Jiangyong Gu

Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology

Background Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products. Methodology We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model. Conclusions Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins.

Drug-target network and polypharmacology studies of a Traditional Chinese Medicine for type II diabetes mellitus

Many Traditional Chinese Medicines (TCMs) are effective to relieve complicated diseases such as type II diabetes mellitus (T2DM). In this work, molecular docking and network analysis were employed to elucidate the action mechanism of a medical composition which had clinical efficacy for T2DM. We found that multiple active compounds contained in this medical composition would target multiple proteins related to T2DM and the biological network would be shifted. We predicted the key players in the medical composition and some of them have been reported in literature. Meanwhile, several compounds such as Rheidin A, Rheidin C, Sennoside C, procyanidin C1 and Dihydrobaicalin were notable although no one have reported their pharmacological activity against T2DM. The association between active compounds, target proteins and other diseases was also discussed.

Surface-initiated molecularly imprinted polymeric column: In situ synthesis and application for semi-preparative separation by high performance liquid chromatography

In this work, we prepared a monolithic and surface initiated molecularly imprinted polymeric (MIP) column for HPLC and explored its application for template separation from plant extract. The silica beads (40-60 μm) were coupled with initiator on the surface and then packed in to a stainless steel HPLC column. The pre-polymerization mixture (the template, functional monomer and crosslinker were emodin, acrylamide and divinylbenzene, respectively) was injected into the column and polymerized by thermal initiation. The prepared MIP column exhibited excellent retention capability and large imprinted factor for template (the retention time and imprinted factor for emodin on MIP column were 16.26 min and 7.21, respectively). Moreover, the emodin-molecularly imprinted polymeric column could be applied to separate emodin from alcohol extract of Rheum palmatum L. at semi-preparative scale and 1.2 mg of emodin was obtained in 4 h.

Traditional Chinese herbs as chemical resource library for drug discovery of anti-infective and anti-inflammatory

ETHNOPHARMACOLOGICAL RELEVANCE Infection is a major group of diseases which caused significant mortality and morbidity worldwide. Traditional Chinese herbs have been used to treat infective diseases for thousands years. The numerous clinical practices in disease therapy make it a large chemical resource library for drug discovery. MATERIALS AND METHODS In this study, we collected 1156 kinds of herbs and 22,172 traditional Chinese medicinal compounds (Tcmcs). The chemical informatics and network pharmacology were employed to analyze the anti-infective effects of herbs and Tcmcs. In order to evaluate the drug likeness of Tcmcs, the molecular descriptors of Tcmcs and FDA-approved drugs were calculated and the chemical space was constructed on the basis of principal component analysis in the eight descriptors. On purpose to estimate the effects of Tcmcs to the targets of FDA-approved anti-infective or anti-inflammatory drugs, the molecular docking was employed. After that, docking score weighted predictive models were used to predict the anti-infective or anti-inflammatory efficacy of herbs. RESULTS The distribution of herbs in the phylogenetic tree showed that most herbs were distributed in family of Asteraceae, Fabaceae and Lamiaceae. Tcmcs were well coincide with drugs in chemical space, which indicated that most Tcmcs had good drug-likeness. The predictive models obtained good specificity and sensitivity with the AUC values above 0.8. At last, 389 kinds of herbs were obtained which were distributed in 100 families, by using the optimal cutoff values in ROC curves. These 389 herbs were widely used in China for treatment of infection and inflammation. CONCLUSION Traditional Chinese herbs have a considerable number of drug-like natural products and predicted activities to the targets of approved drugs, which would give us an opportunity to use these herbs as a chemical resource library for drug discovery of anti-infective and anti-inflammatory.

CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology

BackgroundCardiovascular disease (CVD) is the leading cause of death and associates with multiple risk factors. Herb medicines have been used to treat CVD long ago in china and several natural products or derivatives (e.g., aspirin and reserpine) are most common drugs all over the world. The objective of this work was to construct a systematic database for drug discovery based on natural products separated from CVD-related medicinal herbs and to research on action mechanism of herb medicines.DescriptionThe cardiovascular disease herbal database (CVDHD) was designed to be a comprehensive resource for virtual screening and drug discovery from natural products isolated from medicinal herbs for cardiovascular-related diseases. CVDHD comprises 35230 distinct molecules and their identification information (chemical name, CAS registry number, molecular formula, molecular weight, international chemical identifier (InChI) and SMILES), calculated molecular properties (AlogP, number of hydrogen bond acceptor and donors, etc.), docking results between all molecules and 2395 target proteins, cardiovascular-related diseases, pathways and clinical biomarkers. All 3D structures were optimized in the MMFF94 force field and can be freely accessed.ConclusionsCVDHD integrated medicinal herbs, natural products, CVD-related target proteins, docking results, diseases and clinical biomarkers. By using the methods of virtual screening and network pharmacology, CVDHD will provide a platform to streamline drug/lead discovery from natural products and explore the action mechanism of medicinal herbs. CVDHD is freely available at http://pkuxxj.pku.edu.cn/CVDHD.

