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Published in 2019
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Synergistic Effect of Network-Based Multicomponent Drugs: An Investigation on the Treatment of Non-Small-Cell Lung Cancer with Compound Liuju Formula.

Authors: Su X, Jiang M, Li Y, Zhu J, Zheng C, Chen X, Zhou J, Xiao W, Wang Y, Li Y

Abstract: Lung cancer is the most common cause of cancer death with high morbidity and mortality, which non-small-cell lung cancer (NSCLC) accounting for the majority. Traditional Chinese Medicine (TCM) is effective in the treatment of complex diseases, especially cancer. However, TCM is still in the conceptual stage. The interaction between different components remains unknown due to its multicomponent and multitarget characteristics. In this study, compound Liuju formula was taken as an example to isolate compounds with synergistic biological activity through systems pharmacology strategy. Through pharmacokinetic evaluation, 37 potentially active compounds were screened out. Meanwhile, 116 targets of these compounds were obtained by combing with the target prediction model. Through network analysis, we found that multicomponent drugs can present a synergistic effect through regulating inflammatory signaling pathway, invasion pathway, proliferation, and apoptosis pathway. Finally, it was confirmed that the bioactive compounds of compound Liuju formula have not only a killing effect on NSCLC tumor cells but also a synergistic effect on inhibiting the secretion of correlative inflammatory mediators, including TNF-alpha and IL-1beta. The systems pharmacology method was applied in this study, which provides a new direction for analyzing the mechanism of TCM.
Published in 2019
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Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes.

Authors: Yi HC, You ZH, Guo ZH

Abstract: A key aim of post-genomic biomedical research is to systematically understand and model complex biomolecular activities based on a systematic perspective. Biomolecular interactions are widespread and interrelated, multiple biomolecules coordinate to sustain life activities, any disturbance of these complex connections can lead to abnormal of life activities or complex diseases. However, many existing researches usually only focus on individual intermolecular interactions. In this work, we revealed, constructed, and analyzed a large-scale molecular association network of multiple biomolecules in human by integrating associations among lncRNAs, miRNAs, proteins, drugs, and diseases, in which various associations are interconnected and any type of associations can be predicted. We propose Molecular Association Network (MAN)-High-Order Proximity preserved Embedding (HOPE), a novel network representation learning based method to fully exploit latent feature of biomolecules to accurately predict associations between molecules. More specifically, network representation learning algorithm HOPE was applied to learn behavior feature of nodes in the association network. Attribute features of nodes were also adopted. Then, a machine learning model CatBoost was trained to predict potential association between any nodes. The performance of our method was evaluated under five-fold cross validation. A case study to predict miRNA-disease associations was also conducted to verify the prediction capability. MAN-HOPE achieves high accuracy of 93.3% and area under the receiver operating characteristic curve of 0.9793. The experimental results demonstrate the novelty of our systematic understanding of the intermolecular associations, and enable systematic exploration of the landscape of molecular interactions that shape specialized cellular functions.
Published in 2019
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Network Pharmacology Reveals the Molecular Mechanism of Cuyuxunxi Prescription in Promoting Wound Healing in Patients with Anal Fistula.

Authors: Qu Y, Zhang Z, Lu Y, Zheng, Wei Y

Abstract: Background: The healing process of the surgical wound of anal fistulotomy is much slower because of the presence of stool within the wound. Cuyuxunxi (CYXX) prescription is a Chinese herbal fumigant that is being used to wash surgical wound after anal fistulotomy. This study aimed at investigating the molecular mechanism of CYXX prescription using a network pharmacology-based strategy. Materials and Methods: The active compounds in each herbal medicine were retrieved from the traditional Chinese medicine systems pharmacology (TCMSP) database and in Traditional Chinese Medicine Integrated Database (TCMID) analysis platform based on the criteria of oral bioavailability >/=40% and drug-likeness >/=0.2. The disease-related target genes were extracted from the Comparative Toxicogenomics Database. Protein-protein interaction network was built for the overlapped genes as well as functional enrichment analysis. Finally, an ingredient-target genes-pathway network was built by integrating all information. Results: A total of 375 chemical ingredients of the 5 main herbal medicines in CYXX prescription were retrieved from TCMSP database and TCMID. Among the 375 chemical ingredients, 59 were active compounds. Besides, 325 target genes for 16 active compounds in 3 herbal medicines were obtained. Functional enrichment analysis revealed that these overlapped genes were significantly related with immune response, biosynthesis of antibiotics, and complement and coagulation cascades. A comprehensive network which contains 133 nodes (8 disease nodes, 3 drug nodes, 8 ingredients, 103 target gene nodes, 7 GO nodes, and 4 pathway nodes) was built. Conclusion: The network built in this study might aid in understanding the action mechanism of CYXX prescription at molecular level to pathway level.
Published in 2019
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Five-Feature Model for Developing the Classifier for Synergistic vs. Antagonistic Drug Combinations Built by XGBoost.

