In silico prediction of chemical mechanism of action via an improved network-based inference method

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

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

A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

Unraveling the Nephroprotective Potential of Papaverine against Cisplatin Toxicity through Mitigating Oxidative Stress and Inflammation: Insights from In Silico, In Vitro, and In Vivo Investigations

Cisplatin is a potent compound in anti-tumor chemotherapy; however, its clinical utility is hampered by dose-limiting nephrotoxicity. This study investigated whether papaverine could mitigate cisplatin-induced kidney damage while preserving its chemotherapeutic efficacy. Integrative bioinformatics analysis predicted papaverine modulation of the mechanistic pathways related to cisplatin renal toxicity; notably, mitogen-activated protein kinase 1 (MAPK1) signaling. We validated protective effects in normal kidney cells without interfering with cisplatin cytotoxicity on a cancer cell line. Concurrent in vivo administration of papaverine alongside cisplatin in rats prevented elevations in nephrotoxicity markers, including serum creatinine, blood urea nitrogen, and renal oxidative stress markers (malondialdehyde, inducible nitric oxide synthase (iNOS), and pro-inflammatory cytokines), as tumor necrosis factor alpha (TNF-α), monocyte chemoattractant protein 1 (MCP-1), and interleukin-6 (IL-6). Papaverine also reduced apoptosis markers such as Bcl2 and Bcl-2-associated X protein (Bax) and kidney injury molecule-1 (KIM-1), and histological damage. In addition, it upregulates antioxidant enzymes like catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx) while boosting anti-inflammatory signaling interleukin-10 (IL-10). These effects were underlined by the ability of Papaverine to downregulate MAPK-1 expression. Overall, these findings show papaverine could protect against cisplatin kidney damage without reducing its cytotoxic activity. Further research would allow the transition of these results to clinical practice.

Network-Based Methods and Their Applications in Drug Discovery

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

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods

Introduction: Drug-target interaction (DTI) prediction is a key step in drug function discovery and repositioning. The emergence of large-scale heterogeneous biological networks provides an opportunity to identify drug-related target genes, which led to the development of several computational methods for DTI prediction. Methods: Considering the limitations of conventional computational methods, a novel tool named LM-DTI based on integrated information related to lncRNAs and miRNAs was proposed, which adopted the graph embedding (node2vec) and the network path score methods. First, LM-DTI innovatively constructed a heterogeneous information network containing eight networks composed of four types of nodes (drug, target, lncRNA, and miRNA). Next, the node2vec method was used to obtain feature vectors of drug as well as target nodes, and the path score vector of each drug-target pair was calculated using the DASPfind method. Finally, the feature vectors and path score vectors were merged and input into the XGBoost classifier to predict potential drug-target interactions. Results and Discussion: The 10-fold cross validations evaluate the classification accuracies of the LM-DTI. The prediction performance of LM-DTI in AUPR reached 0.96, which showed a significant improvement compared with those of conventional tools. The validity of LM-DTI has also been verified by manually searching literature and various databases. LM-DTI is scalable and computing efficient; thus representing a powerful drug relocation tool that can be accessed for free at http://www.lirmed.com:5038/lm_dti.

Investigation of Anti-Alzheimer's Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods

Alzheimer's disease (AD), a neurodegenerative disease with no cure, affects millions of people worldwide and has become one of the biggest healthcare challenges. Some investigated compounds play anti-AD roles at the cellular or the animal level, but their molecular mechanisms remain unclear. In this study, we designed a strategy combining network-based and structure-based methods together to identify targets for anti-AD sarsasapogenin derivatives (AAs). First, we collected drug-target interactions (DTIs) data from public databases, constructed a global DTI network, and generated drug-substructure associations. After network construction, network-based models were built for DTI prediction. The best bSDTNBI-FCFP_4 model was further used to predict DTIs for AAs. Second, a structure-based molecular docking method was employed for rescreening the prediction results to obtain more credible target proteins. Finally, in vitro experiments were conducted for validation of the predicted targets, and Nrf2 showed significant evidence as the target of anti-AD compound AA13. Moreover, we analyzed the potential mechanisms of AA13 for the treatment of AD. Generally, our combined strategy could be applied to other novel drugs or compounds and become a useful tool in identification of new targets and elucidation of disease mechanisms. Our model was deployed on our NetInfer web server (http://lmmd.ecust.edu.cn/netinfer/).

