A network-based multi-target computational estimation scheme for anticoagulant activities of compounds

In silico perturbation of drug targets in pan-cancer analysis combining multiple networks and pathways

The knowledge of cancer cell response to conventional therapies is crucial in order to choose the correct therapy of patients affected by cancer. The major problem is generally attributed to the lack of specific biological processes able to predict the therapy efficacy. Here, we optimized a computational method for the analysis of gene networks able to detect and quantify the effects of a drug in a pan-cancer study. Overall, our method, using several network topological measures has identified a cancer gene network with a key role in biological processes. The gene network, able to classify with a good performance cancer vs normal samples, was modulated in silico to evaluate the effects of new or approved drugs. This computational model could offer an interesting hint to decipher molecular mechanisms contributing to resistance or inefficacy of drugs.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

Systematic Understanding of the Mechanism of Salvianolic Acid A via Computational Target Fishing

Salvianolic acid A (SAA) is one of the most abundant water-soluble and potent anti-oxidative compounds isolated from Danshen, a traditional Chinese medicine. A systematic overview of its mechanism of action is yet to be performed. In the present study, the druggability of SAA was measured using the TCMSP server, and potential targets of SAA were identified by PharmMapper and DRAR-CPI. Intersecting targets were then assessed by GeneMANIA and GO pathway analysis, and drug-target-pathway networks were constructed to give a visual view. The results showed that SAA has good druggability, and 13 putative protein targets were identified. Network analysis showed that these targets were associated with cancer, metabolism and other physiological processes. In summary, SAA is predicted to target multiple proteins and pathways to form a network that exerts systematic pharmacological effects.

Inflammatory pathway network-based drug repositioning and molecular phenomics

Inflammation is a protective biological response to body/tissue damage that involves immune cells, blood vessels and molecular mediators. In this work, we constructed the pathway network of inflammation, including 11 sub-pathways of inflammatory factors. Pathway-based network efficiency and network flux were adopted to evaluate drug efficacy. By using approved and experimentally validated anti-inflammatory drugs as training sets, a predictive model was built to screen potential anti-inflammatory drugs from approved drugs in DrugBank. This drug repositioning approach would bring a fast and cheap way to find new indications for approved drugs. Moreover, molecular phenomics profiles of the expression of inflammatory factors will provide new insight into the drug mechanism of action.

Pharmacodynamics and potential synergistic effects of Mai-Luo-Ning injection on cardiovascular protection, based on molecular docking

As a computer-assisted approach, molecular docking has been universally applied in drug research and development and plays an important role in the investigation and evaluation of herbal medicines. Herein, the method was used to estimate the pharmacodynamics of Mai-Luo-Ning injection, a traditional Chinese compound herbal prescription. Through investigating the interactions between several important proteins in cardiovascular system and characteristic components of the formula, its effect on cardiovascular protection was evaluated. Results showed the differences in the interactions between each component and the selected target proteins and revealed the possible mechanisms for synergistic effects of various characteristic components on cardiovascular protection. The study provided scientific evidence supporting the mechanistic study of the interactions among multi-components and targets, offering a general approach to investigating the pharmacodynamics of complicated materials in compound herbal prescriptions.

Network pharmacology study on the mechanism of traditional Chinese medicine for upper respiratory tract infection

Traditional Chinese medicine (TCM) is a multi-component and multi-target agent and could treat complex diseases in a holistic way, especially infection diseases. However, the underlying pharmacology remains unclear. Fortunately, network pharmacology by integrating system biology and polypharmacology provides a strategy to address this issue. In this work, Reduning Injection (RDN), a well-used TCM treatment in the clinic for upper respiratory tract infections (URTIs), was investigated to interpret the molecular mechanism and predict new clinical directions by integrating molecular docking, network analysis and cell-based assays. 32 active ingredients and 38 potential targets were identified. In vitro experiments confirmed the bioactivities of the compounds against lipopolysaccharide (LPS)-stimulated PGE2 and NO production in RAW264.7 cells. Moreover, network analysis showed that RDN could not only inhibit viral replication but also alleviate the sickness symptoms of URTIs through directly targeting the key proteins in the respiratory viral life cycle and indirectly regulating host immune systems. In addition, other clinical applications of RDN such as neoplasms, cardiovascular diseases and immune system diseases were predicted on the basis of the relationships between targets and diseases.

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

Background

Quantitative 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.

Results

Network 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.

Conclusions

This 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.

Insights into the inhibition and mechanism of compounds against LPS-induced PGE2 production: a pathway network-based approach and molecular dynamics simulations

In comparison to the current target-based screening approach, it is increasingly evident that active lead compounds based on disease-related phenotypes are more likely to be translated to clinical trials during drug development. That is, because human diseases are in essence the outcome of the abnormal function of multiple genes, especially in complex diseases. Therefore, as a conventional technology in the early phase of active lead compound discovery, computational methods that can connect molecular interactions and disease-related phenotypes to evaluate the efficacy of compounds are in urgently required. In this work, a computational approach that integrates molecular docking and pathway network analysis (network efficiency and network flux) was developed to evaluate the efficacy of a compound against LPS-induced Prostaglandin E2(PGE2) production. The predicted results were then validated in vitro, and a correlation with the experimental results was analyzed using linear regression. In addition, molecular dynamics (MD) simulations were performed to explore the molecular mechanism of the most potent compounds. There were 12 hits out of 28 predicted ingredients separated from Reduning injection (RDN). The predicted results have a good agreement with the experimental inhibitory potency (IC50) (correlation coefficient = 0.80). The most potent compounds could target several proteins to regulate the pathway network. This might partly interpret the molecular mechanism of RDN on fever. Meanwhile, the good correlation of the computational model with the wet experimental results might bridge the gap between molecule-target interactions and phenotypic response, especially for multi-target compounds. Therefore, it would be helpful for active lead compound discovery, the understanding of the multiple targets and synergic essence of traditional Chinese medicine (TCM).

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

A systematic study of chemogenomics of carbohydrates

We explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach.

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

Background

Cardiovascular 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.

Description

The 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.

Conclusions

CVDHD 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.

Network-based target ranking for polypharmacological therapies

With the growing understanding of complex diseases, the focus of drug discovery has shifted from the well-accepted "one target, one drug" model, to a new "multi-target, multi-drug" model, aimed at systemically modulating multiple targets. In this context, polypharmacology has emerged as a new paradigm to overcome the recent decline in productivity of pharmaceutical research. However, finding methods to evaluate multicomponent therapeutics and ranking synergistic agent combinations is still a demanding task. At the same time, the data gathered on complex diseases has been progressively collected in public data and knowledge repositories, such as protein-protein interaction (PPI) databases. The PPI networks are increasingly used as universal platforms for data integration and analysis. A novel computational network-based approach for feasible and efficient identification of multicomponent synergistic agents is proposed in this paper. Given a complex disease, the method exploits the topological features of the related PPI network to identify possible combinations of hit targets. The best ranked combinations are subsequently computed on the basis of a synergistic score. We illustrate the potential of the method through a study on Type 2 Diabetes Mellitus. The results highlight its ability to retrieve novel target candidates, which role is also confirmed by the analysis of the related literature.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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.

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.

Network-based drug discovery by integrating systems biology and computational technologies

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.