Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes

Resveratrol Promotes Self-digestion to Put Cancer to Sleep

Resveratrol, a natural polyphenol present in a variety of food stuff, has been shown to exert preventive and curative anticancer activity in several in vitro and in vivo models. Such chemopreventive/anticancer activity has been linked to biochemical and epigenetic modifications of multiple pathways involved in carcinogenesis and metastasization. In this commentary, we focus on the recent work done in our laboratory showing that resveratrol has potential to prevent and cure cancer by promoting epigenetic-mediated autophagy-dependent tumor dormancy, an effect associated with re-education of the cancer-associated fibroblasts and reduced production of inflammatory cytokines in the tumor microenvironment. The clinical translation of the current knowledge on resveratrol anticancer activity is also discussed.

Preclinical evidence for preventive and curative effects of resveratrol on xenograft cholangiocarcinogenesis

Cholangiocarcinoma (CCA), the malignant tumor of bile duct epithelial cells, is a relatively rare yet highly lethal cancer. In this work, we tested the ability of Resveratrol (RV) to prevent and cure CCA xenograft in nude mice and investigated molecular mechanisms underpinning such anticancer effect. Human CCA cells were xenografted in mice that were or not treated prior to or after to transplantation with RV. Tumor growth was monitored and analyzed for the markers of cell proliferation, apoptosis, and autophagy. TCGA was interrogated for the molecules possibly targeted by RV. RV could inhibit the growth of human CCA xenograft when administered after implantation and could reduce the growth or even impair the implantation of the tumors when administered prior the transplantation. RV inhibited CCA cell proliferation, induced apoptosis with autophagy, and strongly reduced the presence of CAFs and production of IL-6. Interrogation of CCA dataset in TCGA database revealed that the expression of IL-6 Receptor (IL-6R) inversely correlated with that of MAP-LC3 and BECLIN-1, and that low expression of IL-6R and of MIK67, two pathways downregulated by RV, associated with better survival of CCA patients. Our data demonstrate that RV elicits a strong preventive and curative anticancer effect in CCA by limiting the formation of CAFs and their release of IL-6, and this results in up-regulation of autophagy and apoptosis in the cancer cells. These findings support the clinical use of RV as a primary line of prevention in patients exposed at risk and as an adjuvant therapeutics in CCA patients.

Hydrogen sulfide and sulfaceutic or sulfanutraceutic agents: Classification, differences and relevance in preclinical and clinical studies

Hydrogen sulfide (H2S) has been extensively studied as a signal molecule in the body for the past 30 years. Researchers have conducted studies using both natural and synthetic sources of H2S, known as H2S donors, which have different characteristics in terms of how they release H2S. These donors can be inorganic salts or have various organic structures. In recent years, certain types of sulfur compounds found naturally in foods have been characterized as H2S donors and explored for their potential health benefits. These compounds are referred to as "sulfanutraceuticals," a term that combines "nutrition" and "pharmaceutical". It is used to describe products derived from food sources that offer additional health advantages. By introducing the terms "sulfaceuticals" and "sulfanutraceuticals," we categorize sulfur-containing substances based on their origin and their use in both preclinical and clinical research, as well as in dietary supplements.

Integrated network pharmacology and experimental analysis unveil multi-targeted effect of 18α- glycyrrhetinic acid against non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most malignant types of cancer with soaring incidence rates worldwide, attributed to its heterogeneity and complex etiology. Evidently, alternative anti-cancer therapies comprising traditional medicines and natural products have gained attention for their ability to act as chemopreventive agents with minimal toxicities, either alone or in combination. Accumulating studies have substantiated the inevitability of network pharmacology studies for effectively mapping molecular targets of natural products against multifaceted diseases, including cancer. The 18α-Glycyrrhetinic acid (18α-GA), a triterpenoid found in licorice plants, has shown promising medicinal properties, although, its mechanism of action against NSCLC yet remains elusive. The present study was conducted to explore the anti- NSCLC potential of 18α-GA, employing integrative network pharmacology, molecular docking, and experimental research. Initially, network analysis revealed 181 common targets of 18α-GA in NSCLC as shown in the “compound-target- disease” network employing Cytoscape 3.8.2. Further analyses identified EGFR, AKT1, PI3KR1, MAPK1, IGF1, and SRC as the most crucial hub targets of 18α-GA against NSCLC. Moreover, molecular docking simulations and functional enrichment analyses indicated the involvement of multiple signaling pathways in suppressing NSCLC. Subsequent in-vitro studies verified the antiproliferative effect of 18α-GA on two NSCLC cancer cell lines, H1299 and A549. Mechanistically, 18α-GA arrested cell cycle at the G1 phase, induced apoptosis, decreased migratory potential, and protein expression levels of EGFR-PI3K/AKT, as examined by flow cytometry, morphological assessment, RT-PCR, and western blot. In conclusion, this study delineates the therapeutic potential and underlying mechanism(s) of 18α-GA as a putative novel drug against NSCLC. However, further studies are warranted to elucidate the complete molecular mechanism(s) using animal models of NSCLC.