Platelet aggregation pathway network-based approach for evaluating compounds efficacy.

Traditional Chinese medicines (TCMs) contain a large quantity of compounds with multiple biological activities. By using multitargets docking and network analysis in the context of pathway network of platelet aggregation, we proposed network efficiency and network flux model to screen molecules which can be used as drugs for antiplatelet aggregation. Compared with traditional single-target screening methods, network efficiency and network flux take into account the influences which compounds exert on the whole pathway network. The activities of antiplatelet aggregation of 19 active ingredients separated from TCM and 14 nonglycoside compounds predicated from network efficiency and network flux model show good agreement with experimental results (correlation coefficient = 0.73 and 0.90, resp.). This model can be used to evaluate the potential bioactive compounds and thus bridges the gap between computation and clinical indicator.

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

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.

Quantitative modeling of dose–response and drug combination based on pathway network

BackgroundQuantitative description of dose–response of a drug for complex systems is essential for treatment of diseases and drug discovery. Given the growth of large-scale biological data obtained by multi-level assays, computational modeling has become an important approach to understand the mechanism of drug action. However, due to complicated interactions between drugs and cellular targets, the prediction of drug efficacy is a challenge, especially for complex systems. And the biological systems can be regarded as networks, where nodes represent molecular entities (DNA, RNA, protein and small compound) and processes, edges represent the relationships between nodes. Thus we combine biological pathway-based network modeling and molecular docking to evaluate drug efficacy.ResultsNetwork efficiency (NE) and network flux (NF) are both global measures of the network connectivity. In this work, we used NE and NF to quantitatively evaluate the inhibitory effects of compounds against the lipopolysaccharide-induced production of prostaglandin E2. The edge values of the pathway network of this biological process were reset according to the Michaelis-Menten equation, which used the binding constant and drug concentration to determine the degree of inhibition of the target protein in the pathway. The combination of NE and NF was adopted to evaluate the inhibitory effects. The dose–response curve was sigmoid and the EC50 values of 5 compounds were in good agreement with experimental results (R2 = 0.93). Moreover, we found that 2 drugs produced maximal synergism when they were combined according to the ratio between each EC50.ConclusionsThis quantitative model has the ability to predict the dose–response relationships of single drug and drug combination in the context of the pathway network of biological process. These findings are valuable for the evaluation of drug efficacy and thus provide an effective approach for pathway network-based drug discovery.

Probing the G‑quadruplex from hsa-miR-3620-5p and inhibition of its interaction with the target sequence

G-quadruplexes have been reported to exist both in human genome and transcriptome and are of great interests due to their important biological functions. Up to now, the formation and property of G-quadruplex in human mature microRNAs has not been explored yet. In this study, we discovered that hsa-miR-3620-5p, a guanine rich human mature microRNA, could fold into a stable parallel G-quadruplex in near physiological condition for the first time. We explored the formation, folding pattern and binding affinity of the miR-3620-5p G-quadruplex by ESI-MS, CD, NMR and SPR. The results indicated that its high-order structure was comprised of three G-quartets with two bases in each parallel loop stretching outward and two bases flanking at each end. In addition, sanguinarine, a natural alkaloid screened from traditional Chinese medicine was characterized to have high binding affinity and thermodynamic stabilization effects through π-π stacking interaction with the external G-quartets. Furthermore, the potent interaction of sanguinarine with miR-3620-5p G-quadruplex could block the base pairing between miR-3620-5p and its target sequence. Therefore, our study revealed the possibility of regulating microRNA functions using potent G-quadruplex binders, and could provide a new approach to affect the microRNA:mRNA interactions.

System-level Study on Synergism and Antagonism of Active Ingredients in Traditional Chinese Medicine by Using Molecular Imprinting Technology

In this work, synergism and antagonism among active ingredients of traditional Chinese medicine (TCM) were studied at system-level by using molecular imprinting technology. Reduning Injection (RDNI), a TCM injection, was widely used to relieve fever caused by viral infection diseases in China. Molecularly imprinted polymers (MIPs) synthesized by sol-gel method were used to separate caffeic acid (CA) and analogues from RDNI without affecting other compounds. It can realize the preparative scale separation. The inhibitory effects of separated samples of RDNI and sample combinations in prostaglandin E2 biosynthesis in lipopolysaccharide-induced RAW264.7 cells were studied. The combination index was calculated to evaluate the synergism and antagonism. We found that components which had different scaffolds can produce synergistic anti-inflammatory effect inside and outside the RDNI. Components which had similar scaffolds exhibited the antagonistic effect, and the antagonistic effects among components could be reduced to some extent in RDNI system. The results indicated MIPs with the characteristics of specific adsorption ability and large scale preparation can be an effective approach to study the interaction mechanism among active ingredients of complex system such as TCM at system-level. And this work would provide a new idea to study the interactions among active ingredients of TCM.