Design and synthesis of new bicyclic diketopiperazines as scaffolds for receptor probes of structurally diverse functionality

Diketopiperazines (DKPs) are a common motif in various biologically active natural products, and hence they may be useful scaffolds for the rational design of receptor probes and therapeutic agents. We constructed a new bicyclic scaffold that combines a DKP bridged with a 10-membered ring. In this way we obtained a three-dimensional molecular skeleton, with several amendable sites that provide a starting point to design a new combinatorial library having diverse substituent groups. Structural variation is based upon the flexibility of alkylation of the nitrogen atoms of the DKP and on the side-chain olefin. We obtained a 10-membered secondary ring through a ring-closure metathesis reaction using the second generation Grubbs catalyst. Rings containing both O-ethers and S-ethers were compared. N-Alkyl or arylalkyl groups were introduced optionally at the two Nalpha-atoms. This is a general scheme that will allow us to test rings of varying sizes, linkages, and stereochemical parameters. The DKP derivatives were tested for activity in astrocytoma cells expressing receptors coupled to phospholipase C. Inhibitory effects were observed for signaling elicited by activation of human nucleotide P2Y receptors but not m3 muscarinic receptors. Compound 20 selectively inhibited calcium mobilization (IC50 value of 486 +/- 16 nM) and phosphoinositide turnover elicited by a selective P2Y1 receptor agonist, but this compound did not compete for binding of a radiolabeled nucleotide-competitive receptor antagonist. Therefore, the new class of DKP derivatives shows utility as pharmacological tools for P2Y receptors.     

3-D QSAR for intrinsic activity of 5-HT1A receptor ligands by the method of comparative molecular field analysis

The affinity of a ligand for a receptor is usually expressed in terms of the dissociation constant (K_i) of the drug-receptor complex, conveniently measured by the inhibition of radioligand binding. However, a ligand can be an antagonist, a partial agonist, or a full agonist, a property largely independent of its receptor affinity. This property can be quantitated as intrinsic activity (1A), which can range from 0 for a full antagonist to 1 for a full agonist. Although quantitative structure–activity relationship (QSAR) methods have been applied to the prediction of receptor affinity with considerable success, the prediction of IA, even qualitatively, has rarely been attempted. Because most traditional QSAR methods are limited to congeneric series, and there are often major structural differences between agonists and antagonists, this lack of success in predicting IA is understandable. To overcome this limitation, we used the method of comparative molecular field analysis (CoMFA), which, unlike traditional Hansch analysis, permits the inclusion of structurally dissimilar compounds in a single QSAR model. A structurally diverse set of 5-hydroxytryptamine_1A (5-HT_1A) receptor ligands, with literature IA data (determined by the inhibition of 5-HT sensitive forskolin-stimulated adenylate cyclase), was used to develop a 3-D QSAR model correlating intrinsic activity with molecular structure properties of 5HT_1A receptor ligands. This CoMFA model had a crossvalidated r² of 0.481, five components and final conventional r² of 0.943. The receptor model suggests that agonist and antagonist ligands can share parts of a common binding site on the receptor, with a primary agonist binding region that is also occupied by antagonists and a secondary binding site accommodating the excess bulk present in the sidechains of many antagonists and partial agonists. The CoMFA steric field graph clearly shows that agonists tend to be “flatter” (more coplanar) than antagonists, consistent with the difference between the 5-HT_1A agonist and antagonist pharmacophores proposed by Hibert and coworkers. The CoMFA electrostatic field graph suggests that, in the region surrounding the essential protonated aliphatic amino group, the positive molecular electrostatic potential may be weaker in antagonists as compared to agonists. Together, the steric and electrostatic maps suggest that in the secondary binding site region increased hydrophobic binding may enhance antagonist activity. These results demonstrate that CoMFA is capable of generating a statistically crossvalidated 3-D QSAR model that can successfully distinguish between agonist and antagonist 5-HT_1A ligands. To the best of our knowledge, this is the first time this or any other QSAR method has been successfully applied to the correlation of structure with IA rather than potency or affinity. The analysis has suggested various structural features associated with agonist and antagonist behaviors of 5-HT_1A ligands and thus should assist in the future design of drugs that act via 5-HT_1A receptors. © 1993 John Wiley & Sons, Inc.     

Identification and characterization of mGlu3 ligands using a high throughput FLIPR assay for detection of agonists, antagonists, and allosteric modulators

When targeting G-protein coupled receptors (GPCRs) in early stage drug discovery, or for novel targets, the type of ligand most likely to produce the desired therapeutic effect may be unknown. Therefore, it can be desirable to identify potential lead compounds from multiple categories: agonists, antagonists, and allosteric modulators. In this study, we developed a triple addition calcium flux assay using FLIPR Tetra to identify multiple ligand classes for the metabotropic glutamate receptor 3 (mGlu3), using a cell line stably co-expressing the human G-protein-coupled mGlu3 receptor, a promiscuous G-protein (G(α16)), and rat Glast, a glutamate transporter. Compounds were added to the cells followed by stimulation with EC(10) and then EC(80) concentration of glutamate, the physiological agonist for mGlu receptors. This format produced a robust assay, facilitating the identification of agonists, positive allosteric modulators and antagonists/negative allosteric modulators. Follow up experiments were conducted to exclude false positives. Using this approach, we screened a library of approximately 800,000 compounds using FLIPR Tetra and identified viable leads for all three ligand classes. Further characterization revealed the selectivity of individual ligands.     

Structure-activity relationships of 2-(benzothiazolylthio)acetamide class of CCR3 selective antagonist

The structure activity relationships of novel selective CCR3 receptor antagonists, 2-(benzothiazolylthio)acetamimde derivatives were described. A lead structure (1a) was discovered from the screening of the focused library that was based on the structure of our dual antagonists for the human CCR1 and CCR3 receptors. Derivatization of 1a including incorporation of substituent(s) into each benzene ring of the benzothiazole and piperidine side chain resulted in the identification of potent and selective compounds (1b, r, s) exhibiting nano-molar binding affinity (IC(50)s: 1.5-3.0 nM) and greater than 800-fold selectivity for the CCR3 receptor over the CCR1 receptor.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global ( E HOMO ) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

Three-dimensional quantitative structure-activity relationship of nucleosides acting at the A3 adenosine receptor: analysis of binding and relative efficacy

The binding affinity and relative maximal efficacy of human A3 adenosine receptor (AR) agonists were each subjected to ligand-based three-dimensional quantitative structure-activity relationship analysis. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) used as training sets a series of 91 structurally diverse adenosine analogues with modifications at the N6 and C2 positions of the adenine ring and at the 3', 4', and 5' positions of the ribose moiety. The CoMFA and CoMSIA models yielded significant cross-validated q2 values of 0.53 (r2 = 0.92) and 0.59 (r2 = 0.92), respectively, and were further validated by an external test set (25 adenosine derivatives), resulting in the best predictive r2 values of 0.84 and 0.70 in each model. Both the CoMFA and the CoMSIA maps for steric or hydrophobic, electrostatic, and hydrogen-bonding interactions well reflected the nature of the putative binding site previously obtained by molecular docking. A conformationally restricted bulky group at the N6 or C2 position of the adenine ring and a hydrophilic and/or H-bonding group at the 5' position were predicted to increase A3AR binding affinity. A small hydrophobic group at N6 promotes receptor activation. A hydrophilic and hydrogen-bonding moiety at the 5' position appears to contribute to the receptor activation process, associated with the conformational change of transmembrane domains 5, 6, and 7. The 3D-CoMFA/CoMSIA model correlates well with previous receptor-docking results, current data of A3AR agonists, and the successful conversion of the A3AR agonist into antagonists by substitution (at N6) or conformational constraint (at 5'-N-methyluronamide).     

Structural basis for engineering of retinoic acid receptor isotype-selective agonists and antagonists.