Large-scale comparison of machine learning algorithms for target prediction of natural products

Natural products (NPs) and their derivatives are important resources for drug discovery. There are many in silico target prediction methods that have been reported, however, very few of them distinguish NPs from synthetic molecules. Considering the fact that NPs and synthetic molecules are very different in many characteristics, it is necessary to build specific target prediction models of NPs. Therefore, we collected the activity data of NPs and their derivatives from the public databases and constructed four datasets, including the NP dataset, the NPs and its first-class derivatives dataset, the NPs and all its derivatives and the ChEMBL26 compounds dataset. Conditions, including activity thresholds and input features, were explored to access the performance of eight machine learning methods of target prediction of NPs, including support vector machines (SVM), extreme gradient boosting, random forests, K-nearest neighbor, naive Bayes, feedforward neural networks (FNN), convolutional neural networks and recurrent neural networks. As a result, the NPs and all their derivatives datasets were selected to build the best NP-specific models. Furthermore, the consensus models, as well as the voting models, were additionally applied to improve the prediction performance. More evaluations were made on the external validation set and the results demonstrated that (1) the NP-specific model performed better on the target prediction of NPs than the traditional models training on the whole compounds of ChEMBL26. (2) The consensus model of FNN + SVM possessed the best overall performance, and the voting model can significantly improve recall and specificity.

A Comparative Study of Systems Pharmacology and Gene Chip Technology for Predicting Targets of a Traditional Chinese Medicine Formula in Primary Liver Cancer Treatment

Background: The systems pharmacology approach is a target prediction model for traditional Chinese medicine and has been used increasingly in recent years. However, the accuracy of this model to other prediction models is yet to be established.

Objective: To compare the systems pharmacology modelwithexperimental gene chip technology by using these models to predict targets of a traditional Chinese medicine formulain the treatment of primary liver cancer.

Methods: Systems pharmacology and gene chip target predictions were performed for the traditional Chinese medicine formula ZhenzhuXiaojiTang (ZZXJT). A third square alignment was performed with molecular docking.

Results: Identification of systems pharmacology accounted for 17% of targets, whilegene chip-predicted outcomes accounted for 19%.Molecular docking showed that the top ten targets (excludingcommon targets) of the system pharmacology model had better binding free energies than the gene chip model using twocommon targets as a benchmark. For both models, the core drugs predictions were more consistent than the core small molecules predictions.

Conclusion:In this study, the identified targets of systems pharmacology weredissimilar to those identified by gene chip technology; whereas the core drug and small molecule predictions were similar.

Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses

Background

Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical–disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood.

Results

With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses. In the algorithm, we systematically identified TFs that regulate the expression of chemically induced genes by integrating transcriptome data from chemical induction experiments and almost all publicly available ChIP-seq data (consisting of 13,558 experiments). By relating the resultant chemical–TF associations to a repository of associated proteins for a wide range of diseases, we made a comprehensive prediction of chemical–TF–disease associations, which could then be used to account for drug MoAs. Using this approach, we predicted that: (1) cisplatin promotes the anti-tumor activity of TP53 family members but suppresses the cancer-inducing function of MYCs; (2) inhibition of RELA and E2F1 is pivotal for leflunomide to exhibit antiproliferative activity; and (3) CHD8 mediates valproic acid-induced autism.

Conclusions

Our proposed approach has the potential to elucidate the MoAs for both approved drugs and candidate compounds from an epigenetic perspective, thereby revealing new therapeutic targets, and to guide the discovery of unexpected therapeutic effects, side effects, and novel targets and actions.

