Network pharmacology study of Citrus reticulata and Pinellia ternata in the treatment of non-small cell lung cancer

It has been recognized that Citrus reticulata and Pinellia ternata have a good therapeutic effect on NSCLC. However, the potential mechanism of C. reticulata and P. ternata in the treatment of NSCLC based on network pharmacology analysis is not clear. The “Drug-Component-Target-Disease” network was constructed by Cytoscape, and the protein interaction (PPI) network was constructed by STRING. Our study indicated that 18 active ingredients of C. reticulata and P. Ternata were screened from the TCMSP database, and 56 target genes of C. reticulata and P. Ternata for the treatment of NSCLC were identified, and we constructed the “Drug-Component-Target-Disease” network. In this study, we screened 56 PPI core genes to establish a PPI network. We concluded that the network pharmacology mechanism of the effect of C. reticulata and P. Ternata on NSCLC may be closely related to the protein expressed by TP53, ESR1, FOS, NCOA3 and MAPK8, and these may play the therapeutic roles by regulating the IL-17 signaling pathway, antigen processing and presentation, microRNAs in cancer and endocrine resistance.

Modeling of microRNA-derived disease network repurposes methotrexate for the prevention and therapy of abdominal aortic aneurysm in mice

Abdominal aortic aneurysm (AAA) is a highly lethal vascular disease characterized by permanent dilatation of the abdominal aorta. The main purpose of the current study is to search for noninvasive medical therapies for abdominal aortic aneurysm (AAA), for which there is currently no effective drug therapy. Network medicine represents a cutting-edge technology, as analysis and modeling of disease networks can provide critical clues regarding the etiology of specific diseases and which therapeutics may be effective. Here, we proposed a novel algorithm to quantify disease relations based on a large accumulated microRNA-disease association dataset and then built a disease network that covered 15 disease classes and included 304 diseases. Analysis revealed a number of patterns for these diseases; for example, diseases tended to be clustered and coherent in the network. Surprisingly, we found that AAA showed the strongest similarity with rheumatoid arthritis and systemic lupus erythematosus, both of which are autoimmune diseases, suggesting that AAA could be one type of autoimmune disease in etiology. Based on this observation, we further hypothesized that drugs for autoimmune disease could be repurposed for the prevention and therapy of AAA. Finally, animal experiments confirmed that methotrexate, a drug for autoimmune disease, was able to prevent the formation and inhibit the development of AAA.

Shexiang Tongxin Dropping Pill Protects Against Chronic Heart Failure in Mice via Inhibiting the ERK/MAPK and TGF-β Signaling Pathways

Background: Chronic heart failure (CHF) is a major public health problem with high mortality and morbidity worldwide. Shexiang Tongxin Dropping Pill (STDP) is a widely used traditional Chinese medicine preparation for coronary heart disease and growing evidence proves that STDP exerts beneficial effects on CHF in the clinic. However, the molecular mechanism of the therapeutic effects of STDP on CHF remains largely unknown.Objective: This study aimed to elucidate the mechanism of action of STDP against CHF by integrating network pharmacology analysis and whole-transcriptome sequencing.Methods: First, the mouse model of CHF was established by the transverse aortic constriction (TAC) surgery, and the efficacy of STDP against CHF was evaluated by assessing the alterations in cardiac function, myocardial fibrosis, and cardiomyocyte hypertrophy with echocardiography, Masson’s trichrome staining, and wheat germ agglutinin staining. Next, a CHF disease network was constructed by integrating cardiovascular disease-related genes and the transcriptome sequencing data, which was used to explore the underlying mechanism of action of STDP. Then, the key targets involved in the effects of STDP on CHF were determined by network analysis algorithms, and pathway enrichment analysis was performed to these key genes. Finally, important targets in critical pathway were verified in vivo.Results: STDP administration obviously improved cardiac function, relieved cardiomyocyte hypertrophy, and ameliorated myocardial fibrosis in CHF mice. Moreover, STDP significantly reversed the imbalanced genes that belong to the disease network of CHF in mice with TAC, and the number of genes with the reverse effect was 395. Pathway analysis of the crucial genes with recovery efficiency revealed that pathways related to fibrosis and energy metabolism were highly enriched, while TGF-β pathway and ERK/MAPK pathway were predicted to be significantly affected. Consistently, validation experiments confirmed that inhibiting ERK/MAPK and TGF-β signaling pathways via reduction of the phosphorylation level of Smad3 and ERK1/2 is the important mechanism of STDP against CHF.Conclusion: Our data demonstrated that STDP can recover the imbalanced CHF network disturbed by the modeling of TAC through the multi-target and multi-pathway manner in mice, and the mechanisms are mainly related to inhibition of ERK/MAPK and TGF-β signaling pathways.

