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.

[1]  Clemencia Pinilla,et al.  Strategies for the use of mixture-based synthetic combinatorial libraries: scaffold ranking, direct testing in vivo, and enhanced deconvolution by computational methods. , 2008, Journal of combinatorial chemistry.

[2]  G. Koob,et al.  Precipitation of morphine withdrawal syndrome in rats by administration of mu-, delta- and kappa-selective opioid antagonists , 1992, Neuropharmacology.

[3]  Marvin Johnson,et al.  Concepts and applications of molecular similarity , 1990 .

[4]  David M. Ferguson,et al.  Molecular recognition of opioid receptor ligands , 2006, The AAPS Journal.

[5]  M. Eguchi Recent advances in selective opioid receptor agonists and antagonists , 2004, Medicinal research reviews.

[6]  Clemencia Pinilla,et al.  Advances in the use of synthetic combinatorial chemistry: Mixture-based libraries , 2003, Nature Medicine.

[7]  R. Lahti,et al.  U-50,488: a selective and structurally novel non-Mu (kappa) opioid agonist. , 1983, The Journal of pharmacology and experimental therapeutics.

[8]  R. Houghten,et al.  Selective Ligands for the μ, δ, and κ Opioid Receptors Identified from a Single Mixture Based Tetrapeptide Positional Scanning Combinatorial Library* , 1998, The Journal of Biological Chemistry.

[9]  G. Uhl,et al.  Human kappa opiate receptor second extracellular loop elevates dynorphin's affinity for human mu/kappa chimeras. , 1994, The Journal of biological chemistry.

[10]  C. Mathias,et al.  Differential cardiovascular and respiratory responses to central administration of selective opioid agonists in conscious rabbits: correlation with receptor distribution , 1989, British journal of pharmacology.

[11]  M. Ivanovic,et al.  Steric interactions and the activity of fentanyl analogs at the mu-opioid receptor. , 2006, Bioorganic & medicinal chemistry.

[12]  David M. Ferguson,et al.  A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist , 2006, J. Comput. Aided Mol. Des..

[13]  Clemencia Pinilla,et al.  Conformation-opioid activity relationships of bicyclic guanidines from 3D similarity analysis. , 2008, Bioorganic & medicinal chemistry.

[14]  Jürgen Bajorath,et al.  Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. , 2007, Drug discovery today.

[15]  J. Mccall,et al.  [3H]U-69593 a highly selective ligand for the opioid κ receptor , 1985 .

[16]  S. Muchmore,et al.  The Use of Three‐Dimensional Shape and Electrostatic Similarity Searching in the Identification of a Melanin‐Concentrating Hormone Receptor 1 Antagonist , 2006, Chemical biology & drug design.

[17]  Clemencia Pinilla,et al.  A Similarity‐based Data‐fusion Approach to the Visual Characterization and Comparison of Compound Databases , 2007, Chemical biology & drug design.

[18]  T. Yaksh,et al.  In vivo studies on spinal opiate receptor systems mediating antinociception. II. Pharmacological profiles suggesting a differential association of mu, delta and kappa receptors with visceral chemical and cutaneous thermal stimuli in the rat. , 1984, The Journal of pharmacology and experimental therapeutics.

[19]  J. Jenkins,et al.  A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes. , 2004, Journal of medicinal chemistry.

[20]  H. Mosberg,et al.  Direct dependence studies in rats with agents selective for different types of opioid receptor. , 1988, The Journal of pharmacology and experimental therapeutics.

[21]  José L. Medina-Franco,et al.  Characterization of Activity Landscapes Using 2D and 3D Similarity Methods: Consensus Activity Cliffs , 2009, J. Chem. Inf. Model..

[22]  Irina D. Pogozheva,et al.  Homology modeling of opioid receptor-ligand complexes using experimental constraints , 2005, The AAPS Journal.

[23]  P. Portoghese,et al.  Selective blockage of delta opioid receptors prevents the development of morphine tolerance and dependence in mice. , 1991, The Journal of pharmacology and experimental therapeutics.

[24]  J. Leander A kappa opioid effect: increased urination in the rat. , 1983, The Journal of pharmacology and experimental therapeutics.

[25]  R. Hill,et al.  Pharmacological profile of PD 117302, a selective κ‐opioid agonist , 1987 .

[26]  S. Filipek,et al.  Molecular Dynamics of µ Opioid Receptor Complexes with Agonists and Antagonists , 2008 .

[27]  P. Hawkins,et al.  Comparison of shape-matching and docking as virtual screening tools. , 2007, Journal of medicinal chemistry.

[28]  R A Houghten,et al.  Mixture-based synthetic combinatorial libraries. , 1999, Journal of medicinal chemistry.

[29]  Matthew J Sykes,et al.  Prediction of metabolism by cytochrome P450 2C9: alignment and docking studies of a validated database of substrates. , 2008, Journal of medicinal chemistry.

[30]  Gerhard Klebe,et al.  Ligand-supported homology modeling of g-protein-coupled receptor sites: models sufficient for successful virtual screening. , 2004, Angewandte Chemie.

[31]  J. Simon,et al.  The kappa-opioid receptor: evidence for the different subtypes. , 1993, Life sciences.

[32]  Richard A. Houghten,et al.  Solid-Phase Synthesis of Substituted 2,3-Diketopiperazines from Reduced Polyamides , 2000 .

[33]  Yongping Yu,et al.  Combinatorial chemistry: libraries from libraries, the art of the diversity-oriented transformation of resin-bound peptides and chiral polyamides to low molecular weight acyclic and heterocyclic compounds. , 2004, The Journal of organic chemistry.

[34]  D. Ferguson,et al.  Molecular docking reveals a novel binding site model for fentanyl at the mu-opioid receptor. , 2000, Journal of medicinal chemistry.

[35]  Y. Martin,et al.  Do structurally similar molecules have similar biological activity? , 2002, Journal of medicinal chemistry.

[36]  K. Palczewski,et al.  Crystal Structure of Rhodopsin: A G‐Protein‐Coupled Receptor , 2002, Chembiochem : a European journal of chemical biology.

[37]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[38]  M. E. Lewis,et al.  Anatomy of CNS opioid receptors , 1988, Trends in Neurosciences.