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Karacoline, identified by network pharmacology, reduces degradation of the extracellular matrix in intervertebral disc degeneration via the NF-κB signaling pathway

Karacoline is a compound found in the plant Aconitum kusnezoffii Reichb. Although Aconitum kusnezoffii Reichb is widely used for the treatment of pain, very few studies have been carried out on the use of karacoline due to its potential toxicity. In this study, we selected key matrix metalloproteina...

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Detalles Bibliográficos
Autores principales: Zhou, Xiaoli, Hong, Yingying, Zhan, Yulin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Xi'an Jiaotong University 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037626/
https://www.ncbi.nlm.nih.gov/pubmed/32123596
http://dx.doi.org/10.1016/j.jpha.2019.07.002
Descripción
Sumario:Karacoline is a compound found in the plant Aconitum kusnezoffii Reichb. Although Aconitum kusnezoffii Reichb is widely used for the treatment of pain, very few studies have been carried out on the use of karacoline due to its potential toxicity. In this study, we selected key matrix metalloproteinases (MMPs), collagen II, and aggrecan as targets due to their association with intervertebral disc degeneration (IDD). Using these targets, we then used network pharmacology to predict a series of molecules that might exert therapeutic effects on IDD. Of these molecules, karacoline was predicted to have the best effect. Tumor necrosis factor (TNF)-α is known to promote the degeneration of the extracellular matrix in IDD. We therefore applied different concentrations of karacoline (0, 1.25, or 12.88 μM) along with 100 ng/mL TNF-α to rat nucleus pulposus cells and found that karacoline reduced the expression of MMP-14 in IDD by inhibiting the nuclear factor (NF)-κB pathway, while collagen II and aggrecan expression was increased. This suggested that extracellular matrix degradation was inhibited by karacoline (P < 0.05). Our data therefore reveal a new clinical application of karacoline and provide support for the use of network pharmacology in predicting novel drugs.