In Silico Prediction and Bioactivity Evaluation of Chemical Ingredients Against Influenza A Virus From Isatis tinctoria L

Influenza A virus (IAV) is one of the major causes of seasonal endemic diseases and unpredictable periodic pandemics. Due to the high mutation rate and drug resistance, it poses a persistent threat and challenge to public health. Isatis tinctoria L. (Banlangen, BLG), a traditional herbal medicine widely used in Asian countries, has been reported to possess strong efficacy on respiratory viruses, including IAV. However, its effective anti-IAV components and the mechanism of actions (MOAs) are not yet fully elucidated. In this study, we first summarized the chemical components and corresponding contents in BLG according to current available chemical analysis literature. We then presented a network-based in silico framework for identifying potential drug candidates against IAV from BLG. A total of 269 components in BLG were initially screened by drug-likeness and ADME (absorption, distribution, metabolism, and excretion) evaluation. Thereafter, network predictive models were built via the integration of compound–target networks and influenza virus–host proteins. We highlighted 23 compounds that possessed high potential as anti-influenza virus agents. Through experimental evaluation, six compounds, namely, eupatorin, dinatin, linarin, tryptanthrin, indirubin, and acacetin, exhibited good inhibitory activity against wild-type H1N1 and H3N2. Particularly, they also exerted significant effects on drug-resistant strains. Finally, we explored the anti-IAV MOAs of BLG and showcased the potential biological pathways by systems pharmacology analysis. In conclusion, this work provides important information on BLG regarding its use in the development of anti-IAV drugs, and the network-based prediction framework proposed here also offers a powerfulful strategy for the in silico identification of novel drug candidates from complex components of herbal medicine.

Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome

Despite that several therapeutic agents have exhibited promising prevention or treatment on Coronavirus disease-2019 (COVID-19), there is no specific drug discovered for this pandemic. Targeting virus-host interactome provides a more effective strategy for antivirus drug discovery compared with targeting virus proteins. In this study, we developed a network-based infrastructure to prioritize promising drug candidates from natural products and approved drugs via targeting host proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We firstly measured the network distances between drug targets and COVID-19 disease module utilizing the network proximity approach, and identified 229 approved drugs as well as 432 natural products had significant associations with SARS-CoV-2. After searching for previous literature evidence, we found that 60.7% (139/229) of approved drugs and 39.6% (171/432) of natural products were confirmed with antivirus or anti-inflammation. We further integrated our network-based predictions and validated anti-SARS-CoV-2 activities of some compounds. Four drug candidates, including hesperidin, isorhapontigenin, salmeterol, and gallocatechin-7-gallate, have exhibited activity on SARS-COV-2 virus-infected Vero cells. Finally, we showcased the mechanism of actions of isorhapontigenin and salmeterol via network analysis. Overall, this study offers forceful approaches for in silico identification of drug candidates on COVID-19, which may facilitate the discovery of antiviral drug therapies.

Andrographolide and its derivatives: Current achievements and future perspectives

Natural product andrographolide isolated from the plant Andrographis paniculata shows a plethora of biological activities, including anti-tumor, anti-bacterial, anti-inflammation, anti-virus, anti-fibrosis, anti-obesity, immunomodulatory and hypoglycemic activities. Based on extensive chemical structural modifications, a series of andrographolide derivatives with improved bioavailability and druggability has been developed. Moreover, greater understanding of their mechanisms of action at the molecular and cellular level has been thoroughly investigated. In this review, we give an outlook for the therapeutical potential of andrographolide and its derivatives in diverse diseases and highlighted the drug design, pharmacokinetic and mechanistic studies for the past ten years, together with a brief overview of the pharmacological effects. Notably, we focused to provide a critical enlightenment of the area of andrographolide and its derivatives with the intent of indicating the future perspectives, challenges and limitations. We believe that this review paper will benefit drug discovery where andrographolide was used as a template, shed light on the identification of drug targets for andrographolide and its analogs, as well as increase our knowledge for using them for therapeutic application, including the treatment for various forms of cancers.

