Multiscale Modeling of Drug-induced Effects of ReDuNing Injection on Human Disease: From Drug Molecules to Clinical Symptoms of Disease

Abstract ReDuNing injection (RDN) is a patented traditional Chinese medicine, and the components of it were proven to have antiviral and important anti-inflammatory activities. Several reports showed that RDN had potential effects in the treatment of influenza and pneumonia. Though there were several experimental reports about RDN, the experimental results were not enough and complete due to that it was difficult to predict and verify the effect of RDN for a large number of human diseases. Here we employed multiscale model by integrating molecular docking, network pharmacology and the clinical symptoms information of diseases and explored the interaction mechanism of RDN on human diseases. Meanwhile, we analyzed the relation among the drug molecules, target proteins, biological pathways, human diseases and the clinical symptoms about it. Then we predicted potential active ingredients of RDN, the potential target proteins, the key pathways and related diseases. These attempts may offer several new insights to understand the pharmacological properties of RDN and provide benefit for its new clinical applications and research. Introduction Traditional Chinese medicine (TCM), which mainly consists of herbal medicines, has been practiced over thousands of years. Today it is still regarded as an important source for drug discovery and is growing in popularity worldwide. Therefore understanding the action mechanism of TCM is significant for the treatment of disease. But it is difficult to study the action mechanism of TCM using routine methods due to that herbal formulae are mixtures of lots of chemical ingredients and most of ingredients may interact with multiple targets1 weakly or moderately. TCM classified a patient by their ZHENG (TCM syndrome or pattern)2. Although the high throughput technologies such as gene expression microarrays3, proteomics4, and metabolomics5 were used to study the mechanisms of TCM, the experiments still have limitations which come from complex nature of TCM. With the rapid progress of bioinformatics, network pharmacology was proposed to be a promising way to understand the mechanism of TCM6,7,8, and it was successfully applied to analyze the anti-rheumatoid arthritis formulae Qing-Luo-Yin9. To date, accumulating evidence suggests that the network pharmacology analysis is a powerful way to study the act mechanisms of herbal formula10. With the advent of big data era, our thinking, technology and methodology are being transformed. Along with the development and application of the Internet information technology, the information of drug molecules, target proteins, biological pathways, diseases, clinical data of diagnosis and treatments have been accumulated dramatically, which generates big data in medical field. The research target was shifted from the “causality inference” to the “correlativity analysis” gradually11. The characteristics of TCM information had strong similarity to that of big data12. The advent of the big data era provided both opportunities and challenges for TCM such as the new computational methods and technologies13. Wang et al developed kernel-based methods to integrate drug related omics data sources14. The drug-gene-disease coherent subnetwork concept was proposed by Li et al to group the biological function related drugs, diseases and genes15. The multiscal network pharmacology methods and technologies facilitated the evaluation of effect of drug, disease treatment, disease prediction and prevention, and practice in the treatment in terms of TCM and promoted the further research in drug discovery. With the development of computer science, it is easier and faster to process the big data than before. Computer modeling not only saved the cost in money and time but also improved the efficiency of experiments. The Connectivity Map (CMAP) database contains more than 7,000 expression profiles representing treatments from 1,309 compounds16, and it provides a useful tool for TCM when combined with microarray analysis. ReDuNing injection (RDN) is a patented TCM, which contains the effective components extracted from three herbs17, Artemisia annua L., Gardenia jasminoides E. and Lonicera japonica T. The components have been proven that they had potential antiviral, significant anti-inflammatory and immunomodulatory activities18,19,20. It also showed potential effects in the therapy of influenza21 and pneumonia22,23. Recently, several experimental studies related to the RDN were reported. For example, Tang et al. evaluated the protective effects of RDN on lipopolysaccharide-induced acute lung injury in rats and its underlying mechanisms of action17. Zhang et al. assessed the efficacy and safety of RDN for fever, rash and ulcers in children with mild hand, foot, and mouth disease, and no adverse reactions were observed24. Li et al. developed a method for screening and analyzing the potential bioactive components from RDN using macrophage cell extraction and ultra-high performance liquid chromatography coupled with mass spectrometry25. Zhang et al. investigated the effect of RDN in the clinic for upper respiratory tract infections by using molecular docking, network analysis and cell-based assays26. Finally, they identified 32 active ingredients and 38 potential targets. Chang et al. developed and validated a method for screening and determining the concentration of seven antioxidants of RDN27. Yang et al. studied the effect of RDN for influenza diseases using a novel systems pharmacology-based strategy28. Although the above studies proved that RDN had some important effects in the therapy of several diseases such as influenza and upper respiratory tract infections, the results were still not complete due to that the category of disease was limited, and there were little reports about the effects of RDN for other human diseases such as cancer and inherited metabolic disease. It may be because the mechanisms of these diseases are so complex that it is difficult to study their mechanisms using experimental methods. Therefore, most of the underlying principles of RDN were still unclear and it is necessary to be explored further by using other methods such as computer modeling. In many ways, drug development is the ultimate multiscale optimization29. The importance of multiscale modeling is fittingly recognized by the award of the 2013 Nobel Prize in chemistry. Drug development and preclinical-to-clinical mapping provided an ideal context for multiscale modeling11. The case for multiscale modeling approaches to improve the efficiency of drug discovery and development has been made over several years in the past30. Here we employed multiscale computer model by integrating molecular docking, network pharmacology and the clinical synopses information of diseases to explore the interaction mechanism of RDN on human disease. By using these methods, we analyzed and predicted potential active ingredients of RDN, the potential target pro (...truncated)