mTOR x MAPK x Akt x PKC

Last update 08 May 2025

Basic Info

Related Targets

mTORMAPKAkt[+1]

Drugs associated with mTOR x MAPK x Akt x PKC

GAP-107B8

Target

Akt x MAPK x PKC x mTOR

Mechanism

Akt inhibitors [+3]

Active Org.

PharmaGap, Inc.

Originator Org.

PharmaGap, Inc.

Active Indication

Ovarian Cancer

Inactive Indication-

Drug Highest PhasePreclinical

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with mTOR x MAPK x Akt x PKC

100 Translational Medicine associated with mTOR x MAPK x Akt x PKC

0 Patents (Medical) associated with mTOR x MAPK x Akt x PKC

104

Literatures (Medical) associated with mTOR x MAPK x Akt x PKC

01 Apr 2025·Journal of Ethnopharmacology

Integrated network pharmacology, proteomics, molecular docking, and experiments in vivo and in vitro to explore the efficacy and potential mechanism of bufalin against hepatocellular carcinoma angiogenesis

Article

Author: Guo, Ran ; Yang, Shuohui ; Li, Yuanchao ; Li, Zehua ; Zhou, Lingwei ; Sun, Kang ; Wen, Qingqing ; Fu, Guifeng

ETHNOPHARMACOLOGICAL RELEVANCEBufalin is a potent bioactive compound extracted from the venom of toads such as Bufo gargarizans. It has rich pharmacological effects, and its traditional applications mainly include anti-cancer, anti-inflammatory and analgesic, especially in cancer treatment, which has been a hot topic of research. Prior research has suggested that bufalin may have anti-tumor angiogenic effects. However, the efficacy and mechanism of bufalin inhibiting hepatocellular carcinoma (HCC) angiogenesis have yet to be further investigated.AIM OF THE STUDYAn extensive detailed strategy via network pharmacology, proteomics, histopathological analysis, molecular docking, in vitro experiments, and in vivo magnetic resonance imaging (MRI) examinations were adopted to investigate the efficacy and mechanisms of bufalin against HCC angiogenesis.MATERIALS AND METHODSMicro-vessel density (MVD) and intravoxel incoherent motion (IVIM) perfusion-related parameters based on magnetic resonance diffusion-weighted imaging were used to identify the effect of bufalin against HCC angiogenesis. Potential bufalin and HCC targets were gathered from appropriate databases. The STRING database was used to construct the target protein interaction networks. The "clusterprofiler" package (version 4.2.2) in R was applied to conduct the target-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) analysis. Network pharmacology and proteomics were integrated to identify key targets and pathways related to bufalin against HCC angiogenesis. Molecular docking and Western Blot were utilized to validate the findings.RESULTSAnalysis through IVIM and MVD showed that bufalin could inhibit HCC angiogenesis in nude mice models. A total of 159 common targets of bufalin and HCC were identified by network pharmacology. GO analysis revealed that these targets focused on multiple angiogenesis-related biological processes, including endothelial cell proliferation and migration, sprouting angiogenesis, and regulation of angiogenesis. The KEGG enrichment results suggested that bufalin could regulate multiple signaling pathways to inhibit HCC angiogenesis, including VEGF, MAPK, PI3K-Akt, mTOR, and HIF-1 signaling pathways. MAPK1, MAPK14, PRKCA, EIF4E, and APEX1 might be critical targets in regulating the above pathways. The molecular docking and Western blot analysis verified the effects of bufalin on target proteins.CONCLUSIONThis study demonstrated that bufalin might inhibit HCC angiogenesis by regulating multiple targets and pathways. These findings offer theoretical insights and experimental foundations for the clinical application and commercial development of bufalin in the treatment of HCC.

01 Jan 2025·IET Systems Biology

Analysis of type 2 diabetes mellitus‐related genes by constructing the pathway‐based weighted network

Article

Author: Zhang, Xue‐Yan ; Xu, Chuan‐Yun ; Luo, Hong ; Zhang, Xu‐Sheng ; Cao, Ke‐Fei

AbstractComplex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway‐based weighted network model of T2DM‐related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power‐law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: MAPK1, PIK3CD, PIK3CA, PIK3R1, AKT2, AKT1, KRAS, TNF, MAPK8, PRKCA, IL6 and MTOR. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.

25 Dec 2024·Sheng li xue bao : [Acta physiologica Sinica]

[Research progress on molecular mechanism related to skeletal muscle atrophy].

Review

Author: Wang, Yong-Ping ; Ke, Yi-Bing ; Guo, Hao-Ran ; Abudoukeremu, Dawuti

The maintenance of skeletal muscle quality involves various signal pathways that interact with each other. Under normal physiological conditions, these intersecting signal pathways regulate and coordinate the hypertrophy and atrophy of skeletal muscles, balancing the protein synthesis and degradation of muscle. When the total rate of protein synthesis exceeds that of protein degradation, the muscle gradually becomes enlarged, while when the total rate of protein synthesis is lower than that of protein degradation, the muscle shrinks. Myocyte atrophy mainly involves two protein degradation pathways, namely ubiquitin-proteasome and autophagy-lysosome. Protein degradation pathway is activated during muscle atrophy, resulting in the loss of muscle mass. Muscle atrophy can occur under various conditions such as malnutrition, aging and cachexia. Skeletal muscle atrophy caused by orthopedic diseases mainly includes disuse muscular atrophy caused by fracture and denervation muscular atrophy. The signal pathways that control and coordinate protein synthesis and degradation in skeletal muscle include insulin-like growth factor 1 (IGF1)-Akt-mammalian target of rapamycin (mTOR), myostatin-activin A-Smad, G protein α inhibitory peptide 2 (Gαi2)-PKC, nuclear factor κB (NF-κB), ectodysplasin A2 receptor (EDA2R)-NF-κB inducing kinase (NIK) and mitogen-activated protein kinase (MAPK) pathways. This paper provides a comprehensive review of the protein degradation pathways in skeletal muscle atrophy and the associated signal pathways regulating protein degradation in muscular atrophy.

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