CancerHSP: anticancer herbs database of systems pharmacology

Computer-Aided Drug Design in Research on Chinese Materia Medica: Methods, Applications, Advantages, and Challenges

Chinese materia medica (CMM) refers to the medicinal substances used in traditional Chinese medicine. In recent years, CMM has become globally prevalent, and scientific research on CMM has increasingly garnered attention. Computer-aided drug design (CADD) has been employed in Western medicine research for many years, contributing significantly to its progress. However, the role of CADD in CMM research has not been systematically reviewed. This review briefly introduces CADD methods in CMM research from the perspectives of computational chemistry (including quantum chemistry, molecular mechanics, and quantum mechanics/molecular mechanics) and informatics (including cheminformatics, bioinformatics, and data mining). Then, it provides an exhaustive discussion of the applications of these CADD methods in CMM research through rich cases. Finally, the review outlines the advantages and challenges of CADD in CMM research. In conclusion, despite the current challenges, CADD still offers unique advantages over traditional experiments. With the development of the CMM industry and computer science, especially driven by artificial intelligence, CADD is poised to play an increasingly pivotal role in advancing CMM research.

The role and application of bioinformatics techniques and tools in drug discovery

The process of drug discovery and development is both lengthy and intricate, demanding a substantial investment of time and financial resources. Bioinformatics techniques and tools can not only accelerate the identification of drug targets and the screening and refinement of drug candidates, but also facilitate the characterization of side effects and the prediction of drug resistance. High-throughput data from genomics, transcriptomics, proteomics, and metabolomics make significant contributions to mechanics-based drug discovery and drug reuse. This paper summarizes bioinformatics technologies and tools in drug research and development and their roles and applications in drug research and development, aiming to provide references for the development of new drugs and the realization of precision medicine.

Network pharmacology: an efficient but underutilized approach in oral, head and neck cancer therapy-a review

The application of network pharmacology (NP) has advanced our understanding of the complex molecular mechanisms underlying diseases, including neck, head, and oral cancers, as well as thyroid carcinoma. This review aimed to explore the therapeutic potential of natural network pharmacology using compounds and traditional Chinese medicines for combating these malignancies. NP serves as a pivotal tool that provides a comprehensive view of the interactions among compounds, genes, and diseases, thereby contributing to the advancement of disease treatment and management. In parallel, this review discusses the significance of publicly accessible databases in the identification of oral, head, and neck cancer-specific genes. These databases, including those for head and neck oral cancer, head and neck cancer, oral cancer, and genomic variants of oral cancer, offer valuable insights into the genes, miRNAs, drugs, and genetic variations associated with these cancers. They serve as indispensable resources for researchers, clinicians, and drug developers, contributing to the pursuit of precision medicine and improved treatment of these challenging malignancies. In summary, advancements in NP could improve the globalization and modernization of traditional medicines and prognostic targets as well as aid in the development of innovative drugs. Furthermore, this review will be an eye-opener for researchers working on drug development from traditional medicines by applying NP approaches.

The impact of altering the concentration of coffee constituents on their anticancer effect on oral squamous cell carcinoma cell line - in vitro study

Introduction

Oral squamous cell carcinoma (OSCC) is one of the most common malignancies of the head and neck, which attracts much attention because of its increasing incidence and poor outcome. Coffee is one of the most popular beverages that are globally consumed. It consists of several phytochemical constituents, such as polyphenols, caffeine, and chlorogenic acid (CGA). Those constituents account for the potential effects on several diseases, including cancer. It has been reported that coffee exerts significant cytotoxicity against OSCC via inhibition of epidermal growth factor receptor tyrosine kinase (EGFR-TK) and up-regulation of apoptotic proteins, such as caspase-3 and caspase-9. The current study aims to measure the concentration of caffeine and CGA in 3 different types of coffee extracts, unroasted green coffee (GC), medium-roasted coffee (MRC), and decaffeinated coffee.

Material and methods

The cytotoxic effect against OSCC-25 cell lines was evaluated and correlated with the concentration of constituents in each extract. The mechanisms of cytotoxicity were also studied by assessing the effect of each extract on caspase-3 and caspase-9 levels, in addition to the inhibitory effect on EGFR-TK.

Results

It was found that the caffeine concentration was higher in MRC than in GC because of the roasting process. However, the concentration of caspase-3 and -9 and the inhibitory effect on EGFR-TK were much higher in GC than MRC-treated cells because of the higher concentration of CGA.

