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

Network-based identification and mechanism exploration of active ingredients against Alzheimer's disease via targeting endoplasmic reticulum stress from traditional chinese medicine

Alzheimer's disease is a neurodegenerative disease that leads to dementia and poses a serious threat to the health of the elderly. Traditional Chinese medicine (TCM) presents as a promising novel therapeutic therapy for preventing and treating dementia. Studies have shown that natural products derived from kidney-tonifying herbs can effectively inhibit AD. Furthermore, endoplasmic reticulum (ER) stress is a critical factor in the pathology of AD. Regulation of ER stress is a crucial approach to prevent and treat AD. Thus, in this study, we first collected kidney-tonifying herbs, integrated chemical ingredients from multiple TCM databases, and constructed a comprehensive drug-target network. Subsequently, we employed the endophenotype network (network proximity) method to identify potential active ingredients in kidney-tonifying herbs that prevented AD via regulating ER stress. By combining the predicted outcomes, we discovered that 32 natural products could ameliorate AD pathology via regulating ER stress. After a comprehensive evaluation of the multi-network model and systematic pharmacological analyses, we further selected several promising compounds for in vitro testing in the APP-SH-SY5Y cell model. Experimental results showed that echinacoside and danthron were able to effectively reduce ER stress-mediated neuronal apoptosis by inhibiting the expression levels of BIP, p-PERK, ATF6, and CHOP in APP-SH-SY5Y cells. Overall, this study utilized the endophenotype network to preliminarily decipher the effective material basis and potential molecular mechanism of kidney-tonifying Chinese medicine for prevention and treatment of AD.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

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.

Systems pharmacology dissection of pharmacological mechanisms of Xiaochaihu decoction against human coronavirus

BACKGROUND

Although coronavirus disease 2019 (COVID-19) pandemic is still rage worldwide, there are still very limited treatments for human coronaviruses (HCoVs) infections. Xiaochahu decoction (XCHD), which is one of the traditional Chinese medicine (TCM) prescriptions in Qingfeipaidu decoction (QFPDD), is widely used for COVID-19 treatment in China and able to relieve the symptoms of fever, fatigue, anorexia, and sore throat. To explore the role and mechanisms of XCHD against HCoVs, we presented an integrated systems pharmacology framework in this study.

METHODS

We constructed a global herb-compound-target (H-C-T) network of XCHD against HCoVs. Multi-level systems pharmacology analyses were conducted to highlight the key XCHD-regulated proteins, and reveal multiple HCoVs relevant biological functions affected by XCHD. We further utilized network-based prediction, drug-likeness analysis, combining with literature investigations to uncover the key ani-HCoV constituents in XCHD, whose effects on anit-HCoV-229E virus were validated using cytopathic effect (CPE) assay. Finally, we proposed potential molecular mechanisms of these compounds against HCoVs via subnetwork analysis.

RESULTS

Based on the systems pharmacology framework, we identified 161 XCHD-derived compounds interacting with 37 HCoV-associated proteins. An integrated pathway analysis revealed that the mechanism of XCHD against HCoVs is related to TLR signaling pathway, RIG-I-like receptor signaling pathway, cytoplasmic DNA sensing pathway, and IL-6/STAT3 pro-inflammatory signaling pathway. Five compounds from XCHD, including betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1 exerted inhibitory activity against HCoV-229E virus in Huh7 cells using in vitro CPE assay.

CONCLUSION

Our work presented a comprehensive systems pharmacology approach to identify the effective molecules and explore the molecular mechanism of XCHD against HCoVs.

Systems pharmacology-based mechanism exploration of Acanthopanax senticosusin for Alzheimer's disease using UPLC-Q-TOF-MS, network analysis, and experimental validation

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease, characterized by progressive cognitive dysfunction and memory loss. However, the disease-modifying treatments for AD are still lacking. Traditional Chinese herbs, have shown their potentials as novel treatments for complex diseases, such as AD.

PURPOSE

This study was aimed at investigating the mechanism of action (MOA) of Acanthopanax senticosusin (AS) for treatment of AD.

