|关键词||异质网络 药物协同 中药方剂 并发疾病 谱聚类 随机森林 随机游走 网络模体|
|其他摘要||Drug synergy refers to the interplay between different drugs, which can enhance drugs’ overall therapeutic effects. Drug synergy is of great value in the field of drug research and development. Complex diseases like cancer and AIDs are caused by multiple factors, with various biological processes being disturbed or changed. Single drug is insufficient to handle the systematic pathology. In contrast, combination drugs or TCM formulae, both of which are composed of multiple efficacy components with synergistic effects, can treat the complex diseases by cooperately acting on multiple pathological processes. With the number of candidate components increases, the number of all possible combinations will grow exponentially, it will be both time and resource-consuming to screen synergistic interactions by experimental methods. Besides, the synergy mechanism has not been fully defined. Consequently, how to predict synergistic effects and illustrate the mechanism based on currently available biomedical data resources has become a matter of concern.|
Considering the systematic and integral features of complex diseases, the paper integrated various kinds of data resources by heterogeneous network to describe the complicated interactions between drugs and human body’s bio-system. Based on these heterogeneous networks, drug synergy patterns were explored by ranking, classification, clustering and sub-graph mining algorithms, from different perspectives, including combination drugs, TCM formulae, drug interactions and comorbidities. More specifically, the main content of this paper has been concluded as bellow:
(1) A “protein-pathway” heterogeneous network based method was proposed to predict the synergistic effects of combination drugs. This method integrated protein and pathway databases to build a “protein-pathway” heterogeneous network, based on which a synergy score was defined to evaluate the association between different drugs considering both pathway dependencies and protein interactions within individual pathways. Experiments show that this method could achieve better performance than pre-existing network pharmacology methods. In addition, by extracting the subgraph of synergistic combination drugs on the heterogeneous network, this method can offer meaningful mechanistic hypotheses on drug synergy.
(2) A TCM formula is composed of numerous ingredients. To uncover the synergistic mechanism of these ingredients, this paper put forward a heterogeneous biology network based method. The synergy score between each two ingredients was calculated based on the “protein-pathway” heterogeneous network. Then all ingredients were clustered based on the scores, and specific disease-related pathway network and “ingredient-pathway-biomarker” network were built to further analyze the functions of each cluster, as well as to reveal the synergistic mechanism. This paper has applied such method on “Jin Chai” antiviral capsule to elucidate its synergistic effects. The results show that different ingredients cooperate with each other by targeting on multiple mutual-associated pathways which play crucial functions in the life cycle of influenza virus.
(3) A classification model based on multi-dimensional association features was built to classify drug interactions into three types - synergistic, antagonistic and independent interactions. First, multi-dimensional association features were calculated considering the relationships of drugs on aspects of chemical structures, ATC codes, targets, enzymes, pathways, gene ontology and so on. Based on such features, a random forest classification model was trained to classify three types of drug interactions. This model is of good accuracy and explainability, it can not only achieve good accuracy, but also help explore the potential mechanism of drug interactions.
(4) How to effectively combine different types of drugs in the treatment of comorbidity is of great importance in clinical practice. As to this problem, this paper put forward an electronic medical record (EMR) based method. First, chi-square test and relative risk coefficient were utilized to discover the significant associations among diseases and drugs from the EMRs, and a “disease-drug” heterogeneous network was constructed to describe the disease-disease, disease-drug and drug-drug associations. Then, combination medication patterns of comorbidity diseases, which can depict how different types of drugs can be combined to treat two comorbidity diseases, were mined from the heterogeneous network through random walk and network motif analysis algorithms. The method can reveal the medication principles for comorbidities and help improve treatment efficiency of comorbidities.
|陈迪. 基于异质网络的药物协同模式挖掘关键技术研究[D]. 北京. 中国科学院大学,2016.|
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