BACKGROUND: Many synthetic retinoids have been generated that exhibit a distinct pattern of agonist/antagonist activities with the three retinoic acid receptors (RARalpha, RARbeta and RARgamma). Because these retinoids are selective tools with which to dissect the pleiotropic functions of the natural pan-agonist, retinoic acid, and might constitute new therapeutic drugs, we have determined the structural basis of their receptor specificity and compared their activities in animal and yeast cells. RESULTS: There are only three divergent amino acid residues in the ligand binding pockets (LBPs) of RARalpha, RARbeta and RARgamma. We demonstrate here that the ability of monospecific (class I) retinoid agonists and antagonists to bind to and induce or inhibit transactivation by a given isotype is directly linked to the nature of these residues. The agonist/antagonist potential of class II retinoids, which bind to all three RARs but depending on the RAR isotype have the potential to act as agonists or antagonists, was also largely determined by the three divergent LBP residues. These mutational studies were complemented by modelling, on the basis of the three-dimensional structures of the RAR ligand-binding domains, and a comparison of the retinoid agonist/antagonist activities in animal and yeast cells. CONCLUSIONS: Our results reveal the rational basis of RAR isotype selectivity, explain the existence of class I and II retinoids, and provide a structural concept of ligand-mediated antagonism. Interestingly, the agonist/antagonist characteristics of retinoids are not conserved in yeast cells, suggesting that yeast co-regulators interact with RARs in a different way than the animal cell homologues do.     

3D-pharmacophore models for selective A2A and A2B adenosine receptor antagonists

Three-dimensional pharmacophore models were generated for A2A and A2B adenosine receptors (ARs) based on highly selective A2A and A2B antagonists using the Catalyst program. The best pharmacophore model for selective A2A antagonists (Hypo-A2A) was obtained through a careful validation process. Four features contained in Hypo-A2A (one ring aromatic feature (R), one positively ionizable feature (P), one hydrogen bond acceptor lipid feature (L), and one hydrophobic feature (H)) seem to be essential for antagonists in terms of binding activity and A2A AR selectivity. The best pharmacophore model for selective A2B antagonists (Hypo-A2B) was elaborated by modifying the Catalyst common features (HipHop) hypotheses generated from the selective A2B antagonists training set. Hypo-A2B also consists of four features: one ring aromatic feature (R), one hydrophobic aliphatic feature (Z), and two hydrogen bond acceptor lipid features (L). All features play an important role in A2B AR binding affinity and are essential for A2B selectivity. Both A2A and A2B pharmacophore models have been validated toward a wide set of test molecules containing structurally diverse selective antagonists of all AR subtypes. They are capable of identifying correspondingly high potent antagonists and differentiating antagonists between subtypes. The results of our study will act as a valuable tool for retrieving structurally diverse compounds with desired biological activities and designing novel selective adenosine receptor ligands.     

Solid-phase synthesis of a 6-phenylquinolin-2(1H)-one library directed toward nuclear hormone receptors

A library of 6-phenylquinolin-2(1H)-ones with diversity at position 1 and the ortho, meta, and para positions of the pendant phenyl ring has been synthesized using solid-phase parallel synthetic techniques. A key step in the synthesis of the library is a tandem alkylation cleavage in which diversity can be introduced at position 1 simultaneously to the cleavage from the resin. The yields of this step were significantly improved over what has previously been reported by addition of cesium carbonate to scavenge the acid that is formed during the reaction. Furthermore, we have shown that the solid support linkage is tolerant to Suzuki coupling and etherification reaction conditions and that selective cleavage of the linkage can take place in the presence of esters. The resulting 6-phenylquinolin-2(1H)-one library was screened against a panel of nuclear hormone receptors (androgen, estrogen alpha and beta isoforms, glucocorticoid, mineralocorticoid, and progesterone). Certain members of this library display moderate affinity for several of these receptors, and consequently, the 6-phenylquinolin-2(1H)-one core of the library may be considered a privileged structure for nuclear hormone receptors. In contrast, other members of the library display high selectivity for a particular receptor. The highest affinity ligand (9{2,1,1}) possesses an affinity of 330 nM for the androgen receptor, whereas the most selective ligand (9{2,4,1}) displays an affinity of 900 nM for the androgen receptor and a selectivity of 140-fold over the next highest affinity receptor.     

