Systems pharmacology for traditional Chinese medicine with application to cardio-cerebrovascular diseases

Identified as a treasure of natural herbal products,traditional Chinese medicine(TCM)has attracted extensive attention for their moderate treatment effect and lower side effect.Cardio-cerebrovascular diseases(CCVD)are a leading cause of death.TCM is used in China to prevent and treat CCVD.However,the complexity of TCM poses challenges in understanding the mechanisms of herbs at a systems-level,thus hampering the modernization and globalization of TCM.A novel model,termed traditional Chinese medicine systems pharmacology(TCMSP)analysis platform,which relies on the theory of systems pharmacology and integrates absorption,distribution,metabolism,excretion and toxicity(ADME/T)evaluation,target prediction and network/pathway analysis,was proposed to address these problems.Here,we review the development of systems pharmacology,the TCMSP approach and its applications in the investigations of CCVD and compare it with other methods.TCMSP assists in uncovering the mechanisms of action of herbal formulas used in treating CCVD.It can also be applied in ascertaining the different syndrome patterns of coronary artery disease,decoding the multi-scale mechanisms of herbs,and in understanding the mechanisms of herbal synergism.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes(DAGs),which are important for understanding disease initiation and developing precision therapeutics.However,DAGs often contain large amounts of redundant or false positive information,leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases.In this study,a networkoriented gene entropy approach(NOGEA)is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks.In addition,we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk.Master genes may also be used to extract the underlying information of different diseases,thus revealing mechanisms of disease comorbidity.More importantly,approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network,which provides a new way for predicting drug-disease associations.Through this method,11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments.Collectively,the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence,thus providing a valuable strategy for drug efficacy screening and repositioning.NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.