Andrographis paniculata (Burm. f.) Wall. ex Nees: An Updated Review of Phytochemistry, Antimicrobial Pharmacology, and Clinical Safety and Efficacy

Infectious disease (ID) is one of the top-most serious threats to human health globally, further aggravated by antimicrobial resistance and lack of novel immunization options. Andrographis paniculata (Burm. f.) Wall. ex Nees and its metabolites have been long used to treat IDs. Andrographolide, derived from A. paniculata, can inhibit invasive microbes virulence factors and regulate the host immunity. Controlled clinical trials revealed that A. paniculata treatment is safe and efficacious for acute respiratory tract infections like common cold and sinusitis. Hence, A. paniculata, mainly andrographolide, could be considered as an excellent candidate for antimicrobial drug development. Considering the importance, medicinal values, and significant role as antimicrobial agents, this study critically evaluated the antimicrobial therapeutic potency of A. paniculata and its metabolites, focusing on the mechanism of action in inhibiting invasive microbes and biofilm formation. A critical evaluation of the secondary metabolites with the aim of identifying pure compounds that possess antimicrobial functions has further added significant values to this study. Notwithstanding that A. paniculata is a promising source of antimicrobial agents and safe treatment for IDs, further empirical research is warranted.

In silico identification of natural products from Traditional Chinese Medicine for cancer immunotherapy

Advances in immunotherapy have revolutionized treatments in many types of cancer. Traditional Chinese Medicine (TCM), which has a long history of clinical adjuvant application against cancer, is emerging as an important medical resource for developing innovative cancer treatments, including immunotherapy. In this study, we developed a quantitative and systems pharmacology-based framework to identify TCM-derived natural products for cancer immunotherapy. Specifically, we integrated 381 cancer immune response-related genes and a compound-target interaction network connecting 3273 proteins and 766 natural products from 66 cancer-related herbs based on literature-mining. Via systems pharmacology-based prediction, we uncovered 182 TCM-derived natural products having potential anti-tumor immune responses effect. Importantly, 32 of the 49 most promising natural products (success rate = 65.31%) are validated by multiple evidence, including published experimental data from clinical studies, in vitro and in vivo assays. We further identified the mechanism-of-action of TCM in cancer immunotherapy using network-based functional enrichment analysis. We showcased that three typical natural products (baicalin, wogonin, and oroxylin A) in Huangqin (Scutellaria baicalensis Georgi) potentially overcome resistance of known oncology agents by regulating tumor immunosuppressive microenvironments. In summary, this study offers a novel and effective systems pharmacology infrastructure for potential cancer immunotherapeutic development by exploiting the medical wealth of natural products in TCM.

Effect of berberine nanoemulsion Photodynamic therapy on cervical carcinoma cell line

Cervical carcinoma is the most common gynecological cancer among young and adult women. There has been increasing interest in natural sources for cervical carcinoma treatment, especially for active compounds from plant extracts as antineoplastic agents. Berberine is an example of one these promising natural products. It is a natural isoquinoline alkaloid and comes from plants, such as Berbis, Coptis, and Hydrastis. It is widely used in Chinese medicine and has demonstrated activity against various cancer cell lines. This work aims to analyze the efficiency of berberine-containing nanoemulsions as photosensitizing agents in photodynamic therapy and their interaction with cervical carcinoma cells and immortalized human keratinocyte cell line. Among all groups tested, berberine nanoemulsions combined with photodynamic therapy induced the most statistically significant phototoxicity in the evaluated cell lines. Fluorescence microscopy demonstrated that the compound was present for up to 48 h when berberine nanoemulsions were used. The reactive oxygen species assay showed an increase in reactive oxygen species in the two studied cell lines after treatment of berberine-containing nanoemulsion combined with photodynamic therapy. The autophagy trial showed significant increases in cell death when berberine-containing nanoemulsion treatment was combined with photodynamic therapy when compared to trichostatin A treatment as a positive control. However, caspase-3 activity did not significantly increase in cervical carcinoma cells and immortalized human keratinocyte cell line. The results suggest that nanoemulsions with berberine have potential for use as photosensitizing agents in photodynamic therapy to treat cervical carcinoma.

Systems pharmacology-based approach to investigate the mechanisms of Danggui-Shaoyao-san prescription for treatment of Alzheimer's disease

Background

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly, characterized by a progressive and irreversible loss of memory and cognitive abilities. Currently, the prevention and treatment of AD still remains a huge challenge. As a traditional Chinese medicine (TCM) prescription, Danggui-Shaoyao-san decoction (DSS) has been demonstrated to be effective for alleviating AD symptoms in animal experiments and clinical applications. However, due to the complex components and biological actions, its underlying molecular mechanism and effective substances are not yet fully elucidated.

