Quantitative and systems pharmacology 2. In silico polypharmacology of G protein-coupled receptor ligands via network-based approaches

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

SPTBN2 suppresses ferroptosis in NSCLC cells by facilitating SLC7A11 membrane trafficking and localization

The function of SLC7A11 in the process of ferroptosis is well-established, as it regulates the synthesis of glutathione (GSH), thereby influencing tumor development along with drug resistance in non-small cell lung cancer (NSCLC). However, the determinants governing SLC7A11's membrane trafficking and localization remain unknown. Our study identified SPTBN2 as a ferroptosis suppressor, enhancing NSCLC cells resistance to ferroptosis inducers. Mechanistically, SPTBN2, through its CH domain, interacted with SLC7A11 and connected it with the motor protein Arp1, thus facilitating the membrane localization of SLC7A11 - a prerequisite for its role as System Xc-, which mediates cystine uptake and GSH synthesis. Consequently, SPTBN2 suppressed ferroptosis through preserving the functional activity of System Xc- on the membrane. Moreover, Inhibiting SPTBN2 increased the sensitivity of NSCLC cells to cisplatin through ferroptosis induction, both in vitro and in vivo. Using Abrine as a potential SPTBN2 inhibitor, its efficacy in promoting ferroptosis and sensitizing NSCLC cells to cisplatin was validated. Collectively, SPTBN2 is a potential therapeutic target for addressing ferroptosis dysfunction and cisplatin resistance in NSCLC.

Targeting ferroptosis unveils a new era for traditional Chinese medicine: a scientific metrology study

In the past 11 years, there has been a surge in studies exploring the regulatory effect of Traditional Chinese Medicine (TCM) on ferroptosis. However, a significant gap persists in comprehensive scientometric analysis and scientific mapping research, especially in tracking the evolution, primary contributors, and emerging research focal points. This study aims to comprehensively update the advancements in targeting ferroptosis with various TCMs during the previous 11 years. The data, covering the period from 1 January 2012, to 30 November 2023, were retrieved from the Web of Science database. For in-depth scientometric and visualized analyses, a series of advanced analytical instruments were employed. The findings highlight China's predominant role, accounting for 71.99% of total publications and significantly shaping research in this domain. Noteworthy productivity was observed at various institutions, including Guangzhou University of Chinese Medicine, Chengdu University of Traditional Chinese Medicine, and Zhejiang University. Thomas Efferth emerged as the foremost author within this field, while Frontiers in Pharmacology boasted the highest publication count. This study pinpointed hepatocellular carcinoma, chemical and drug-induced liver injury, mitochondrial diseases, acute kidney injury, and liver failure as the most critical disorders addressed in this research realm. The research offers a comprehensive bibliometric evaluation, enhancing our understanding of the present status of TCM therapy in managing ferroptosis-related diseases. Consequently, it aids both seasoned researchers and newcomers by accelerating access to vital information and fostering innovative concept extraction within this specialized field.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Mechanisms of multi-omics and network pharmacology to explain traditional chinese medicine for vascular cognitive impairment: A narrative review

BACKGROUND

The term "vascular cognitive impairment" (VCI) describes various cognitive conditions that include vascular elements. It increases the risk of morbidity and mortality in the elderly population and is the most common cognitive impairment associated with cerebrovascular disease. Understanding the etiology of VCI may aid in identifying approaches to target its possible therapy for the condition. Treatment of VCI has focused on vascular risk factors. There are no authorized conventional therapies available right now. The medications used to treat VCI are solely approved for symptomatic relief and are not intended to prevent or slow the development of VCI.

PURPOSE

The function of Chinese medicine in treating VCI has not yet been thoroughly examined. This review evaluates the preclinical and limited clinical evidence to comprehend the "multi-component, multi-target, multi-pathway" mechanism of Traditional Chinese medicine (TCM). It investigates the various multi-omics approaches in the search for the pathological mechanisms of VCI, as well as the new research strategies, in the hopes of supplying supportive evidence for the clinical treatment of VCI.

METHODS

This review used the Preferred Reporting Items for Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements. Using integrated bioinformatics and network pharmacology approaches, a thorough evaluation and analysis of 25 preclinical studies published up to July 1, 2023, were conducted to shed light on the mechanisms of TCM for vascular cognitive impairment. The studies for the systematic review were located using the following databases: PubMed, Web of Science, Scopus, Cochrane, and ScienceDirect.

