Jinan Wang

TCMSP: a database of systems pharmacology for drug discovery from herbal medicines

BackgroundModern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed.DescriptionThe traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski’s rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development.ConclusionsThe particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.

A System-Level Investigation into the Mechanisms of Chinese Traditional Medicine: Compound Danshen Formula for Cardiovascular Disease Treatment

Compound Danshen Formula (CDF) is a widely used Traditional Chinese Medicine (TCM) which has been extensively applied in clinical treatment of cardiovascular diseases (CVDs). However, the underlying mechanism of clinical administrating CDF on CVDs is not clear. In this study, the pharmacological effect of CDF on CVDs was analyzed at a systemic point of view. A systems-pharmacological model based on chemical, chemogenomics and pharmacological data is developed via network reconstruction approach. By using this model, we performed a high-throughput in silico screen and obtained a group of compounds from CDF which possess desirable pharmacodynamical and pharmacological characteristics. These compounds and the corresponding protein targets are further used to search against biological databases, such as the compound-target associations, compound-pathway connections and disease-target interactions for reconstructing the biologically meaningful networks for a TCM formula. This study not only made a contribution to a better understanding of the mechanisms of CDF, but also proposed a strategy to develop novel TCM candidates at a network pharmacology level.

Systems approaches and polypharmacology for drug discovery from herbal medicines: An example using licorice

ETHNOPHARMACOLOGICAL RELEVANCE Licorice, one of the oldest and most popular herbal medicines in the world, has been widely used in traditional Chinese medicine as a cough reliever, anti-inflammatory, anti-anabrosis, immunomodulatory, anti-platelet, antiviral (hepatitis) and detoxifying agent. Licorice was used as an example to show drug discovery from herbal drugs using systems approaches and polypharmacology. AIM OF THE STUDY Herbal medicines are becoming more mainstream in clinical practice and show value in treating and preventing diseases. However, due to its extreme complexity both in chemical components and mechanisms of action, deep understanding of botanical drugs is still difficult. Thus, a comprehensive systems approach which could identify active ingredients and their targets in the crude drugs and more importantly, understand the biological basis for the pharmacological properties of herbal medicines is necessary. MATERIALS AND METHODS In this study, a novel systems pharmacology model that integrates oral bioavailability screening, drug-likeness evaluation, blood-brain barrier permeation, target identification and network analysis has been established to investigate the herbal medicines. RESULTS The comprehensive systems approach effectively identified 73 bioactive components from licorice and 91 potential targets for this medicinal herb. These 91 targets are closely associated with a series of diseases of respiratory system, cardiovascular system, and gastrointestinal system, etc. These targets are further mapped to drug-target and drug-target-disease networks to elucidate the mechanism of this herbal medicine. CONCLUSION This work provides a novel in silico strategy for investigation of the botanical drugs containing a huge number of components, which has been demonstrated by the well-studied licorice case. This attempt should be helpful for understanding definite mechanisms of action for herbal medicines and discovery of new drugs from plants.

Large-scale exploration and analysis of drug combinations

MOTIVATION Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered. RESULTS We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. 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. PEA also evaluates the adverse effects (AUC = 0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications. AVAILABILITY AND IMPLEMENTATION The PreDC database is available at http://sm.nwsuaf.edu.cn/lsp/predc.php.

Systems pharmacology strategies for drug discovery and combination with applications to cardiovascular diseases

ETHNOPHARMACOLOGICAL RELEVANCE Multi-target therapeutics is a promising paradigm for drug discovery which is expected to produce greater levels of efficacy with fewer adverse effects and toxicity than monotherapies. Medical herbs featuring multi-components and multi-targets may serve as valuable resources for network-based multi-target drug discovery. MATERIALS AND METHODS In this study, we report an integrated systems pharmacology platform for drug discovery and combination, with a typical example applied to herbal medicines in the treatment of cardiovascular diseases. RESULTS First, a disease-specific drug-target network was constructed and examined at systems level to capture the key disease-relevant biology for discovery of multi-targeted agents. Second, considering an integration of disease complexity and multilevel connectivity, a comprehensive database of literature-reported associations, chemicals and pharmacology for herbal medicines was designed. Third, a large-scale systematic analysis combining pharmacokinetics, chemogenomics, pharmacology and systems biology data through computational methods was performed and validated experimentally, which results in a superior output of information for systematic drug design strategies for complex diseases. CONCLUSIONS This strategy integrating different types of technologies is expected to help create new opportunities for drug discovery and combination.

System-level multi-target drug discovery from natural products with applications to cardiovascular diseases

The term systems pharmacology describes a field of study that uses computational and experimental approaches to broaden the view of drug actions rooted in molecular interactions and advance the process of drug discovery. The aim of this work is to stick out the role that the systems pharmacology plays across the multi-target drug discovery from natural products for cardiovascular diseases (CVDs). Firstly, based on network pharmacology methods, we reconstructed the drug–target and target–target networks to determine the putative protein target set of multi-target drugs for CVDs treatment. Secondly, we reintegrated a compound dataset of natural products and then obtained a multi-target compounds subset by virtual-screening process. Thirdly, a drug-likeness evaluation was applied to find the ADME-favorable compounds in this subset. Finally, we conducted in vitro experiments to evaluate the reliability of the selected chemicals and targets. We found that four of the five randomly selected natural molecules can effectively act on the target set for CVDs, indicating the reasonability of our systems-based method. This strategy may serve as a new model for multi-target drug discovery of complex diseases.

Systems pharmacology exploration of botanic drug pairs reveals the mechanism for treating different diseases.

