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1.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
doi:10.1038/msb.2011.5
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
2.  Assessment of Anti-Influenza Activity and Hemagglutination Inhibition of Plumbago indica and Allium sativum Extracts 
Pharmacognosy Research  2016;8(2):105-111.
Background:
Human influenza is a seasonal disease associated with significant morbidity and mortality. Anti-flu ayurvedic/herbal medicines have played a significant role in fighting the virus pandemic. Plumbagin and allicin are commonly used ingredients in many therapeutic remedies, either alone or in conjunction with other natural substances. Evidence suggests that these extracts are associated with a variety of pharmacological activities.
Objective:
To evaluate anti-influenza activity from Plumbago indica and Allium sativum extract against Influenza A (H1N1)pdm09.
Materials and Methods:
Different extraction procedures were used to isolate the active ingredient in the solvent system, and quantitative HPLTC confirms the presence of plumbagin and allicin. The cytotoxicity was carried out on Madin-Darby Canine kidney cells, and the 50% cytotoxic concentration (CC50) values were below 20 mg/mL for both plant extracts. To assess the anti-influenza activity, two assays were employed, simultaneous and posttreatment assay.
Results:
A. sativum methanolic and ethanolic extracts showed only 14% reduction in hemagglutination in contrast to P. indica which exhibited 100% reduction in both simultaneous and posttreatment assay at concentrations of 10 mg/mL, 5 mg/mL, and 1 mg/mL.
Conclusions:
Our results suggest that P. indica extracts are good candidates for anti-influenza therapy and should be used in medical treatment after further research.
SUMMARY
The search for natural antiviral compounds from plants is a promising approach in the development of new therapeutic agents. In the past century, several scientific efforts have been directed toward identifying phytochemicals capable of inhibiting virus. Knowledge of ethnopharmacology can lead to new bioactive plant compounds suitable for drug discovery and development. Macromolecular docking studies provides most detailed possible view of drug-receptor interaction where the structure of drug is designed based on its fit to three dimensional structures of receptor site rather than by analogy to other active structures or random leads. Our previous studies indicate that Allicin sand Plumbagin could be used as the potent multi drug targets against the Neuraminidase, Hemagglutinin and M2 protein channel of influenza A (H1N1) pdm09. This in-vittro study has shown that P. indica L. and A. sativum extracts can inhibit influenza A (H1N1)pdm09 virus by inhibiting viral nucleoprotein synthesis and polymerase activity.
doi:10.4103/0974-8490.172562
PMCID: PMC4780135  PMID: 27034600
Allium sativum; Anti-influenza activity; Cytotoxicity; Hemagglutination; Plumbago indica
3.  Proposed correlation of modern processing principles for Ayurvedic herbal drug manufacturing: A systematic review 
Ancient Science of Life  2014;34(1):8-15.
Quality Ayurvedic herbal medicines are potential, low-cost solutions for addressing contemporary healthcare needs of both Indian and global community. Correlating Ayurvedic herbal preparations with modern processing principles (MPPs) can help develop new and use appropriate technology for scaling up production of the medicines, which is necessary to meet the growing demand. Understanding the fundamental Ayurvedic principles behind formulation and processing is also important for improving the dosage forms. Even though Ayurvedic industry has adopted technologies from food, chemical and pharmaceutical industries, there is no systematic study to correlate the traditional and modern processing methods. This study is an attempt to provide a possible correlation between the Ayurvedic processing methods and MPPs. A systematic literature review was performed to identify the Ayurvedic processing methods by collecting information from English editions of classical Ayurveda texts on medicine preparation methods. Correlation between traditional and MPPs was done based on the techniques used in Ayurvedic drug processing. It was observed that in Ayurvedic medicine preparations there were two major types of processes, namely extraction, and separation. Extraction uses membrane rupturing and solute diffusion principles, while separation uses volatility, adsorption, and size-exclusion principles. The study provides systematic documentation of methods used in Ayurveda for herbal drug preparation along with its interpretation in terms of MPPs. This is the first step which can enable improving or replacing traditional techniques. New technologies or use of existing technologies can be used to improve the dosage forms and scaling up while maintaining the Ayurvedic principles similar to traditional techniques.
doi:10.4103/0257-7941.150768
PMCID: PMC4342652  PMID: 25737605
Ayurvedic extraction principle; Ayurvedic herbal drug preparation; Ayurvedic separation principle; Modern processing principle
4.  DTome: a web-based tool for drug-target interactome construction 
BMC Bioinformatics  2012;13(Suppl 9):S7.
