Chemogenomics Approaches to Rationalizing the Mode-of-Action of Traditional Chinese and Ayurvedic Medicines

Sulfur fertiliser enhancement of Erigeron breviscapus (Asteraceae) quality by improving plant physiological responses and reducing soil cadmium bioavailability

Erigeron breviscapus (Vant.) Hand.-Mazz. is an important medicinal plant; however, its quality is severely diminished by cadmium (Cd) pollution. Sulfur fertilisation can improve the production and application of E. breviscapus. This study examined Cd stress alleviation in the soil-plant system and determined the plant growth response after the application of sulfur fertiliser. The soil Cd concentration used in the treatments was 100 g·kg^-1, and the sulfur fertiliser application rates were 0.1, 0.2, and 0.3 g·kg^-1. Using pot experiments, we explored the impacts of high, medium, and low amounts of sulfur fertiliser on Cd accumulation and the quality and activity of E. breviscapus. The results showed that the application of sulfur fertiliser promoted Cd transformation to residual Cd under oxidation conditions, reducing Cd accumulation in E. breviscapus. Throughout the growth period, the application of sulfur fertiliser increased the soluble protein content and antioxidant enzyme activity, which alleviated Cd toxicity. The net photosynthetic rate, transpiration rate, intercellular CO₂ concentration, chlorophyll level, and leaf width increased significantly. The biomass content of E. breviscapus also increased. Sulfur fertiliser improves the quality of herbaceous medicinal plants by reducing Cd accumulation and increasing scutellarin, chlorogenic, isochlorogenic acid B, and isochlorogenic acid C contents. A reasonable application of sulfur fertiliser is essential for improving E. breviscapus quality. This study provides a new method to reduce the ecological risk of planting herbaceous medicinal plants in Cd-contaminated soil.

Network-based modeling of herb combinations in traditional Chinese medicine

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

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

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

Data Mining and Systematic Pharmacology to Reveal the Mechanisms of Traditional Chinese Medicine in Recurrent Respiratory Tract Infections' Treatment

Traditional Chinese medicine (TCM) was widely used in the treatment of recurrent respiratory tract infections (RRTIs) in East Asia, but its mechanism was not clear because of its complex prescription rules. This research prospectively collected 100 prescriptions of RRTI children treated with TCM. The characteristics of TCM in prescriptions were described and analyzed, and the rules of prescriptions were analyzed by hierarchical clustering and association rules. The results showed that the principle of RRTI was to pay equal attention to cold and mild, and six new meaningful prescriptions were obtained. Among them, the new prescription composed of Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizoma (Baizhu), Saposhnikoviae Radix (Fangfeng), Angelicae Sinensis Radix (Danggui), and Paeoniae Radix Rubra (Chishao) was an important method to treat RRTI. In order to explore the mechanism of the new prescription, the research obtained the action target of each herb of the core prescription on Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine, TCMIP v2.0. The target genes were enriched by Metascape, and 93 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained. According to the classification and statistics of KEGG type, it was found that the new prescription mainly intervened in the metabolic pathway dominated by amino acid metabolism. In addition, there were also many interventions in the nervous system-, endocrine system-, and digestive system-related pathways. This study summarized the prescription rule of TCM in the treatment of RRTI, analyzed the mechanism of supplementing deficiency, and provided a new idea for the treatment of RRTI.

Integrating bioassay data for improved prediction of drug-target interaction

Identifying drug targets is one of the major tasks in drug discovery. As experimental identification of targets is rather challenging, development of computational methods is necessary for efficient identification of drug-target interaction. Traditional computational method, such as docking, is based solely on the chemical structure, which is not available for most of the targets. On the other hand, bioassay data might contain information helpful for prediction of drug-target interaction. In this study, a feature enrichment method integrating bioassay and chemical structure data was developed to predict drug-target interaction. Using a large-scale benchmark on the datasets, we demonstrated that the model adopting integrated fingerprint outperformed the one using chemical fingerprint. Influence of the false positive hits in bioassays and algorithm-related factors on the model performance were also investigated. The results suggested that prediction by using integrated fingerprint was robust to false positive hits, the choice of classifiers, and different random splits of the datasets.

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account

The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as early as possible in the drug-discovery process. Here, we provide detailed instructions for capitalizing on bioactivity predictions to elucidate the MoAs of small molecules and infer their underlying phenotypic effects. We illustrate how these predictions can be used to infer the underlying antidepressive effects of marketed drugs. We also provide the necessary functionalities to model cytotoxicity data using single and ensemble machine-learning algorithms. Finally, we give detailed instructions on how to calculate confidence intervals for individual predictions using the conformal prediction framework. © 2019 by John Wiley & Sons, Inc.