Modelling of ORL1 receptor-ligand interactions

An opioid receptor like (ORL1) receptor is one of a family of G-protein-coupled receptors (GPCR); it represents a new pharmaceutical target with extensive therapeutic potential for the regulation of important biological functions such as nociception, mood disorders, drug abuse, learning or cardiovascular control. Although the crystal structure of the inactive form of the ORL1 receptor has been determined, little is known about its activation. By using X-ray structures of the β2-adrenegic receptor in its inactive (2RH1) and active (3P0G) states as templates, inactive and active homology models of the ORL1 receptor were constructed. Structurally diverse sets of strongly binding antagonists and agonists were docked with both ORL1 receptor forms. The major receptor-ligand interactions responsible for antagonist and agonist binding were identified. Although both sets of ligands, agonists and antagonists, bind to the same region of the receptor, they occupy partially different binding pockets. Agonists bind to the inactive receptor in a slightly different manner than antagonists. This difference is more pronounced in binding to the active ORL1 receptor model and points to the amino acids at the extracellular end of TM6, suggesting that this region is important for receptor-activation.     

Three-dimensional models of glutamate receptors

Structural models of glutamate receptors have been produced as part of a multidisciplinary study of neuronal function--both ligand/receptor interactions and ion transport--at the atomic level. The models have concentrated on the agonist binding and transmembrane domains of ionotropic (i.e. ligand-gated) glutamate receptors (iGluRs), and have aided our understanding of the molecular determinants of (1) ligand binding and (2) channel activity. The model building process involved a combination of homology modelling, distance geometry, molecular mechanics, protein-ligand and protein-protein docking, electrostatic calculations and manual adjustment, in conjunction with restraints from site-directed mutagenesis, ligand binding and electrophysiological studies. The initial models were used to produce hypotheses which were tested experimentally; these models have been subsequently refined as part of an extremely effective multidisciplinary study using an iterative molecular modelling/experimental verification cycle in which restraints derived from experimental studies are used at all stages, and the findings from one round of modelling are used as restraints in the next. By studying a variety of agonists and antagonists, details have been built up of (1) those residues involved in ligand binding and (2) the role of agonist binding (i.e. agonist-induced conformational change) in channel gating. The models also aid our understanding of the conductance properties of the channels.     

Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A<Subscript>2A</Subscript> receptor

We report an investigation designed to explore alternative approaches for ranking of docking poses in the search for antagonists of the adenosine A_2A receptor, an attractive target for structure-based virtual screening. Calculation of 3D similarity of docking poses to crystallographic ligand(s) as well as similarity of receptor–ligand interaction patterns was consistently superior to conventional scoring functions for prioritizing antagonists over decoys. Moreover, the use of crystallographic antagonists and agonists, a core fragment of an antagonist, and a model of an agonist placed into the binding site of an antagonist-bound form of the receptor resulted in a significant early enrichment of antagonists in compound rankings. Taken together, these findings showed that the use of binding modes of agonists and/or antagonists, even if they were only approximate, for similarity assessment of docking poses or comparison of interaction patterns increased the odds of identifying new active compounds over conventional scoring.     

Harnessing the Anti-Nociceptive Potential of NK2 and NK3 Ligands in the Design of New Multifunctional μ/δ-Opioid Agonist–Neurokinin Antagonist Peptidomimetics