RESULTS

We discovered that the multi-omics analysis approach would hasten the discovery of the role of TCM in the treatment of VCI. It will explore components, compounds, targets, and pathways, slowing the progression of VCI from the perspective of inhibiting oxidative stress, stifling neuroinflammation, increasing cerebral blood flow, and inhibiting iron deposition by a variety of molecular mechanisms, which have significant implications for the treatment of VCI.

CONCLUSION

TCM is a valuable tool for developing dementia therapies, and further research is needed to determine how TCM components may affect the operation of the neurovascular unit. There are still some limitations, although several research have offered invaluable resources for searching for possible anti-dementia medicines and treatments. To gain new insights into the molecular mechanisms that precisely modulate the key molecules at different levels during pharmacological interventions-a prerequisite for comprehending the mechanism of action and determining the potential therapeutic value of the drugs-further research should employ more standardized experimental methods as well as more sophisticated science and technology. Given the results of this review, we advocate integrating chemical and biological component analysis approaches in future research on VCI to provide a more full and objective assessment of the standard of TCM. With the help of bioinformatics, a multi-omics analysis approach will hasten the discovery of the role of TCM in the treatment of VCI, which has significant implications for the treatment of VCI.

Multi-omics approaches for in-depth understanding of therapeutic mechanism for Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) is extensively utilized in clinical practice due to its therapeutic and preventative treatments for various diseases. With the development of high-throughput sequencing and systems biology, TCM research was transformed from traditional experiment-based approaches to a combination of experiment-based and omics-based approaches. Numerous academics have explored the therapeutic mechanism of TCM formula by omics approaches, shifting TCM research from the "one-target, one-drug" to "multi-targets, multi-components" paradigm, which has greatly boosted the digitalization and internationalization of TCM. In this review, we concentrated on multi-omics approaches in principles and applications to gain a better understanding of TCM formulas against various diseases from several aspects. We first summarized frequently used TCM quality assessment methods, and suggested that incorporating both chemical and biological ingredients analytical methods could lead to a more comprehensive assessment of TCM. Secondly, we emphasized the significance of multi-omics approaches in deciphering the therapeutic mechanism of TCM formulas. Thirdly, we focused on TCM network analysis, which plays a vital role in TCM-diseases interaction, and serves for new drug discovery. Finally, as an essential source for storing multi-omics data, we evaluated and compared several TCM databases in terms of completeness and reliability. In summary, multi-omics approaches have infiltrated many aspects of TCM research. With the accumulation of omics data and data-mining resources, deeper understandings of the therapeutic mechanism of TCM have been acquired or will be gained in the future.

Scaffold Hopping Strategy to Identify Prostanoid EP4 Receptor Antagonists for Cancer Immunotherapy

Cancer cells can effectively suppress the natural immune response in humans, and prostaglandin E₂ (PGE₂) is a key mediator in the development of tumor cell resistance to immunotherapy. As a major contributor to PGE₂-elicited immunosuppressive activity, the EP4 receptor promotes tumor development and progression in the tumor microenvironment, and the development of selective and potent EP4 receptor antagonists should have promising potential for tumor immunotherapy. Aiming at improving the drug-like properties, a series of 4,7-dihydro-5H-thieno[2,3-c]pyran derivatives were designed and synthesized through a scaffold hopping strategy. The most promising compound 47 exhibited good EP4 antagonistic activity and excellent subtype selectivity, as well as favorable drug-like properties. It effectively suppressed the expression of multiple immunosuppression-related genes in macrophages. Meanwhile, oral administration of compound 47, alone or in combination with anti-PD-1 antibody, significantly enhanced the antitumor immune response and inhibited tumor growth in the mouse CT26 colon carcinoma model.