METHODS

In this study, we firstly identified the chemical constituents in Acanthopanax senticosusin (AS) utilizing ultra-high performance liquid chromatography coupled with Q-TOF-mass spectrometry (UPLC-Q-TOF-MS), and next built the drug-target network of these compounds. We next performed the systems pharmacology-based analysis to preliminary explore the MOA of AS against AD. Moreover, we applied the network proximity approach to identify the potential anti-AD components in AS. Finally, experimental validations, including animal behavior test, ELISA and TUNEL staining, were conducted to verify our systems pharmacology-based analysis.

RESULTS

60 chemical constituents in AS were identified via the UPLC-Q-TOF-MS approach. The systems pharmacology-based analysis indicated that AS might exert its therapeutic effects on AD via acetylcholinesterase and apoptosis signaling pathway. To explore the material basis of AS against AD, we further identified 15 potential anti-AD components in AS. Consistently, in vivo experiments demonstrated that AS could protect cholinergic nervous system damage and decrease neuronal apoptosis caused by scopolamine.

CONCLUSION

Overall, this study applied systems pharmacology approach, via UPLC-Q-TOF-MS, network analysis, and experimental validation to decipher the potential molecular mechanism of AS against AD.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review

Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.

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

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

Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities

As coronavirus disease 2019 (COVID-19) threatens human health globally, infectious disorders have become one of the most challenging problem for the medical community. Natural products (NP) have been a prolific source of antimicrobial agents with widely divergent structures and a range vast biological activities. A dataset comprising 618 articles, including 646 NP-based compounds from 672 species of natural sources with biological activities against 21 infectious pathogens from five categories, was assembled through manual selection of published articles. These data were used to identify 268 NP-based compounds classified into ten groups, which were used for network pharmacology analysis to capture the most promising lead-compounds such as agelasine D, dicumarol, dihydroartemisinin and pyridomycin. The distribution of maximum Tanimoto scores indicated that compounds which inhibited parasites exhibited low diversity, whereas the chemistries inhibiting bacteria, fungi, and viruses showed more structural diversity. A total of 331 species of medicinal plants with compounds exhibiting antimicrobial activities were selected to classify the family sources. The family Asteraceae possesses various compounds against C. neoformans, the family Anacardiaceae has compounds against Salmonella typhi, the family Cucurbitacea against the human immunodeficiency virus (HIV), and the family Ancistrocladaceae against Plasmodium. This review summarizes currently available data on NP-based antimicrobials against refractory infections to provide information for further discovery of drugs and synthetic strategies for anti-infectious agents.

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.

Chemical Distance Measurement and System Pharmacology Approach Uncover the Novel Protective Effects of Biotransformed Ginsenoside C-Mc against UVB-Irradiated Photoaging

Long-term exposure to ultraviolet light induces photoaging and may eventually increase the risk of skin carcinogenesis. Rare minor ginsenosides isolating from traditional medicine Panax (ginseng) have shown biomedical efficacy as antioxidation and antiphotodamage agents. However, due to the difficulty of component extraction and wide variety of ginsenoside, the identification of active antiphotoaging ginsenoside remains a huge challenge. In this study, we proposed a novel in silico approach to identify potential compound against photoaging from 82 ginsenosides. Specifically, we calculated the shortest distance between unknown and known antiphotoaging ginsenoside set in the chemical space and applied chemical structure similarity assessment, drug-likeness screening, and ADMET evaluation for the candidates. We highlighted three rare minor ginsenosides (C-Mc, Mx, and F2) that possess high potential as antiphotoaging agents. Among them, C-Mc deriving from American ginseng (Panax quinquefolius L.) was validated by wet-lab experimental assays and showed significant antioxidant and cytoprotective activity against UVB-induced photodamage in human dermal fibroblasts. Furthermore, system pharmacology analysis was conducted to explore the therapeutic targets and molecular mechanisms through integrating global drug-target network, high quality photoaging-related gene profile from multiomics data, and skin tissue-specific expression protein network. In combination with in vitro assays, we found that C-Mc suppressed MMP production through regulating the MAPK/AP-1/NF-κB pathway and expedited collagen synthesis via the TGF-β/Smad pathway, as well as enhanced the expression of Nrf2/ARE to hold a balance of endogenous oxidation. Overall, this study offers an effective drug discovery framework combining in silico prediction and in vitro validation, uncovering that ginsenoside C-Mc has potential antiphotoaging properties and might be a novel natural agent for use in oral drug, skincare products, or functional food.

