Synthesis and evaluation of C9 alkoxy analogues of (-)-stepholidine as dopamine receptor ligands

Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.

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“…[36] These important discoveries have provided valuable information for the development of D3 selective ligands. [22, 23]…”

Section: Introductionmentioning

confidence: 99%

“…[28] Previous studies have highlighted the challenges of designing specific antagonists against the dopamine D3 receptor. [21, 23, 37]…”

Section: Introductionmentioning

confidence: 99%

Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes

et al. 2019

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“…Olivercona et al [ 54 ] developed a sequence-based generative model for molecular de novo design and have illustrated its application in predicting molecules with specified desirable properties. Another strategy employed by this group includes various types of autoencoder models that rendered new structures that were predicted to be active against dopamine type receptor 2 [ 55 , 56 , 57 , 58 ]. Even pharmaceutical giants such as Bayer Healthcare and Roche have attributed their recent success in developing optimized pharmacophores to computer-assisted drug design technologies [ 59 , 60 ].…”

Section: Artificial Intelligence (Ai) In Drug Design and Molecularmentioning

confidence: 99%

Deep Learning in Drug Discovery and Medicine; Scratching the Surface

et al. 2018

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The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy. Indeed, precision medicine-based therapies that first found their place in oncology are rapidly finding uses in autoimmune, renal and other diseases. More recently a new renaissance that is shaping everyday life is making its way into healthcare. Drug discovery and medicine that started with Ayurveda in India are now benefiting from an altogether different artificial intelligence (AI)—one which is automating the invention of new chemical entities and the mining of large databases in health-privacy-protected vaults. Indeed, disciplines as diverse as language, neurophysiology, chemistry, toxicology, biostatistics, medicine and computing have come together to harness algorithms based on transfer learning and recurrent neural networks to design novel drug candidates, a priori inform on their safety, metabolism and clearance, and engineer their delivery but only on demand, all the while cataloging and comparing omics signatures across traditionally classified diseases to enable basket treatment strategies. This review highlights inroads made and being made in directed-drug design and molecular therapy.

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“…[8] THPBs such as (S)-stepholidine, (S)-isocorypalmine, (S)-govadine, and (S)-tetrahydropalmatine ( Figure 1) have featured in several studies investigating their bioactivities as dopamine receptor ligands. [9][10][11][12][13][14] There are five subtypes of dopamine receptor (D 1 R-D 5 R) and these are grouped into two families: D 1 -like and D 2 -like based on differences in signal transduction mechanisms and structure. The D 1 -like family includes the structurally related D 1 R and D 5 R subtypes, while the D 2 -like family is represented by D 2 R-D 4 R. [15] Structure-activity relationship (SAR) studies to date indicate that THPBs exert a preference for binding to D 1 -like receptors although several compounds also display good affinity for D 2 -like receptors.…”

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confidence: 99%

“…The D 1 -like family includes the structurally related D 1 R and D 5 R subtypes, while the D 2 -like family is represented by D 2 R-D 4 R. [15] Structure-activity relationship (SAR) studies to date indicate that THPBs exert a preference for binding to D 1 -like receptors although several compounds also display good affinity for D 2 -like receptors. [9,11,13,16] Their profound dopamine receptor activities and interesting polypharmacology have led to the examination of THPBs in animal studies relevant to dopamine-related disorders including schizophrenia, Parkinsons disease, and drug abuse. [12,14,[17][18][19][20][21][22][23][24][25] Our lab recently discovered a number of THPBs with strong D 1 R affinity (ranging in K i values from 6 to 40 nM) and strong σ 2 receptor (σ 2 R) affinity (ranging in K i values from 1 to 107 nM).…”

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confidence: 99%

First synthesis of thiazepino[3,4‐a]isoquinolines, a facile new synthetic route to diazepino[3,4‐a]isoquinolines and assessment of their dopamine and σ receptor affinities

Cordone

Krishna

Harding

2020

Journal of Heterocyclic Chem

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Heterocycles that bear the novel 5,6,14,14a-tetrahydro-8H-benzo[6,7][1,4] thiazepino[3,4-a]isoquinoline and the 5,6,14,14a-tetrahydro-8H-13l2-benzo [6,7][1,4]diazepino[3,4-a]isoquinoline frameworks were synthesized in a facile manner. These tetrahydroprotoberberine (THPB)-inspired scaffolds demonstrate selective affinity for the σ 1 R in contrast to the naturally occurring THPB congeners that show D 1 R and σ 2 R selectivity.

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Synthesis and evaluation of C9 alkoxy analogues of (-)-stepholidine as dopamine receptor ligands

Cited by 11 publications

References 44 publications

Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes

Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes

Deep Learning in Drug Discovery and Medicine; Scratching the Surface

First synthesis of thiazepino[3,4‐a]isoquinolines, a facile new synthetic route to diazepino[3,4‐a]isoquinolines and assessment of their dopamine and σ receptor affinities

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