Network-based drug repurposing: A critical review

New drug development for a disease is a tedious time taking, complex and expensive process. Even if it is done, still the chances for success of newly developed drugs are very low. Modern reports state that repurposing the pre-existing drugs will have more efficient functioning than newly developed drugs. This repurposing process will save time, reduce expenses and provide more success rate. The only limitation for this repurposing is getting a desired pharmacological and characteristic parameter of various drugs from vast data available about a huge number of drugs, their effects, and target mechanisms. This drawback can be avoided by introducing computational methods of analysis. This includes various network analysis types that use various biological processes and relationships with various drugs to make data interpretation a simple process. Some of the data sets now available in standard and simplified forms include gene expression, drug-target interactions, protein networks, electronic health records, clinical trial results, and drug adverse event reports. Integrating various data sets and interpretation methods gives way for a more efficient and easy way to repurpose an exact drug for desired target and effect. In this review, we are going to discuss briefly various computational biological network analysis methods like gene regulatory networks, metabolic networks, protein-protein interaction networks, drug-target interaction networks, drug-disease association networks, drug-drug interaction networks, drug-side effects networks, integrated network-based methods, semantic link networks, and isoform-isoform networks. Along with these, we have also briefly presented limitations, predicting methods, data sets used of various biological networks used of the drug for drug repurposing.

wSDTNBI: a novel network-based inference method for virtual screening

In recent years, the rapid development of network-based methods for the prediction of drug-target interactions (DTIs) provides an opportunity for the emergence of a new type of virtual screening (VS), namely, network-based VS. Herein, we reported a novel network-based inference method named wSDTNBI. Compared with previous network-based methods that use unweighted DTI networks, wSDTNBI uses weighted DTI networks whose edge weights are correlated with binding affinities. A two-pronged approach based on weighted DTI and drug-substructure association networks was employed to calculate prediction scores. To show the practical value of wSDTNBI, we performed network-based VS on retinoid-related orphan receptor γt (RORγt), and purchased 72 compounds for experimental validation. Seven of the purchased compounds were confirmed to be novel RORγt inverse agonists by in vitro experiments, including ursonic acid and oleanonic acid with IC50 values of 10 nM and 0.28 μM, respectively. Moreover, the direct contact between ursonic acid and RORγt was confirmed using the X-ray crystal structure, and in vivo experiments demonstrated that ursonic acid and oleanonic acid have therapeutic effects on multiple sclerosis. These results indicate that wSDTNBI might be a powerful tool for network-based VS in drug discovery.

A unified drug-target interaction prediction framework based on knowledge graph and recommendation system

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

Discovery of Natural Products Targeting NQO1 via an Approach Combining Network-Based Inference and Identification of Privileged Substructures

NAD(P)H:quinone oxidoreductase 1 (NQO1) has been shown to be a potential therapeutic target for various human diseases, such as cancer and neurodegenerative disorders. Recent advances in computational methods, especially network-based methods, have made it possible to identify novel compounds for a target with high efficiency and low cost. In this study, we designed a workflow combining network-based methods and identification of privileged substructures to discover new compounds targeting NQO1 from a natural product library. According to the prediction results, we purchased 56 compounds for experimental validation. Without the assistance of privileged substructures, 31 compounds (31/56 = 55.4%) showed IC₅₀ < 100 μM, and 11 compounds (11/56 = 19.6%) showed IC₅₀ < 10 μM. With the assistance of privileged substructures, the two success rates were increased to 61.8 and 26.5%, respectively. Seven natural products further showed inhibitory activity on NQO1 at the cellular level, which may serve as lead compounds for further development. Moreover, network analysis revealed that osthole may exert anticancer effects against multiple cancer types by inhibiting not only carbonic anhydrases IX and XII but also NQO1. Our workflow and computational methods can be easily applied in other targets and become useful tools in drug discovery and development.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Reprogramming immunosuppressive myeloid cells facilitates immunotherapy for colorectal cancer

Immune checkpoint blockade (ICB) has a limited effect on colorectal cancer, underlining the requirement of co-targeting the complementary mechanisms. Here, we identified prostaglandin E2 (PGE2 ) receptor 4 (EP4) as the master regulator of immunosuppressive myeloid cells (IMCs), which are the major driver of resistance to ICB therapy. PGE2 -bound EP4 promotes the differentiation of immunosuppressive M2 macrophages and myeloid-derived suppressor cells (MDSCs) and reduces the expansion of immunostimulated M1 macrophages. To explore the immunotherapeutic role of EP4 signaling, we developed a novel and selective EP4 antagonist TP-16. TP-16 effectively blocked the function of IMCs and enhanced cytotoxic T-cell-mediated tumor elimination in vivo. Cell co-culture experiments revealed that TP-16 promoted T-cell proliferation, which was impaired by tumor-derived CD11b+ myeloid cells. Notably, TP-16 and anti-PD-1 combination therapy significantly impeded tumor progression and prolonged mice survival. We further demonstrated that TP-16 increased responsiveness to anti-PD-1 therapy in an IMC-related spontaneous colorectal cancer mouse model. In summary, this study demonstrates that inhibition of EP4-expressing IMCs may offer a potential strategy for enhancing the efficacy of immunotherapy for colorectal cancer.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

