Structure-Based Prediction of Subtype Selectivity of Histamine H3 Receptor Selective Antagonists in Clinical Trials

Publication: Research - peer-reviewJournal article – Annual report year: 2011

  • Author: Kim, Soo-Kyung

    Materials and Process Simulation Center (MC139-74), California Institute of Technology, United States

  • Author: Fristrup, Peter

    Organic Chemistry, Department of Chemistry, Technical University of Denmark, Kemitorvet, byg. 201, 2800, Kgs. Lyngby, Denmark

  • Author: Abrol, Ravinder

    Materials and Process Simulation Center (MC139-74), California Institute of Technology, United States

  • Author: Goddard, William A., III

    Materials and Process Simulation Center (MC139-74), California Institute of Technology, United States

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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 H3 histamine receptor (hH3HR) antagonists have been proposed for specific therapeutic applications, including treatment of Alzheimer’s disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity.(1) 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 (H1, H2, H3, and H4) using the GEnSeMBLE (GPCR ensemble of structures in membrane bilayer environment) Monte Carlo protocol,(2) 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 × 1020 poses to predict the optimum ligand–protein structures for various agonists and antagonists. We find that E2065.46 contributes most in binding H3 selective agonists (5, 6, 7) in agreement with experimental mutation studies. We also find that conserved E5.46/S5.43 in both of hH3HR and hH4HR are involved in H3/ H4 subtype selectivity. In addition, we find that M3786.55 in hH3HR provides additional hydrophobic interactions different from hH4HR (the corresponding amino acid of T3236.55 in hH4HR) to provide additional subtype bias. From these studies, we developed a pharmacophore model based on our predictions for known hH3HR 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 D1143.32, 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.

Original languageEnglish
JournalJournal of Chemical Information and Modeling
Publication date2011
Volume51
Pages3264-3274
ISSN1549-9596
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 10
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