecular interaction Estimating Individual Group Components in Ligand-Receptor Interactions and Co- Operativity Some Rules of Thumb STEREOCHEMISTRY IN DRUG DESIGN Ian J. Kennedy and David E. Jane Introduction What Are Stereoisomers? The Origin of Stereospecificity in Molecular Recognition Why is Stereochemistry Important in Drug Design? Methods of Obtaining Pure Stereoisomers Analytical Methods of Determining Purity of Stereoisomers COMPUTER-AIDED DEVELOPMENT OF THREE-DIMENSIONAL PHARMACOPHORE MODELS Tommy Liljefors and Ingrid Petterson Structure-and Pharmacophore-Based Ligand Design The Pharmacophore Concept Basic Principles and a Step-By-Step Procedure Pharmacophore Elements and Their Representations The Receptor-Bound or 'Active' Conformation Molecular Superimposition Receptor-Excluded and Receptor-Essential Volumes Solvation Effects Examples of 3D-Pharmacophore Models and Their Use QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS AND EXPERIMENTAL DESIGN Ulf Norinder and Thomas Hoberg Introduction Hansch Analysis Physico-Chemical Properties Applications of Hansch Equations Pattern Recognition 3D-QSAR Methodologies Experimental Design RECEPTORS: STRUCTURE, FUNCTION AND PHARMACOLOGY Hans Brauner-Osborne Introduction Receptor Structure and Function Receptor Pharmacology

Structures of a diverse set of colchicine binding site inhibitors in complex with tubulin provide a rationale for drug discovery.

Abstract: Microtubules are dynamic assemblies of αβ-tubulin heterodimers and have been recognized as highly attractive targets for cancer chemotherapy. A broad range of agents bind to tubulin and interfere with microtubule assembly. Despite having a long history of characterization, colchicine binding site inhibitors (CBSIs) have not yet reached the commercial phase as anti-cancer drugs to date. We determined the structures of tubulin complexed with a set of structurally diverse CBSIs (lexibulin, nocodazole, plinabulin and tivantinib), among which nocodazole and tivantinib are both binary-function inhibitors targeting cancer-related kinases and microtubules simultaneously. High resolution structures revealed the detailed interactions between these ligands and tubulin. Our results showed that the binding modes of the CBSIs were different from previous docking models, highlighting the importance of crystal structure information in structure-based drug design. A real structure-based pharmacophore was proposed to rationalize key common interactions of the CBSIs at the colchicine domain. Our studies provide a solid structural basis for developing new anti-cancer agents for the colchicine binding site. Database The atomic coordinates and structure factors for tubulin complexed with lexibulin, nocodazole, plinabulin and tivantinib have been deposited in the Protein Data Bank under accession codes 5CA0, 5CA1, 5C8Y and 5CB4, respectively.

Recent Developments on 1,2,4-Triazole Nucleus in Anticancer Compounds: A Review

Abstract: 1,2,4-triazole is an important nucleus present in a large number of compounds. More than thirty-five compounds containing this nucleus are introduced into the market. 1,2,4-triazole nucleus is stable to metabolism and acts as an important pharmacophore by interacting at the active site of a receptor as hydrogen bond acceptor and as a donor. Due to its polar nature, the triazole nucleus can increase the solubility of the ligand and it can significantly improve the pharmacological profile of the drug. A large number of 1,2,4-triazole derivatives are reported to possess a wide range of bioactivities including anti-cancer activity. This review article describes the role of 1,2,4-triazole nucleus in different types of anti-cancer agents such as nucleoside based anti-cancer agents, kinase inhibitors, tubulin modulators, aromatase and steroid sulfatase inhibitors, methionine aminopeptidase inhibitors, tankyrase inhibitors and metal complex based anti-cancer agents. It is expected that the current review article will provide insight into various ligand-receptor interactions and help in the rational design and development of novel 1,2,4-triazole based anti-cancer drugs with improved selectivity for cancer cells.

Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs.

Abstract: PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand–target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand–pharmacophore fit score...