NetInfer: A Web Server for Prediction of Targets and Therapeutic and Adverse Effects via Network-Based Inference Methods

In this study, we developed a web server named NetInfer for prediction of targets and therapeutic and adverse effects via network-based inference methods. Compared with our previously developed standalone version of NetInfer, this web server provides a user-friendly interface. With the web server, users can easily predict potential target proteins, microRNAs, Anatomical Therapeutic Chemical (ATC) classification codes, or adverse drug events for small molecules of their interests in a few steps. Most of the prediction models were constructed on the basis of our previous studies, where those models have been evaluated systematically and demonstrated high performance. The high-quality models can generate accurate predictions. As a case study, we predicted ATC codes and target proteins for several drugs. The predicted therapeutic effects of these drugs on cardiovascular diseases and their potential molecular mechanisms were validated by the literature. This successful case study demonstrated that our web server would be a powerful tool in drug repositioning and systems pharmacology. The web server of NetInfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.

Discovery and Characterization of 1H-1,2,3-Triazole Derivatives as Novel Prostanoid EP4 Receptor Antagonists for Cancer Immunotherapy

The prostanoid EP4 receptor is one of the key receptors associated with inflammatory mediator PGE₂-elicited immunosuppression in the tumor microenvironment. Blockade of EP4 signaling to enhance immunity-mediated tumor elimination has recently emerged as a promising strategy for cancer immunotherapy. In our efforts to discover novel subtype-selective EP4 antagonists, we designed and synthesized a class of 1H-1,2,3-triazole-based ligands that display low nanomolar antagonism activity toward the human EP4 receptor and excellent subtype selectivity. The most promising compound 59 exhibits single-digit nanomolar potency in the EP4 calcium flux and cAMP-response element reporter assays and effectively suppresses the expression of multiple immunosuppression-related genes in macrophage cells. On the basis of its favorable ADMET properties, compound 59 was chosen for further in vivo biological evaluation. Oral administration of compound 59 significantly inhibited tumor growth in the mouse CT26 colon carcinoma model accompanied by enhanced infiltration of cytotoxic T lymphocytes in the tumor tissue.

Functional analysis of Rossmann-like domains reveals convergent evolution of topology and reaction pathways

Rossmann folds are ancient, frequently diverged domains found in many biological reaction pathways where they have adapted for different functions. Consequently, discernment and classification of their homologous relations and function can be complicated. We define a minimal Rossmann-like structure motif (RLM) that corresponds for the common core of known Rossmann domains and use this motif to identify all RLM domains in the Protein Data Bank (PDB), thus finding they constitute about 20% of all known 3D structures. The Evolutionary Classification of protein structure Domains (ECOD) classifies RLM domains in a number of groups that lack evidence for homology (X-groups), which suggests that they could have evolved independently multiple times. Closely related, homologous RLM enzyme families can diverge to bind different ligands using similar binding sites and to catalyze different reactions. Conversely, non-homologous RLM domains can converge to catalyze the same reactions or to bind the same ligand with alternate binding modes. We discuss a special case of such convergent evolution that is relevant to the polypharmacology paradigm, wherein the same drug (methotrexate) binds to multiple non-homologous RLM drug targets with different topologies. Finally, assigning proteins with RLM domain to the Enzyme Commission classification suggest that RLM enzymes function mainly in metabolism (and comprise 38% of reference metabolic pathways) and are overrepresented in extant pathways that represent ancient biosynthetic routes such as nucleotide metabolism, energy metabolism, and metabolism of amino acids. In fact, RLM enzymes take part in five out of eight enzymatic reactions of the Wood-Ljungdahl metabolic pathway thought to be used by the last universal common ancestor (LUCA). The prevalence of RLM domains in this ancient metabolism might explain their wide distribution among enzymes.

Top-100 Most Cited Publications Concerning Network Pharmacology: A Bibliometric Analysis

Background

Network pharmacology (NP) has become an increasingly important focus in the drug research field over the past decade. However, no study to date has mapped the current status of NP. Therefore, we performed a bibliometric study to evaluate the top 100 cited papers on NP.