Authors: Ji X, Tong W, Liu Z, Shi T

Abstract: Combinatorial drug therapy can improve the therapeutic effect and reduce the corresponding adverse events. In silico strategies to classify synergistic vs. antagonistic drug pairs is more efficient than experimental strategies. However, most of the developed methods have been applied only to cancer therapies. In this study, we introduce a novel method, XGBoost, based on five features of drugs and biomolecular networks of their targets, to classify synergistic vs. antagonistic drug combinations from different drug categories. We found that XGBoost outperformed other classifiers in both stratified fivefold cross-validation (CV) and independent validation. For example, XGBoost achieved higher predictive accuracy than other models (0.86, 0.78, 0.78, and 0.83 for XGBoost, logistic regression, naive Bayesian, and random forest, respectively) for an independent validation set. We also found that the five-feature XGBoost model is much more effective at predicting combinatorial therapies that have synergistic effects than those with antagonistic effects. The five-feature XGBoost model was also validated on TCGA data with accuracy of 0.79 among the 61 tested drug pairs, which is comparable to that of DeepSynergy. Among the 14 main anatomical/pharmacological groups classified according to WHO Anatomic Therapeutic Class, for drugs belonging to five groups, their prediction accuracy was significantly increased (odds ratio < 1) or reduced (odds ratio > 1) (Fisher's exact test, p < 0.05). This study concludes that our five-feature XGBoost model has significant benefits for classifying synergistic vs. antagonistic drug combinations.
Published in 2019
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Deciphering the Pharmacological Mechanism of the Herb Radix Ophiopogonis in the Treatment of Nasopharyngeal Carcinoma by Integrating iTRAQ-Coupled 2-D LC-MS/MS Analysis and Network Investigation.

Authors: Feng X, Shi H, Chao X, Zhao F, Song L, Wei M, Zhang H

Abstract: The herb Radix Ophiopogonis (RO) has been used effectively to treat nasopharyngeal carcinoma (NPC) as an adjunctive therapy. Due to the complexity of the traditional Chinese herbs, the pharmacological mechanism of RO's action on NPC remains unclear. To address this problem, an integrative approach bridging proteome experiments with bioinformatics prediction was employed. First, differentially expressed protein profile from NPC serum samples was established using isobaric tag for relative and absolute quantification (iTRAQ) coupled 2-D liquid chromatography (LC)-MS/MS analysis. Second, the RO putative targets were predicted using Traditional Chinese Medicines Integrated Database and known therapeutic targets of NPC were collected from Drugbank and OMIM databases. Then, a network between RO putative targets and NPC known therapeutic targets was constructed. Third, based on pathways enrichment analysis, an integrative network was constructed using DAVID and STRING database in order to identify potential candidate targets of RO against NPC. As a result, we identified 13 differentially expressed proteins from clinical experiments compared with the healthy control. And by bioinformatics investigation, 12 putative targets of RO were selected. Upon interactions between experimental and predicted candidate targets, we identified three key candidate targets of RO against NPC: VEGFA, TP53, and HSPA8, by calculating the nodes' topological features. In conclusion, this integrative pharmacology-based analysis revealed the anti-NPC effects of RO might be related to its regulatory impact via the PI3K-AKT signaling pathway, the Wnt signaling pathway, and the cAMP signaling pathway by targeting VEGFA, TP53, and HSPA8. The findings of potential key targets may provide new clues for NPC's treatments with the RO adjunctive therapy.
Published in 2019
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Identification of Cancer Hallmarks Based on the Gene Co-expression Networks of Seven Cancers.

