Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles

Deciphering the Molecular Complexity of Hepatocellular Carcinoma: Unveiling Novel Biomarkers and Therapeutic Targets Through Advanced Bioinformatics Analysis

BACKGROUND

Hepatocellular carcinoma (HCC) represents a primary liver tumor characterized by a bleak prognosis and elevated mortality rates, yet its precise molecular mechanisms have not been fully elucidated. This study uses advanced bioinformatics techniques to discern differentially expressed genes (DEGs) implicated in the pathogenesis of HCC. The primary objective is to discover novel biomarkers and potential therapeutic targets that can contribute to the advancement of HCC research.

METHODS

The bioinformatics analysis in this study primarily utilized the Gene Expression Omnibus (GEO) database as data source. Initially, the Transcriptome analysis console (TAC) screened for DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network of the proteins associated to the identified DEGs with the STRING database. We obtained our hub genes using Cytoscape and confirmed the results through the GEPIA database. Furthermore, we assessed the prognostic significance of the identified hub genes using the GEPIA database. To explore the regulatory interactions, a miRNA-gene interaction network was also constructed, incorporating information from the miRDB database. For predicting the impact of gene overexpression on drug effects, we utilized CANCER DP.

RESULTS

A comprehensive analysis of HCC gene expression profiles revealed a total of 4716 DEGs, consisting of 2430 upregulated genes and 2313 downregulated genes in HCC sample compared to healthy control group. These DEGs exhibited significant enrichment in key pathways such as the PI3K-Akt signaling pathway, nuclear receptors meta-pathway, and various metabolism-related pathways. Further exploration of the PPI network unveiled the P53 signaling pathway and pyrimidine metabolism as the most prominent pathways. We identified 10 hub genes (ASPM, RRM2, CCNB1, KIF14, MKI67, SHCBP1, CENPF, ANLN, HMMR, and EZH2) that exhibited significant upregulation in HCC samples compared to healthy control group. Survival analysis indicated that elevated expression levels of these genes were strongly associated with changes in overall survival in HCC patients. Lastly, we identified specific miRNAs that were found to influence the expression of these genes, providing valuable insights into potential regulatory mechanisms underlying HCC progression.

CONCLUSION

The findings of this study have successfully identified pivotal genes and pathways implicated in the pathogenesis of HCC. These novel discoveries have the potential to significantly enhance our understanding of HCC at the molecular level, opening new ways for the development of targeted therapies and improved prognosis evaluation.

Unraveling the mechanisms of Fenugreek seed for managing different gynecological disorders: steroidal saponins and isoflavones revealed as key bioactive metabolites

This study employed network pharmacology-based analysis and reverse molecular docking to investigate the molecular targets and pathways associated with gynecological disorders, particularly those related to steroidal hormones and their receptors, and the potential therapeutic effects of fenugreek (Trigonella foenum-graecum L.) constituents. The STITCH 5.0 database was utilized to identify potential molecular targets, and a compound-target network was constructed. The main targets associated with gynecological disorders included estrogen receptor beta (ESR2), estrogen-related receptor gamma (GPER1), oxytocin receptor (OXTR), progesterone receptor (PGR), prolactin receptor (PRLR), and several enzymes involved in sex hormone biosynthesis. Additionally, network topological analysis revealed that specific compounds, such as quercetin, luteolin, genistein, and vitexin, had significant interactions with the identified targets. Reverse molecular docking analysis confirmed the interactions between the identified compounds and target proteins where quercetin, luteolin, genistein, 4'-methylgenistein, trigoneoside IIB, diosgenin, and vitexin possessed the highest combined docking scores, indicating their multi-target nature. The results highlighted the potential of steroidal saponins, isoflavones, and flavones as active constituents of fenugreek with implications for lactation, reproductive processes, and estrogenic activity. The chemical profiling of saponin-enriched and flavonoid-enriched fractions using UPLC/MS/MS further supported the presence of these bioactive compounds. In an animal model study, the steroidal saponins-enriched fraction of fenugreek seed exhibited a significant increase in the body weight of lactating female rats and serum prolactin levels while the flavonoids-enriched fraction showed an increase in serum estradiol levels and improved the histological structure of ovaries.

A Study Against Colon Cancer Mechanism of Xanthium sibiricum Herba Based on Computer Simulation and Bioinformatics

INTRODUCTION

Cancer is one of the leading causes of death worldwide, accounting for nearly one in six deaths in 2020. As a folk medicine, Xanthium sibiricum Herba (XSH) has been used many times in clinical practice for the treatment of various diseases. With the increasing number of cancer patients, there is a clinical need to find effective anti-cancer drugs.

AIM

This study aims to explores the bioactivity and the anti-cancer mechanism of XSH.

METHODS

In this study, bioinformatics, network pharmacology, molecular docking, molecular dynamics simulation techniques, and apoptosis assay were used to explore the bioactivity and the anti- cancer mechanism of XSH.

RESULTS

Finally, seven active ingredients in XSH after the screening were obtained, the two most active compounds were β-sitosterol and aloe-emodin, and good anti-cancer activity of XSH was predicted.

DISCUSSION

Four core targets were obtained from the PPI network map, namely Caspase-3 (CASP3), Transcription factor AP-1 (JUN), Myc proto-oncogene protein (MYC), and cellular tumor antigen p53 (TP53). GO and KEGG analyses showed that the mechanism of XSH anti-cancer is mainly related to the apoptosis process, and the main signaling pathways are enriched in the p53 signaling pathway, Apoptosis, and MAPK signaling. The molecular docking and molecular dynamics simulation results showed that CASP3, JUN, MYC, and TP53 had a high affinity with β- sitosterol and aloe-emodin. Bioinformatics analyses demonstrated the importance of core targets. Apoptosis assay showed that XSH could significantly promote the apoptosis of cancer cells, and inhibit their proliferation and migration, especially colon cancer cells.

