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Neurochemistry international · 2012

Structural insight into receptor-selectivity for lurasidone.

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Ichikawa Osamu, Okazaki Kazuhiko, Nakahira Hiroyuki, Maruyama Megumi, Nagata Ryu, Tokuda Kumiko, Horisawa Tomoko, Yamazaki Kazuto

Аннотация

Lurasidone is a novel antipsychotic agent with high affinity for dopamine D(2), 5-hydroxyltryptamine 5-HT(2A), and 5-HT(7) receptors. Lurasidone has negligible affinity for histamine H(1) and muscarinic M(1) receptors, which are thought to contribute to side effects such as weight gain, sedation, and worsening of cognitive deficits. Our interests focus on why lurasidone has such high selectivity for only a part of these aminergic G-protein coupled receptors (GPCRs) and the different binding profile from ziprasidone, which has the same benzisothiazolylpiperazine moiety as lurasidone. In order to address these issues, we constructed structural models of lurasidone-GPCR complexes by homology modeling of receptors, exhaustive docking of ligand, and molecular dynamics simulation-based refinement of complexes. This computational study gave reliable structural models for D(2), 5-HT(2A), and 5-HT(7), which had overall structural complementarities with a salt bridge anchor at the center of the lurasidone molecule, but not for H(1) and M(1) owing to steric hindrance between the norbornane-2,3-dicarboximide and/or cyclohexane part of lurasidone and both receptors. By comparison with the structural models of olanzapine-GPCRs and ziprasidone-GPCRs constructed using the same computational protocols, it was suggested that the bulkiness of the norbornane-2,3-dicarboximide part and the rigidity and the bulkiness of the cyclohexyl linker gave lurasidone high selectivity for the desired aminergic GPCRs. Finally, this structural insight was validated by a binding experiment of the novel benzisothiazolylpiperazine derivatives. This knowledge on the structural mechanism behind the receptor selectivity should help to design new antipsychotic agents with preferable binding profiles, and the established computational protocols realize virtual screening and structure-based drug design for other central nervous system drugs with desired selectivity for multiple targets.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты