Computational systems pharmacology reveals an antiplatelet and neuroprotective mechanism of Deng-Zhan-Xi-Xin injection in the treatment of ischemic stroke

2019 ◽  
Vol 147 ◽  
pp. 104365 ◽  
Author(s):  
Jing Zhao ◽  
Chao Lv ◽  
Qiuling Wu ◽  
Huawu Zeng ◽  
Xin Guo ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Systems Pharmacology ◽  
Computational Systems ◽  
Xi Xin

scholarly journals Identification of Key Ischemic Stroke Genes by Computational Systems Biology

2021 ◽  
Author(s):  
Rongting Yue ◽  
Abhishek Dutta
Keyword(s):  
Ischemic Stroke ◽  
Experimental Validation ◽  
Causes Of Death ◽  
Expression Patterns ◽  
Computational Systems Biology ◽  
Protein Protein Interaction ◽  
Gene Modules ◽  
High Connectivity ◽  
Computational Systems ◽  
Weighted Correlation

Stroke is one of the leading causes of death in humans. Even if patients survive from stroke, they may suffer sequelae such as disability. Treatment for strokes remains unsatisfactory due to an incomplete understanding of its mechanisms. This study investigates Ischemic Stroke (IS), a primary subtype of stroke, through analyses based on microarray data. Limma (in R)derives differentially expressed genes, and the protein-protein interaction (PPI) network is mapped from the database. Gene co-expression patterns are obtained for clustering gene modules by the Weighted Correlation Network Analysis (WGCNA), and genes with high connectivity in the significantly co-expressed modules are selected as key regulators. Common hubs are identified as Cdkn1a, Nes and Anxa2. Based on our analyses, we hypothesize that these hubs might play a key role in the onset and progression of IS. Result suggests the potential of identifying unexplored key regulators by the systemic method used in this work. Further analyses aim at expanding candidate genes for screening biomarkers for IS, and experimental validation is required on identified potential hubs.

scholarly journals Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose

2019 ◽  
Vol 10 (8) ◽  
pp. 3486-3499 ◽  
Author(s):  
Jin Cheng ◽  
Siyi Wang ◽  
Weiwei Lin ◽  
Nan Wu ◽  
Yuanqiang Wang ◽  
...  
Keyword(s):  
Systems Pharmacology ◽  
Computational Systems

scholarly journals A quantitative systems pharmacology analysis of KRAS G12C covalent inhibitors

2017 ◽  
Author(s):  
Edward C. Stites ◽  
Andrey S. Shaw
Keyword(s):  
Human Cancer ◽  
Conventional Wisdom ◽  
Ras Signaling ◽  
Systems Pharmacology ◽  
Ras Pathway ◽  
Ras Protein ◽  
Oncogenic Mutation ◽  
Ras Activation ◽  
Covalent Inhibitors ◽  
Computational Systems

AbstractThe KRAS oncogene is the most common, activating, oncogenic mutation in human cancer. KRAS has proven difficult to target effectively. Two different strategies have recently been described for covalently targeting the most common activating KRAS mutant in lung cancer, KRAS G12C. Previously, we have developed a computational model of the processes that regulate Ras activation and this model has proven useful for understanding the complex behaviors of Ras signaling. Here, we use this model to perform a computational systems pharmacology analysis of KRAS G12C targeted covalent inhibitors. After updating our model to include Ras protein turnover, we verified the validity of our model for problems in this domain by comparing model behaviors with experimental behaviors. The model naturally reproduces previous experimental data, including several experimental observations that were interpreted as being contrary to conventional wisdom. Overall, this suggests that our model describes the Ras system well, including those areas where conventional wisdom struggles. We then used the model to investigate possible strategies to improve the ability of KRAS G12C inhibitors to inhibit Ras pathway signaling. We identify one, as of yet unexplored mechanism, that, if optimized, could improve the effectiveness of one class of KRAS inhibitor. We also simulated resistance to targeted therapies and found that resistance promoting mutations may reverse which class of KRAS G12C inhibitor inhibits the system better, suggesting that there may be value to pursuing both types of KRAS G12C inhibitors. Overall, this work demonstrates that systems biology approaches can provide insights that inform the drug development process.