CONCLUSION

This study uncovered the main active components, bioactivities, and potential targets of XSH, and further revealed the multi-component, multi-target, and multi-pathway mechanism of XSH for cancer treatment and promoting apoptosis.

The Potential of Eleutherine bulbosa in Inducing Apoptosis and Inhibiting Cell Cycle in Breast Cancer: A Network Pharmacology Approach and In Vitro Experiments

OBJECTIVE

The objective of this study was to evaluate the potential and mechanisms of phytochemicals in Eleutherine bulbosa (EBE) in inducing apoptosis and inhibiting the cell cycle in breast cancer through a network pharmacology approach and in vitro validation.

METHODS

This research employed a literature review approach to identify active anti-cancer compounds and utilized a network pharmacology approach to predict the mechanisms of action of EBE compounds in breast cancer. In addition, in vitro experiments were conducted using MTT method to evaluate the effects of EBE on the cytotoxicity of T47D cells, and the flow cytometry method was employed to determine the impact of EBE on apoptosis and the cell cycle.

RESULTS

The network pharmacology analysis revealed that EBE had an impact on 42 genes involved in breast cancer, including 23 important target genes implicated in the pathophysiology of breast cancer. Pathway analysis using the KEGG database showed a close association between EBE and crucial signaling pathways in breast cancer, including P53 signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, apoptosis and cell cycle. In vitro experiments demonstrated that EBE exhibited moderate anti-cancer activity. Furthermore, EBE demonstrated significant potential in inducing apoptosis in breast cancer cells, with a percentage of apoptotic cells reaching 93.6%. Additionally, EBE was observed to disrupt the cell cycle, leading to a significant increase in the sub G1 and S phases, and a significant decrease in the G2-M and G1 phases.

CONCLUSION

EBE has the potential to be an anti-cancer agent through various mechanisms, including apoptosis induction and cell cycle inhibition in breast cancer cells. These findings provide new insights into the potential of EBE as an alternative treatment for breast cancer.

Pyrrolizine/indolizine-bearing (un)substituted isoindole moiety: design, synthesis, antiproliferative and MDR reversal activities, and in silico studies

Two new series of pyrrolizine/indolizine derivative-bearing (un)substituted isoindole moiety were designed and synthesized. The anticancer potential of the new compounds was evaluated against hepatocellular carcinoma (HepG-2), colorectal carcinoma, colon cancer (HCT-116), and breast cancer (MCF-7) cell lines. Compounds 6d and 6o were the most potent derivatives with IC50 values ranging from 6.02 to 13.87 μM against HePG-2, HCT-116, and MCF-7 cell lines. Moreover, methyl analog of the fluoro-substituted indolizine derivative 6m revealed significant antiproliferative activity against HePG-2, HCT-116, and MCF-7 cancer cell lines with IC50 values of 11.97, 28.37, and 19.87 μM, respectively. The most active anticancer analogs, 6d, 6m, and 6o, were inspected for their putative mechanism of action by estimating their epidermal growth factor receptor (EGFR) and cyclin-dependent kinase (CDK 2) inhibitory activities. Thus, compound 6o displayed the most inhibitory activity against EGFR and CDK 2 with IC50 values of 62 and 118 nM, respectively. Additionally, the quantitative real-time PCR analysis for the P-glycoprotein effect of compounds 6d, 6m, and 6o was performed, in which compound 6o illustrated significant down-regulation of P-gp against the HepG-2 cell line by 0.2732 fold. Mechanistic studies for the most active compounds involving the reversal doxorubicin (DOX) effect of compounds 6d, 6m, and 6o were performed, which illustrated cytotoxic activity with IC50 22.27, 3.88, and 8.79 μM, respectively. Moreover, the apoptotic activity of the most active derivative 6o on HCT-116 cancer cells showed accumulation in the G1 and S phases of the cell cycle.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma

BACKGROUND

We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results.

METHODS

The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso.

RESULTS

The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI.

CONCLUSIONS

Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe.

Screening of core genes and prediction of ceRNA regulation mechanism of circRNAs in nasopharyngeal carcinoma by bioinformatics analysis

Background: Nasopharyngeal carcinoma (NPC) represents a highly aggressive malignant tumor. Competing endogenous RNAs (ceRNA) regulation is a common regulatory mechanism in tumors. The ceRNA network links the functions between mRNAs and ncRNAs, thus playing an important regulatory role in diseases. This study screened the potential key genes in NPC and predicted regulatory mechanisms using bioinformatics analysis.

Methods: The merged microarray data of three NPC-related mRNA expression microarrays from the Gene Expression Omnibus (GEO) database and the expression data of tumor samples or normal samples from the nasopharynx and tonsil in The Cancer Genome Atlas (TCGA) database were both subjected to differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The results from two different databases were intersected with WGCNA results to obtain potential regulatory genes in NPC, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. The hub-gene in candidate genes was discerned through Protein-Protein Interaction (PPI) analysis and its upstream regulatory mechanism was predicted by miRwalk and circbank databases.

Results: Totally 68 upregulated genes and 96 downregulated genes in NPC were screened through GEO and TCGA. According to WGCNA, the NPC-related modules were screened from GEO and TCGA analysis results, and the genes in the modules were obtained. After the results of differential analysis and WGCNA were intersected, 74 differentially expressed candidate genes associated with NPC were discerned. Finally, fibronectin 1 (FN1) was identified as a hub-gene in NPC. Prediction of upstream regulatory mechanisms of FN1 suggested that FN1 may be regulated by ceRNA mechanisms involving multiple circRNAs, thereby influencing NPC progression through ceRNA regulation.

Conclusion: FN1 is identified as a key regulator in NPC development and is likely to be regulated by numerous circRNA-mediated ceRNA mechanisms.

Deciphering the protective effect of Buzhong Yiqi Decoction on osteoporotic fracture through network pharmacology and experimental validation

BACKGROUND

Osteoporotic fracture (OPF) is one of the most common skeletal diseases in an aging society. The Chinese medicine formula Buzhong Yiqi Decoction (BZYQD) is commonly used for treating OPF. However, the essential bioactive compounds and the underlying molecular mechanisms that promote fracture repair remain unclear.

METHODS

We used network pharmacology and experimental animal validation to address this issue. First, 147 bioactive BZYQD compounds and 32 target genes for treating OPF were screened and assessed. A BZYQD-bioactive compound-target gene-disease network was constructed using the Cytoscape software. Functional enrichment showed that the candidate target genes were enriched in oxidative stress- and inflammation-related biological processes and multiple pathways, including nuclear factor kappa B (NF-κB), and mitogen-activated protein kinase (MAPK) signaling pathways. Furthermore, an OPF rat model was established and treated with BZYQD.

RESULTS

The results revealed that BZYQD ameliorated OPF characteristics, including femoral microarchitecture, biomechanical properties, and histopathological changes, in a dose-dependent manner. Results of enzyme-linked immunosorbent assay showed that BZYQD reduced the serum's pro-inflammatory cytokines [Tumor necrosis factor-alpha (TNF-α), Interleukin (IL)-1β, and IL-6] and improved oxidative stress-related factors [glutathione (GSH) and superoxide dismutase (SOD)]. BZYQD significantly decreased the protein expression of NF-κB in OPF rat femurs, suppressed NF-κB activation, and activated the nuclear factor-erythroid factor 2-related factor (Nrf2)/heme oxygenase 1 (HO-1) and p38 MAPK as well ERK pathways.

CONCLUSIONS

Our results suggest that BZYQD could improve inflammation and oxidative stress during fracture repair by suppressing NF-κB and activating Nrf2/MAPK signaling pathways.

Scutellaria baicalensis in the Treatment of Hepatocellular Carcinoma: Network Pharmacology Analysis and Experimental Validation

Objective

The aim of the study was to use a network pharmacological method and experimental validation to examine the mechanism of Scutellaria baicalensis (SB) against hepatocellular carcinoma (HCC).

Methods

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and GeneCards were used for screening of targets of SB for the treatment of HCC. Cytoscape (3.7.2) software was used to construct the "drug-compound-intersection target interaction" interaction network. The STING database was used to analyze the interactions of the previous intersecting targets. The results were visualized and processed by performing GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis at the target sites. The core targets were docked with the active components by AutoDockTools-1.5.6 software. We used cellular experiments to validate the bioinformatics predictions.

Results

A total of 92 chemical components and 3258 disease targets including 53 intersecting targets were discovered. The results showed that wogonin and baicalein, the main chemical components of SB, could inhibit the viability and proliferation of hepatocellular carcinoma cells, promote apoptosis through the mitochondrial apoptotic pathway, and effectively act on AKT1, RELA, and JUN targets.

Conclusion

SB has multiple components and targets in the treatment of HCC, providing possible potential targets for the treatment of HCC and providing a basis for further research.

Integrated analysis of Dendrobium nobile extract Dendrobin A against pancreatic ductal adenocarcinoma based on network pharmacology, bioinformatics, and validation experiments

Background: Dendrobium nobile (D. nobile), a traditional Chinese medicine, has received attention as an anti-tumor drug, but its mechanism is still unclear. In this study, we applied network pharmacology, bioinformatics, and in vitro experiments to explore the effect and mechanism of Dendrobin A, the active ingredient of D. nobile, against pancreatic ductal adenocarcinoma (PDAC). Methods: The databases of SwissTargetPrediction and PharmMapper were used to obtain the potential targets of Dendrobin A, and the differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The protein-protein interaction (PPI) network for Dendrobin A anti-PDAC targets was constructed based on the STRING database. Molecular docking was used to assess Dendrobin A anti-PDAC targets. PLAU, one of the key targets of Dendrobin A anti-PDAC, was immunohistochemically stained in clinical tissue arrays. Finally, in vitro experiments were used to validate the effects of Dendrobin A on PLAU expression and the proliferation, apoptosis, cell cycle, migration, and invasion of PDAC cells. Results: A total of 90 genes for Dendrobin A anti-PDAC were screened, and a PPI network for Dendrobin A anti-PDAC targets was constructed. Notably, a scale-free module with 19 genes in the PPI indicated that the PPI is highly credible. Among these 19 genes, PLAU was positively correlated with the cachexia status while negatively correlated with the overall survival of PDAC patients. Through molecular docking, Dendrobin A was found to bind to PLAU, and the Dendrobin A treatment led to an attenuated PLAU expression in PDAC cells. Based on clinical tissue arrays, PLAU protein was highly expressed in PDAC cells compared to normal controls, and PLAU protein levels were associated with the differentiation and lymph node metastatic status of PDAC. In vitro experiments further showed that Dendrobin A treatment significantly inhibited the proliferation, migration, and invasion, inducing apoptosis and arresting the cell cycle of PDAC cells at the G2/M phase. Conclusion: Dendrobin A, a representative active ingredient of D. nobile, can effectively fight against PDAC by targeting PLAU. Our results provide the foundation for future PDAC treatment based on D. nobile.

2,3-Dihydro-Quinazolin-4(1H)-One as a Fluorescent Sensor for Hg2+ Ion and its Docking Studies in Cancer Treatment

The 2,3-dihydro-quinazolin-4(1H)-one was synthesised via the deployment of SBA-Pr-SO3H and its application was explored as a highly selective fluorescent sensor for Hg2+ ion; fluorescence intensity was decreased selectively by Hg2+ ions. Furthermore, this compound also indicated for its superb anti-interference ability among other ions. It is important to mention that this compound could be employed to detect a very low amount of Hg2+ ions, which are highly toxic and general contaminants. The docking study shows that the molecule, 2,3-dihydro-quinazolin-4(1H)-one, is a good inhibitor for the 5ACC enzyme.

Sanguisorba officinalis L. suppresses non-small cell lung cancer via downregulating the PI3K/AKT/mTOR signaling pathway based on network pharmacology and experimental investigation

Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Sanguisorba officinalis L. (SOL), a traditional Chinese herbal medicine called Diyu, has been shown to have potent antitumor effects. However, the role of SOL in suppressing NSCLC remains unknown. Methods: Network pharmacology was employed for acquiring the potential targets and mechanisms of SOL in NSCLC. Based on the predictions of network pharmacology, we used CCK8 and EdU assays to investigate cell proliferation, flow cytometry to investigate apoptosis, wound healing assay to investigate cell migration, and transwell assay to investigate cell invasion in vitro. Western blot was employed for detecting the potential proteins, including signaling pathways and apoptosis. The A549-bearing athymic nude mice were employed to verify the effect on cell proliferation and apoptosis in vivo. Results: SOL significantly inhibited the proliferation, migration and invasion of NSCLC cells in a dose-dependent manner. Flow cytometry showed that the apoptotic ratio and ROS level of NSCLC cells increased significantly with increasing concentrations. AKT and the PI3K-AKT signaling pathway were analyzed as the most relevant target and pathway via network pharmacology predictions. Western blotting revealed that the expression levels of p-PI3K, p-AKT, and p-mTOR in NSCLC cells treated with SOL were significantly downregulated, while cleaved PARP-1 and caspase-3 were upregulated in a dose-dependent manner. The results in the mouse xenograft model were consistent with those in NSCLC cell lines. Conclusion: SOL downregulated the PI3K/AKT/mTOR signaling pathway to suppress NSCLC.

Virtual Screening and Network Pharmacology-Based Study to Explore the Pharmacological Mechanism of Clerodendrum Species for Anticancer Treatment

BACKGROUND

Cancer is a second leading cause of death in the world, killing approximately 3500 per million people each year. Therefore, the drugs with multitarget pharmacology based on biological networks are crucial to investigate the molecular mechanisms of cancer drugs and repurpose the existing drugs to reduce adverse effects. Clerodendrum is a diversified genus with a wide range of economic and pharmacological properties. Limited studies were conducted on the genus's putative anticancer properties and the mechanisms of action based on biological networks remains unknown. This study was aimed to construct the possible compound/target/pathway biological networks for anticancer effect of Clerodendrum sp. using docking weighted network pharmacological approach and to investigate its potential mechanism of action.

METHODS

A total of 194 natural Clerodendrum sp. Compounds were retrieved from public databases and screened using eight molecular descriptors. The cancer-associated gene targets were retrieved from databases and the function of the target genes with related pathways were examined. Cytoscape v3.7.2 was used to build three major networks: compound-target network, target-target pathway network, and compound-target-pathway network.

RESULTS

Our finding indicates that the anticancer activity of Clerodendrum sp. involves 6 compounds, 9 targets, and 63 signaling pathways, resulting in multicompounds, multitargets, and multipathways networks. Additionally, molecular dynamics (MD) simulations were used to estimate the binding affinity of the best hit protein-ligand complexes. Conclusion. This study suggests the potential anticancer activity of Clerodendrum sp. which could further contribute to scavenger novel compounds for the development of new alternative anticancer drugs.

What are the challenges with multi-targeted drug design for complex diseases?

INTRODUCTION

Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account.

AREAS COVERED

In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases.

EXPERT OPINION

Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.

Deciphering the Effects and Mechanisms of Yi-Fei-San-Jie-pill on Non-Small Cell Lung Cancer With Integrating Network Target Analysis and Experimental Validation

Non-small cell lung cancer (NSCLC), which accounts for 85% of lung cancer cases, calls for better therapy. Yi-Fei-San-Jie-pill (YFSJ), a well-applicated traditional Chinese medicine formula, was reported to be effective in the treatment of NSCLC. However, its anti-tumor mechanism still needs to be fully elucidated. Herein, a reliable preclinical orthotopic but not subcutaneous model of NSCLC in mice was established to evaluate the anti-cancer properties and further validate the mechanisms of YFSJ. A bioinformatic analysis was executed to identify the potential targets and key pathways of YFSJ on NSCLC. In detail, the anti-tumor effect of YFSJ and the autophagy inhibitor 3-MA was evaluated according to the tumor fluorescence value and comparison of different groups' survival times. As a result, YFSJ markedly decreased tumor size and prolonged survival time in contrast with those in the orthotopic model group (p < 0.05), and it also significantly regulated the protein expression levels of apoptosis- and autophagy-related proteins. In conclusion, this study provides convincing evidence that YFSJ could inhibit the growth of tumors and prolong the survival time of tumor-bearing mice based on the NSCLC orthotopic model, and its anti-tumor effect was closely associated with the promotion of apoptosis and interference of autophagy coupled with regulation of immune infiltration.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Identifying effective drug combinations to treat cancer is a challenging task, either experimentally or computationally. Here, the authors develop a bipartite network modelling approach to propose drug combination strategies in acute myeloid leukaemia using patient and cell line drug screening data.

Revealing Potential Bioactive Compounds and Mechanisms of Lithospermum erythrorhizon against COVID-19 via Network Pharmacology Study

Lithospermum erythrorhizon (LE) is known in Korean traditional medicine for its potent therapeutic effect and antiviral activity. Currently, coronavirus (COVID-19) disease is a developing global pandemic that can cause pneumonia. A precise study of the infection and molecular pathway of COVID-19 is therefore obviously important. The compounds of LE were identified from the Natural Product Activity and Species Source (NPASS) database and screened by SwissADME. The targets interacted with the compounds and were selected using the Similarity Ensemble Approach (SEA) and Swiss Target Prediction (STP) methods. PubChem was used to classify targets linked to COVID-19. The protein-protein interaction (PPI) networks and signaling pathways-targets-bioactive compounds (STB) networks were constructed by RPackage. Lastly, we performed the molecular docking test (MDT) to verify the binding affinity between significant complexes through AutoDock 1.5.6. The Natural Product Activity and Species Source (NPASS) revealed a total of 82 compounds from LE, which interacted with 1262 targets (SEA and STP), and 249 overlapping targets were identified. The 19 final overlapping targets from the 249 targets and 356 COVID-19 targets were ultimately selected. A bubble chart exhibited that inhibition of the MAPK signaling pathway could be a key mechanism of LE on COVID-19. The three key targets (RELA, TNF, and VEGFA) directly related to the MAPK signaling pathway, and methyl 4-prenyloxycinnamate, tormentic acid, and eugenol were related to each target and had the most stable binding affinity. The three bioactive effects on the three key targets might be synergistic effects to alleviate symptoms of COVID-19 infection. Overall, this study shows that LE can play a role in alleviating COVID-19 symptoms, revealing that the three components (bioactive compounds, targets, and mechanism) are the most significant elements of LE against COVID-19. However, the promising mechanism of LE on COVID-19 is only predicted on the basis of mining data; the efficacy of the chemical compounds and the affinity between compounds and the targets in experiment was ignored, which should be further substantiated through clinical trials.

iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation

Cancer is one of the most deadly diseases that annually kills millions of people worldwide. The investigation on anticancer medicines has never ceased to seek better and more adaptive agents with fewer side effects. Besides chemically synthetic anticancer compounds, natural products are scientifically proved as a highly potential alternative source for anticancer drug discovery. Along with experimental approaches being used to find anticancer drug candidates, computational approaches have been developed to virtually screen for potential anticancer compounds. In this study, we construct an ensemble computational framework, called iANP-EC, using machine learning approaches incorporated with evolutionary computation. Four learning algorithms (k-NN, SVM, RF, and XGB) and four molecular representation schemes are used to build a set of classifiers, among which the top-four best-performing classifiers are selected to form an ensemble classifier. Particle swarm optimization (PSO) is used to optimise the weights used to combined the four top classifiers. The models are developed by a set of curated 997 compounds which are collected from the NPACT and CancerHSP databases. The results show that iANP-EC is a stable, robust, and effective framework that achieves an AUC-ROC value of 0.9193 and an AUC-PR value of 0.8366. The comparative analysis of molecular substructures between natural anticarcinogens and nonanticarcinogens partially unveils several key substructures that drive anticancerous activities. We also deploy the proposed ensemble model as an online web server with a user-friendly interface to support the research community in identifying natural products with anticancer activities.

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug's representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Machine learning approaches for drug combination therapies

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.

Comparative analysis of molecular fingerprints in prediction of drug combination effects

Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

Jiangzhi Granule attenuates non-alcoholic steatohepatitis by suppressing TNF/NFκB signaling pathway-a study based on network pharmacology

Jiangzhi Granule is a commonly used traditional Chinese medicine for treating non-alcoholic fatty liver disease. However, its key ingredients and underlying mechanisms for attenuating nonalcoholic steatohepatitis (NASH) remain unclear. To address this issue, UPLC-TOF-MS based chemical profiling, network pharmacology and animal experimental validation were employed. First, a total of 56 main ingredients of Jiangzhi Granule and 38 ingredients in the blood and liver (after oral administration) were identified. Then, 170 potential targets of the absorbed ingredients and 50 targets of NASH were identified, and 10 overlapped genes were identified as candidate targets of Jiangzhi Granule for NASH treatment. A Jiangzhi Granule-ingredients-targets-disease network was constructed using Cytoscape software, which included eight main ingredients (such as emodin, resveratrol and quercetin) and 10 candidate targets (such as TNF, IL6 and CCL2). Functional enrichment indicated that the candidate targets were enriched in multiple pathways (such as the TNF signaling pathway). Furthermore, a NASH mice model was constructed and intervened with Jiangzhi Granule. The results revealed that Jiangzhi Granule could ameliorate NASH characteristics, such as histopathological changes and liver cholesterol level. Meanwhile, Jiangzhi Granule significantly decreased the mRNA and protein expression of TNFα in NASH mice liver, suppressed NFκB activation, and inhibited the expression of macrophage activation marker F4/80 and M1-type polarization marker CD11b/CD11c. ELISA assay indicated that Jiangzhi Granule reduced pro-inflammatory cytokines (including TNFα, IL-1β and IL-6) in the liver. Collectively, our results suggested that Jiangzhi Granule could attenuate NASH by suppressing TNF/NFκB signaling mediated macrophage M1-type polarization.

Topological network measures for drug repositioning

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

Network-based modeling of herb combinations in traditional Chinese medicine

Traditional Chinese medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein–protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.

Therapeutic Potential of Natural Psychoactive Drugs for Central Nervous System Disorders: A Perspective from Polypharmacology

In the drug development, the formation of highly selective ligands has been unsuccessful in the treatment of central nervous system disorders. Multi-target ligands, from the polypharmacology paradigm, are being proposed as treatments for these complex disorders, since they offer enhanced efficacy and a strong safety profile. Natural products are the best examples of multi-target compounds, so they are of high interest within this paradigm. Additionally, recent research on psychoactive drugs of natural origin, such as ayahuasca and cannabis, has demonstrated the promising therapeutic potential for the treatment of some psychiatric and neurological disorders. In this text, we describe how research on psychoactive drugs can be effectively combined with the polypharmacology paradigm, providing ayahuasca and cannabis research as examples. The advantages and disadvantages are also discussed.