Polypharmacology-based approach for screening TCM against coinfection of Mycoplasma gallisepticum and Escherichia coli

Natural products and their unique polypharmacology offer significant advantages for finding novel therapeutics particularly for the treatment of complex diseases. Meanwhile, Traditional Chinese Medicine exerts overall clinical benefits through a multi-component and multi-target approach. In this study, we used the previously established co-infection model of Mycoplasma gallisepticum and Escherichia coli as a representative of complex diseases. A new combination consisting of 6 herbs were obtained by using network pharmacology combined with transcriptomic analysis to reverse screen TCMs from the Chinese medicine database, containing Isatdis Radix, Forsythia Fructus, Ginkgo Folium, Mori Cortex, Licorice, and Radix Salviae. The results of therapeutic trials showed that the Chinese herbal compounds screened by the target network played a good therapeutic effect in the case of co-infection. In summary, these data suggested a new method to validate target combinations of natural products that can be used to optimize their multiple structure-activity relationships to obtain drug-like natural product derivatives.

Investigation of the mechanism of Shen Qi Wan prescription in the treatment of T2DM via network pharmacology and molecular docking

Shen Qi Wan (SQW) prescription has been used to treat type 2 diabetes mellitus (T2DM) for thousands of years, but its pharmacological mechanism is still unclear. The network pharmacology method was used to reveal the potential pharmacological mechanism of SQW in the treatment of T2DM in this study. Nine core targets were identified through protein-protein interaction (PPI) network analysis and KEGG pathway enrichment analysis, which were AKT1, INSR, SLC2A1, EGFR, PPARG, PPARA, GCK, NOS3, and PTPN1. Besides, this study found that SQW treated the T2DM through insulin resistance (has04931), insulin signaling pathway (has04910), adipocytokine signaling pathway (has04920), AMPK signaling pathway (has04152) and FoxO signaling pathway (has04068) via ingredient-hub target-pathway network analysis. Finally, molecular docking was used to verify the drug-target interaction network in this research. This study provides a certain explanation for treating T2DM by SQW prescription, and provides a certain angle and method for researchers to study the mechanism of TCM in the treatment of complex diseases.

Supplementary information

The online version contains supplementary material available at 10.1007/s40203-022-00124-2.

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

An-Gong-Niu-Huang-Wan (AGNHW) is a well-known formula for treating cerebrovascular diseases, with roles including clearing away heat, detoxification, and wake-up consciousness. In recent years, AGNHW has been commonly used for the treatment of ischemic stroke, but the mechanism by which AGNHW relieves stroke has not been clearly elucidated. In the current study, we developed a multiple systems pharmacology-based framework to identify the potential antistroke ingredients in AGNHW and explore the underlying mechanisms of action (MOA) of AGNHW against stroke from a holistic perspective. Specifically, we performed a network-based method to identify the potential antistroke ingredients in AGNHW by integrating the drug-target network and stroke-associated genes. Furthermore, the oxygen-glucose deprivation/reoxygenation (OGD/R) model was used to validate the anti-inflammatory effects of the key ingredients by determining the levels of inflammatory cytokines, including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. The antiapoptotic effects of the key ingredients were also confirmed in vitro. Integrated pathway analysis of AGNHW revealed that it might regulate three biological signaling pathways, including IL-17, TNF, and PI3K-AKT, to play a protective role in stroke. Moreover, 30 key antistroke ingredients in AGNHW were identified via network-based in silico prediction and were confirmed to have known neuroprotective effects. After drug-like property evaluation and pharmacological validation in vitro, scutellarein (SCU) and caprylic acid (CA) were selected for further antistroke investigation. Finally, systems pharmacology-based analysis of CA and SCU indicated that they might exert antistroke effects via the apoptotic signaling pathway and inflammatory response, which was further validated in an in vitro stroke model. Overall, the current study proposes an integrative systems pharmacology approach to identify antistroke ingredients and demonstrate the underlying pharmacological MOA of AGNHW in stroke, which provides an alternative strategy to investigate novel traditional Chinese medicine formulas for complex diseases.

Drug Repurposing for Newly Emerged Diseases via Network-Based Inference on A Gene-Disease-Drug Network

Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 ± 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.

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

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

ADENet: a novel network-based inference method for prediction of drug adverse events

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.