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.

Molecular dynamics simulations, docking and MMGBSA studies of newly designed peptide-conjugated glucosyloxy stilbene derivatives with tumor cell receptors

In this work, for the first time, we designed derivatives of beta-D-glucosyloxy-3-hydroxy-trans-stiblene-2-carboxylic acid (GHS), by conjugating GHS with tumor targeting peptides RPARPAR and GGKRPAR to target over-expressed receptors in tumor cells. The sequences RPARPAR and GGKRPAR are known to target the neuropilin1 (NRP1) receptor due to the C-terminal Arg domain; however, their effectiveness has never been examined with other commonly over-expressed receptors in tumor cells, particularly of chronic lymphocytic leukemia that include integrin α1β1 and CD22. By conjugating these peptides with GHS, which is known for its inherent anti-cancer properties, the goal is to further enhance tumor cell targeting by developing compounds that can target multiple receptors. The physicochemical properties of the conjugates and individual peptides were analyzed using Turbomole and COSMOthermX20 in order to determine their hydrogen bond accepting and donating capabilities. The web server POCASA was used in order to determine the surface cavities and binding pockets of the three receptors. To explore the binding affinities, we conducted molecular docking studies with the peptides and the conjugates with each of the receptors. After molecular docking, the complexes were analyzed using Protein-Ligand Interaction Profiler to determine the types of interactions involved. Molecular dynamics simulation studies were conducted to explore the stability of the receptor-ligand complexes. Our results indicated that in most cases the conjugates showed higher binding and stability with the receptors. Additionally, highly stable complexes of conjugates were obtained with CD22, NRP1 and in most cases with the integrin α1β1 receptor as well. The binding energies were calculated for each of the receptor ligand complexes through trajectory analysis using MMGBSA studies. SwissADME studies revealed that the compounds showed low GI absorption and were not found to be CYP inhibitors and had bioavailability score that would allow them to be considered as potential drug candidates. Overall, our results for the first time show that the designed conjugates can target multiple over-expressed receptors in tumor cells and may be potentially developed as future therapeutics for targeting tumor cells.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor

Background

The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs.

Results

After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R²) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs.

Conclusion

Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04389-w.

Systems pharmacology approach uncovers the therapeutic mechanism of medicarpin against scopolamine-induced memory loss

BACKGROUND

Medicarpin is a natural pterocarpan-type phytoalexin widely distributed in many traditional Chinese medicines, such as Astragali Radix. A previous study showed that Astragali Radix demonstrated promising protective effects in neurons. However, there is no reported study on the neuroprotective function and the underlying mechanism of Medicarpin.

PURPOSE

This study aimed to demonstrate the neuroprotective effect of Medicarpin on Alzheimer's disease (AD) and explore the therapeutic mechanisms.

METHOD

First, we carried out animal behavioral tests and biochemical analysis to assess the anti-AD potential of Medicarpin for ameliorating spatial learning and memory and modulating cholinergic metabolism in scopolamine-induced amnesic mice. Subsequently, network proximity prediction was used to measure the network distance between the Medicarpin target network and AD-related endophenotype module. We identified Medicarpin-regulated AD pathological processes and highlighted the key disease targets via network analysis. Finally, experimental approaches including Nissl staining and Western blotting were conducted to validate our network-based findings.

RESULT

In this study, we first observed that Medicarpin can ameliorate cognitive and memory dysfunction and significantly modulate cholinergic metabolism in scopolamine-induced amnesic mice. We then proposed an endophenotype network-based framework to comprehensively explore the AD therapeutic mechanisms of Medicarpin by integrating 25 AD-related endophenotype modules, gold-standard AD seed genes, an experimentally validated drug-target network of Medicarpin, and a global human protein-protein interactome. In silico prediction revealed that the effect of Medicarpin is highly relevant to neuronal apoptosis and synaptic plasticity, which was validated by experimental assays. Network analysis and Western blotting further identified two key targets, GSK-3β and MAPK14 (p38), in the AD-related protein regulatory network, which play key roles in the regulation of neuronal apoptosis and synaptic plasticity by Medicarpin.