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

Abstract: Blockade of human ether-a-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

Structure-guided development of heterodimer-selective GPCR ligands

Abstract: Crystal structures of G protein-coupled receptor (GPCR) ligand complexes allow a rational design of novel molecular probes and drugs. Here we report the structure-guided design, chemical synthesis and biological investigations of bivalent ligands for dopamine D2 receptor/neurotensin NTS1 receptor (D2R/NTS1R) heterodimers. The compounds of types 1-3 consist of three different D2R pharmacophores bound to an affinity-generating lipophilic appendage, a polyethylene glycol-based linker and the NTS1R agonist NT(8-13). The bivalent ligands show binding affinity in the picomolar range for cells coexpressing both GPCRs and unprecedented selectivity (up to three orders of magnitude), compared with cells that only express D2Rs. A functional switch is observed for the bivalent ligands 3b,c inhibiting cAMP formation in cells singly expressing D2Rs but stimulating cAMP accumulation in D2R/NTS1R-coexpressing cells. Moreover, the newly synthesized bivalent ligands show a strong, predominantly NTS1R-mediated β-arrestin-2 recruitment at the D2R/NTS1R-coexpressing cells.

Structure-Based Inhibitor Design for Evaluation of a Cyp3A4 Pharmacophore Model.

Abstract: Human cytochrome P450 3A4 (CYP3A4) is a key xenobiotic-metabolizing enzyme that oxidizes and clears the majority of drugs. CYP3A4 inhibition may lead to drug-drug interactions, toxicity, and other adverse effects but, in some cases, could be beneficial and enhance therapeutic efficiency of coadministered pharmaceuticals that are metabolized by CYP3A4. On the basis of our investigations of analogs of ritonavir, a potent CYP3A4 inactivator and pharmacoenhancer, we have built a pharmacophore model for a CYP3A4-specific inhibitor. This study is the first attempt to test this model using a set of rationally designed compounds. The functional and structural data presented here agree well with the proposed pharmacophore. In particular, we confirmed the importance of a flexible backbone, the H-bond donor/acceptor moiety, and aromaticity of the side group analogous to Phe-2 of ritonavir and demonstrated the leading role of hydrophobic interactions at the sites adjacent to the heme and phenylalanine cluster in the ligand binding process. The X-ray structures of CYP3A4 bound to the rationally designed inhibitors provide deeper insights into the mechanism of the CYP3A4-ligand interaction. Most importantly, two of our compounds (15a and 15b) that are less complex than ritonavir have comparable submicromolar affinity and inhibitory potency for CYP3A4 and, thus, could serve as templates for synthesis of second generation inhibitors for further evaluation and optimization of the pharmacophore model.

Elucidation of the Teixobactin Pharmacophore

Abstract: This paper elucidates the teixobactin pharmacophore by comparing the arginine analogue of teixobactin Arg10-teixobactin to seven homologues with varying structure and stereochemistry. The roles of the guanidinium group at position 10, the stereochemistry of the macrolactone ring, and the “tail” comprising residues 1–5 are investigated. The guanidinium group is not necessary for activity; Lys10-teixobactin is more active than Arg10-teixobactin against Gram-positive bacteria in minimum inhibitory concentration (MIC) assays. The relative stereochemistry of the macrolactone ring is important. Diastereomer l-Thr8,Arg10-teixobactin is inactive, and diastereomer d-allo-Ile11,Arg10-teixobactin is less active. The macrolactone ring is critical; seco-Arg10-teixobactin is inactive. The absolute stereochemistry is not important; the enantiomer ent-Arg10-teixobactin is comparable in activity. The hydrophobic N-terminal tail is important. Truncation of residues 1–5 results in loss of activity, and replacement of residu...

Medicinal Attributes of Imidazo[1,2-a]pyridine Derivatives: An Update

Abstract: The imidazo[1,2-a]pyridine scaffold is recognized as a privileged structure as it represents a promising area for identification of lead structures towards the discovery of new synthetic drug molecules. Several commercial drugs such as Zolpidem, Olprinone, Soraprazan and many other compounds in biological testing and preclinical evaluation, illustrate the wide therapeutic spectrum in this class of drug scaffolds. The present manuscript represents the assimilation of literature pertaining to medicinal aspects of this pharmacophore including the structure-activity relationships.