Present and future challenges in therapeutic designing using computational approaches

Currently, various computational methods are being used for the purpose of therapeutic design. The advent of the Coronavirus disease-2019 (COVID-19) pandemic has created a lot of problems due to which the development of effective treatment options is urgently needed. Computational intelligence is used in the control, prevention, prediction, diagnosis, and treatment of the disease. Several important drug targets have been identified in severe acute respiratory syndrome-Coronavirus-2 using in silico methods. Computer-aided drug design includes a variety of theoretical and computational approaches that are part of modern drug discovery. Advances in machine learning methods and their applications speed up the drug discovery process. Exploration of nucleic acid-based therapeutics is playing an important role in healthcare also. But a lot of challenges have also been seen that complicate the therapeutic design. Therefore, investigation of challenges associated with therapeutic design is important, and the present chapter is aimed to cover various therapeutic design approaches and challenges associated with them. Moreover, the role of computational strategies in the exploration of potential therapeutics against COVID-19 has been investigated.

Acetylenic Synthetic Betulin Derivatives Inhibit Akt and Erk Kinases Activity, Trigger Apoptosis and Suppress Proliferation of Neuroblastoma and Rhabdomyosarcoma Cell Lines

Neuroblastoma (NB) and rhabdomyosarcoma (RMS), the most common pediatric extracranial solid tumors, still represent an important clinical challenge since no effective treatment is available for metastatic and recurrent disease. Hence, there is an urgent need for the development of new chemotherapeutics to improve the outcome of patients. Betulin (Bet), a triterpenoid from the bark of birches, demonstrated interesting anti-cancer potential. The modification of natural phytochemicals with evidenced anti-tumor activity, including Bet, is one of the methods of receiving new compounds for potential implementation in oncological treatment. Here, we showed that two acetylenic synthetic Bet derivatives (ASBDs), EB5 and EB25/1, reduced the viability and proliferation of SK-N-AS and TE671 cells, as measured by MTT and BrdU tests, respectively. Moreover, ASBDs were also more cytotoxic than temozolomide (TMZ) and cisplatin (cis-diaminedichloroplatinum [II], CDDP) in vitro, and the combination of EB5 with CDDP enhanced anti-cancer effects. We also showed the slowdown of cell cycle progression at S/G2 phases mediated by EB5 using FACS flow cytometry. The decreased viability and proliferation of pediatric cancers cells after treatment with ASBDs was linked to the reduced activity of kinases Akt, Erk1/2 and p38 and the induction of apoptosis, as investigated using Western blotting and FACS. In addition, in silico analyses of the ADMET profile found EB5 to be a promising anti-cancer drug candidate that would benefit from further investigation.

DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning

Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug-target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

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.

Application of Machine Learning Techniques in Drug-target Interactions Prediction

BACKGROUND

Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field.

RESULTS

The machine learning methods can be divided into three categories basically: Supervised methods, Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category.

CONCLUSION

Every prediction model has both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.

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

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

Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches

Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug-pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug-pathway associations based on substructure-drug-pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug-pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug-pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.

Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy

Advancements in systems biology have resulted in the development of network pharmacology, leading to a paradigm shift from "one-target, one-drug" to "target-network, multi-component therapeutics". We employ a chimeric approach involving in-vivo assays, gene expression analysis, cheminformatics, and network biology to deduce the regulatory actions of a multi-constituent Ayurvedic concoction, Amalaki Rasayana (AR) in animal models for its effect in pressure-overload cardiac hypertrophy. The proteomics analysis of in-vivo assays for Aorta Constricted and Biologically Aged rat models identify proteins expressed under each condition. Network analysis mapping protein-protein interactions and synergistic actions of AR using multi-component networks reveal drug targets such as ACADM, COX4I1, COX6B1, HBB, MYH14, and SLC25A4, as potential pharmacological co-targets for cardiac hypertrophy. Further, five out of eighteen AR constituents potentially target these proteins. We propose a distinct prospective strategy for the discovery of network pharmacological therapies and repositioning of existing drug molecules for treating pressure-overload cardiac hypertrophy.

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.

Insights into the Molecular Mechanisms of Liuwei Dihuang Decoction via Network Pharmacology

The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target network-based inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A "target-(pathway)-target" (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.

FPSC-DTI: drug-target interaction prediction based on feature projection fuzzy classification and super cluster fusion

Identifying drug-target interactions (DTIs) is an important part of drug discovery and development. However, identifying DTIs is a complex process that is time consuming, costly, long, and often inefficient, with a low success rate, especially with wet-experimental methods. Computational methods based on drug repositioning and network pharmacology can effectively overcome these defects. In this paper, we develop a new fusion method, called FPSC-DTI, that fuses feature projection fuzzy classification (FP) and super cluster classification (SC) to predict DTI. As the experimental result, the mean percentile ranking (MPR) that was yielded by FPSC-DTI achieved 0.043, 0.084, 0.072, and 0.146 on enzyme, ion channel (IC), G-protein-coupled receptor (GPCR), and nuclear receptor (NR) datasets, respectively. And the AUC values exceeded 0.969 over all four datasets. Compared with other methods, FPSC-DTI obtained better predictive performance and became more robust.