Conclusions

Decaffeinated coffee exerts lower cytotoxic effects because it was totally deprived of caffeine and CGA during the decaffeination process.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) meridian is the key theoretical guidance of prescription against tumor in clinical practice. However, there is no scientific and systematic verification of therapeutic action of herbs under meridians context. Several studies have determined the Chinese herbal medicine (CHM) phytochemicals for intrinsic attribute or meridians classification based on artificial intelligence (AI) tools. However, it is challenging to represent the complex molecular structures with large heterogeneity through the current technologies. In addition, the multiple correspondence between herbs and meridians has not been paid much attention.

AIM OF THE STUDY

We aim to develop an AI framework to classify multi-target meridians through the topological structure of phytochemicals.

MATERIALS AND METHODS

A total of 354 anti-cancer herbs, their corresponding TCM meridians and 5471 ingredient compounds were collected from public databases of CancerHSP, ETCM, and Hit 2.0. The statistical analysis of herbal and compound datasets, clustering analysis of the associated cancers, and correlational analysis of meridian tropism were preliminary conducted. Then a deep learning (DL) hybrid model named GRMC consisting of graph convolutional network (GCN) and recurrent neural network (RNN) was employed to generate the meridian multi-label sequences based on molecular graph.

RESULTS

The curing herbs against tumors have tight relationships to lung, liver, stomach, and spleen meridians. These herbs behave different properties in curing certain cancer. Certain cancer types have co-occurrence such as ovarian, bladder and cervical cancer. Compounds have multitarget meridians with characteristics of higher-order correlations. Compared with the other state-of-the-art algorithms on the datasets and previous methods dealing with conventional fixed fingerprints of herbal compounds, the proposed GRMC has superior overall performance on testing dataset with the one error of 0.183, hamming loss of 0.112, mean averaged accuracy (MAA) of 0.855, mean averaged precision (MAP) of 0.891, mean averaged recall (MAR) of 0.812, and mean averaged F1 score (MAF) of 0.849.

CONCLUSIONS

The proposed method can predict multi-targeted meridians through neural graph features in herbal compounds and outperforms several comparison methods. It could provide a basis for understanding the molecular scientific evidence of TCM meridians.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes'/proteins' connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein-protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways' components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.

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.

Investigation of Anti-Liver Cancer Activity of the Herbal Drug FDY003 Using Network Pharmacology

Globally, liver cancer (LC) is the sixth-most frequently occurring and the second-most fatal malignancy, responsible for 0.83 million deaths annually. Although the application of herbal drugs in cancer therapies has increased, their anti-LC activity and relevant mechanisms have not been fully studied from a systems perspective. To address these issues, we conducted a system-perspective network pharmacological investigation into the activity and mechanisms underlying the action of the herbal drug. FDY003 reduced the viability of human LC treatment. FDY003 reduced the viability of human LC cells and elevated their chemosensitivity. There were a total of 16 potential bioactive chemical components in FDY003 and they had 91 corresponding targets responsible for the pathological processes in LC. These FDY003 targets were functionally involved in regulating the survival, proliferation, apoptosis, and cell cycle of LC cells. Additionally, we found that FDY003 may target key signaling cascades connected to diverse LC pathological mechanisms, namely, PI3K-Akt, focal adhesion, IL-17, FoxO, MAPK, and TNF pathways. Overall, this study contributed to integrative mechanistic insights into the anti-LC potential of FDY003.

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

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

Network Pharmacology-Based Dissection of the Comprehensive Molecular Mechanisms of the Herbal Prescription FDY003 Against Estrogen Receptor-Positive Breast Cancer

Estrogen receptor-positive breast cancer (ERPBC) is the commonest subtype of breast cancer, with a high prevalence, incidence, and mortality. Herbal drugs are increasingly being used to treat ERPBC, although their mechanisms of action are not fully understood. Therefore, in this study, we aimed to analyze the therapeutic properties of FDY003, a herbal anti-ERPBC prescription, using a network pharmacology approach. FDY003 decreased the viability of human ERPBC cells and sensitized them to tamoxifen, an endocrine drug that is widely used in the treatment of ERPBC. The network pharmacology analysis revealed 18 pharmacologically active components in FDY003 that may interact with and regulate 66 therapeutic targets. The enriched gene ontology terms for the FDY003 targets were associated with the modulation of cell survival and death, cell proliferation and growth arrest, and estrogen-associated cellular processes. Analysis of the pathway enrichment of the targets showed that FDY003 may target a variety of ERPBC-associated pathways, including the PIK3-Akt, focal adhesion, MAPK, and estrogen pathways. Overall, these data provide a comprehensive mechanistic insight into the anti-ERPBC activity of FDY003.