Background
Understanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process.
Results
We designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome.
Conclusions
As demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions.
doi:10.1186/1471-2105-13-S9-S7
PMCID: PMC3372450  PMID: 22901092
5.  A Plant-Derived Morphinan as a Novel Lead Compound Active against Malaria Liver Stages  
PLoS Medicine  2006;3(12):e513.
Background
The global spread of multidrug–resistant malaria parasites has led to an urgent need for new chemotherapeutic agents. Drug discovery is primarily directed to the asexual blood stages, and few drugs that are effective against the obligatory liver stages, from which the pathogenic blood infection is initiated, have become available since primaquine was deployed in the 1950s.
Methods and Findings
Using bioassay-guided fractionation based on the parasite's hepatic stage, we have isolated a novel morphinan alkaloid, tazopsine, from a plant traditionally used against malaria in Madagascar. This compound and readily obtained semisynthetic derivatives were tested for inhibitory activity against liver stage development in vitro (P. falciparum and P. yoelii) and in vivo (P. yoelii). Tazopsine fully inhibited the development of P. yoelii (50% inhibitory concentration [IC50] 3.1 μM, therapeutic index [TI] 14) and P. falciparum (IC50 4.2 μM, TI 7) hepatic parasites in cultured primary hepatocytes, with inhibition being most pronounced during the early developmental stages. One derivative, N-cyclopentyl-tazopsine (NCP-tazopsine), with similar inhibitory activity was selected for its lower toxicity (IC50 3.3 μM, TI 46, and IC50 42.4 μM, TI 60, on P. yoelii and P. falciparum hepatic stages in vitro, respectively). Oral administration of NCP-tazopsine completely protected mice from a sporozoite challenge. Unlike the parent molecule, the derivative was uniquely active against Plasmodium hepatic stages.
Conclusions
A readily obtained semisynthetic derivative of a plant-derived compound, tazopsine, has been shown to be specifically active against the liver stage, but inactive against the blood forms of the malaria parasite. This unique specificity in an antimalarial drug severely restricts the pressure for the selection of drug resistance to a parasite stage limited both in numbers and duration, thus allowing researchers to envisage the incorporation of a true causal prophylactic in malaria control programs.
A derivative of a morphinan alkaloid, tazopsine, from a plant used against malaria in Madagascar, is active against the hepatic stages ofPlasmodium species.
Editors' Summary
Background.
The parasite that causes malaria has quickly developed resistance to many of the drugs that are commonly used to treat this disease. As a result, new drugs and drug combinations are needed. In some parts of the world where antimalarial drugs are failing due to resistance, or are not available to everyone, people often turn to traditional herbal remedies instead. These traditional plant remedies can be a useful starting point for development of new drugs, but the process of developing effective new drugs from plant remedies is long and complicated. An important initial step is to isolate and identify the active compounds from plants and then see how well these compounds perform against malaria parasites in laboratory tests. If the tests are successful, such compounds could then progress to experiments in animals and possibly eventually human trials. One plant used widely in Madagascar for treatment of malaria is Strychnopsis thouarsii; the traditional remedy consists of the plant stem bark boiled in water.
Why Was This Study Done?
The group of researchers doing this study wanted to discover candidates for new malaria drugs. They therefore wanted to find out which molecular compounds in the stem bark of S. thouarsii contained antimalarial activity, and what particular stage of the malaria parasite's life cycle these compounds had an effect on. The researchers suspected that the agents in this plant bark had some activity against the “liver stage” of malaria infection in humans. This is the first stage of infection, after a person has been bitten by a malaria-infected mosquito, and before blood cells are invaded by malaria parasites (which then causes the disease symptoms). Very few drugs currently in existence have an effect on the “liver stage” of infection, but activity at this stage would be tremendously useful because it could mean a drug is better for prevention of malaria than others in existence.
What Did the Researchers Do and Find?