Insights into the mechanism of Arnebia euchroma on leukemia via network pharmacology approach

BACKGROUND

Arnebia euchroma (A. euchroma) is a traditional Chinese medicine (TCM) used for the treatment of blood diseases including leukemia. In recent years, many studies have been conducted on the anti-tumor effect of shikonin and its derivatives, the major active components of A. euchroma. However, the underlying mechanism of action (MoA) for all the components of A. euchroma on leukemia has not been explored systematically.

METHODS

In this study, we analyzed the MoA of A. euchroma on leukemia via network pharmacology approach. Firstly, the chemical components and their concentrations in A. euchroma as well as leukemia-related targets were collected. Next, we predicted compound-target interactions (CTIs) with our balanced substructure-drug-target network-based inference (bSDTNBI) method. The known and predicted targets of A. euchroma and leukemia-related targets were merged together to construct A. euchroma-leukemia protein-protein interactions (PPIs) network. Then, weighted compound-target bipartite network was constructed according to combination of eight central attributes with concentration information through Cytoscape. Additionally, molecular docking simulation was performed to calculate whether the components and predicted targets have interactions or not.

RESULTS

A total of 65 components of A. euchroma were obtained and 27 of them with concentration information, which were involved in 157 targets and 779 compound-target interactions (CTIs). Following the calculation of eight central attributes of targets in A. euchroma-leukemia PPI network, 37 targets with all central attributes greater than the median values were selected to construct the weighted compound-target bipartite network and do the KEGG pathway analysis. We found that A. euchroma candidate targets were significantly associated with several apoptosis and inflammation-related biological pathways, such as MAPK signaling, PI3K-Akt signaling, IL-17 signaling, and T cell receptor signaling pathways. Moreover, molecular docking simulation demonstrated that there were eight pairs of predicted CTIs had the strong binding free energy.

CONCLUSIONS

This study deciphered that the efficacy of A. euchroma in the treatment of leukemia might be attributed to 10 targets and 14 components, which were associated with inhibiting leukemia cell survival and inducing apoptosis, relieving inflammatory environment and inhibiting angiogenesis.

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.

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

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

TCMIO: A Comprehensive Database of Traditional Chinese Medicine on Immuno-Oncology

Advances in immuno-oncology (IO) are making immunotherapy a powerful tool for cancer treatment. With the discovery of an increasing number of IO targets, many herbs or ingredients from traditional Chinese medicine (TCM) have shown immunomodulatory function and antitumor effects via targeting the immune system. However, knowledge of underlying mechanisms is limited due to the complexity of TCM, which has multiple ingredients acting on multiple targets. To address this issue, we present TCMIO, a comprehensive database of Traditional Chinese Medicine on Immuno-Oncology, which can be used to explore the molecular mechanisms of TCM in modulating the cancer immune microenvironment. Over 120,000 small molecules against 400 IO targets were extracted from public databases and the literature. These ligands were further mapped to the chemical ingredients of TCM to identify herbs that interact with the IO targets. Furthermore, we applied a network inference-based approach to identify the potential IO targets of natural products in TCM. All of these data, along with cheminformatics and bioinformatics tools, were integrated into the publicly accessible database. Chemical structure mining tools are provided to explore the chemical ingredients and ligands against IO targets. Herb–ingredient–target networks can be generated online, and pathway enrichment analysis for TCM or prescription is available. This database is functional for chemical ingredient structure mining and network analysis for TCM. We believe that this database provides a comprehensive resource for further research on the exploration of the mechanisms of TCM in cancer immunity and TCM-inspired identification of novel drug leads for cancer immunotherapy. TCMIO can be publicly accessed at http://tcmio.xielab.net.