CONCLUSIONS

This study presented a powerful endophenotype network-based strategy to explore the mechanisms of action (MOAs) of new AD therapeutics, and first identified Medicarpin as a potential anti-AD candidate by targeting multiple pathways.

Regulation of the PTEN/PI3K/AKT pathway in RCC using the active compounds of natural products in vitro

Since Professor Tu Youyou won the 2015 Nobel Prize in Physiology and Medicine for the discovery of artemisinin, which is used to treat malaria, increased attention has been paid to the extracts obtained from plants, in order to analyze their biological activities, particularly with regard to their antitumor activity. Therefore, the present study explored the biochemical properties of seven natural plant extracts on renal cell carcinoma (RCC). 786-O and OS-RC-2 cells were cultured and treated with different concentrations of the extracts. Then, cell viability, the IC₅₀ value and proliferation was determined using a Cell Counting Kit-8 assay. Apoptosis and cell cycle distribution were evaluated via flow cytometry. The expression levels of proteins were assessed using western blotting, and cellular morphology was observed using a light microscope. The results showed that sophoricoside, aucubin, notoginsenoside R1 and ginsenoside Rg1 did not exhibit a cytotoxic effect on RCC cells, whereas ginsenoside Re and allicin exhibited a very slight inhibitory effect. Naringenin possessed the highest activity of the analyzed extracts. The IC₅₀ values of naringenin on 786-O and OS-RC-2 cells were 8.91±0.33 and 7.78±2.65 µM, respectively. In addition, naringenin notably inhibited the proliferation of RCC cells by decreasing Ki67 expression, blocked cell cycle progression in the G₂ phase by regulating expression of cell cycle proteins, and increased apoptosis by upregulating caspase-8 expression, downregulating Bcl-2 expression and altering the cellular morphology. Furthermore, naringenin inhibited cell proliferation and promoted apoptosis by upregulating the expression of PTEN at the protein level, downregulated the expression of PI3K and phosphorylated-(p-)AKT, but did not affect the expression of AKT, mTOR or p-mTOR. The seven plant extracts analyzed showed differing degrees of anti-RCC activity. Sophoricoside, aucubin, notoginsenoside R1 and ginsenoside Rg1 did not exhibit notable anti-RCC activity, whereas the effect of ginsenoside Re and allicin on RCC was considerably weak. However, naringenin showed potent anti-proliferative, apoptosis inducing and cell cycle arresting activity on RCC cells via regulation of the PTEN/PI3K/AKT signaling pathway.

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.

A chemoinformatic analysis of atoms, scaffolds and functional groups in natural products

In the quest to know why natural products (NPs) have often been considered as privileged scaffolds for drug discovery purposes, many investigations into the differences between NPs and synthetic compounds have been carried out. Several attempts to answer this question have led to the investigation of the atomic composition, scaffolds and functional groups (FGs) of NPs, in comparison with synthetic drugs analysis. This chapter briefly describes an atomic enumeration method for chemical libraries that has been applied for the analysis of NP libraries, followed by a description of the main differences between NPs of marine and terrestrial origin in terms of their general physicochemical properties, most common scaffolds and “drug-likeness” properties. The last parts of the work describe an analysis of scaffolds and FGs common in NP libraries, focusing on huge NP databases, e.g. those in the Dictionary of Natural Products (DNP), NPs from cyanobacteria and the largest chemical class of NP – terpenoids.

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.

Systems pharmacology approach uncovers Ligustilide attenuates experimental colitis in mice by inhibiting PPARγ-mediated inflammation pathways

