Systems Pharmacology-based Study Of Chinese Medicine Therapy And Cardiovascular Diseases

Systems pharmacology, as an emerging field of post-genome era that integrates systems biology, pharmacology and other disciplines to accelerate the process of drug discovery, target discovery, and understanding of complex disease mechanisms. To study mechanism of cardiovascular disease and find new targets from the system level, further develop multi-target drugs for cardiovascular disease ── drug combinations, this study established an systems pharmacology model that integrates network pharmacology, statistics, pharmacokinetics, pharmacology for prediction and evaluation of pharmacology mechanism research of cardiovascular disease and the development of drug combinations. The main findings inchluding three parts:(1) To quantitatively characterize the complex relationships between cardiovascular drugs, targets, disease genes and disorders, we construct a series of networked relationships including cardiovascular drug-target, gene-disease, drug-disease, and protein-protein interaction networks by integrating publicly available drug data. We believe that within- and between-studies of these networks will provide a more comprehensive and profound understanding of the cardiovascular disease pathogenesis and drug action. Herein, we apply integrated network analysis and mainly focus on three areas that are critical to cardiovascular systems biology and pharmacology: 1) the extent of polypharmacological effects of cardiovascular agents; 2) the relationships between drug targets and disease genes in biological networks; and 3) the genetic and molecular connections between different cardiovascular diseases. All these CVD-associated factors and their multi-layer interactions are integrated and provided in a comprehensive database CVDSP for readers to explore information interactively(http://sm.nwsuaf.edu.cn/lsp/cvdsp.php).(2) The large-scale prediction of drug combination by a systems method is rarely considered.We report a systems pharmacology framework to predict drug combinations on a computational model, termed PEA(Probability Ensemble Approach), for analysis of both the efficacy and adverse effects of drug combinations. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity(AUC = 0.90), which was further validated by independent data or new experimental assays. After the large-scale prediction of drug combination, we build a drug combination database: PreDC which is a database of predicted and experimentally determined drug combinations(http://sm.nwsuaf.edu.cn/lsp/predc.php). This database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications.(3) We propose for the first time a systemspharmacological strategy for systematic pursuit of optimal drug combinations. This strategy aids to drug discovery from three categories: 1) systematically identify and understand the pharmacological information for relevant diseases; 2) utilize disease associated target space to screen out effective herbal molecules; and 3) combine biological network analysis and experimental validation to discover novel drug combinations. The accomplishment of this model will demonstrate the power of the combined approach for enhancing our understanding of molecular interconnectedness and its potential regulation in the control of complex diseases.In summary, our work provided an systems pharmacology framework for the integrated study of complex mechnism of drug network and drug combination treatment of cardiovascular diseases. Systems study of network mechanism of cardiovascular disease and the development of prediction system for drug combination not only provides an effective way for cardiovascular disease study but also provide new idea for development of new drugs and target detection and new treatment strategy. Our study of systems pharmacology integrating different types of technologies is expected to help create new opportunities for drug discovery and combination, especially study of traditional medicine.