Methods

We searched the Web of Science Core Collection from its inception to February 25, 2019, using the terms “network pharmacology” and “systems pharmacology.” Key data, including title, publication year, number of citations, authors, countries/regions, organizations, and journals, were retrieved and analyzed using Excel 2016 and VOSviewer 1.6.10.

Results

The total number of citations for the 100 cited papers ranged from 21 to 1,238, published in 53 journals, from 2005 to 2017. The top three journals with the most publications on NP were Clinical Pharmacology & Therapeutics (n = 8, IF2017 = 6.544), Journal of Ethnopharmacology (n = 8, IF2017 = 3.115), and PLoS One (n = 7, IF2017 = 2.766). Most published articles were from the USA (n = 41) and China (n = 35). The most active author was Wang Yonghua from the Northwest A&F University, and of the 100 publications, 14 listing his name. The most frequently used substantive terms included “drug discovery,” “traditional Chinese medicine (TCM),” “in-vitro,” “cancer,” and “cardiovascular disease.” Conclusions. The USA and China made the greatest contribution to NP research. The current NP research mainly focused on NP methods (including experimental validation) and using them to explore the molecular mechanisms of TCM for some critical diseases such as cardiovascular diseases and cancers. Furthermore, we believe some guidelines should be developed to regulate NP studies.

Systems pharmacology approach to investigate the molecular mechanisms of herb Rhodiola rosea L. radix

Rhodiola rosea L. radix (RRL) is one of the most popular medical herb which has been widely used for the treatment of different diseases effectively, including cardiovascular diseases and nerve system diseases. However, due to the multiple compounds in RRL, the underlying molecular mechanisms of RRL are remained unclear. To decipher the action mechanisms of RRL from a systematic perspective, a systems pharmacology approach integrated absorption, distribution, metabolism, and excretion (ADME) system, drug targeting, and network analysis was introduced. First, by the ADME screening system and the target fishing process, 56 potential active compounds and 62 targets were obtained, respectively. In addition, compound-target network demonstrated that most compounds interacted with multiple targets, indicating that RRL may enhance its therapeutic effects probably through hitting on multiple targets in a holistic level. Moreover, target-pathway network and gene ontology analysis showed that multiple targets of RRL were involved in several biological pathways, i.e. Neuroactive ligand-receptor interaction, calcium signaling pathway, adrenergic signaling in cardiomyocytes, and VEGF signaling pathway, which dissecting the therapeutic effects of RRL on various diseases, such as cardiovascular diseases, depression, adaptation diseases, etc. In summary, this work successfully explains the potential active compounds and the multi-scale curative action mechanisms of RRL for treating various diseases; meanwhile, it implies that RRL could be applied as a novel therapeutic agent in arthritic diseases. Most importantly, this work provides an in silico strategy to understand the action mechanisms of herbal medicines from molecular/system levels, which will promote the new drug development of traditional Chinese medicine.

Network-Based Methods for Prediction of Drug-Target Interactions

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.

A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer's Disease

Traditional Chinese medicine (TCM) is typically prescribed as formula to treat certain symptoms. A TCM formula contains hundreds of chemical components, which makes it complicated to elucidate the molecular mechanisms of TCM. Here, we proposed a computational systems pharmacology approach consisting of network link prediction, statistical analysis, and bioinformatics tools to investigate the molecular mechanisms of TCM formulae. Taking formula Tian-Ma-Gou-Teng-Yin as an example, which shows pharmacological effects on Alzheimer’s disease (AD) and its mechanism is unclear, we first identified 494 formula components together with corresponding 178 known targets, and then predicted 364 potential targets for these components with our balanced substructure-drug–target network-based inference method. With Fisher’s exact test and statistical analysis we identified 12 compounds to be most significantly related to AD. The target genes of these compounds were further enriched onto pathways involved in AD, such as neuroactive ligand–receptor interaction, serotonergic synapse, inflammatory mediator regulation of transient receptor potential channel and calcium signaling pathway. By regulating key target genes, such as ACHE, HTR2A, NOS2, and TRPA1, the formula could have neuroprotective and anti-neuroinflammatory effects against the progression of AD. Our approach provided a holistic perspective to study the relevance between TCM formulae and diseases, and implied possible pharmacological effects of TCM components.