Mining Natural Products with Anticancer Biological Activity through a Systems Biology Approach

Natural products, like turmeric, are considered powerful antioxidants which exhibit tumor-inhibiting activity and chemoradioprotective properties. Nowadays, there is a great demand for developing novel, affordable, efficacious, and effective anticancer drugs from natural resources. In the present study, we have employed a stringent in silico methodology to mine and finally propose a number of natural products, retrieved from the biomedical literature. Our main target was the systematic search of anticancer products as anticancer agents compatible to the human organism for future use. In this case and due to the great plethora of such products, we have followed stringent bioinformatics methodologies. Our results taken together suggest that natural products of a great diverse may exert cytotoxic effects in a maximum of the studied cancer cell lines. These natural compounds and active ingredients could possibly be combined to exert potential chemopreventive effects. Furthermore, in order to substantiate our findings and their application potency at a systems biology level, we have developed a representative, user-friendly, publicly accessible biodatabase, NaturaProDB, containing the retrieved natural resources, their active ingredients/fractional mixtures, the types of cancers that they affect, and the corresponding experimentally verified target genes.

Investigating the Mechanism of Scutellariae barbata Herba in the Treatment of Colorectal Cancer by Network Pharmacology and Molecular Docking

Background

Colorectal cancer (CRC) is one of the most common gastrointestinal tumors, which accounts for approximately 10% of all diagnosed cancers and cancer deaths worldwide per year. Scutellariae barbatae Herba (SBH) is one of the most frequently used traditional Chinese medicine (TCM) in the treatment of CRC. Although many experiments have been carried out to explain the mechanisms of SBH, the mechanisms of SBH have not been illuminated fully. Thus, we constructed a network pharmacology and molecular docking to investigate the mechanisms of SBH.

Methods

We adopted active constituent prescreening, target predicting, protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, differentially expressed gene analysis, and molecular docking to establish a system pharmacology database of SBH against CRC.

Results

A total of 64 active constituents of SBH were obtained and 377 targets were predicted, and the result indicated that quercetin, luteolin, wogonin, and apigenin were the main active constituents of SBH. Glucocorticoid receptor (NR3C1), pPhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA), cellular tumor antigen p53 (TP53), transcription factor AP-1 (JUN), mitogen-activated protein kinase 1 (MAPK1), Myc protooncogene protein (MYC), cyclin-dependent kinase 1 (CDK1), and broad substrate specificity ATP-binding cassette transporter ABCG2 (ABCG2) were the major targets of SBH in the treatment of CRC. GO analysis illustrated that the core biological process regulated by SBH was the regulation of the cell cycle. Thirty pathways were presented and 8 pathways related to CRC were involved. Molecular docking presented the binding details of 3 key targets with 6 active constituents.

Conclusions

The mechanisms of SBH against CRC depend on the synergistic effect of multiple active constituents, multiple targets, and multiple pathways.

A Comprehensive Understanding of the Anticancer Mechanisms of FDY2004 Against Cervical Cancer Based on Network Pharmacology

Herbal drugs are continuously being developed and used as effective therapeutics for various cancers, such as cervical cancer (CC); however, their mechanisms of action at a systemic level have not been explored fully. To study such mechanisms, we conducted a network pharmacological investigation of the anti-CC mechanisms of FDY2004, an herbal drug consisting of Moutan Radicis Cortex, Persicae Semen , and Rhei Radix et Rhizoma. We found that FDY2004 inhibited the viability of human CC cells. By performing pharmacokinetic evaluation and network analysis of the phytochemical components of FDY2004, we identified 29 bioactive components and their 116 CC-associated pharmacological targets. Gene ontology enrichment analysis showed that the modulation of cellular functions, such as apoptosis, growth, proliferation, and survival, might be mediated through the FDY2004 targets. The therapeutic targets were also key components of CC-associated oncogenic and tumor-suppressive pathways, including PI3K-Akt, human papillomavirus infection, IL-17, MAPK, TNF, focal adhesion, and viral carcinogenesis pathways. In conclusion, our data present a comprehensive insight for the mechanisms of the anti-CC properties of FDY2004.