Opioid agonists are well-established analgesics, widely prescribed for acute but also chronic pain. However, their efficiency comes with the price of drastically impacting side effects that are inherently linked to their prolonged use. To answer these liabilities, designed multiple ligands (DMLs) offer a promising strategy by co-targeting opioid and non-opioid signaling pathways involved in nociception. Despite being intimately linked to the Substance P (SP)/neurokinin 1 (NK1) system, which is broadly examined for pain treatment, the neurokinin receptors NK2 and NK3 have so far been neglected in such DMLs. Herein, a series of newly designed opioid agonist-NK2 or -NK3 antagonists is reported. A selection of reported peptidic, pseudo-peptidic, and non-peptide neurokinin NK2 and NK3 ligands were covalently linked to the peptidic μ-opioid selective pharmacophore Dmt-DALDA (H-Dmt-d-Arg-Phe-Lys-NH₂) and the dual μ/δ opioid agonist H-Dmt-d-Arg-Aba-βAla-NH₂ (KGOP01). Opioid binding assays unequivocally demonstrated that only hybrids SBL-OPNK-5, SBL-OPNK-7 and SBL-OPNK-9, bearing the KGOP01 scaffold, conserved nanomolar range μ-opioid receptor (MOR) affinity, and slightly reduced affinity for the δ-opioid receptor (DOR). Moreover, NK binding experiments proved that compounds SBL-OPNK-5, SBL-OPNK-7, and SBL-OPNK-9 exhibited (sub)nanomolar binding affinity for NK2 and NK3, opening promising opportunities for the design of next-generation opioid hybrids.     

Structure-based prediction of subtype selectivity of histamine H3 receptor selective antagonists in clinical trials

Histamine receptors (HRs) are excellent drug targets for the treatment of diseases, such as schizophrenia, psychosis, depression, migraine, allergies, asthma, ulcers, and hypertension. Among them, the human H(3) histamine receptor (hH(3)HR) antagonists have been proposed for specific therapeutic applications, including treatment of Alzheimer's disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity. However, many of these drug candidates cause undesired side effects through the cross-reactivity with other histamine receptor subtypes. In order to develop improved selectivity and activity for such treatments, it would be useful to have the three-dimensional structures for all four HRs. We report here the predicted structures of four HR subtypes (H(1), H(2), H(3), and H(4)) using the GEnSeMBLE (GPCR ensemble of structures in membrane bilayer environment) Monte Carlo protocol, sampling ～35 million combinations of helix packings to predict the 10 most stable packings for each of the four subtypes. Then we used these 10 best protein structures with the DarwinDock Monte Carlo protocol to sample ～50?000 × 10(20) poses to predict the optimum ligand-protein structures for various agonists and antagonists. We find that E206(5.46) contributes most in binding H(3) selective agonists (5, 6, 7) in agreement with experimental mutation studies. We also find that conserved E5.46/S5.43 in both of hH(3)HR and hH(4)HR are involved in H(3)/ H(4) subtype selectivity. In addition, we find that M378(6.55) in hH(3)HR provides additional hydrophobic interactions different from hH(4)HR (the corresponding amino acid of T323(6.55) in hH(4)HR) to provide additional subtype bias. From these studies, we developed a pharmacophore model based on our predictions for known hH(3)HR selective antagonists in clinical study [ABT-239 1, GSK-189,254 2, PF-3654746 3, and BF2.649 (tiprolisant) 4] that suggests critical selectivity directing elements are: the basic proton interacting with D114(3.32), the spacer, the aromatic ring substituted with the hydrophilic or lipophilic groups interacting with lipophilic pockets in transmembranes (TMs) 3-5-6 and the aliphatic ring located in TMs 2-3-7. These 3D structures for all four HRs should help guide the rational design of novel drugs for the subtype selective antagonists and agonists with reduced side effects.     

Multifunctional Antibody Agonists Targeting Glucagon‐like Peptide‐1, Glucagon,and Glucose‐Dependent Insulinotropic Polypeptide Receptors

Glucagon‐like peptide‐1 (GLP‐1) receptor (GLP‐1R), glucagon (GCG) receptor (GCGR), and glucose‐dependent insulinotropic polypeptide (GIP, also known as gastric inhibitory polypeptide) receptor (GIPR), are three metabolically related peptide hormone receptors. A novel approach to the generation of multifunctional antibody agonists that activate these receptors has been developed. Native or engineered peptide agonists for GLP‐1R, GCGR, and GIPR were fused to the N‐terminus of the heavy chain or light chain of an antibody, either alone or in pairwise combinations. The fusion proteins have similar in vitro biological activities on the cognate receptors as the corresponding peptides, but circa 100‐fold longer plasma half‐lives. The GLP‐1R mono agonist and GLP‐1R/GCGR dual agonist antibodies both exhibit potent effects on glucose control and body weight reduction in mice, with the dual agonist antibody showing enhanced activity in the latter.     