Cryptotanshinone Is a Intervention for ER-Positive Breast Cancer: An Integrated Approach to the Study of Natural Product Intervention Mechanisms

Background: Resistance to endocrine therapy has hampered clinical treatment in patients with ER-positive breast cancer (BRCA). Studies have confirmed that cryptotanshinone (CPT) has cytotoxic effects on BRCA cells and can significantly inhibit the proliferation and metastasis of ER-positive cancer cells.

Methods: We analyzed the gene high-throughput data of ER-positive and negative BRCA to screen out key gene targets for ER-positive BRCA. Finally, the effects of CPT on BRCA cells (MCF-7 and MDA-MB-231) were examined, and quantitative RT-PCR was used to evaluate the expression of the key targets during CPT intervention.

Results: A total of 169 differentially expressed genes were identified, and revealed that CPT affects the ER-positive BRCA cells by regulating CDK1, CCNA2, and ESR1. The overall experimental results initially show that MCF-7 cells were more sensitive to CPT than MDA-MB-231 cells, and the expression of ESR1 was not affected in the BRCA cells during CPT intervention, while the expression of CDK1 and CCNA2 were significantly down-regulated.

Conclusion: CPT can inhibit the proliferation and migration of BRCA cells by regulating CDK1, CCNA2, and ESR1, especially in ER-positive BRCA samples. On the one hand, our research has discovered the possible mechanism that CPT can better interfere with ER+ BRCA; on the other hand, the combination of high-throughput data analysis and network pharmacology provides valuable information for identifying the mechanism of drug intervention in the disease.

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This paper had been uploaded to arXiv : https://arxiv.org/abs/2009.10931.

Desirable drug-drug interactions or when a matter of concern becomes a renewed therapeutic strategy

Drug-drug interactions are sometimes considered to be detrimental and responsible for adverse effects. In some cases, however, some are stakeholders of the efficiency of the treatment and this combinatorial strategy is exploited by some drug associations, including levodopa (L-Dopa) and dopadecarboxylase inhibitors, β-lactam antibiotics and clavulanic acid, 5-fluorouracil (5-FU) and folinic acid, and penicillin and probenecid. More recently, some drug-drug combinations have been integrated in modern drug design strategies, aiming to enhance the efficiency of already marketed drugs with new compounds acting not only as synergistic associations, but also as real boosters of activity. In this review, we provide an update of examples of such strategies, with a special focus on microbiology and oncology.

Phytochemical Information and Biological Activities of Quinolizidine Alkaloids in Sophora: A Comprehensive Review

Quinolizidine alkaloids, a main form of alkaloids found in the genus Sophora, have been shown to have many pharmacological effects. This review aims to summarize the photochemical reports and biological activities of quinolizidine alkaloids in Sophora. The collected information suggested that a total of 99 quinolizidine alkaloids were isolated and detected from different parts of Sophora plants, represented by lupinine-type, cytisine-type, sparteine-type, and matrine-type. However, quality control needs to be monitored because it could provide basic information for the reasonable and efficient use of quinolizidine alkaloids as medicines and raw materials. The nonmedicinal parts may be promising to be used as a source of quinolizidine alkaloid raw materials and to reduce the waste of resources and environmental pollution. In addition, the diversity of chemical compounds based on the alkaloid scaffold to make a biological compound library needs to be extended, which may reduce toxicity and find new bioactivities of quinolizidine alkaloids. The bioactivities most reported are in the fields of antitumor activity along with the effects on the cardiovascular system. However, those studies rely on theoretical research, and novel drugs based on quinolizidine alkaloids are expected.

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.

Identification of Active Compounds of Mahuang Fuzi Xixin Decoction and Their Mechanisms of Action by LC-MS/MS and Network Pharmacology

The decoction is an important dosage form of traditional Chinese medicine (TCM) administration. The Mahuang Fuzi Xixin decoction (MFXD) is widely used to treat allergic rhinitis (AR) in China. However, its active compounds and therapeutic mechanisms are unclear. The aim of this study was to establish an integrative method to identify the bioactive compounds and reveal the mechanisms of action of MFXD. LC-MS/MS was used to identify the compounds in MFXD, followed by screening for oral bioavailability. TCMSP, BindingDB, STRING, DAVID, and KEGG databases and algorithms were used to gather information. Cytoscape was used to visualize the networks. Twenty-four bioactive compounds were identified, and thirty-seven predicted targets of these compounds were associated with AR. DAVID analysis suggested that these compounds exert their therapeutic effects by modulating the Fc epsilon RI, B-cell receptor, Toll-like receptor, TNF, NF-κB, and T-cell receptor signaling pathways. The PI3K/AKT and cAMP signaling pathways were also implicated. Ten of the identified compounds, quercetin, pseudoephedrine, ephedrine, β-asarone, methylephedrine, α-linolenic acid, cathine, ferulic acid, nardosinone, and higenamine, seemed to account for most of the beneficial effects of MFXD in AR. This study showed that LC-MS/MS followed by network pharmacology analysis is useful to elucidate the complex mechanisms of action of TCM formulas.

DrugComb: an integrative cancer drug combination data portal

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users’ own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

UNaProd: A Universal Natural Product Database for Materia Medica of Iranian Traditional Medicine

BACKGROUND

Iranian traditional medicine (ITM) is a holistic medical system that uses a wide range of medicinal substances to treat disease. Reorganization and standardization of the data on ITM concepts is a necessity for optimal use of this rich source. In an initial step towards this goal, we created a database of ITM materia medica. Main Body. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, a database of natural medicinal substances was created using both text mining methods and manual editing. UNaProd, a Universal Natural Product database for materia medica of ITM, is currently host to 2696 monographs, from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. Currently, systems biology, and more precisely systems medicine and pharmacology, can be an aid in providing rationalizations for many traditional medicines and elucidating a great deal of knowledge they can offer to guide future research in medicine.

CONCLUSIONS

A database of materia medica is a stepping stone in creating a systems pharmacology platform of ITM that encompasses the relationships between the drugs, their targets, and diseases. UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features, respectively, and it is freely available at http://jafarilab.com/unaprod/.