Uncovering the Anti-Lung-Cancer Mechanisms of the Herbal Drug FDY2004 by Network Pharmacology

With growing evidence on the therapeutic efficacy and safety of herbal drugs, there has been a substantial increase in their application in the lung cancer treatment. Meanwhile, their action mechanisms at the system level have not been comprehensively uncovered. To this end, we employed a network pharmacology methodology to elucidate the systematic action mechanisms of FDY2004, an anticancer herbal drug composed of Moutan Radicis Cortex, Persicae Semen, and Rhei Radix et Rhizoma, in lung cancer treatment. By evaluating the pharmacokinetic properties of the chemical compounds present in FDY2004 using herbal medicine-associated databases, we identified its 29 active chemical components interacting with 141 lung cancer-associated therapeutic targets in humans. The functional enrichment analysis of the lung cancer-related targets of FDY2004 revealed the enriched Gene Ontology terms, involving the regulation of cell proliferation and growth, cell survival and death, and oxidative stress responses. Moreover, we identified key FDY2004-targeted oncogenic and tumor-suppressive pathways associated with lung cancer, including the phosphatidylinositol 3-kinase-Akt, mitogen-activated protein kinase, tumor necrosis factor, Ras, focal adhesion, and hypoxia-inducible factor-1 signaling pathways. Overall, our study provides novel evidence and basis for research on the comprehensive anticancer mechanisms of herbal medicines in lung cancer treatment.

A Network Pharmacology Study on the Molecular Mechanisms of FDY003 for Breast Cancer Treatment

Herbal medicines have drawn considerable attention with regard to their potential applications in breast cancer (BC) treatment, a frequently diagnosed malignant disease, considering their anticancer efficacy with relatively less adverse effects. However, their mechanisms of systemic action have not been understood comprehensively. Based on network pharmacology approaches, we attempted to unveil the mechanisms of FDY003, an herbal drug comprised of Lonicera japonica Thunberg, Artemisia capillaris Thunberg, and Cordyceps militaris, against BC at a systemic level. We found that FDY003 exhibited pharmacological effects on human BC cells. Subsequently, detailed data regarding the biochemical components contained in FDY003 were obtained from comprehensive herbal medicine-related databases, including TCMSP and CancerHSP. By evaluating their pharmacokinetic properties, 18 chemical compounds in FDY003 were shown to be potentially active constituents interacting with 140 BC-associated therapeutic targets to produce the pharmacological activity. Gene ontology enrichment analysis using g:Profiler indicated that the FDY003 targets were involved in the modulation of cellular processes, involving the cell proliferation, cell cycle process, and cell apoptosis. Based on a KEGG pathway enrichment analysis, we further revealed that a variety of oncogenic pathways that play key roles in the pathology of BC were significantly enriched with the therapeutic targets of FDY003; these included PI3K-Akt, MAPK, focal adhesion, FoxO, TNF, and estrogen signaling pathways. Here, we present a network-perspective of the molecular mechanisms via which herbal drugs treat BC.

Systems Pharmacology Study of the Anticervical Cancer Mechanisms of FDY003

Increasing data support that herbal medicines are beneficial in the treatment of cervical cancer; however, their mechanisms of action remain to be elucidated. In the current study, we used a systems pharmacology approach to explore the pharmacological mechanisms of FDY003, an anticancer herbal formula comprising Lonicera japonica Thunberg, Artemisia capillaris Thunberg, and Cordyceps militaris (Linn.) Link, in the treatment of cervical cancer. Through the pharmacokinetic assessment of absorption-distribution-metabolism-excretion characteristics, we found 18 active compounds that might interact with 106 cervical cancer-related targets responsible for the pharmacological effects. FDY003 targets were significantly associated with gene ontology terms related to the regulation of cellular behaviors, including cell proliferation, cell cycle processes, cell migration, cell apoptosis, cell death, and angiogenesis. The therapeutic targets of the herbal drug were further enriched in various oncogenic pathways that are implicated in the tumorigenesis and progression of cervical cancer, including the phosphatidylinositol 3-kinase, mitogen-activated protein kinase, focal adhesion, human papillomavirus infection, and tumor necrosis factor signaling pathways. Our study provides a systematic approach to explore the anticancer properties of herbal medicines against cervical cancer.