Identification of the Active Constituents and Significant Pathways of Shen-qi-Yi-zhu Decoction on Antigastric Cancer: A Network Pharmacology Research and Experimental Validation

Shen-qi-Yi-zhu decoction (SQYZD) is an empirical prescription with antigastric cancer (GC) property created by Xu Jing-fan, a National Chinese Medical Master. However, its underlying mechanisms are still unclear. Based on network pharmacology and experimental verification, this study puts forward a systematic method to clarify the anti-GC mechanism of SQYZD. The active ingredients of SQYZD and their potential targets were acquired from the TCMSP database. The target genes related to GC gathered from GeneCards, DisGeNET, OMIM, TTD, and DrugBank databases were imported to establish protein-protein interaction (PPI) networks in GeneMANIA. Cytoscape was used to establish the drug-ingredients-targets-disease network. The hub target genes collected from the SQYZD and GC were parsed via GO and KEGG analysis. Our findings from network pharmacology were successfully validated using an in vitro HGC27 cell model experiment. In a word, this study proves that the combination of network pharmacology and in vitro experiments is effective in clarifying the potential molecular mechanism of traditional Chinese medicine (TCM).

Therapy Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Is Predictive of Individual Acute Myeloid Leukemia (AML) Patient Probability of Clinical Response (CR) and Overall Survival (OS): Mycare-023