Authors: Yu LH, Huang QW, Zhou XH

Abstract: Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in cancer hallmarks are likely to be cancer drivers. However, there is no appropriate method in the current literature for identifying genetic cancer hallmarks, especially considering the interrelationships among the genes. Here, we hypothesized that "dense clusters" (or "communities") in the gene co-expression networks of cancer patients may represent functional units regarding cancer formation and progression, and the communities present in the co-expression networks of multiple types of cancer may be cancer hallmarks. Consequently, we mined the conserved communities in the gene co-expression networks of seven cancers in order to identify candidate hallmarks. Functional annotation of the communities showed that they were mainly related to immune response, the cell cycle and the biological processes that maintain basic cellular functions. Survival analysis using the genes involved in the conserved communities verified that two of these hallmarks could predict the survival risks of cancer patients in multiple types of cancer. Furthermore, the genes involved in these hallmarks, one of which was related to the cell cycle, could be useful in screening for cancer drugs.
Published in 2019
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Attractor - a new turning point in drug discovery.

Authors: Hou X, Li M, Jia C, Zhang X, Wang Y

Abstract: Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.
Published in 2019
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Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives.

Authors: Romano JD, Tatonetti NP

Abstract: The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.
Published in 2019
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Unveiling the Kinomes of Leishmania infantum and L. braziliensis Empowers the Discovery of New Kinase Targets and Antileishmanial Compounds.

Authors: Borba JVB, Silva AC, Ramos PIP, Grazzia N, Miguel DC, Muratov EN, Furnham N, Andrade CH

Abstract: Leishmaniasis is a neglected tropical disease caused by parasites of the genus Leishmania (NTD) endemic in 98 countries. Although some drugs are available, current treatments deal with issues such as toxicity, low efficacy, and emergence of resistance. Therefore, there is an urgent need to identify new targets for the development of new antileishmanial drugs. Protein kinases (PKs), which play an essential role in many biological processes, have become potential drug targets for many parasitic diseases. A refined bioinformatics pipeline was applied in order to define and compare the kinomes of L. infantum and L. braziliensis, species that cause cutaneous and visceral manifestations of leishmaniasis in the Americas, the latter being potentially fatal if untreated. Respectively, 224 and 221 PKs were identified in L. infantum and L. braziliensis overall. Almost all unclassified eukaryotic PKs were assigned to six of nine major kinase groups and, consequently, most have been classified into family and subfamily. Furthermore, revealing the kinomes for both Leishmania species allowed for the prioritization of potential drug targets that could be explored for discovering new drugs against leishmaniasis. Finally, we used a drug repurposing approach and prioritized seven approved drugs and investigational compounds to be experimentally tested against Leishmania. Trametinib and NMS-1286937 inhibited the growth of L. infantum and L. braziliensis promastigotes and amastigotes and therefore might be good candidates for the drug repurposing pipeline.
Published in 2019
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Building the drug-GO function network to screen significant candidate drugs for myasthenia gravis.

Authors: Li S, Cao Y, Li L, Zhang H, Lu X, Bo C, Kong X, Liu Z, Chen L, Liu P, Jiao Y, Wang J, Ning S, Wang L

Abstract: Myasthenia gravis (MG) is an autoimmune disease. In recent years, considerable evidence has indicated that Gene Ontology (GO) functions, especially GO-biological processes, have important effects on the mechanisms and treatments of different diseases. However, the roles of GO functions in the pathogenesis and treatment of MG have not been well studied. This study aimed to uncover the potential important roles of risk-related GO functions and to screen significant candidate drugs related to GO functions for MG. Based on MG risk genes, 238 risk GO functions and 42 drugs were identified. Through constructing a GO function network, we discovered that positive regulation of NF-kappaB transcription factor activity (GO:0051092) may be one of the most important GO functions in the mechanism of MG. Furthermore, we built a drug-GO function network to help evaluate the latent relationship between drugs and GO functions. According to the drug-GO function network, 5 candidate drugs showing promise for treating MG were identified. Indeed, 2 out of 5 candidate drugs have been investigated to treat MG. Through functional enrichment analysis, we found that the mechanisms between 5 candidate drugs and associated GO functions may involve two vital pathways, specifically hsa05332 (graft-versus-host disease) and hsa04940 (type I diabetes mellitus). More interestingly, most of the processes in these two pathways were consistent. Our study will not only reveal a new perspective on the mechanisms and novel treatment strategies of MG, but also will provide strong support for research on GO functions.