Chinese Herbal Medicine (MaZiRenWan) Improves Bowel Movement in Functional Constipation Through Down-Regulating Oleamide

In a prospective, randomized, three-arms, controlled clinical study, Chinese Herbal Medicine MaZiRenWan (MZRW, also known as Hemp Seed Pill) demonstrates comparable efficacy with Senna for functional constipation (FC) during an 8-week treatment period. Both MZRW and Senna are better than a placebo; relative to Senna and a placebo, MZRW displayed a more sustained effect during the 8-week follow-up period. The characteristic pharmacological mechanism responsible for this observation is still unclear. To explore this, we collected pre- and post-treatment serum samples of 85 FC patients from MZRW/Senna/placebo treatment groups for pharmacometabolomic analysis. An ultrahigh-performance liquid chromatography-mass spectrometer (UPLC-MS) was used for metabolic profiling and quantification. In vivo studies were conducted in constipated C57BL/6J mice to verify the effects and corresponding mechanism(s) of the action of MZRW. Pearson correlation analysis, paired t-test, one-way ANOVA analysis, χ2 test, and Student t-test were used to interpret the clinical and preclinical data. Changes in levels of circulating oleamide and its derivatives negatively correlate with improvement in complete spontaneous bowel movement (CSBM) in the MZRW group (Pearson r = -0.59, p = 0.00057). The same did not hold true for either Senna or placebo groups. Oleamide is a known regulator of intestinal motility. MZRW treatment resulted in reduced levels of circulating oleamide in FC patients. Experimental verification showed that MZRW attenuated oleamide-induced slow intestinal motility in mice. MZRW decreased oleamide levels in serum, ileum, and colon in normal mice, but increased expression of colonic fatty acid amide hydrolase (FAAH). In conclusion, MZRW improved bowel movement in FC by down-regulating oleamide, possibly by enhancing FAAH-mediated degradation. Our findings suggest a novel therapeutic strategy for FC.

Evaluation of the combinatorial effect of Tinospora cordifolia and Zingiber officinale on human breast cancer cells

The present study was aimed to investigate the anticancer potential of the combination treatment of Tinospora cordifolia (TC) and Zingiber officinale (ZO) using network pharmacology approach. In silico analysis of the anticancer activity of TC + ZO was carried out using Cytoscape 3.2.0 software to elucidate the mechanism. The MTT assay confirms the combination of TC and ZO is more active (IC₅₀; 2 μg ml^-1) as compared to TC (509 μg ml^-1) and ZO (1 mg ml^-1) alone in MCF-7 cells. The TC + ZO combination treatment inhibits DNA synthesis, migration, and induces apoptosis in MCF-7 cells as compared to TC and ZO alone at a concentration of 1 µg ml^-1. TC + ZO combination treatment arrested cell cycle significantly at the G₀/G₁ phase. The proposed synergistic activity of the two herbs in the treatment of several cancers was correlated with an appropriate associated target/s, based on the pharmacological network. Interestingly, when both the plants used in combination, were found to regulate a total of 16 genes in 27 types of cancers. Further, ALOX5, MMP2, and MMP9 genes were identified as major targets which are responsible for the TC + ZO anticancer activity. According to merged and sub-networks of source-bioactive, bioactive-target, target-disease of TC, ZO alone and their combination; MMP9 was selected for validation purpose. The real-time PCR analysis confirmed that the TC + ZO combination treatment significantly down-regulated MMP9 mRNA expression by fivefold via up-regulation of its downstream target ER-α by 3.5-fold. In conclusion, the network analysis and in vitro validation confirmed the potent synergistic activity of TC + ZO combination treatment in breast cancer.