Methods

In this study, we firstly systematically reviewed and summarized the molecular effects of DSS against AD based on current literatures of in vivo studies. Furthermore, an integrated systems pharmacology framework was proposed to explore the novel anti-AD mechanisms of DSS and identify the main active components. We further developed a network-based predictive model for identifying the active anti-AD components of DSS by mapping the high-quality AD disease genes into the global drug-target network.

Results

We constructed a global drug-target network of DSS consisting 937 unique compounds and 490 targets by incorporating experimental and computationally predicted drug–target interactions (DTIs). Multi-level systems pharmacology analyses revealed that DSS may regulate multiple biological pathways related to AD pathogenesis, such as the oxidative stress and inflammatory reaction processes. We further conducted a network-based statistical model, drug-likeness analysis, human intestinal absorption (HIA) and blood-brain barrier (BBB) penetration prediction to uncover the key ani-AD ingredients in DSS. Finally, we highlighted 9 key ingredients and validated their synergistic role against AD through a subnetwork.

Conclusion

Overall, this study proposed an integrative systems pharmacology approach to disclose the therapeutic mechanisms of DSS against AD, which also provides novel in silico paradigm for investigating the effective substances of complex TCM prescription.

Uncovering the Mechanisms of Cryptotanshinone as a Therapeutic Agent Against Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a fatal and dominant form of liver cancer that currently has no effective treatment or positive prognosis. In this study, we explored the antitumor effects of cryptotanshinone (CPT) against HCC and the molecular mechanisms underlying these effects using a systems pharmacology and experimental validation approach. First, we identified a total of 296 CPT targets, 239 of which were also HCC-related targets. We elucidated the mechanisms by which CPT affects HCC through multiple network analysis, including CPT-target network analysis, protein-protein interaction network analysis, target-function network analysis, and pathway enrichment analysis. In addition, we found that CPT induced apoptosis in Huh7 and MHCC97-H ells due to increased levels of cleaved PARP, Bax, and cleaved caspase-3 and decreased Bcl-2 expression. CPT also induced autophagy in HCC cells by increasing LC3-II conversion and the expression of Beclin1 and ATG5, while decreasing the expression of p62/SQSTM1. Autophagy inhibitors (3-methyladenine and chloroquine) enhanced CPT-induced proliferation and apoptosis, suggesting that CPT-induced autophagy may protect HCC cells against cell death. Furthermore, CPT was found to inhibit the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway. Interestingly, activation of PI3K by insulin-like growth factor-I inhibited CPT-induced apoptosis and autophagy, suggesting that the PI3K/AKT/mTOR signaling pathway is involved in both CPT-induced apoptosis and autophagy. Finally, CPT was found to inhibit the growth of Huh7 xenograft tumors. In conclusion, we first demonstrated the antitumor effects of CPT in Huh7 and MHCC97-H cells, both in vitro and in vivo. We elucidated the potential antitumor mechanism of CPT, which involved inducing apoptosis and autophagy by inhibiting the PI3K/Akt/mTOR signaling pathway. Our findings may provide valuable insights into the clinical application of CPT, serving as a potential candidate therapeutic agent for HCC treatment.

The Inhibitory Effect of Cordycepin on the Proliferation of MCF-7 Breast Cancer Cells, and its Mechanism: An Investigation Using Network Pharmacology-Based Analysis

Cordyceps militaris is a well-known medicinal mushroom. It is non-toxic and has clinical health benefits including cancer inhibition. However, the anticancer effects of C. militaris cultured in brown rice on breast cancer have not yet been reported. In this study, we simultaneously investigated the anticancer effects of cordycepin and an extract of C. militaris cultured in brown rice on MCF-7 human breast cancer cells using a cell viability assay, cell staining with Hoechst 33342, and an image-based cytometric assay. The C. militaris concentrate exhibited significant MCF-7 cell inhibitory effects, and its IC50 value was 73.48 µg/mL. Cordycepin also exhibited significant MCF-7 cell inhibitory effects, and its IC50 value was 9.58 µM. We applied network pharmacological analysis to predict potential targets and pathways of cordycepin. The gene set enrichment analysis showed that the targets of cordycepin are mainly associated with the hedgehog signaling, apoptosis, p53 signaling, and estrogen signaling pathways. We further verified the predicted targets related to the apoptosis pathway using western blot analysis. The C. militaris concentrate and cordycepin exhibited the ability to induce apoptotic cell death by increasing the cleavage of caspase-7 -8, and -9, increasing the Bcl-2-associated X protein/ B-cell lymphoma 2 (Bax/Bcl-2) protein expression ratio, and decreasing the protein expression of X-linked inhibitor of apoptosis protein (XIAP) in MCF-7 cells. Consequently, the C. militaris concentrate and cordycepin exhibited significant anticancer effects through their ability to induce apoptosis in breast cancer cells.