Background: Although some genomic biomarkers have been integrated into therapeutic decision-making for the management of AML, the complete remission and cure rates have significant margin for improvement. Except for a few targeted therapies, genomic assessments offer limited guidance on treatment. Nevertheless, comprehensive molecular profiling of AML discloses a complex and heterogeneous disease network that impacts the efficacy of individual chemotherapeutics differently in individual patients. The Cellworks Computational Omics Biology Model (CBM) was developed using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The Cellworks Biosimulation Platform uses the CBM to model each patient's unique cancer and predict personalized responses to standard AML drugs, identify novel drug combinations for treatment-refractory patients and optimize treatment selection to improve outcomes. Methods: A prospectively designed study involving observational data from 416 de novo AML patients was used to test the hypothesis that biosimulation using the Cellworks Biosimulation Platform predicts clinical response to individual drugs and estimates likelihood of response and survival better than physician prescribed treatment (PPT) alone. Cytogenetic and molecular data obtained from clinical trials including AMLSG 07-04, Beat AML, TCGA and PubMed publications was used to create personalized in silico models of each patient's AML and generate a Singula™ biosimulation report with a Therapy Response Index (TRI) to determine the efficacy of specific chemotherapeutic agents. The impact of specific AML agents on each patient's disease network was biosimulated to determine a treatment efficacy score by estimating the effect of chemotherapy on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. The mechanism of action of each drug was mapped to each patient's genome and biological consequences determined response. Multivariate logistic regression models for clinical response and likelihood ratio tests were used to assess the contribution of the Cellworks Biosimulation Platform beyond PPT. Similarly, multivariate Cox proportional hazards models were used to test the hypothesis that the Cellworks Biosimulation Platform is predictive of overall survival (OS) and provides predictive information beyond PPT alone. Scoring quantifies the benefit of each drug used to treat each patient's AML. Kaplan-Meier curves, associated log rank tests, and median OS are provided for patients predicted by predefined low and high treatment benefit groups. Results: The TRI Score, scaled from 0 to 100, predicted complete response (CR) (likelihood ratio χ 12 = 52.54, p < 0.0001). Specific leukemia therapies generated a variable likelihood of benefit for individual patients. Notably, Cellworks biosimulation was able to predict treatment benefit or failure better than PPT alone (likelihood ratio χ 12 = 14.86, p < 0.0001). The use of therapy biosimulation to select therapy is estimated to increase the odds of CR by 19% per every 25 units of the TRI Score. TRI was also a significant predictor of OS (likelihood ratio χ 12 = 80.41, p < 0.0001) and provides predictive information above and beyond PPT alone (likelihood ratio χ 12 = 58.70, p < 0.0001 ). Inclusion of the Cellworks Biosimulation Platform is estimated to reduce the hazard ratio for death above and beyond PPT alone by 16% per every 25 units of the TRI Score. Furthermore, predictiveness curves suggest that approximately 25% of de novo AML patients had low probabilities of CR resulting in lower OS and could benefit substantially from inclusion of drugs and combinations identified by biosimulation into frontline management. Conclusions: By predicting the impact of aberrations and copy number alterations on drug response, the Cellworks Biosimulation Platform can improve treatment outcomes for AML patients. The Cellworks TRI predicts response and OS beyond PPT alone, and the Cellworks Biosimulation Platform provides individualized, networked-based alternate treatment options for patients predicted to be non-responders to standard care. Disclosures Howard: Sanofi: Consultancy, Other: Speaker fees; Cellworks Group Inc.: Consultancy; Servier: Consultancy. Watson: Cellworks Group Inc.: Consultancy, Other: Advisor; CellMax Life: Consultancy, Other: Advisor; AlloVir: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAi Health: Consultancy, Membership on an entity's Board of Directors or advisory committees. Castro: Cellworks Group Inc.: Current Employment; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau; Exact sciences Inc.: Consultancy; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Kapoor: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Rajagopalan: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Roy: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Raju: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Agios: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings.

Identification of Key Pathways and Targets of Kai Xin San in The Treatment of Alzheimer's Disease Based On a Network Pharmacology Approach and Experimental Validation

Background: Alzheimer's disease (AD) is the commonest neurodegenerative disease characterized with a progressive loss of cognitive functions and memory decline. Kai Xin San (KXS), a traditional Chinese herbal classic prescription, has been used to ameliorate cognitive dysfunction for thousands of years. However, its specific pharmacological molecular mechanisms have not been fully clarified.Methods: The ingredients of KXS and their corresponding targets were firstly screened from ETCM database. AD-related target proteins were obtained from Malacards database and DisGeNet database. Venn diagram was used to intersect the common targets between KXS and AD. Then, key ingredients and key targets were identified from compound-target-disease network and protein-protein interaction (PPI) network analysis respectively. Moreover, the binding affinity between the key ingredients and targets were verified by molecular docking. KEGG enrichment analysis further predicted the potential key signaling pathway involved in the treatment of KXS on AD, and the predicted signaling pathway was validated via experimental approach.Results: A total of 38 ingredients and 469 corresponding targets were screened, and 264 target proteins associated with AD were obtained. Compound-target-disease network and PPI identified the key active ingredients and targets, which correlate with the treatment of KXS on AD. Molecular docking revealed a good binding affinity between key ingredients and targets. KEGG pathway analysis suggested the potential effect of KXS in treatment of AD via Aβ-GSK3β-Tau pathway. Aβ1-42-injected induced a decline in spatial learning and memory and upregulated the expression of GSK3β and CDK5 along with the downregulated PP1 and PP2 expression. However, KXS significantly improve the cognitive deficits induced by Aβ1-42, decrease the GSK3β and CDK5 levels and increase the expression of PP1 and PP2.Conclusions: Our research elucidated that KXS exerted neuroprotective effects through regulating the Aβ-GSK3β-Tau signaling pathway, which provided a novel insight into the therapeutic mechanism of KXS in treatment of AD.