The Yin-Yang Property of Chinese Medicinal Herbs Relates to Chemical Composition but Not Anti-Oxidative Activity: An Illustration Using Spleen-Meridian Herbs

"Yin-Yang" and "Five Elements" theories are the basis theories of Traditional Chinese Medicine (TCM). To probe and clarify the theoretical basis of these ancient Chinese wisdoms, extensive efforts have been taken, however, without a full success. In the classification of TCM herbs, hot, cold and neutral herbs are believed to possess distinct profile of chemical compositions of which the compounds should have different polarity and mass: this view provides a new perspective for further illustration. To understand the chemical properties of TCMs in the classification of "Yin-Yang" and "Five Elements," 15 commonly used herbs attributed to spleen-meridian were selected for analyses. Chemically standardized water extracts, 50% ethanol extracts and 90% ethanol extracts were prepared and subjected to different analytic measurements. Principle component analysis (PCA) of full spectrum of HPLC, NMR and LC-MS of the extracts were established. The results revealed that the LC-MS profile showed a strong correlation with the "Yin-Yang" classification criterion. The Yang-stimulating herbs generally contain more compounds with lower molecular weight and less polar property. Additionally, a comprehensive anti-oxidative profiles of selected herbs were developed, and the results showed that its correlation with cold and hot properties of TCM, however, was rather low. Taken together, the "Yin-Yang" nature of TCM is closely related to the physical properties of the ingredients, such as polarity and molecular mass; while such classification has little correlation with anti-oxidative property. Therefore, the present results provide a new direction in probing the basic principle of TCM classification.

Natural products chemistry: The emerging trends and prospective goals

The role and contributions of natural products chemistry in advancements of the physical and biological sciences, its interdisciplinary domains, and emerging of new avenues by providing novel applications, constructive inputs, thrust, comprehensive understanding, broad perspective, and a new vision for future is outlined. The developmental prospects in bio-medical, health, nutrition, and other interrelated sciences along with some of the emerging trends in the subject area are also discussed as part of the current review of the basic and core developments, innovation in techniques, advances in methodology, and possible applications with their effects on the sciences in general and natural products chemistry in particular. The overview of the progress and ongoing developments in broader areas of the natural products chemistry discipline, its role and concurrent economic and scientific implications, contemporary objectives, future prospects as well as impending goals are also outlined. A look at the natural products chemistry in providing scientific progress in various disciplines is deliberated upon.

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

TCMAnalyzer: A Chemo- and Bioinformatics Web Service for Analyzing Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has been widely used and proven effective in long term clinical practice. However, the molecular mechanism of action for many TCMs remains unclear due to the complexity of many ingredients and their interactions with biological receptors. This is one of the major roadblocks in TCM modernization. In order to solve this problem, we have developed TCMAnalyzer, which is a free web-based toolkit allowing a user to (1) identify the potential compounds that are responsible for the bioactivities for a TCM herb through scaffold-activity relation searches using structural search techniques, (2) investigate the molecular mechanism of action for a TCM herb at the systemic level, and (3) explore the potentially targeted bioactive herbs. The toolkit can result in TCM networks that demonstrate the relations among natural product molecules (small molecular ligands), putative protein targets, pathways, and diseases. These networks are graphically depicted to reveal the mechanism of actions for a TCM herb or to identify new molecular scaffolds for new chemotherapies. TCMAnalyzer is freely available at http://www.rcdd.org.cn/tcmanalyzer .

MOST: most-similar ligand based approach to target prediction

BACKGROUND

Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power.

RESULTS

Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction.

CONCLUSIONS

Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.

Phylogenetic Tree Analysis of the Cold-Hot Nature of Traditional Chinese Marine Medicine for Possible Anticancer Activity

Traditional Chinese Marine Medicine (TCMM) represents one of the medicinal resources for research and development of novel anticancer drugs. In this study, to investigate the presence of anticancer activity (AA) displayed by cold or hot nature of TCMM, we analyzed the association relationship and the distribution regularity of TCMMs with different nature (613 TCMMs originated from 1,091 species of marine organisms) via association rules mining and phylogenetic tree analysis. The screened association rules were collected from three taxonomy groups: (1) Bacteria superkingdom, Phaeophyceae class, Fucales order, Sargassaceae family, and Sargassum genus; (2) Viridiplantae kingdom, Streptophyta phylum, Malpighiales class, and Rhizophoraceae family; (3) Holothuroidea class, Aspidochirotida order, and Holothuria genus. Our analyses showed that TCMMs with closer taxonomic relationship were more likely to possess anticancer bioactivity. We found that the cluster pattern of marine organisms with reported AA tended to cluster with cold nature TCMMs. Moreover, TCMMs with salty-cold nature demonstrated properties for softening hard mass and removing stasis to treat cancers, and species within Metazoa or Viridiplantae kingdom of cold nature were more likely to contain AA properties. We propose that TCMMs from these marine groups may enable focused bioprospecting for discovery of novel anticancer drugs derived from marine bioresources.