A Novel Binding Mode Reveals Two Distinct Classes of NMDA Receptor GluN2B-selective Antagonists.

Abstract: N-methyl-d-aspartate receptors (NMDARs) are glutamate-gated ion channels that play key roles in brain physiology and pathology. Because numerous pathologic conditions involve NMDAR overactivation, subunit-selective antagonists hold strong therapeutic potential, although clinical successes remain limited. Among the most promising NMDAR-targeting drugs are allosteric inhibitors of GluN2B-containing receptors. Since the discovery of ifenprodil, a range of GluN2B-selective compounds with strikingly different structural motifs have been identified. This molecular diversity raises the possibility of distinct binding sites, although supporting data are lacking. Using X-ray crystallography, we show that EVT-101, a GluN2B antagonist structurally unrelated to the classic phenylethanolamine pharmacophore, binds at the same GluN1/GluN2B dimer interface as ifenprodil but adopts a remarkably different binding mode involving a distinct subcavity and receptor interactions. Mutagenesis experiments demonstrate that this novel binding site is physiologically relevant. Moreover, in silico docking unveils that GluN2B-selective antagonists broadly divide into two distinct classes according to binding pose. These data widen the allosteric and pharmacological landscape of NMDARs and offer a renewed structural framework for designing next-generation GluN2B antagonists with therapeutic value for brain disorders.

Discovery of Potent Succinate-Ubiquinone Oxidoreductase Inhibitors via Pharmacophore-linked Fragment Virtual Screening Approach

Abstract: Succinate-ubiquinone oxidoreductase (SQR) is an attractive target for fungicide discovery. Herein, we report the discovery of novel SQR inhibitors using a pharmacophore-linked fragment virtual screening approach, a new drug design method developed in our laboratory. Among newly designed compounds, compound 9s was identified as the most potent inhibitor with a Ki value of 34 nM against porcine SQR, displaying approximately 10-fold higher potency than that of the commercial control penthiopyrad. Further inhibitory kinetics studies revealed that compound 9s is a noncompetitive inhibitor with respect to the substrate cytochrome c and DCIP. Interestingly, compounds 8a, 9h, 9j, and 9k exhibited good in vivo preventive effects against Rhizoctonia solani. The results obtained from molecular modeling showed that the orientation of the R2 group had a significant effect on binding with the protein.

μ Opioid receptor: novel antagonists and structural modeling.

Abstract: The μ opioid receptor (MOR) is a prominent member of the G protein-coupled receptor family and the molecular target of morphine and other opioid drugs. Despite the long tradition of MOR-targeting drugs, still little is known about the ligand-receptor interactions and structure-function relationships underlying the distinct biological effects upon receptor activation or inhibition. With the resolved crystal structure of the β-funaltrexamine-MOR complex, we aimed at the discovery of novel agonists and antagonists using virtual screening tools, i.e. docking, pharmacophore- and shape-based modeling. We suggest important molecular interactions, which active molecules share and distinguish agonists and antagonists. These results allowed for the generation of theoretically validated in silico workflows that were employed for prospective virtual screening. Out of 18 virtual hits evaluated in in vitro pharmacological assays, three displayed antagonist activity and the most active compound significantly inhibited morphine-induced antinociception. The new identified chemotypes hold promise for further development into neurochemical tools for studying the MOR or as potential therapeutic lead candidates.

Molecular Properties of Drugs Interacting with SLC22 Transporters OAT1, OAT3, OCT1, and OCT2: A Machine-Learning Approach