Systems Pharmacology Approach to Investigate the Mechanism of Kai-Xin-San in Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative disease characterized by cognitive dysfunction. Kai-Xin-San (KXS) is a traditional Chinese medicine (TCM) formula that has been used to treat AD patients for over a thousand years in China. However, the therapeutic mechanisms of KXS for treating AD have not been fully explored. Herein, we used a comprehensive network pharmacology approach to investigate the mechanism of action of KXS in the treatment of AD. This approach consists of construction of multiple networks and Gene Ontology enrichment and pathway analyses. Furthermore, animal experiments were performed to validate the predicted molecular mechanisms obtained from the systems pharmacology-based analysis. As a result, 50 chemicals in KXS and 39 AD-associated proteins were identified as major active compounds and targets, respectively. The therapeutic mechanisms of KXS in treating AD were primarily related to the regulation of four pathology modules, including amyloid beta metabolism, tau protein hyperphosphorylation process, cholinergic dysfunction, and inflammation. In scopolamine-induced cognitive dysfunction mice, we validated the anti-inflammatory effects of KXS on AD by determining the levels of inflammation cytokines including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. We also found cholinergic system dysfunction amelioration of KXS is correlated with upregulation of the cholinergic receptor CHRNB2. In conclusion, our work proposes a comprehensive systems pharmacology approach to explore the underlying therapeutic mechanism of KXS for the treatment of AD.

Drug repositioning by prediction of drug's anatomical therapeutic chemical code via network-based inference approaches

Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug's anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd- and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 ± 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen-glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.

The Methodological Trends of Traditional Herbal Medicine Employing Network Pharmacology

Natural products, including traditional herbal medicine (THM), are known to exert their therapeutic effects by acting on multiple targets, so researchers have employed network pharmacology methods to decipher the potential mechanisms of THM. To conduct THM-network pharmacology (THM-NP) studies, researchers have employed different tools and databases for constructing and analyzing herb–compound–target networks. In this study, we attempted to capture the methodological trends in THM-NP research. We identified the tools and databases employed to conduct THM-NP studies and visualized their combinatorial patterns. We also constructed co-author and affiliation networks to further understand how the methodologies are employed among researchers. The results showed that the number of THM-NP studies and employed databases/tools have been dramatically increased in the last decade, and there are characteristic patterns in combining methods of each analysis step in THM-NP studies. Overall, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was the most frequently employed network pharmacology database in THM-NP studies. Among the processes involved in THM-NP research, the methodology for constructing a compound–target network has shown the greatest change over time. In summary, our analysis describes comprehensive methodological trends and current ideas in research design for network pharmacology researchers.

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.

Predicting biomedical relationships using the knowledge and graph embedding cascade model

Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding to represent biochemical entities and their relations as dense and low-dimensional vectors. We build a cascade learning framework which incorporates semantic features from the knowledge embedding model, and graph features from the graph embedding model, to score the probability of linking. The proposed method performs noticeably better than the models with which it is compared. It predicted links and entities with an accuracy of 93%, and its average hits@10 score has an average of 8.6% absolute improvement compared with original knowledge embedding model, 1.1% to 9.7% absolute improvement compared with other knowledge and graph embedding algorithm. In addition, we designed a meta-path algorithm to detect path relations in the biomedical network. Case studies further verify the value of the proposed model in finding potential relationships between diseases, drugs, genes, treatments, etc. Amongst the findings of the proposed model are the suggestion that VDR (vitamin D receptor) may be linked to prostate cancer. This is backed by evidence from medical databases and published research, supporting the suggestion that our proposed model could be of value to biomedical researchers.

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.

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.

Network-Based Methods for Prediction of Drug-Target Interactions

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

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

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

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

G protein-coupled receptors (GPCRs) are the largest super family with more than 800 membrane receptors. Currently, over 30% of the approved drugs target human GPCRs. However, only approximately 30 human GPCRs have been resolved three-dimensional crystal structures, which limits traditional structure-based drug discovery. Recent advances in network-based systems pharmacology approaches have demonstrated powerful strategies for identifying new targets of GPCR ligands. In this study, we proposed a network-based systems pharmacology framework for comprehensive identification of new drug-target interactions on GPCRs. Specifically, we reconstructed both global and local drug-target interaction networks for human GPCRs. Network analysis on the known drug-target networks showed rational strategies for designing new GPCR ligands and evaluating side effects of the approved GPCR drugs. We further built global and local network-based models for predicting new targets of the known GPCR ligands. The area under the receiver operating characteristic curve of more than 0.96 was obtained for the best network-based models in cross validation. In case studies, we identified that several network-predicted GPCR off-targets (e.g. ADRA2A, ADRA2C and CHRM2) were associated with cardiovascular complications (e.g. bradycardia and palpitations) of the approved GPCR drugs via an integrative analysis of drug-target and off-target-adverse drug event networks. Importantly, we experimentally validated that two newly predicted compounds, AM966 and Ki16425, showed high binding affinities on prostaglandin E2 receptor EP4 subtype with IC₅₀=2.67μM and 6.34μM, respectively. In summary, this study offers powerful network-based tools for identifying polypharmacology of GPCR ligands in drug discovery and development.

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.