Spectroscopic and E-tongue evaluation of medicinal plants: A taste of how rasa can be studied

BACKGROUND

The use of medicinal plants in Ayurveda is based on rasa, generally taken to represent taste as a sensory perception. This chemosensory parameter plays an important role in Ayurvedic pharmacology.

OBJECTIVE

The aim is to explore the use of structuro-functional information deduced from analytical techniques for the rasa-based classification of medicinal plants in Ayurveda.

MATERIALS AND METHODS

Methods of differential sensing and spectroscopic metabolomics have been used in select medicinal plants from three different taste categories (sweet, pungent and multiple taste): Tribulus terrestris, Vitis vinifera and Glycyrrhiza glabra from sweet category; Piper longum, Cuminum cyminum and Capsicum annum from pungent group; Emblica officinalis with five tastes. While Electronic tongue was used for evaluation of the sensorial property of taste, the chemical properties were studied with Nuclear Magnetic Resonance (NMR), Fourier Transform InfraRed (FTIR) and Laser Induced Breakdown Spectroscopy (LIBS).

RESULTS

In terms of taste and phytochemical profiles, all samples were unique but with similarities within each group. While the sensor response in E-tongue showed similarities within the sweet and pungent categories, NMR spectra in the aromatic region showed close similarities between the plants in the sweet category. The sensory, phytochemical and phytoelemental profiles of E. officinalis (with five rasa) in particular, were unique.

CONCLUSION

A combination of sensorial and chemical descriptors is a promising approach for a comprehensive evaluation and fingerprinting of the Ayurvedic pharmacological parameter rasa.

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.

Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

Novel synthetic biscoumarins target tumor necrosis factor-α in hepatocellular carcinoma in vitro and in vivo

TNF is a pleotropic cytokine known to be involved in the progression of several pro-inflammatory disorders. Many therapeutic agents have been designed to counteract the effect of TNF in rheumatoid arthritis as well as a number of cancers. In the present study we have synthesized and evaluated the anti-cancer activity of novel biscoumarins in vitro and in vivo. Among new compounds, BIHC was found to be the most cytotoxic agent against the HepG2 cell line while exhibiting less toxicity toward normal hepatocytes. Furthermore, BIHC inhibited the proliferation of various hepatocellular carcinoma (HCC) cells in a dose- and time-dependent manner. Subsequently, using in silico target prediction, BIHC was predicted as a TNF blocker. Experimental validation was able to confirm this hypothesis, where BIHC could significantly inhibit the recombinant mouse TNF-α binding to its antibody with an IC50 of 16.5 μM. Furthermore, in silico docking suggested a binding mode of BIHC similar to a ligand known to disrupt the native, trimeric structure of TNF, and also validated with molecular dynamics simulations. Moreover, we have demonstrated the down-regulation of p65 phosphorylation and other NF-κB-regulated gene products upon BIHC treatment, and on the phenotypic level the compound shows inhibition of CXCL12-induced invasion of HepG2 cells. Also, we demonstrate that BIHC inhibits infiltration of macrophages to the peritoneal cavity and suppresses the activity of TNF-α in vivo in mice primed with thioglycollate broth and lipopolysaccharide. We comprehensively validated the TNF-α inhibitory efficacy of BIHC in an inflammatory bowel disease mice model.

A screening pattern recognition method finds new and divergent targets for drugs and natural products

Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule-target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug-target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 μM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug's known MOA.

Why is Research on Herbal Medicinal Products Important and How Can We Improve Its Quality?

Research on herbal medicinal products is increasingly published in “Western” scientific journals dedicated primarily to conventional medicines. Publications are concerned mainly not only on the issues of safety and interactions, but also on efficacy. In reviews, a recurring complaint has been a lack of quality studies. In this opinion article, we present the case of Chinese herbal medicines as an example, as they have been extensively used in the global market and increasingly studied worldwide. We analyze the potential reasons for problems and propose some ways forward. As in the case of any drug, clinical trials for safety, efficacy, and/or effectiveness are the ultimate demonstration of therapeutic usefulness of herbal products. These will only make scientific sense when the tested herbal products are authentic, standardized, and quality controlled, if good practice guidelines of evidence-based medicine are followed, and if relevant controls and outcome measures are scientifically defined. Herbal products are complex mixtures, and for such complexity, an obvious approach for mechanistic studies is network pharmacology based on omic tools and approaches, which has already begun to revolutionize the study of conventional drugs, emphasizing networks, interactions, and polypharmacological features behind the action of many drugs.