Inflammatory bowel disease (IBD) is a chronic idiopathic disorder causing inflammation in the gastro-intestinal tract, which is lack of effective drug targets and medications. To identify novel therapeutic agents against consistent targets, we exploited a systems pharmacology-driven framework that incorporates drug-target networks of natural product and IBD disease genes. Our in silico approach found that Ligustilide (LIG), one of the major active components of Angelica acutiloba and Cnidium Officinale, potently attenuated IBD. The following in vivo and in vitro results demonstrated that LIG prevented experimental mice colitis induced by dextran sulfate sodium (DSS) via suppressing inflammatory cell infiltration, the activity of MPO and iNOS, and the expression and production of IL-1β, IL-6, and TNF-α. Subsequently, the network analysis helped to validate that LIG alleviated colitis by inhibiting NF-κB and MAPK/AP-1 pathway through activating PPARγ, which were further confirmed in RAW 264.7 cells and bone marrow-derived macrophages in vitro. In summary, this study reveals that LIG activated PPARγ to inhibit the activation of NF-κB and AP-1 signaling thus eventually alleviated DSS-induced colitis, which has promising activities and may serve as a candidate for the treatment of IBD.Graphical abstract This study suggested novel computational and experimental pharmacology approaches to identify potential IBD therapeutic agents by exploiting polypharmacology of natural products. We demonstrated that LIG could attenuate inflammation in IBD by inhibiting NF-κB and AP-1 pathways via PPARγ activation to reduce the expression of pro-inflammatory cytokines in macrophages. These findings offer comprehensive pre-clinical evidence that LIG may serve as a promising candidate for IBD therapy in the future. Graphical headlights: 1. Systems pharmacology uncovered Ligustilide attenuates experimental colitis in mice. 2. Network-based analysis predicted the mechanism of Ligustilide against IBD, which was validated by inhibiting PPARγ-mediated inflammation pathways. 3. Ligustilide activated PPARγ to inhibit NF-κB and AP-1 activation thus eventually alleviated DSS-induced colitis.4. Ligustilide has promising activities and may serve as a candidate for the treatment of IBD.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

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

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

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.

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.

Prediction of modes of action of components of traditional medicinal preparations

Traditional medicine preparations are used to treat many ailments in multiple regions across the world. Despite their widespread use, the mode of action of these preparations and their constituents are not fully understood. Traditional methods of elucidating the modes of action of these natural products (NPs) can be expensive and time consuming e. g. biochemical methods, bioactivity guided fractionation, etc. In this review, we discuss some methods for the prediction of the modes of action of traditional medicine preparations, both in mixtures and as isolated NPs. These methods are useful to predict targets of NPs before they are experimentally validated. Case studies of the applications of these methods are also provided herein.

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

Rationalization of a traditional liver medicine using systems biology approach and its evaluation in preclinical trial

'Bottom-up', i.e., molecule to medicine strategy for the discovery of new drugs takes enormous time and cost. In most of the cases, inherent toxicity and undesired side effects of the developed drug hinder its way beyond the early stages of development. In this regard, the systems pharmacology can play an excellent role by reducing the cost and time of drug development through rationalization and/or repurposing of traditional drugs with known side effects. In the present study, our aim was to develop an integrated systems biology method for the prediction of active ingredients of a traditional medicine and their potential targets inside the body. Further, we evaluated the predictive capacity of the developed method in a preclinical animal model. Here, we have prepared a formulation (SKP17LIV01) from an extract of eight medicinal plants traditionally used as liver medicine and identified the constituents using UHPLC-MS technique. Using systems biology approach, we have rationalized the components of the formulation for potential use in the treatment of heavy metal-induced hepatotoxicity. The active ingredients and potential therapeutic targets were also predicted. A detailed biochemical, histopathological and molecular study on the mice model of lead toxicity confirms the efficacy of the formulation as per prediction by the systems pharmacology approach. The study may open a new frontier for re-discovery of drugs that are already used in traditional medicine.

STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products

Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.

Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs

The ancient tradition of taking parts of a plant or preparing plant extracts for treating certain discomforts and maladies has long been lacking a scientific rationale to support its preparation and still widespread use in several parts of the world. In an attempt to address this challenge, we collected and integrated data connecting metabolites, plants, diseases, and proteins. A mechanistic hypothesis is generated when a metabolite is known to be present in a given plant, that plant is known to be used to treat a certain disease, that disease is known to be linked to the function of a given protein, and that protein is finally known or predicted to interact with the original metabolite. The construction of plant–protein networks from mutually connected metabolites and diseases facilitated the identification of plausible mechanisms of action for plants being used to treat analgesia, hypercholesterolemia, diarrhea, catarrh, and cough. Additional concrete examples using both experimentally known and computationally predicted, and subsequently experimentally confirmed, metabolite–protein interactions to close the connection circle between metabolites, plants, diseases, and proteins offered further proof of concept for the validity and scope of the approach to generate mode of action hypotheses for some of the therapeutic uses of remedial herbs.