Abstract: Statistical analysis was performed on physicochemical descriptors of ∼250 drugs known to interact with one or more SLC22 "drug" transporters (i.e., SLC22A6 or OAT1, SLC22A8 or OAT3, SLC22A1 or OCT1, and SLC22A2 or OCT2), followed by application of machine-learning methods and wet laboratory testing of novel predictions. In addition to molecular charge, organic anion transporters (OATs) were found to prefer interacting with planar structures, whereas organic cation transporters (OCTs) interact with more three-dimensional structures (i.e., greater SP3 character). Moreover, compared with OAT1 ligands, OAT3 ligands possess more acyclic tetravalent bonds and have a more zwitterionic/cationic character. In contrast, OCT1 and OCT2 ligands were not clearly distinquishable form one another by the methods employed. Multiple pharmacophore models were generated on the basis of the drugs and, consistent with the machine-learning analyses, one unique pharmacophore created from ligands of OAT3 possessed cationic properties similar to OCT ligands; this was confirmed by quantitative atomic property field analysis. Virtual screening with this pharmacophore, followed by transport assays, identified several cationic drugs that selectively interact with OAT3 but not OAT1. Although the present analysis may be somewhat limited by the need to rely largely on inhibition data for modeling, wet laboratory/in vitro transport studies, as well as analysis of drug/metabolite handling in Oat and Oct knockout animals, support the general validity of the approach-which can also be applied to other SLC and ATP binding cassette drug transporters. This may make it possible to predict the molecular properties of a drug or metabolite necessary for interaction with the transporter(s), thereby enabling better prediction of drug-drug interactions and drug-metabolite interactions. Furthermore, understanding the overlapping specificities of OATs and OCTs in the context of dynamic transporter tissue expression patterns should help predict net flux in a particular tissue of anionic, cationic, and zwitterionic molecules in normal and pathophysiological states.

Structural Insights into the Pharmacophore of Vinca Domain Inhibitors of Microtubules

Abstract: Antibody-drug conjugates (ADCs) have achieved great success in cancer therapy in recent years. Some peptidyl microtubule inhibitors consisting of natural and unnatural amino acids, such as monomethyl auristatin E (MMAE) and F (MMAF), are extremely cytotoxic and have been used as a payload in ADCs. However, their precise molecular interaction with tubulin and microtubules remains unclear. We determined the crystal structures of tubulin in complex with three ultra-potent peptidyl microtubule inhibitors [MMAE, taltobulin (HTI- 286), and tubulysin M] at 2.5 A. Our data showed that the three peptides bound to the vinca domain and shared a common and key pharmacophore containing two consecutive hydrophobic groups (Val, Ile-like side chain). These groups protruded in opposite directions into hydrophobic pockets on the tubulin β and α subunits. Nitrogen and oxygen atoms from the same backbone formed hydrogen bonds with Asn329 from the α subunit and Asp179 from the β subunit in a direction normal to the surface formed by the aforementioned hydrophobic groups. In addition, our crystal structure data indicated that tubulysin M bound to the β subunit alone, providing a structural explanation for its higher affinity. We also compared the conformations of two representative structurally different vinca domain compounds, ustiloxin D and vinblastine, with those of the aforementioned peptidyl ligands, and found that they shared a similar pharmacophore. Our findings lay a foundation for the rational design of novel vinca domain ligands and may facilitate the development of microtubule inhibitors with high specificity, affinity, and efficiency as payloads for ADCs in cancer therapy.

Discovery of a Selective Aurora A Kinase Inhibitor by Virtual Screening

Abstract: Here we report the discovery of a selective inhibitor of Aurora A, a key regulator of cell division and potential anticancer target. We used the atom category extended ligand overlap score (xLOS), a 3D ligand-based virtual screening method recently developed in our group, to select 437 shape and pharmacophore analogs of reference kinase inhibitors. Biochemical screening uncovered two inhibitor series with scaffolds unprecedented among kinase inhibitors. One of them was successfully optimized by structure-based design to a potent Aurora A inhibitor (IC50 = 2 nM) with very high kinome selectivity for Aurora kinases. This inhibitor locks Aurora A in an inactive conformation and disrupts binding to its activator protein TPX2, which impairs Aurora A localization at the mitotic spindle and induces cell division defects. This phenotype can be rescued by inhibitor-resistant Aurora A mutants. The inhibitor furthermore does not induce Aurora B specific effects in cells.