A Systems Pharmacology Approach Uncovers Wogonoside as an Angiogenesis Inhibitor of Triple-Negative Breast Cancer by Targeting Hedgehog Signaling

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease that lacks clinically actionable genetic alterations that limit targeted therapies. Here we explore a systems pharmacology approach that integrates drug-target networks and large-scale genomic profiles of TNBC and identify wogonoside, one of the major active flavonoids, as a potent angiogenesis inhibitor. We validate that wogonoside attenuates cell migration, tube formation, and rat aorta microvessel outgrowth, and reduces formation of blood vessels in chicken chorioallantoic membrane and TNBC cell-induced Matrigel plugs. In addition, wogonoside inhibits growth and angiogenesis in TNBC cell xenograft models. This network-based approach predicts, and we empirically validate, wogonoside's antiangiogenic effects resulting from vascular endothelial growth factor secretion. Mechanistically, wogonoside inhibits Gli1 nuclear translocation and transcriptional activities associated with Hedgehog signaling, by promoting Smoothened degradation in a proteasome-dependent mechanism. This study offers a powerful, integrated, systems pharmacology-based strategy for oncological drug discovery and identifies wogonoside as a potential TNBC angiogenesis inhibitor.

An Insight Into the Molecular Mechanism of Berberine Towards Multiple Cancer Types Through Systems Pharmacology

Over the past several decades, natural products with poly-pharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Berberine (PubChem CID: 2353), a soliloquies quaternary alkaloid, has been validated to exert powerful effects in many cancers. However, the underlying molecular mechanism is not yet fully elucidated. In this study, we summarized the molecular effects of berberine against multiple cancers based on current available literatures. Furthermore, a systems pharmacology infrastructure was developed to discover new cancer indications of berberine and explore their molecular mechanisms. Specifically, we incorporated 289 high-quality protein targets of berberine by integrating experimental drug-target interactions (DTIs) extracted from literatures and computationally predicted DTIs inferred by network-based inference approach. Statistical network models were developed for identification of new cancer indications of berberine through integration of DTIs and curated cancer significantly mutated genes (SMGs). High accuracy was yielded for our statistical models. We further discussed three typical cancer indications (hepatocarcinoma, lung adenocarcinoma, and bladder carcinoma) of berberine with new mechanisms of actions (MOAs) based on our systems pharmacology framework. In summary, this study systematically provides a powerful strategy to identify potential anti-cancer effects of berberine with novel mechanisms from a systems pharmacology perspective.

Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery

As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.

Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map

BACKGROUND AND OBJECTIVE

Traditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the "multi-component, multi-target" nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map.

METHODS

We interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results.

RESULTS

We clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) - a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in http://tanlab.ucdenver.edu/TCMHub.

CONCLUSIONS

Molecular mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases.

In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

Backgrounds and Aims

Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI.

Materials and Methods

In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM.

Results

16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity.

Conclusion

Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs.

In Silico Oncology Drug Repositioning and Polypharmacology

Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.

Systems pharmacology-based investigation of Sanwei Ganjiang Prescription: related mechanisms in liver injury

Liver injury remains a significant global health problem and has a variety of causes, including oxidative stress (OS), inflammation, and apoptosis of liver cells. There is currently no curative therapy for this disorder. Sanwei Ganjiang Prescription (SWGJP), derived from traditional Chinese medicine (TCM), has shown its effectiveness in long-term liver damage therapy, although the underlying molecular mechanisms are still not fully understood. To explore the underlining mechanisms of action for SWGJP in liver injury from a holistic view, in the present study, a systems pharmacology approach was developed, which involved drug target identification and multilevel data integration analysis. Using a comprehensive systems approach, we identified 43 candidate compounds in SWGJP and 408 corresponding potential targets. We further deciphered the mechanisms of SWGJP in treating liver injury, including compound-target network analysis, target-function network analysis, and integrated pathways analysis. We deduced that SWGJP may protect hepatocytes through several functional modules involved in liver injury integrated-pathway, such as Nrf2-dependent anti-oxidative stress module. Notably, systems pharmacology provides an alternative way to investigate the complex action mode of TCM.