Systems biology based drug repositioning for development of cancer therapy

Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.

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

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

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE_DB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.

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.

Structural insights and influence of V599 mutations on the overall dynamics of BRAF protein against its kinase domains

Mutations in the BRAF gene are well known for their oncogenic effects. Point mutations in V599 are particularly oncogenic and are considered important for therapeutic purposes. Along with wild type, other V599 mutated BRAF variants viz. V599E, V599D and V599R are reported and crystals of the former two with inhibitor (BAY43-9006) are further detailed. Both wild-type and mutated BRAF forms show similar interaction patterns with BAY43-9006, but the 599th residue did not show any involvement in the interactions. Upon BAY43-9006 binding, kinase domains of both forms were found adopting essentially identical conformations. However, BAY43-9006 shows a varied activity profile in the case of the wild and V599E variant of the BRAF protein. Furthermore, MMGBSA binding energy results for all four BRAF variants, further revealed the importance of the 599th residue. In-depth analysis viz. molecular dynamics, residue correlation studies and residue interaction network (RIN) analyses were conducted, providing a deep insight into the 599th residue and its impact on the overall dynamics of BRAF protein. Our findings reveal that the mutated residue at the 599th position not only changed the BAY43-9006-BRAF binding behaviour but also produced a massive impact on the overall dynamic behaviour of the protein. The insights obtained herein could be of great relevance for designing new BRAF inhibitors aimed at getting ideal activity against all BRAF forms.

Efficient ligand discovery from natural herbs by integrating virtual screening, affinity mass spectrometry and targeted metabolomics

Although natural herbs have been a rich source of compounds for drug discovery, identification of bioactive components from natural herbs suffers from low efficiency and prohibitive cost of the conventional bioassay-based screening platforms. Here we develop a new strategy that integrates virtual screening, affinity mass spectrometry (MS) and targeted metabolomics for efficient discovery of herb-derived ligands towards a specific protein target site. Herb-based virtual screening conveniently selects herbs of potential bioactivity whereas affinity MS combined with targeted metabolomics readily screens candidate compounds in a high-throughput manner. This new integrated approach was benchmarked on screening chemical ligands that target the hydrophobic pocket of the nucleoprotein (NP) of Ebola viruses for which no small molecule ligands have been reported. Seven compounds identified by this approach from the crude extracts of three natural herbs were all validated to bind to the NP target in pure ligand binding assays. Among them, three compounds isolated from Piper nigrum (HJ-1, HJ-4 and HJ-6) strongly promoted the formation of large NP oligomers and reduced the protein thermal stability. In addition, cooperative binding between these chemical ligands and an endogenous peptide ligand was observed, and molecular docking was employed to propose a possible mechanism. Taken together, we established a platform integrating in silico and experimental screening approaches for efficient discovery of herb-derived bioactive ligands especially towards non-enzyme protein targets.

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.

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

BACKGROUNDS AND AIMS

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Discovery of Novel Potential Human Targets of Resveratrol by Inverse Molecular Docking

Resveratrol is a polyphenol known for its antioxidant and anti-inflammatory properties, which support its use as a treatment for variety of diseases. There are already known connections of resveratrol to chemoprevention of cancer because of its ability to prevent tumor initiation and inhibit tumor promotion and progression. Resveratrol is also believed to be important in cardiovascular diseases and neurological disorders, such as Alzheimer's disease. Using an inverse molecular docking approach, we sought to find new potential targets of resveratrol. Docking of resveratrol into each ProBiS predicted binding site of >38 000 protein structures from the Protein Data Bank was examined, and a number of novel potential targets into which resveratrol was docked successfully were found. These explain known actions or predict new effects of resveratrol. The results included three human proteins that are already known to bind resveratrol. A majority of proteins discovered however have no already described connections with resveratrol. We report new potential target human proteins and proteins connected with different organisms into which resveratrol can dock. Our results reveal previously unknown potential target human proteins, whose connection with cardiovascular and neurological disorders could lead to new potential treatments for variety of diseases. We believe that our research could help in future experimental studies on revestratol bioactivity in humans.

In Silico Oncology Drug Repositioning and Polypharmacology

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

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

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

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

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