scholarly journals Systems Pharmacology-Based Approach to Comparatively Study the Independent and Synergistic Mechanisms of Danhong Injection and Naoxintong Capsule in Ischemic Stroke Treatment

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Junfeng Zhu ◽  
Xiaojiao Yi ◽  
Yiwen Zhang ◽  
Zongfu Pan ◽  
Like Zhong ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Gap Junction ◽  
Signaling Pathway ◽  
Combined Effect ◽  
Brain Inflammation ◽  
Therapeutic Effects ◽  
Systems Pharmacology ◽  
Chemical Screening ◽  
Vegf Signaling ◽  
Danhong Injection

To provide evidence for the better clinical use of traditional Chinese medicine preparations (TCMPs), comparison of the pharmacological mechanisms between TCMPs with similar therapeutic effect is necessary. However, methodology for dealing with this issue is still scarce. Danhong injection (DHI) and Naoxintong capsule (NXT) are representative TCMPs for ischemic stroke (IS) treatment, which are also frequently used in combination. Here they were employed as research objects to demonstrate the feasibility of systems pharmacology approach in elucidation of the independent and combined effect of TCMPs. By incorporating chemical screening, target prediction, and network construction, a feasible systems pharmacology model has been established to systematically uncover the underlying action mechanisms of DHI, NXT, or their pair in IS treatment. Systematic analysis of the created TCMP-Compound-Target-Disease network revealed that DHI and NXT shared common targets such as PTGS2, F2, ADRB1, IL6, ALDH2, and CCL2, which were involved in the vasomotor system regulation, blood-brain barrier disruption, redox imbalance, neurotrophin activity, and brain inflammation. In comparative mechanism study, the merged DHI/NXT-IS PPI network and pathway enrichment analysis indicated that DHI and NXT exerted the therapeutic effects mainly through immune system and VEGF signaling pathways. Meanwhile, they had their own unique pathways, e.g., calcium signaling pathway for DHI and gap junction for NXT. While for their synergistic mechanism, DHI and NXT participated in chemokine signaling pathway, T cell receptor signaling pathway, VEGF signaling pathway, gap junction, and so on. Our study provided an optimized strategy for dissecting the different and combined effect of TCMPs with similar actions.

scholarly journals Identification of Key Ischemic Stroke Genes by Computational Systems Biology

2021 ◽  
Author(s):  
Rongting Yue ◽  
Abhishek Dutta
Keyword(s):  
Ischemic Stroke ◽  
Experimental Validation ◽  
Causes Of Death ◽  
Expression Patterns ◽  
Computational Systems Biology ◽  
Protein Protein Interaction ◽  
Gene Modules ◽  
High Connectivity ◽  
Computational Systems ◽  
Weighted Correlation

Abstract Stroke is one of the leading causes of death in humans. Even if patients survive from stroke, they may suffer sequelae such as disability. Treatment for strokes remains unsatisfactory due to an incomplete understanding of its mechanisms. This study investigates Ischemic Stroke (IS), a primary subtype of stroke, through analyses based on microarray data. Limma (in R)derives differentially expressed genes, and the protein-protein interaction (PPI) network is mapped from the database. Gene co-expression patterns are obtained for clustering gene modules by the Weighted Correlation Network Analysis (WGCNA), and genes with high connectivity in the significantly co-expressed modules are selected as key regulators. Common hubs are identified as Cdkn1a, Nes and Anxa2. Based on our analyses, we hypothesize that these hubs might play a key role in the onset and progression of IS. Result suggests the potential of identifying unexplored key regulators by the systemic method used in this work. Further analyses aim at expanding candidate genes for screening biomarkers for IS, and experimental validation is required on identified potential hubs.

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