A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin

Vucicevic, Jelica; Srdiċ-Rajiċ, Tatjana; Pieroni, Marco; Laurila, Jonne M.; Perović, Vladimir; Tassini, Sabrina; Azzali, Elisa; Costantino, Gabriele; Glišić, Sanja; Agbaba, Danica; Scheinin, Mika; Nikolic, Katarina; Radi, Marco; Veljković, Nevena

doi:10.1016/j.bmc.2016.05.043

Cited by 19 publications

(19 citation statements)

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“…Experimental design and statistical analysis. The number of animals to be used in each experiment were predetermined based on analyses of similar experiments in the literature and supplemented as needed based on observed effect sizes (Savchenko and Boughter, 2011;Silberman et al, 2013;Flavin et al, 2014;Ghamari-Langroudi et al, 2015;Kupferschmidt et al, 2015Kupferschmidt et al, , 2017Holleran et al, 2016). For experiments involving male and female mice, a minimum of three mice of both sexes was used to allow for sex difference statistical comparisons to be performed.…”

Section: Methodsmentioning

confidence: 99%

“…Anxiety disorder prevalence is as high as 22.8% (Kessler et al, 2005) and treatments are only partially effective with significant adverse effects (Griebel and Holmes, 2013). Further, anxiety and negative affect can occur during withdrawal and abstinence from drugs of abuse and alcohol (Sinha et al, 1999Kassel et al, 2003;Heilig et al, 2010;Blaine et al, 2016). Therefore, anxiolytics may also be a viable treatment modality for addiction Bruijnzeel, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Agonists at the ␣ 2 -adrenergic receptor (␣ 2 -AR) inhibit norepinephrine signaling to induce anxiolysis and decrease the stress response, among other actions (Buffalari et al, 2012;Ji et al, 2014;Strawn et al, 2017). For example, the ␣ 2A -AR agonist guanfacine elicits antidepressant-like effects in the forced swim test (Mineur et al, 2015) and ␣ 2A -AR KO mice show elevated baseline anxiety-and depressive-like behaviors (Schramm et al, 2001;Lähdesmäki et al, 2002). Further, in rodent models of addiction, ␣ 2 -AR agonists block stress-induced reinstatement of drugseeking behaviors (Erb et al, 2000;Mantsch et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Direct administration of ␣ 2 -AR agonists to the bed nucleus of the stria terminalis (BNST) inhibits both stress-induced anxietylike behavior (Schweimer et al, 2005) and drug-seeking behavior (Delfs et al, 2000;Wang et al, 2001). As part of the extended amygdala, the BNST functions in stress-reward integration and has been implicated in both anxiety disorders (Adhikari, 2014) and reinstatement of drug-seeking behaviors (Koob, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…However, stimulatory effects on neuronal activity have been reported for this and other G i -GPCRs (Federman et al, 1992;Andrade, 1993;Winder and Conn, 1993). Guanfacine upregulates expression of the immediate early gene cfos, an indirect marker of neuronal activity, in the BNST and other regions (Savchenko and Boughter, 2011). In the prefrontal cortex, ␣ 2A -AR activation has been shown to decrease the open probability of hyperpolarization-activated and cyclic nucleotide-gated cation (HCN) channels localized to the dendritic neck separating the dendritic spine from the dendrite and the soma to increase fidelity of synaptic current transmission and pyramidal neuron network activity .…”

Section: Introductionmentioning

confidence: 99%

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Dorsal BNST α_2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

et al. 2018

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Stress is a precipitating agent in neuropsychiatric disease and initiates relapse to drug-seeking behavior in addicted patients. Targeting the stress system in protracted abstinence from drugs of abuse with anxiolytics may be an effective treatment modality for substance use disorders. α-adrenergic receptors (α-ARs) in extended amygdala structures play key roles in dampening stress responses. Contrary to early thinking, α-ARs are expressed at non-noradrenergic sites in the brain. These non-noradrenergic α-ARs play important roles in stress responses, but their cellular mechanisms of action are unclear. In humans, the α-AR agonist guanfacine reduces overall craving and uncouples craving from stress, yet minimally affects relapse, potentially due to competing actions in the brain. Here, we show that heteroceptor α-ARs postsynaptically enhance dorsal bed nucleus of the stria terminalis (dBNST) neuronal activity in mice of both sexes. This effect is mediated by hyperpolarization-activated cyclic nucleotide-gated cation channels because inhibition of these channels is necessary and sufficient for excitatory actions. Finally, this excitatory action is mimicked by clozapine--oxide activation of the G-coupled DREADD hM4Di in dBNST neurons and its activation elicits anxiety-like behavior in the elevated plus maze. Together, these data provide a framework for elucidating cell-specific actions of GPCR signaling and provide a potential mechanism whereby competing anxiogenic and anxiolytic actions of guanfacine may affect its clinical utility in the treatment of addiction. Stress affects the development of neuropsychiatric disorders including anxiety and addiction. Guanfacine is an α2A-adrenergic receptor (α2A-AR) agonist with actions in the bed nucleus of the stria terminalis (BNST) that produces antidepressant actions and uncouples stress from reward-related behaviors. Here, we show that guanfacine increases dorsal BNST neuronal activity through actions at postsynaptic α2A-ARs via a mechanism that involves hyperpolarization-activated cyclic nucleotide gated cation channels. This action is mimicked by activation of the designer receptor hM4Di expressed in the BNST, which also induces anxiety-like behaviors. Together, these data suggest that postsynaptic α2A-ARs in BNST have excitatory actions on BNST neurons and that these actions can be phenocopied by the so-called "inhibitory" DREADDs, suggesting that care must be taken regarding interpretation of data obtained with these tools.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dorsal BNST α_2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

et al. 2018

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Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma

et al. 2020

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Advances in machine intelligence‐driven virtual screening approaches for big‐data

Kumar,

Acharya

2023

Medicinal Research Reviews

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Virtual screening (VS) is an integral and ever‐evolving domain of drug discovery framework. The VS is traditionally classified into ligand‐based (LB) and structure‐based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as “big‐data” in the public domain demands modern and advanced machine intelligence‐driven VS approaches to screen hit molecules from ultra‐large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big‐data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

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A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin

Cited by 19 publications

References 40 publications

Dorsal BNST α_2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

Dorsal BNST α_2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma

Advances in machine intelligence‐driven virtual screening approaches for big‐data

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A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin

Cited by 19 publications

References 40 publications

Dorsal BNST α2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

Dorsal BNST α2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma

Advances in machine intelligence‐driven virtual screening approaches for big‐data

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Dorsal BNST α_2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors

Dorsal BNST α_2A-Adrenergic Receptors Produce HCN-Dependent Excitatory Actions That Initiate Anxiogenic Behaviors