Chinese herbal medicine promote tissue differentiation in colorectal cancer by activating HSD11B2

BACKGROUND

Colorectal cancer is a common malignant tumor of the digestive tract. In recent years, the incidence rate has increased year by year and is showing a younger trend. The application of Chinese herbal medicine (CHM) is one of the important methods for the treatment of colorectal cancer. CHM refers to the main therapeutic drugs based on traditional Chinese medicine (TCM), which is still valued. Many effective anticancer small-molecule compounds are derived from CHMs, and their effective anticancer ingredients and targets must be clarified and to further understand the molecular mechanisms by which CHM affects cancer.

METHODS

We analyzed the ingredients in CHM that were found to be effective against colorectal cancer and constructed an interaction network among these ingredients and the target protein. By analyzing the number of connections in the network and their type of interaction, we identified the key target protein Corticosteroid 11-beta-dehydrogenase isozyme 2, the enzyme encoded by HSD11B2. Analyses of HSD11B2 expression, survival curve, and co-expressed genes helped clarify the correlation between HSD11B2 and colorectal cancer as well as its underlying molecular mechanism.

RESULTS

We determined that the anticancer ingredients contained in Sanguisorba officinalis, Patrinia scabiosaefolia, and Smilax china had more connections to the target proteins found in colorectal cancer. In the interaction network, eight small-molecule compounds had an activating effect on HSD11B2. The expression of the HSD11B2 was markedly decreased in colorectal cancer tissues and was positively correlated with the overall survival time of patients. In addition, co-expression analyses showed a close relationship between HSD11B2 and tissue-specific genes in colorectal tissues. The expression levels of HSD11B2 in well-, moderately, and poorly differentiated tissues progressively decreased.

CONCLUSION

The HSD11B2 protein was a key CHM target for treating colorectal cancer. The key role of CHM may lie in activating HSD11B2 and further promoting tissue differentiation in colorectal cancer.

Compound-based Chinese medicine formula: From discovery to compatibility mechanism

ETHNOPHARMACOLOGICAL RELEVANCE

Chinese medicine formula (CMF) has a long history of clinical use in the treatment of various diseases under the guidance of traditional Chinese medicine (TCM) theory. The application of CMF can be divided into three levels, crude extracts, homologous compounds mixture, and specific compounds. However, the modern scientific connotation of the CMF theory has not been clarified.

AIM OF THE REVIEW

To critically evaluate the research strategy for the investigation of compound-based CMF (CCMF).

MATERIALS AND METHODS

The related information was collected from the scientific databases, including CNKI, Elsevier, ScienceDirect, PubMed, SpringerLink, Web of Science, and Wiley Online.

RESULTS

The research design including discovery, screening, optimization, pharmacodynamics models, and target research techniques including the targets for compatibility compounds were evaluated. Essentially it has been evaluated that the in vitro multicellular three-dimensional culture or organoid model has been proposed for the optimization model for compatibility research of CCMF. Based on these, the traditional compatibility theory of CMF, such as Monarch-Minister-Assistant-Guide (Jun-Chen-Zuo-Shi in Chinese), can probably be elucidated by the CCMF research.

CONCLUSIONS

CCMF has the clear advantage of providing the exact composition and controllable quality of modern medicines, in addition to having the characteristics of multi-ingredients and multi-targets synergistic effects of TCM. However, CCMF is still associated with challenges which need to be addressed for its future use.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

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

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

Drug databases and their contributions to drug repurposing

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.

In silico polypharmacology of natural products

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era.

Pharmacokinetic profiling of anticancer phytocompounds using computational approach

INTRODUCTION

Natural products exhibit diverse scaffolds and are considered as suitable candidates for development of leads. However, poor pharmacokinetics often acts as a hindrance during the drug discovery process.

OBJECTIVE

With a view of exploring the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of plant-based anticancer compounds, open-access databases (NPACT, CancerHSP and TaxKB) were analysed to identify molecules having properties favourable for drug ability.

METHODOLOGY

Our workflow involved identification of molecules capable of passing each of the ADMET barriers based on physicochemical properties of molecules, and physiological barriers and factors.

RESULTS

The results revealed that out of 5086 phytomolecules, 63% were orally absorbable and 52% distributable. Also, an appreciable proportion of these compounds (45%) could be metabolised and excreted. Furthermore, 28% were found to be non-toxic for cardio toxicity and central nervous system (CNS) activity. Additionally, comparison against known anticancer drugs (reference dataset) revealed that the three libraries exhibit similar trends, thus providing additional confidence to the predictions. Overall, 28% of the molecular dataset was found to have suitable pharmacokinetic properties. We have also discussed a few natural products which exhibit favourable ADMET as well as low nano-micromolar in vitro anticancer activity.

CONCLUSION

We have created an interactive database (ADMETCan), which provides access to predicted ADMET of these anticancer phytomolecules. The ease of availability of this dataset is expected to minimise failure rate of these compounds and thus is expected to be beneficial to the scientific community involved in anticancer identification and development.

Uncovering the pharmacological response of novel sesquiterpene derivatives that differentially alter gene expression and modulate the cell cycle in cancer cells

The present study aimed to assess the pharmacological anticancer profile of three natural and five synthetic sesquiterpenes developed by total chemical synthesis. To this end, their properties at the cellular and molecular level were evaluated in a panel of normal and cancer cell lines. The results obtained by performing cytotoxicity assays and gene expression analysis by reverse transcription-quantitative polymerase chain reaction showed that: i) Among the sesquiterpene derivatives analyzed, VDS58 exhibited a notable anticancer profile within attached (U-87 MG and MCF-7) and suspension (K562 and MEL-745) cancer cell cultures; however, U-87 MG cells were able to recover their proliferation capacity rapidly after 48 h of exposure; ii) gene expression profiling of U-87 MG cells, in contrast to K562 cells, showed a transient induction of cyclin-dependent kinase inhibitor 1A (CDKN1) expression; iii) the expression levels of transforming growth factor β1 (TGFB1) increased after 12 h of exposure of U-87 MG cells to VDS58 and were maintained at this level throughout the treatment period; iv) in K562 cells exposed to VDS58, TGFB1 expression levels were upregulated for 48 h and decrease afterwards; and v) the re-addition of VDS58 in U-87 MG cultures pretreated with VDS58 resulted in a notable increase in the expression of caspases (CASP3 and CASP9), BCL2‑associated agonist of cell death (BAD), cyclin D1, CDK6, CDKN1, MYC proto-oncogene bHLH transcription factor (MYC), TGFB1 and tumor suppressor protein p53. This upregulation persisted only for 24 h for the majority of genes, as afterwards, only the expression of TGFB1 and MYC was maintained at high levels. Through bioinformatic pathway analysis of RNA-Seq data of parental U-87 MG and K562 cells, substantial variation was reported in the expression profiles of the genes involved in the regulation of the cell cycle. This was associated with the differential pharmacological profiles observed in the same cells exposed to VDS58. Overall, the data presented in this study provide novel insights into the molecular mechanisms of action of sesquiterpene derivatives by dysregulating the expression levels of genes associated with the cell cycle of cancer cells.

Current situation and future usage of anticancer drug databases

Cancer is a deadly disease with increasing incidence and mortality rates and affects the life quality of millions of people per year. The past 15 years have witnessed the rapid development of targeted therapy for cancer treatment, with numerous anticancer drugs, drug targets and related gene mutations been identified. The demand for better anticancer drugs and the advances in database technologies have propelled the development of databases related to anticancer drugs. These databases provide systematic collections of integrative information either directly on anticancer drugs or on a specific type of anticancer drugs with their own emphases on different aspects, such as drug-target interactions, the relationship between mutations in drug targets and drug resistance/sensitivity, drug-drug interactions, natural products with anticancer activity, anticancer peptides, synthetic lethality pairs and histone deacetylase inhibitors. We focus on a holistic view of the current situation and future usage of databases related to anticancer drugs and further discuss their strengths and weaknesses, in the hope of facilitating the discovery of new anticancer drugs with better clinical outcomes.

Recent advances and effective strategies in the discovery and applications of natural products

Natural products are the most representative form of conventional therapy as compared to any other traditional or alternative medicine systems.