Multifunctional Antibody Agonists Targeting Glucagon‐like Peptide‐1, Glucagon,and Glucose‐Dependent Insulinotropic Polypeptide Receptors

Glucagon‐like peptide‐1 (GLP‐1) receptor (GLP‐1R), glucagon (GCG) receptor (GCGR), and glucose‐dependent insulinotropic polypeptide (GIP, also known as gastric inhibitory polypeptide) receptor (GIPR), are three metabolically related peptide hormone receptors. A novel approach to the generation of multifunctional antibody agonists that activate these receptors has been developed. Native or engineered peptide agonists for GLP‐1R, GCGR, and GIPR were fused to the N‐terminus of the heavy chain or light chain of an antibody, either alone or in pairwise combinations. The fusion proteins have similar in vitro biological activities on the cognate receptors as the corresponding peptides, but circa 100‐fold longer plasma half‐lives. The GLP‐1R mono agonist and GLP‐1R/GCGR dual agonist antibodies both exhibit potent effects on glucose control and body weight reduction in mice, with the dual agonist antibody showing enhanced activity in the latter.     

WIN55212-2 docking to the CB1 cannabinoid receptor and multiple pathways for conformational induction

Key pharmacophoric elements for the (aminoalkyl)indole (AAI) CB1 cannabinoid receptor agonists are the aminoalkyl moiety, the lipophilic aroyl group, and the heterocyclic indole ring. In the present study, the docking space allowed for (R)-[2,3-dihydro-5-methyl-3-[(4-morpholinyl)methyl]pyrrolo[1,2,3-de]-1,4-benzoxazin-6-yl](1-naphthalenyl)methanone (WIN55212-2; 1) within the CB1 receptor was extensively explored by a docking approach that combines Monte Carlo (MC) and molecular dynamics (MD) simulations. The goals were to understand the key binding interactions of AAIs within the CB1 receptor and to examine the role of the ligand in inducing a receptor conformational change. From the findings of extensive SAR studies on the cannabinoid compounds and correlation between AAI binding affinity data and calculated binding energies, we proposed two alternative binding conformations, aroyl-up1 and aroyl-up2. These denote the directionality of the ligand naphthyl ring within the receptor upward with respect to the extracellular side. A comprehensive structural analysis of 1 demonstrated that the aroyl ring moiety could be important as the steric trigger for inducing CB1 receptor conformational change. Thus, it appears that aromatic-aromatic interactions are important not only for the binding of 1 but also for inducing receptor conformational change. It is possible that differences in the nature of the ligand binding could contribute to ligand-specific conformational changes in the receptor.     

Discovery of 2-aminothiazole-4-carboxamides, a novel class of muscarinic M(3) selective antagonists, through solution-phase parallel synthesis

Synthesis and structure-activity relationship of a new class of muscarinic M(3) selective antagonists were described. In the course of searching for a muscarinic M(3) antagonist with a structure distinct from those of the 2-(4,4-difluorocyclopentyl)-2-phenylacetamide derivatives, we identified a thiazole-4-carboxamide derivative (1) as a lead compound in our in-house chemical collection. Since this compound (1) showed relatively low binding affinity (K(i)=140 nM) for M(3) receptors in the human binding assays, we tried to improve its potency and selectivity for M(3) over M(1) and M(2) receptors by derivatization of 1 through a combinatorial approach. A solution-phase parallel synthesis effectively contributed to the optimization of each segment of 1. Thus, we have identified a cyclooctenylmethyl derivative (3e) and a cyclononenylmethyl derivative (3f) as representative M(3) selective antagonists in this class.