Identification of New Human Malaria Parasite Plasmodium falciparum Dihydroorotate Dehydrogenase Inhibitors by Pharmacophore and Structure-Based Virtual Screening

Abstract: Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH), a key enzyme in the de novo pyrimidine biosynthesis pathway, which the Plasmodium falciparum relies on exclusively for survival, has emerged as a promising target for antimalarial drugs. In an effort to discover new and potent PfDHODH inhibitors, 3D-QSAR pharmacophore models were developed based on the structures of known PfDHODH inhibitors and the validated Hypo1 model was used as a 3D search query for virtual screening of the National Cancer Institute database. The virtual hit compounds were further filtered based on molecular docking and Molecular Mechanics/Generalized Born Surface Area binding energy calculations. The combination of the pharmacophore and structure-based virtual screening resulted in the identification of nine new compounds that showed >25% inhibition of PfDHODH at a concentration of 10 μM, three of which exhibited IC50 values in the range of 0.38–20 μM. The most active compound, NSC336047, displayed species-selectivity for ...

Discovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and Docking.

Abstract: The NS5B polymerase is one of the most attractive targets for developing new drugs to block Hepatitis C virus (HCV) infection. We describe the discovery of novel potent HCV NS5B polymerase inhibitors by employing a virtual screening (VS) approach, which is based on random forest (RB-VS), e-pharmacophore (PB-VS), and docking (DB-VS) methods. In the RB-VS stage, after feature selection, a model with 16 descriptors was used. In the PB-VS stage, six energy-based pharmacophore (e-pharmacophore) models from different crystal structures of the NS5B polymerase with ligands binding at the palm I, thumb I and thumb II regions were used. In the DB-VS stage, the Glide SP and XP docking protocols with default parameters were employed. In the virtual screening approach, the RB-VS, PB-VS and DB-VS methods were applied in increasing order of complexity to screen the InterBioScreen database. From the final hits, we selected 5 compounds for further anti-HCV activity and cellular cytotoxicity assay. All 5 compounds were found to inhibit NS5B polymerase with IC50 values of 2.01–23.84 μM and displayed anti-HCV activities with EC50 values ranging from 1.61 to 21.88 μM, and all compounds displayed no cellular cytotoxicity (CC50 > 100 μM) except compound N2, which displayed weak cytotoxicity with a CC50 value of 51.3 μM. The hit compound N2 had the best antiviral activity against HCV, with a selective index of 32.1. The 5 hit compounds with new scaffolds could potentially serve as NS5B polymerase inhibitors through further optimization and development.

Inhibitor design against JNK1 through e-pharmacophore modeling docking and molecular dynamics simulations.

Abstract: c-Jun-NH2 terminal kinases (JNKs) come under a class of serine/threonine protein kinases and are encoded by three genes, namely JNK1, JNK2 and JNK3. Human JNK1 is a cytosolic kinase belonging to mitogen-activated protein kinase (MAPK) family, which plays a major role in intracrinal signal transduction cascade mechanism. Overexpressed human JNK1, a key kinase interacts with other kinases involved in the etiology of many cancers, such as skin cancer, liver cancer, breast cancer, brain tumors, leukemia, multiple myeloma and lymphoma. Thus, to unveil a novel human JNK1 antagonist, receptor-based pharmacophore modeling was performed with the available eighteen cocrystal structures of JNK1 in the protein data bank. Eighteen e-pharmacophores were generated from the 18 cocrystal structures. Four common e-pharmacophores were developed from the 18 e-pharmacophores, which were used as three-dimensional (3D) query for shape-based similarity screening against more than one million small molecules to generate a...

Fragment-Based Approaches to the Development of Mycobacterium tuberculosis CYP121 Inhibitors

Abstract: The essential enzyme CYP121 is a target for drug development against antibiotic resistant strains of Mycobacterium tuberculosis. A triazol-1-yl phenol fragment 1 was identified to bind to CYP121 using a cascade of biophysical assays. Synthetic merging and optimization of 1 produced a 100-fold improvement in binding affinity, yielding lead compound 2 (KD = 15 μM). Deconstruction of 2 into its component retrofragments allowed the group efficiency of structural motifs to be assessed, the identification of more LE scaffolds for optimization and highlighted binding affinity hotspots. Structure-guided addition of a metal-binding pharmacophore onto LE retrofragment scaffolds produced low nanomolar (KD = 15 nM) CYP121 ligands. Elaboration of these compounds to target binding hotspots in the distal active site afforded compounds with excellent selectivity against human drug-metabolizing P450s. Analysis of the factors governing ligand potency and selectivity using X-ray crystallography, UV–vis spectroscopy, and nat...