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.

Polypharmacology or Promiscuity? Structural Interactions of Resveratrol With Its Bandwagon of Targets

Resveratrol (3, 4', 5-trihydroxy-trans-stilbene) is a natural phytoalexin found in grapes and has long been thought to be the answer to the "French Paradox." There is no shortage of preclinical and clinical studies investigating the broad therapeutic activity of resveratrol. However, in spite of many comprehensive reviews published on the bioactivity of resveratrol, there has yet to be a report focused on the variety and complexity of its structural binding properties, and its multi-targeted role. An improved understanding of disease mechanisms at the systems level has enabled targeted polypharmacology to mature into a rational drug discovery approach. Unlike traditional hit-to-lead campaigns that typically optimize activity and selectivity for a single target, polypharmacological drugs aim to selectively target multiple proteins, while avoiding critical off target interactions. This strategy bears promise of improved efficacy and reduced clinical attrition. This review seeks to investigate whether the bioactivity of resveratrol is due to a polypharmacological effect or promiscuity of the phenolic small molecule by examining the modes of binding with its diverse collection of protein targets. We focused on annotated targets, identified via the ChEMBL database, and matched these targets to a representative structure deposited in the Protein Data Bank (PDB), as crystal structures are most informative in understanding modes of binding at the atomic level. We discuss the structural aspects of resveratrol itself that permits binding to multiple proteins in various signaling pathways. Furthermore, we suggest that resveratrol's bioactivity is a result of scaffold promiscuity rather than polypharmacology, and the variety of binding modes across targets display little similarity in the pattern of target interaction.

Quantitative and systems pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease

Despite recent advance of therapeutic development, coronary artery disease (CAD) remains one of the major issues to public health. The use of genomics and systems biology approaches to inform drug discovery and development have offered the possibilities for new target identification and in silico drug repurposing. In this study, we propose a network-based, systems pharmacology framework for target identification and drug repurposing in pharmacologic treatment and chemoprevention of CAD. Specifically, we build in silico models by integrating known drug-target interactions, CAD genes derived from the genetic and genomic studies, and the human protein-protein interactome. We demonstrate that the proposed in silico models can successfully uncover approved drugs and novel natural products in potentially treating and preventing CAD. In case studies, we highlight several approved drugs (e.g., fasudil, parecoxib, and dexamethasone) or natural products (e.g., resveratrol, luteolin, daidzein and caffeic acid) with new mechanism-of-action in chemical intervention of CAD by network analysis. In summary, this study offers a powerful systems pharmacology approach for target identification and in silico drug repurposing on CAD.

Natural scaffolds in anticancer therapy and precision medicine

The diversity of natural compounds is essential for their mechanism of action. The source, structures and structure activity relationship of natural compounds contributed to the development of new classes of chemotherapy agents for over 40 years. The availability of combinatorial chemistry and high-throughput screening has fueled the challenge to identify novel compounds that mimic nature's chemistry and to predict their macromolecular targets. Combining conventional and targeted therapies helped to successfully overcome drug resistance and prolong disease-free survival. Here, we aim to provide an overview of preclinical investigated natural compounds alone and in combination to further improve personalization of cancer treatment.

Resveratrol suppresses human cervical carcinoma cell proliferation and elevates apoptosis via the mitochondrial and p53 signaling pathways

Numerous studies have demonstrated the apoptotic and anti-proliferative effects of resveratrol, a natural polyphenolic phytoalexin, on various cancer cell lines. However, the effects of resveratrol on the regulation of human cervical carcinoma, and the mechanisms underlying these effects, remain to be elucidated. In the present study, the potential mechanisms underlying the effects of resveratrol in HeLa cervical carcinoma cells were investigated. The results revealed that resveratrol inhibited proliferation and induced apoptosis in HeLa human cervical cancer cells in a dose-dependent and time-dependent manner. Resveratrol induced cell shrinkage in HeLa cells and apoptosis accompanied by the activation of caspase-3 and -9. Furthermore, resveratrol upregulated the expression of the pro-apoptotic B-cell lymphoma (Bcl)-2-associated X protein and downregulated the expression of the anti-apoptotic proteins Bcl-2 and Bcl-extra large in HeLa cells. In addition, p53, a protein that is essential for cell survival and cell cycle progression, exhibited elevated expression levels in resveratrol-treated HeLa cells. Therefore, resveratrol may be a promising novel inhibitor of human cervical cancer.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.