Memory-Enhancing Effects of the Crude Extract of Polygala tenuifolia on Aged Mice

Learning and memory disorders arise from distinct age-associated processes, and aging animals are often used as a model of memory impairment. The root of Polygala tenuifolia has been commonly used in some Asian countries as memory enhancer and its memory improvement has been reported in various animal models. However, there is less research to verify its effect on memory functions in aged animals. Herein, the memory-enhancing effects of the crude extract of Polygala tenuifolia (EPT) on normal aged mice were assessed by Morris water maze (MWM) and step-down passive avoidance tests. In MWM tests, the impaired spatial memory of the aged mice was partly reversed by EPT (100 and 200 mg/kg; P < 0.05) as compared with the aged control mice. In step-down tests, the nonspatial memory of the aged mice was improved by EPT (100 and 200 mg/kg; P < 0.05). Additionally, EPT could increase superoxide dismutase (SOD) and catalase (CAT) activities, inhibit monoamine oxidase (MAO) and acetyl cholinesterase (AChE) activities, and decrease the levels of malondialdehyde (MDA) in the brain tissue of the aged mice. The results showed that EPT improved memory functions of the aged mice probably via its antioxidant properties and via decreasing the activities of MAO and AChE.

Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review

An overview of databases andin silicotools for discovery of the hidden therapeutic potential of medicinal plants.

Aqueous ethanolic extract of Tinospora cordifolia as a potential candidate for differentiation based therapy of glioblastomas

Glioblastomas are the most aggressive primary brain tumors and their heterogeneity and complexity often renders them non responsive to various conventional treatments. Search for herbal products having potential anti-cancer activity is an active area of research in the Indian traditional system of medicine i.e., Ayurveda. Tinospora cordifolia, also named as ‘heavenly elixir’ is used in various ayurvedic decoctions as panacea to treat several body ailments. The current study investigated the anti-brain cancer potential of 50% ethanolic extract of Tinospora cordifolia (TCE) using C6 glioma cells. TCE significantly reduced cell proliferation in dose-dependent manner and induced differentiation in C6 glioma cells, resulting in astrocyte-like morphology as indicated by phase contrast images, GFAP expression and process outgrowth data of TCE treated cells which exhibited higher number and longer processes than untreated cells. Reduced proliferation of cells was accompanied by enhanced expression of senescence marker, mortalin and its translocation from perinuclear to pancytoplasmic spaces. Further, TCE showed anti-migratory and anti-invasive potential as depicted by wound scratch assay and reduced expression of plasticity markers NCAM and PSA-NCAM along with MMP-2 and 9. On analysis of the cell cycle and apoptotic markers, TCE treatment was seen to arrest the C6 cells in G0/G1 and G2/M phase, suppressing expression of G1/S phase specific protein cyclin D1 and anti-apoptotic protein Bcl-xL, thus supporting its anti-proliferative and apoptosis inducing potential. Present study provides the first evidence for the presence of anti-proliferative, differentiation-inducing and anti-migratory/anti-metastatic potential of TCE in glioma cells and possible signaling pathways involved in its mode of action. Our primary data suggests that TCE and its active components may prove to be promising phytotherapeutic interventions in gliobalstoma multiformae. 

Linking Ayurveda and Western medicine by integrative analysis

In this article, we discuss our recent work in elucidating the mode-of-action of compounds used in traditional medicine including Ayurvedic medicine. Using computational ('in silico') approach, we predict potential targets for Ayurvedic anti-cancer compounds, obtained from the Indian Plant Anticancer Database given its chemical structure. In our analysis, we observed that: (i) the targets predicted can be connected to cancer pathogenesis i.e. steroid-5-alpha reductase 1 and 2 and estrogen receptor-β, and (ii) predominantly hormone-dependent cancer targets were predicted for the anti-cancer compounds. Through the use of our in silico target prediction, we conclude that understanding how traditional medicine such as Ayurveda work through linking with the 'western' understanding of chemistry and protein targets can be a fruitful avenue in addition to bridging the gap between the two different schools of thinking. Given that compounds used in Ayurveda have been tested and used for thousands of years (although not in the same approach as Western medicine), they can potentially be developed into potential new drugs. Hence, to further advance the case of Ayurvedic medicine, we put forward some suggestions namely: (a) employing and integrating novel analytical methods given the advancements of 'omics' and (b) sharing experimental data and clinical results on studies done on Ayurvedic compounds in an easy and accessible way.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.