Conformation-opioid activity relationships of bicyclic guanidines from 3D similarity analysis

Bis-Cyclic Guanidine Heterocyclic Peptidomimetics as Opioid Ligands with Mixed μ-, κ- and δ-Opioid Receptor Interactions: A Potential Approach to Novel Analgesics

The design and development of analgesics with mixed-opioid receptor interactions has been reported to decrease side effects, minimizing respiratory depression and reinforcing properties to generate safer analgesic therapeutics. We synthesized bis-cyclic guanidine heterocyclic peptidomimetics from reduced tripeptides. In vitro screening with radioligand competition binding assays demonstrated variable affinity for the mu-opioid receptor (MOR), delta-opioid receptor (DOR), and kappa-opioid receptor (KOR) across the series, with compound 1968-22 displaying good affinity for all three receptors. Central intracerebroventricular (i.c.v.) administration of 1968-22 produced dose-dependent, opioid receptor-mediated antinociception in the mouse 55 °C warm-water tail-withdrawal assay, and 1968-22 also produced significant antinociception up to 80 min after oral administration (10 mg/kg, p.o.). Compound 1968-22 was detected in the brain 5 min after intravenous administration and was shown to be stable in the blood for at least 30 min. Central administration of 1968-22 did not produce significant respiratory depression, locomotor effects or conditioned place preference or aversion. The data suggest these bis-cyclic guanidine heterocyclic peptidomimetics with multifunctional opioid receptor activity may hold potential as new analgesics with fewer liabilities of use.

Small Molecule Compounds Identified from Mixture-Based Library Inhibit Binding between Plasmodium falciparum Infected Erythrocytes and Endothelial Receptor ICAM-1

Specific adhesion of P. falciparum parasite-infected erythrocytes (IE) in deep vascular beds can result in severe complications, such as cerebral malaria, placental malaria, respiratory distress, and severe anemia. Cerebral malaria and severe malaria syndromes were associated previously with sequestration of IE to a microvasculature receptor ICAM-1. The screening of Torrey Pines Scaffold Ranking library, which consists of more than 30 million compounds designed around 75 molecular scaffolds, identified small molecules that inhibit cytoadhesion of ICAM-1-binding IE to surface-immobilized receptor at IC₅₀ range down to ~350 nM. With their low cytotoxicity toward erythrocytes and human endothelial cells, these molecules might be suitable for development into potentially effective adjunct anti-adhesion drugs to treat cerebral and/or severe malaria syndromes. Our two-step high-throughput screening approach is specifically designed to work with compound mixtures to make screening and deconvolution to single active compounds fast and efficient.

Ligand/kappa-opioid receptor interactions: insights from the X-ray crystal structure

During the past five years, the three-dimensional structures of 14 different G-protein coupled receptors (GPCRs) have been resolved by X-ray crystallography. The most recently published structures, those of the opioid receptors (ORs), are remarkably important in pain modulation, drug addiction, and mood disorders. These structures, confirmed previously proposed key interactions conferring potency and antagonistic properties, including the well-known interaction with Asp138, conserved in all aminergic GPCRs. In addition, crystallization of the opioid receptors highlighted the potential function of the ECL2 and ICL2 loops. We have previously reported a set of potent and selective kappa opioid receptor peptide agonists, of which ff(D-nle)r-NH₂ is among the most potent and selective ones. These peptides were identified from the deconvolution of a 6,250,000 tetrapeptide combinatorial library. A derivative of this set is currently the subject of a phase 2 clinical trial in the United States. In this work, we describe comparative molecular modeling studies of kappa-OR peptide agonists with the co-crystallized antagonist, JDTic, and also report structure-activity relationships of 23 tetrapeptides. The overall binding and contact interactions are sound and interactions known to favor selectivity and potency were observed. Additional modeling studies will reveal conformational changes that the kappa-OR undergoes upon binding to these peptide agonists.

Molecular scaffold analysis of natural products databases in the public domain

Natural products represent important sources of bioactive compounds in drug discovery efforts. In this work, we compiled five natural products databases available in the public domain and performed a comprehensive chemoinformatic analysis focused on the content and diversity of the scaffolds with an overview of the diversity based on molecular fingerprints. The natural products databases were compared with each other and with a set of molecules obtained from in-house combinatorial libraries, and with a general screening commercial library. It was found that publicly available natural products databases have different scaffold diversity. In contrast to the common concept that larger libraries have the largest scaffold diversity, the largest natural products collection analyzed in this work was not the most diverse. The general screening library showed, overall, the highest scaffold diversity. However, considering the most frequent scaffolds, the general reference library was the least diverse. In general, natural products databases in the public domain showed low molecule overlap. In addition to benzene and acyclic compounds, flavones, coumarins, and flavanones were identified as the most frequent molecular scaffolds across the different natural products collections. The results of this work have direct implications in the computational and experimental screening of natural product databases for drug discovery.

Increased diversity of libraries from libraries: chemoinformatic analysis of bis-diazacyclic libraries

Combinatorial libraries continue to play a key role in drug discovery. To increase structural diversity, several experimental methods have been developed. However, limited efforts have been performed so far to quantify the diversity of the broadly used diversity-oriented synthetic libraries. Herein, we report a comprehensive characterization of 15 bis-diazacyclic combinatorial libraries obtained through libraries from libraries, which is a diversity-oriented synthetic approach. Using MACCS keys, radial and different pharmacophoric fingerprints as well as six molecular properties, it was demonstrated the increased structural and property diversity of the libraries from libraries over the individual libraries. Comparison of the libraries to existing drugs, NCI diversity, and the Molecular Libraries Small Molecule Repository revealed the structural uniqueness of the combinatorial libraries (mean similarity <0.5 for any fingerprint representation). In particular, bis-cyclic thiourea libraries were the most structurally dissimilar to drugs retaining drug-like character in property space. This study represents the first comprehensive quantification of the diversity of libraries from libraries providing a solid quantitative approach to compare and contrast the diversity of diversity-oriented synthetic libraries with existing drugs or any other compound collection.

Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble

Representing the 3D structures of ligands in virtual screenings via multi-conformer ensembles can be computationally intensive, especially for compounds with a large number of rotatable bonds. Thus, reducing the size of multi-conformer databases and the number of query conformers, while simultaneously reproducing the bioactive conformer with good accuracy, is of crucial interest. While clustering and RMSD filtering methods are employed in existing conformer generators, the novelty of this work is the inclusion of a clustering scheme (NMRCLUST) that does not require a user-defined cut-off value. This algorithm simultaneously optimizes the number and the average spread of the clusters. Here we describe and test four inter-dependent approaches for selecting computer-generated conformers, namely: OMEGA, NMRCLUST, RMS filtering and averaged-RMS filtering. The bioactive conformations of 65 selected ligands were extracted from the corresponding protein:ligand complexes from the Protein Data Bank, including eight ligands that adopted dissimilar bound conformations within different receptors. We show that NMRCLUST can be employed to further filter OMEGA-generated conformers while maintaining biological relevance of the ensemble. It was observed that NMRCLUST (containing on average 10 times fewer conformers per compound) performed nearly as well as OMEGA, and both outperformed RMS filtering and averaged-RMS filtering in terms of identifying the bioactive conformations with excellent and good matches (0.5 < RMSD < 1.0 A). Furthermore, we propose thresholds for OMEGA root-mean square filtering depending on the number of rotors in a compound: 0.8, 1.0 and 1.4 for structures with low (1-4), medium (5-9) and high (10-15) numbers of rotatable bonds, respectively. The protocol employed is general and can be applied to reduce the number of conformers in multi-conformer compound collections and alleviate the complexity of downstream data processing in virtual screening experiments.

Endogenous opiates and behavior: 2008

This paper is the 31st consecutive installment of the annual review of research concerning the endogenous opioid system. It summarizes papers published during 2008 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides, opioid receptors, opioid agonists and opioid antagonists. The particular topics that continue to be covered include the molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors related to behavior (Section 2), and the roles of these opioid peptides and receptors in pain and analgesia (Section 3); stress and social status (Section 4); tolerance and dependence (Section 5); learning and memory (Section 6); eating and drinking (Section 7); alcohol and drugs of abuse (Section 8); sexual activity and hormones, pregnancy, development and endocrinology (Section 9); mental illness and mood (Section 10); seizures and neurologic disorders (Section 11); electrical-related activity and neurophysiology (Section 12); general activity and locomotion (Section 13); gastrointestinal, renal and hepatic functions (Section 14); cardiovascular responses (Section 15); respiration and thermoregulation (Section 16); and immunological responses (Section 17).

Identification, structure-activity relationships and molecular modeling of potent triamine and piperazine opioid ligands

Opioid receptors are important targets for pain management. Here, we report the synthesis and biological evaluation of three positional scanning combinatorial libraries, consisting of linear triamines and piperazines. A highly potent (14 nM) and selective (IC(50(mu))/IC(50(kappa))=71; IC(50(delta))/IC(50(kappa))=714) triamine for the kappa-opioid receptor was found. In addition, non-selective mu-kappa binders were obtained, with binding affinities of 54 nM and 22 nM for mu- and kappa-opioid receptors, respectively. Structure-activity relationships of each subset are described. 3D molecular alignments based on shape similarity to internal and external query molecules were carried out. For the combinatorial chemistry dataset studied here a 1.3 similarity cut-off value was observed to be efficient in the rocs-based alignment method. Interactions from the overlays analyzed in the binding sites of homology models of the receptors revealed specific substitution patterns for enhancing binding affinity in the piperazine series. Pharmacophore modeling of the compounds found from the three combinatorial libraries was also performed. The pharmacophore model indicated that the important feature for receptor binding activity with the mu-receptor was the presence of at least one hydrogen bond acceptor and one aromatic hydrophobic group. Whereas for the kappa-receptor two binding modes emerged with one set of compounds employing the hydrogen bond acceptor and aromatic hydrophobic group, and a second set possibly via interactions with the receptor by hydrophobic and ionic salt-bridges.

Chemoinformatic analysis of combinatorial libraries, drugs, natural products, and molecular libraries small molecule repository

A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds, and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity, and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR, and natural products databases. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different than current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries is located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space.

Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs

Activity landscape characterization has been demonstrated to be a valuable tool in lead optimization, virtual screening, and computational modeling of active compounds. In this work, we present a general protocol to explore systematically the activity landscape of a lead series using 11 2D and 3D structural representations. As a test case we employed a set of 48 bicyclic guanidines (BCGs) with kappa-opioid receptor binding affinity, identified in our group. MACCS keys, graph-based three point pharmacophores, circular fingerprints, ROCS shape descriptors, and the TARIS approach, that compares structures based on molecular electrostatic potentials, were employed as orthogonal descriptors. Based on 'activity cliffs' common to a series of descriptors, we introduce the concept of consensus activity cliffs. Results for the current test case suggest that the presence or absence of a methoxybenzyl group may lead to different modes of binding for the active BCGs with the kappa-opioid receptor. The most active compound (IC50 = 37 nM) is involved in a number of consensus cliffs making it a more challenge query for future virtual screening than would be expected from affinity alone. Results also reveal the importance of screening high density combinatorial libraries, especially in the "cliff-rich" regions of activity landscapes. The protocol presented here can be applied to other data sets to develop a consensus model of the activity landscape.