The unequivocal preponderance of biocomputation in clinical virology

Bioinformatics and computer based data simulation and modeling are captivating biological research, delivering great results already and promising to deliver more. As biological research is a complex, intricate, diverse field, any available support is gladly taken. With recent outbreaks and epidemics, pathogens are a constant threat to the global economy and security. Virus related plagues are somehow the most difficult to handle. Biocomputation has provided appreciable help in resolving clinical virology related issues. This review, for the first time, surveys the current status of the role of computation in virus related research. Advances made in the fields of clinical virology, antiviral drug design, viral immunology and viral oncology, through input from biocomputation, have been discussed. The amount of progress made and the software platforms available are consolidated in this review. The limitations of computation based methods are presented. Finally, the challenges facing the future of biocomputation in clinical virology are speculated upon.

Biocomputation in clinical virology.

Systems Pharmacology-based strategy to screen new adjuvant for hepatitis B vaccine from Traditional Chinese Medicine Ophiocordyceps sinensis

Adjuvants are common component for many vaccines but there are still few licensed for human use due to low efficiency or side effects. The present work adopted Systems Pharmacology analysis as a new strategy to screen adjuvants from traditional Chinese medicine. Ophiocordyceps sinensis has been used for many years in China and other Asian countries with many biological properties, but the pharmacological mechanism has not been fully elucidated. First in this study, 190 putative targets for 17 active compounds in Ophiocordyceps sinensis were retrieved and a systems pharmacology-based approach was applied to provide new insights into the pharmacological actions of the drug. Pathway enrichment analysis found that the targets participated in several immunological processes. Based on this, we selected cordycepin as a target compound to serve as an adjuvant of the hepatitis B vaccine because the existing vaccine often fails to induce an effective immune response in many subjects. Animal and cellular experiments finally validated that the new vaccine simultaneously improves the humoral and cellular immunity of BALB/c mice without side effects. All this results demonstrate that cordycepin could work as adjuvant to hepatitis b vaccine and systems-pharmacology analysis could be used as a new method to select adjuvants.

NPCARE: database of natural products and fractional extracts for cancer regulation

Background

Natural products have increasingly attracted much attention as a valuable resource for the development of anticancer medicines due to the structural novelty and good bioavailability. This necessitates a comprehensive database for the natural products and the fractional extracts whose anticancer activities have been verified.

Description

NPCARE (http://silver.sejong.ac.kr/npcare) is a publicly accessible online database of natural products and fractional extracts for cancer regulation. At NPCARE, one can explore 6578 natural compounds and 2566 fractional extracts isolated from 1952 distinct biological species including plants, marine organisms, fungi, and bacteria whose anticancer activities were validated with 1107 cell lines for 34 cancer types. Each entry in NPCARE is annotated with the cancer type, genus and species names of the biological resource, the cell line used for demonstrating the anticancer activity, PubChem ID, and a wealth of information about the target gene or protein. Besides the augmentation of plant entries up to 743 genus and 197 families, NPCARE is further enriched with the natural products and the fractional extracts of diverse non-traditional biological resources.

Conclusions

NPCARE is anticipated to serve as a dominant gateway for the discovery of new anticancer medicines due to the inclusion of a large number of the fractional extracts as well as the natural compounds isolated from a variety of biological resources.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-016-0188-5) contains supplementary material, which is available to authorized users.

Cucurbitacin I elicits the formation of actin/phospho-myosin II co-aggregates by stimulation of the RhoA/ROCK pathway and inhibition of LIM-kinase

Cucurbitacins are cytotoxic triterpenoid sterols isolated from plants. One of their earliest cellular effect is the aggregation of actin associated with blockage of cell migration and division that eventually lead to apoptosis. We unravel here that cucurbitacin I actually induces the co-aggregation of actin with phospho-myosin II. This co-aggregation most probably results from the stimulation of the Rho/ROCK pathway and the direct inhibition of the LIMKinase. We further provide data that suggest that the formation of these co-aggregates is independent of a putative pro-oxidant status of cucurbitacin I. The results help to understand the impact of cucurbitacins on signal transduction and actin dynamics and open novel perspectives to use it as drug candidates for cancer research.

Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties

Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.

Systems Biology - A Pivotal Research Methodology for Understanding the Mechanisms of Traditional Medicine

Objectives:

Systems biology is a novel subject in the field of life science that aims at a systems’ level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era.

Methods:

The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction.

Results:

The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted.

Conclusion:

Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.