Glucagon for refractory asthma exacerbation

Cavallari, Jillian M.; Jawaro, Tara; Awad, Nadia I.; Bridgeman, Patrick

doi:10.1016/j.ajem.2016.09.063

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…These connections are supported by the fact that hormonal agents like glucocorticoids and beta adrenergic agonists have been used for decades to relax the airway musculature in patients with reactive airways disease and chronic obstructive pulmonary disease 22 . Interestingly, our analysis identified glucagon, a peptide hormone that increases blood glucose levels, as a candidate for “Respiratory System” repurposing and this use already has clinical support 23,24 . Additionally, drugs classified as “Respiratory System” and “Dermatological” were also observed to be highly associated because of interactions such as the one between ciclesonide and hydrocortisone (8.43 CATNIP score).…”

Section: Resultsmentioning

confidence: 80%

A machine learning and network framework to discover new indications for small molecules

Gilvary

Elkhader

Madhukar

et al. 2019

Preprint

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23Drug repurposing, identifying novel indications for drugs, bypasses common drug 24 development pitfalls to ultimately deliver therapies to patients faster. However, 25 most repurposing discoveries have been led by anecdotal observations (e.g. 26Viagra) or experimental-based repurposing screens, which are costly, time-27 consuming, and imprecise. Recently, more systematic computational 28 approaches have been proposed, however these rely on utilizing the information 29 from the diseases a drug is already approved to treat. This inherently limits the 30 algorithms, making them unusable for investigational molecules. Here, we 31 present a computational approach to drug repurposing, CATNIP, that requires 32 only biological and chemical information of a molecule. CATNIP is trained with 33 2,576 diverse small molecules and uses 16 different drug similarity features, 34 such as structural, target, or pathway based similarity. This model obtains 35 significant predictive power (AUC = 0.841). Using our model, we created a 36repurposing network to identify broad scale repurposing opportunities between 37 drug types. By exploiting this network, we identified literature-supported 38repurposing candidates, such as the use of systemic hormonal preparations for 39

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Section: Resultsmentioning

confidence: 80%

A machine learning and network framework to discover new indications for small molecules

Gilvary

Elkhader

Madhukar

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…These connections are supported by the fact that hormonal agents like glucocorticoids and beta adrenergic agonists have been used for decades to relax the airway musculature in patients with reactive airways disease and chronic obstructive pulmonary disease [21]. Interestingly, our analysis identified glucagon, a peptide hormone that increases blood glucose levels, as a candidate for "Respiratory System" repurposing and this use already has clinical support [22], [23]. Additionally, drugs classified as "Respiratory System" and "Dermatological" were also observed to be highly associated because of interactions such as the one between ciclesonide and hydrocortisone (8.43 CATNIP score).…”

Section: Plos Computational Biologymentioning

confidence: 66%

A machine learning and network framework to discover new indications for small molecules

et al. 2020

View full text Add to dashboard Cite

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.

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“…Glucagon also has an effect on the contraction of the smooth muscles of the respiratory tract and the inflammatory response [ 64 ]. In a study on laboratory animals, it was shown that the intranasal administration of glucagon inhibits airway obstruction and reduces bronchial tree hyperreactivity and bronchoalveolar inflammation [ 65 , 66 ]. In turn, the level of glucagon increases with obesity, and its concentration varies in a wide range depending on the nature of the food consumed [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Blood Adipokines/Cytokines in Young People with Chronic Bronchitis and Abdominal Obesity

et al. 2022

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The pathogenesis of the development of chronic lung diseases assumes the participation of systemic inflammation factors, as well as hormone-like substances produced by adipose tissue. The aim of this study was to evaluate the associations of certain adipokines/cytokines and chronic bronchitis against the background of abdominal obesity in young people. The study included 1415 people aged 25−44. In total, 115 people were selected by the random numbers method, who were divided into two subgroups: those with chronic bronchitis and abdominal obesity and those with chronic bronchitis without abdominal obesity. A control group of patients with comparable gender and age was also selected. In the group of patients with chronic bronchitis, adiponectin, TNFa and GIP levels were 1.4 times higher. The levels of C-peptide, MCP-1 and PP in the group of chronic bronchitis were 1.3 times higher compared to the control. Adipsin, lipocalin-2, IL-6 and resistin were significantly higher in the group with chronic bronchitis. Glucagon, amylin and ghrelin were 2.2, 2.3 and 3.2 times lower, respectively, in the group of patients with chronic bronchitis. Against the background of abdominal obesity, the probability of having chronic bronchitis increased with an increase in the level of lipocalin-2 and GIP and TNFa.

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Glucagon for refractory asthma exacerbation

Cited by 9 publications

References 5 publications

A machine learning and network framework to discover new indications for small molecules

A machine learning and network framework to discover new indications for small molecules

A machine learning and network framework to discover new indications for small molecules

Blood Adipokines/Cytokines in Young People with Chronic Bronchitis and Abdominal Obesity

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