Integrated network pharmacology and targeted metabolomics to reveal the mechanism of nephrotoxicity of triptolide

Triptolide (TP) is one of the important active components in Tripterygium wilfordii Hook. F., which shows strong anti-inflammatory and immunomodulatory effects. However, a large number of literature studies have reported that TP is the main component causing nephrotoxicity, and the mechanism of nephrotoxicity has not yet been revealed. Therefore, it is of great practical significance to clarify the toxicity mechanism of TP. This study integrated network pharmacology and targeted metabolomics to reveal the nephrotoxicity mechanism of TP. Firstly, network pharmacology screening of 61 action targets related to TP induced nephrotoxicity, with 39 direct targets and 22 indirect targets, was performed. Subsequently, based on a large-scale protein-protein interaction (PPI) and molecular docking validation, the core targets were identified. Based on the above targets and enrichment analysis, the purine metabolism, Toll-like receptor signaling pathway and NF-κB signaling pathway were found play a pivotal role in TP-induced nephrotoxicity. Literature investigation showed that purine and pyrimidine metabolism pathways were closely related to kidney diseases. Therefore, by using the quantitative method of determining endogenous purine and pyrimidine previously established in the laboratory, a targeted metabolomic analysis of TP was carried out. Finally, six nephrotoxicity biomarkers, dihydroorotate, thymidine, 2-deoxyinosine, uric acid, adenosine and xanthine, were found. Combining the above results, the mechanisms underlying the nephrotoxicity of TP were speculated to be due to the over-consumption of xanthine and uric acid, which would result in enormous ROS being released in response to oxidative stress in the body. Furthermore, activation of the Toll-like receptor signalling pathway can promotes the phosphorylation of the downstream protein NF-κB and causes an inflammatory response that ultimately leads to nephrotoxicity.

Ibrutinib as a potential therapeutic option for HER2 overexpressing breast cancer - the role of STAT3 and p21

Treatment response rates to current anticancer therapies for HER2 overexpressing breast cancer are limited and are associated with severe adverse drug reactions. Tyrosine kinases perform crucial roles in cellular processes by mediating cell signalling cascades. Ibrutinib is a recently approved Tyrosine Kinase Inhibitor (TKI) that has been shown be an effective therapeutic option for HER2 overexpressing breast cancer. The molecular mechanisms, pathways, or genes that are modulated by ibrutinib and the mechanism of action of ibrutinib in HER2 overexpressing breast cancer remain obscure. In this study, we have performed a kinome array analysis of ibrutinib treatment in two HER2 overexpressing breast cancer cell lines. Our analysis shows that ibrutinib induces changes in nuclear morphology and causes apoptosis via caspase-dependent extrinsic apoptosis pathway with the activation of caspases-8, caspase-3, and cleavage of PARP1. We further show that phosphorylated STAT3^Y705 is upregulated and phosphorylated p21^T145 is downregulated upon ibrutinib treatment. We propose that STAT3 upregulation is a passive response as a result of induction of DNA damage and downregulation of phosphorylated p21 is promoting cell cycle arrest and apoptosis in the two HER2 overexpressing cell lines. These results suggest that inhibitors of STAT3 phosphorylation may be potential options for combination therapy to help increase the efficacy of ibrutinib against HER2-overexpressing tumors.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

Target Identification of Active Constituents of Shen Qi Wan to Treat Kidney Yang Deficiency Using Computational Target Fishing and Network Pharmacology

Background: Kidney yang deficiency syndrome (KYDS) is one of the most common syndromes treated with traditional Chinese medicine (TCM) among elderly patients. Shen Qi Wan (SQW) has been effectively used in treating various diseases associated with KYDS for hundreds of years. However, due to the complex composition of SQW, the mechanism of action remains unknown.

Purpose: To identify the mechanism of the SQW in the treatment of KYDS and determine the molecular targets of SQW.

Methods: The potential targets of active ingredients in SQW were predicted using PharmMapper. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the Molecule Annotation System (MAS3.0). The protein–protein interaction (PPI) network of these potential targets and “components-targets-pathways” interaction networks were constructed using Cytoscape. We also established a KYDS rat model induced by adenine to investigate the therapeutic effects of SQW. Body weight, rectal temperature, holding power, water intake, urinary output, blood urea nitrogen (BUN), serum creatinine (Scr), adrenocorticotrophic hormone (ACTH), cortisol (CORT), urine total protein (U-TP), and 17-hydroxy-corticosteroid (17-OHCS) were measured. Additionally, the mRNA expression levels of candidates were detected by qPCR.

Results: KYDS-caused changes in body weight, rectal temperature, holding power, water intake, urinary output, BUN, Scr, ACTH, CORT, U-TP, and 17-OHCS were corrected to the baseline values after SQW treatment. We selected the top 10 targets of each component and obtained 79 potential targets, which were mainly enriched in the proteolysis, protein binding, transferase activity, T cell receptor signaling pathway, and focal adhesion. SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2, and MMP9 were identified as targets of SQW in the treatment of KYDS. The administration of SQW significantly suppressed the expression of SRC, HSP90AA1, LCK, and CDK2 and markedly increased the expression of MAPK14, MMP9, and F2. However, HRAS levels remained unchanged.

Conclusion: These findings demonstrated that SQW corrected hypothalamic–pituitary–target gland axis disorder in rats caused by KYDS. SRC, MAPK14, HRAS, HSP90AA1, F2, LCK, CDK2, and MMP9 were determined to the therapeutic target for the further investigation of SQW to ameliorate KYDS.

Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers

Drawing on concepts from experimental biology, computer science, informatics, mathematics and statistics, systems biologists integrate data across diverse platforms and scales of time and space to create computational and mathematical models of the integrative, holistic functions of living systems. Endocrine-related cancers are well suited to study from a systems perspective because of the signaling complexities arising from the roles of growth factors, hormones and their receptors as critical regulators of cancer cell biology and from the interactions among cancer cells, normal cells and signaling molecules in the tumor microenvironment. Moreover, growth factors, hormones and their receptors are often effective targets for therapeutic intervention, such as estrogen biosynthesis, estrogen receptors or HER2 in breast cancer and androgen receptors in prostate cancer. Given the complexity underlying the molecular control networks in these cancers, a simple, intuitive understanding of how endocrine-related cancers respond to therapeutic protocols has proved incomplete and unsatisfactory. Systems biology offers an alternative paradigm for understanding these cancers and their treatment. To correctly interpret the results of systems-based studies requires some knowledge of how in silico models are built, and how they are used to describe a system and to predict the effects of perturbations on system function. In this review, we provide a general perspective on the field of cancer systems biology, and we explore some of the advantages, limitations and pitfalls associated with using predictive multiscale modeling to study endocrine-related cancers.

Identifying the Antiproliferative Effect of Astragalus Polysaccharides on Breast Cancer: Coupling Network Pharmacology With Targetable Screening From the Cancer Genome Atlas

Background:Astragalus polysaccharides (APS), natural plant compounds, have recently emerged as a promising strategy for cancer treatment, but little is known concerning their effects on breast cancer (BC) tumorigenesis.

Methods: We obtained breast cancer genetic data from The Cancer Genome Atlas (TCGA) database, network pharmacology to further clarify its biological properties. Survival analysis and molecular docking techniques were implemented for the final screening to obtain key target information. Our experiments focused on the detection of intervention effects of APS on BC cells (MCF-7 and MDA-MB-231), and quantitative RT-PCR (qRT-PCR) was used to assess the expression of key targets.

Results: A total of 1,439 differentially expressed genes (DEGs) were identified by TCGA and used to build disease networks. Module analysis, gene ontology and pathway analysis revealed characteristic of the DEGs network. Topological properties were used to identify key targets, survival analysis and molecular docking finally found that the targets of APS regulation of BC cells may be CCNB1, CDC6, and p53. Through cell viability, migration and invasion assays, we found that APS interferes with the development of breast cancer in MCF7 and MDA-MB-231 cells in a dose-dependent manner. Furthermore, qRT-PCR verification suggested that the expression of CCNB1 and CDC6 in breast cancer cells was significantly downregulated in response to APS, while expression of the tumor suppressor gene P53 was significantly increased.

Conclusion: Results of this study suggest therapeutic potential for APS in BC treatment, possibly through interventions with CCNB1, CDC6, and P53. Furthermore, these findings illustrate the feasibility of using network pharmacology to connect large-scale target data as a way to discover the mechanism of natural products interfering with disease.

Extended Multitarget Pharmacology of Anticancer Drugs

Multitarget pharmacology of small-molecule cancer drugs significantly contributes to their mechanism of action, side effects, and emergence of drug resistance and opens ways to repurpose, combine, or customize drug therapy. In most cases, the set of targets affected at therapeutic concentrations is not fully characterized and/or the interaction efficacy values are not accurately quantified. We collected information about multiple targets for each cancer drug along with their experimental effective concentrations or binding activities from multiple sources. All multitarget activity values for each drug then were used to build two proximity network pharmacology maps of anticancer drugs and targets of those drugs, respectively. Together with the network map, we showed that the majority of the cancer drugs had substantial multitarget pharmacology based on our current knowledge. In addition, most of the cancer drugs simultaneously affect macromolecular targets from different classes and types. The target subset can further be accentuated and personalized by patient sample-specific expression data. The network maps of cancer drugs and targets as well as all quantified activity data were integrated into a freely available database, CancerDrugMap (http://ruben.ucsd.edu/dnet/maps/drugnet.html). The identified multitarget pharmacology of cancer drugs is essential for improving the efficacy of individually prescribed drugs and drug combinations and minimization of adverse effects.

From single drug targets to synergistic network pharmacology in ischemic stroke

Current one drug–one target–one disease approaches in drug discovery have become increasingly inefficient. Network pharmacology defines disease mechanisms as networks best targeted by multiple, synergistic drugs. Using the high unmet medical need indication stroke, we here develop an integrative in silico approach based on a primary target, NADPH oxidase type 4, to identify a mechanistically related cotarget, NO synthase, for network pharmacology. Indeed, we validate both in vivo and in vitro, including humans, that both NOX4 and NOS inhibition is highly synergistic, leading to a significant reduction of infarct volume, direct neuroprotection, and blood–brain-barrier stabilization. This systems medicine approach provides a ground plan to decrease current failure in the field by being implemented in other complex indications.

Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-based rather than mechanistic approaches have contributed. We here explore a mechanism-based disease definition for network pharmacology. Beginning with a primary causal target, we extend this to a second using guilt-by-association analysis. We then validate our prediction and explore synergy using both cellular in vitro and mouse in vivo models. As a disease model we chose ischemic stroke, one of the highest unmet medical need indications in medicine, and reactive oxygen species forming NADPH oxidase type 4 (Nox4) as a primary causal therapeutic target. For network analysis, we use classical protein–protein interactions but also metabolite-dependent interactions. Based on this protein–metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic cotargets for network pharmacology. We identify the nitric oxide synthase (Nos1 to 3) gene family as the closest target to Nox4. Indeed, when combining a NOS and a NOX inhibitor at subthreshold concentrations, we observe pharmacological synergy as evidenced by reduced cell death, reduced infarct size, stabilized blood–brain barrier, reduced reoxygenation-induced leakage, and preserved neuromotor function, all in a supraadditive manner. Thus, protein–metabolite network analysis, for example guilt by association, can predict and pair synergistic mechanistic disease targets for systems medicine-driven network pharmacology. Such approaches may in the future reduce the risk of failure in single-target and symptom-based drug discovery and therapy.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.