The Mechanisms of Bushen-Yizhi Formula as a Therapeutic Agent against Alzheimer's Disease

Bushen-Yizhi prescription (BSYZ) has been an effective traditional Chinese medicine (TCM) prescription in treating Alzheimer’s disease (AD) for hundreds of years. However, the underlying mechanisms have not been fully elucidated yet. In this work, a systems pharmacology approach was developed to reveal the underlying molecular mechanisms of BSYZ in treating AD. First, we obtained 329 candidate compounds of BSYZ by in silico ADME/T filter analysis and 138 AD-related targets were predicted by our in-house WEGA algorithm via mapping predicted targets into AD-related proteins. In addition, we elucidated the mechanisms of BSYZ action on AD through multiple network analysis, including compound-target network analysis and target-function network analysis. Furthermore, several modules regulated by BSYZ were incorporated into AD-related pathways to uncover the therapeutic mechanisms of this prescription in AD treatment. Finally, further verification experiments also demonstrated the therapeutic effects of BSYZ on cognitive dysfunction in APP/PS1 mice, which was possibly via regulating amyloid-β metabolism and suppressing neuronal apoptosis. In conclusion, we provide an integrative systems pharmacology approach to illustrate the underlying therapeutic mechanisms of BSYZ formula action on AD.

Circadian hormone control in a human-on-a-chip: In vitro biology's ignored component?

Organs-on-Chips (OoCs) are poised to reshape dramatically the study of biology by replicating in vivo the function of individual and coupled human organs. Such microphysiological systems (MPS) have already recreated complex physiological responses necessary to simulate human organ function not evident in two-dimensional in vitro biological experiments. OoC researchers hope to streamline pharmaceutical development, accelerate toxicology studies, limit animal testing, and provide new insights beyond the capability of current biological models. However, to develop a physiologically accurate Human-on-a-Chip, i.e., an MPS homunculus that functions as an interconnected, whole-body, model organ system, one must couple individual OoCs with proper fluidic and metabolic scaling. This will enable the study of the effects of organ-organ interactions on the metabolism of drugs and toxins. Critical to these efforts will be the recapitulation of the complex physiological signals that regulate the endocrine, metabolic, and digestive systems. To date, with the exception of research focused on reproductive organs on chips, most OoC research ignores homuncular endocrine regulation, in particular the circadian rhythms that modulate the function of all organ systems. We outline the importance of cyclic endocrine regulation and the role that it may play in the development of MPS homunculi for the pharmacology, toxicology, and systems biology communities. Moreover, we discuss the critical end-organ hormone interactions that are most relevant for a typical coupled-OoC system, and the possible research applications of a missing endocrine system MicroFormulator (MES-µF) that could impose biological rhythms on in vitro models. By linking OoCs together through chemical messenger systems, advanced physiological phenomena relevant to pharmacokinetics and pharmacodynamics studies can be replicated. The concept of a MES-µF could be applied to other standard cell-culture systems such as well plates, thereby extending the concept of circadian hormonal regulation to much of in vitro biology. Impact statement Historically, cyclic endocrine modulation has been largely ignored within in vitro cell culture, in part because cultured cells typically have their media changed every day or two, precluding hourly adjustment of hormone concentrations to simulate circadian rhythms. As the Organ-on-Chip (OoC) community strives for greater physiological realism, the contribution of hormonal oscillations toward regulation of organ systems has been examined only in the context of reproductive organs, and circadian variation of the breadth of other hormones on most organs remains unaddressed. We illustrate the importance of cyclic endocrine modulation and the role that it plays within individual organ systems. The study of cyclic endocrine modulation within OoC systems will help advance OoC research to the point where it can reliably replicate in vitro key regulatory components of human physiology. This will help translate OoC work into pharmaceutical applications and connect the OoC community with the greater pharmacology and physiology communities.

Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.