Molecular docking based virtual screening of natural compounds as potential BACE1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis.

Abstract: Beta-site APP cleaving enzyme1 (BACE1) catalyzes the rate determining step in the generation of Aβ peptide and is widely considered as a potential therapeutic drug target for Alzheimer’s disease (AD). Active site of BACE1 contains catalytic aspartic (Asp) dyad and flap. Asp dyad cleaves the substrate amyloid precursor protein with the help of flap. Currently, there are no marketed drugs available against BACE1 and existing inhibitors are mostly pseudopeptide or synthetic derivatives. There is a need to search for a potent inhibitor with natural scaffold interacting with flap and Asp dyad. This study screens the natural database InterBioScreen, followed by three-dimensional (3D) QSAR pharmacophore modeling, mapping, in silico ADME/T predictions to find the potential BACE1 inhibitors. Further, molecular dynamics of selected inhibitors were performed to observe the dynamic structure of protein after ligand binding. All conformations and the residues of binding region were stable but the flap adopted a closed...

nido‐Dicarbaborate Induces Potent and Selective Inhibition of Cyclooxygenase‐2

Abstract: Carbaboranes are increasingly studied as pharmacophores, particularly as replacements for aromatic systems. However, especially ortho-carbaborane is prone to degradation of the cluster, which hampers biological application. This study demonstrates that deboronation of the cluster may not only lead to a more active analogue, but can also improve the solubility and stability of a carbaborane-containing inhibitor. Notably, introduction of a nido-dicarbaborate cluster into the cyclooxygenase (COX) inhibitor indomethacin results in remarkably increased inhibitory potency and selectivity for COX-2 relative to the respective phenyl analogue. The first crystal structure of a carbaborane-containing inhibitor bound to COX-2 further reveals a novel binding mode for the inhibitor that is strikingly different from that of indomethacin. These results indicate that nido-dicarbaborate is a promising pharmacophore that exhibits properties which are also highly beneficial for its introduction into other inhibitor classes.

Structure‐Based Virtual Screening for Dopamine D2 Receptor Ligands as Potential Antipsychotics

Abstract: Structure-based virtual screening using a D2 receptor homology model was performed to identify dopamine D2 receptor ligands as potential antipsychotics. From screening a library of 6.5 million compounds, 21 were selected and were subjected to experimental validation. From these 21 compounds tested, ten D2 ligands were identified (47.6% success rate, among them D2 receptor antagonists, as expected) that have additional affinity for other receptors tested, in particular 5-HT2A receptors. The affinity (Ki values) of the compounds ranged from 58 nm to about 24 μM. Similarity and fragment analysis indicated a significant degree of structural novelty among the identified compounds. We found one D2 receptor antagonist that did not have a protonatable nitrogen atom, which is a key structural element of the classical D2 pharmacophore model necessary for interaction with the conserved Asp(3.32) residue. This compound exhibited greater than 20-fold binding selectivity for the D2 receptor over the D3 receptor. We provide additional evidence that the amide hydrogen atom of this compound forms a hydrogen bond with Asp(3.32), as determined by tests of its derivatives that cannot maintain this interaction.

Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening.

Abstract: There is an increasing interest in the development of soluble epoxide hydrolase (sEH) inhibitors, which block the degradation of endogenous anti-inflammatory epoxyeicosatrienoic acids. Within this study, a set of pharmacophore models for sEH inhibitors was developed. The Specs database was virtually screened and a cell-free sEH activity assay was used for the biological investigation of virtual hits. In total, out of 48 tested compounds, 19 were sEH inhibitors with IC50 < 10 μM, representing a prospective true positive hit rate of 40%. Six of these compounds displayed IC50 values in the low nanomolar range. The most potent compound 21, a urea derivative, inhibited sEH with an IC50 = 4.2 nM. The applied approach also enabled the identification of diverse chemical scaffolds, e.g. the pyrimidinone derivative 29 (IC50 = 277 nM). The generated pharmacophore model set therefore represents a valuable tool for the selection of compounds for biological testing.