Multi-herb therapy has been widely used in Traditional Chinese medicine and tailored to meet the specific needs of each individual. However, the potential molecular or systems mechanisms of them to treat various diseases have not been fully elucidated. To address this question, a systems pharmacology approach, integrating pharmacokinetics, pharmacology and systems biology, is used to comprehensively identify the drug-target and drug-disease networks, exemplified by three representative Radix Salviae Miltiorrhizae herb pairs for treating various diseases (coronary heart disease, dysmenorrheal and nephrotic syndrome). First, the compounds evaluation and the multiple targeting technology screen the active ingredients and identify the specific targets for each herb of three pairs. Second, the herb feature mapping reveals the differences in chemistry and pharmacological synergy between pairs. Third, the constructed compound-target-disease network explains the mechanisms of treatment for various diseases from a systematic level. Finally, experimental verification is taken to confirm our strategy. Our work provides an integrated strategy for revealing the mechanism of synergistic herb pairs, and also a rational way for developing novel drug combinations for treatments of complex diseases.

Identification of Novel Potential β-N-Acetyl-D-Hexosaminidase Inhibitors by Virtual Screening, Molecular Dynamics Simulation and MM-PBSA Calculations

Chitinolytic β-N-acetyl-d-hexosaminidases, as a class of chitin hydrolysis enzyme in insects, are a potential species-specific target for developing environmentally-friendly pesticides. Until now, pesticides targeting chitinolytic β-N-acetyl-d-hexosaminidase have not been developed. This study demonstrates a combination of different theoretical methods for investigating the key structural features of this enzyme responsible for pesticide inhibition, thus allowing for the discovery of novel small molecule inhibitors. Firstly, based on the currently reported crystal structure of this protein (OfHex1.pdb), we conducted a pre-screening of a drug-like compound database with 8 × 106 compounds by using the expanded pesticide-likeness criteria, followed by docking-based screening, obtaining 5 top-ranked compounds with favorable docking conformation into OfHex1. Secondly, molecular docking and molecular dynamics simulations are performed for the five complexes and demonstrate that one main hydrophobic pocket formed by residues Trp424, Trp448 and Trp524, which is significant for stabilization of the ligand–receptor complex, and key residues Asp477 and Trp490, are respectively responsible for forming hydrogen-bonding and π–π stacking interactions with the ligands. Finally, the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) analysis indicates that van der Waals interactions are the main driving force for the inhibitor binding that agrees with the fact that the binding pocket of OfHex1 is mainly composed of hydrophobic residues. These results suggest that screening the ZINC database can maximize the identification of potential OfHex1 inhibitors and the computational protocol will be valuable for screening potential inhibitors of the binding mode, which is useful for the future rational design of novel, potent OfHex1-specific pesticides.

Insight into the binding mode and the structural features of the pyrimidine derivatives as human A2A adenosine receptor antagonists

The interaction of 278 monocyclic and bicyclic pyrimidine derivatives with human A2A adenosine receptor (AR) was investigated by employing molecular dynamics, thermodynamic analysis and three-dimensional quantitative structure-activity relationship (3D-QSAR) approaches. The binding analysis reveals that the pyrimidine derivatives are anchored in TM2, 3, 5, 6 and 7 of A2A AR by the aromatic stacking and hydrogen bonding interactions. The key residues involving Phe168, Glu169, and Asn253 stabilize the monocyclic and bicyclic cores of inhibitors. The thermodynamic analysis by molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) approach also confirms the reasonableness of the binding modes. In addition, the ligand-/receptor-based comparative molecular similarity indices analysis (CoMSIA) models of high statistical significance were generated and the resulting contour maps correlate well with the structural features of the antagonists essential for high A2A AR affinity. A minor/bulky group with negative charge at C2/C6 of pyrimidine ring respectively enhances the activity for all these pyrimidine derivatives. Particularly, the higher electron density of the ring in the bicyclic derivatives, the more potent the antagonists. The obatined results might be helpful in rational design of novel candidate of A2A adenosine receptor antagonist for treatment of Parkinsons disease.

Molecular simulation of a series of benzothiazole PI3Kα inhibitors: probing the relationship between structural features, anti-tumor potency and selectivity

The phosphatidylinositol 3-kinase α (PI3Kα) was genetically validated as a promising therapeutic target for developing novel anticancer drugs. In order to explore the structure-activity correlation of benzothiazole series as inhibitors of PI3Kα, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) were performed on 61 promising molecules to build 3D-QSAR models based on both the ligand- and receptor-based methods. The best CoMFA and CoMSIA models had a cross-validated coefficient rcv2 of 0.618 and 0.621, predicted correlation coefficient rpred2 of 0.812 and 0.83, respectively, proving their high correlative and predictive abilities on both the training and test sets. In addition, docking analysis and molecular dynamics simulation (MD) were also applied to elucidate the probable binding modes of these inhibitors at the ATP binding pocket. Based on the contour maps and MD results, some key structural factors responsible for the activity of this series of compounds were revealed as follows: (1) Ring-A has a strong preference for bulky hydrophobic or aromatic groups; (2) Electron-withdrawing groups at the para position of ring-B and hydrophilic substituents in ring-B region may benefit the potency; (3) A polar substituent like -NHSO2- between ring-A and ring-B can enhance the activity of the drug by providing hydrogen bonding interaction with the protein target. The satisfactory results obtained from this work strongly suggest that the developed 3D-QSAR models and the obtained PI3Kα inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and be helpful in future PI3Kα inhibitor design.