First, the researchers wanted to take the traditional herbal remedy—of S. thouarsii bark boiled in water—and find out precisely which molecule in that remedy was responsible for the antimalarial activity. They therefore used a method called chromatography to progressively separate the herbal extract into its distinct components. At each stage of separation, the extract was checked for activity against malaria using a laboratory test. Inactive extracts were disregarded, and the active component then taken on to a further separation round. After many rounds of separation and testing, the researchers got down to a single, apparently new, molecule that was active against malaria in the laboratory test, and this molecule was named tazopsine (in the Malagasy language the word Tazo refers to malaria). In order to find out how effective the molecule was at killing malaria parasites, the researchers took human or mouse liver cells cultured in the laboratory, infected them with malaria parasites (either the malaria parasite that normally infects humans, or a related species that infects mice), and then added tazopsine at different concentrations. The compound completely killed the malaria parasites even at very low concentrations, and had activity against malaria infecting either liver cells or red blood cells. Tazopsine was then given to mice injected with a species of the malaria parasite. The compound protected most mice against malaria infection when it was used at a dosage level lower than the toxic dose. The researchers then tried making a series of different variants of tazopsine in the hope that some variants would be less toxic, but equally active as, the original compound. They found one variant, named NCP-tazopsine, that was much less toxic but just as active as tazopsine, but only against the malaria infecting liver cells.
What Do These Findings Mean?
In these experiments a new molecule, tazopsine, was discovered from a Malagasy plant, and it was found to be active against liver-stage malaria parasites, in laboratory experiments and in mice. This molecule or variants of it could in future become candidate antimalarial drugs in humans. However, much work would need to be done before testing could get to that stage. Different variants of molecules related to tazopsine would need to be tested to find one that has low toxicity, and these variants would need to be fully evaluated in animals to see how they are handled in the body before any trials could begin in humans.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030513
The World Health Organization publishes a minisite containing links to information about all aspects of malaria worldwide, including treatment, prevention, and current programmes for malaria control
Medicines for Malaria Venture is a collaboration between public and private organizations (including the pharmaceutical industry) that aims to fund and manage the development of new drugs for treatment and prevention of malaria
Wikipedia entries for drug discovery and drug development (note: Wikipedia is an internet encyclopedia that anyone can edit)
doi:10.1371/journal.pmed.0030513
PMCID: PMC1716192  PMID: 17194195
6.  Integration of Microfractionation, qNMR and Zebrafish Screening for the In Vivo Bioassay-Guided Isolation and Quantitative Bioactivity Analysis of Natural Products 
PLoS ONE  2013;8(5):e64006.
Natural products (NPs) are an attractive source of chemical diversity for small-molecule drug discovery. Several challenges nevertheless persist with respect to NP discovery, including the time and effort required for bioassay-guided isolation of bioactive NPs, and the limited biomedical relevance to date of in vitro bioassays used in this context. With regard to bioassays, zebrafish have recently emerged as an effective model system for chemical biology, allowing in vivo high-content screens that are compatible with microgram amounts of compound. For the deconvolution of the complex extracts into their individual constituents, recent progress has been achieved on several fronts as analytical techniques now enable the rapid microfractionation of extracts, and microflow NMR methods have developed to the point of allowing the identification of microgram amounts of NPs. Here we combine advanced analytical methods with high-content screening in zebrafish to create an integrated platform for microgram-scale, in vivo NP discovery. We use this platform for the bioassay-guided fractionation of an East African medicinal plant, Rhynchosia viscosa, resulting in the identification of both known and novel isoflavone derivatives with anti-angiogenic and anti-inflammatory activity. Quantitative microflow NMR is used both to determine the structure of bioactive compounds and to quantify them for direct dose-response experiments at the microgram scale. The key advantages of this approach are (1) the microgram scale at which both biological and analytical experiments can be performed, (2) the speed and the rationality of the bioassay-guided fractionation – generic for NP extracts of diverse origin – that requires only limited sample-specific optimization and (3) the use of microflow NMR for quantification, enabling the identification and dose-response experiments with only tens of micrograms of each compound. This study demonstrates that a complete in vivo bioassay-guided fractionation can be performed with only 20 mg of NP extract within a few days.
doi:10.1371/journal.pone.0064006
PMCID: PMC3660303  PMID: 23700445
7.  Biodiversity conservation and drug discovery: Can they be combined? The Suriname and Madagascar experiences 
Pharmaceutical biology  2009;47(8):809-823.
The approach to new drugs through natural products has proved to be the single most successful strategy for the discovery of new drugs, but in recent years its use has been deemphasized by many pharmaceutical companies in favor of approaches based on combinatorial chemistry and genomics, among others.
Drug discovery from natural sources requires continued access to plant, marine, and microbial biomass, and so the preservation of tropical rainforests is an important part of our drug discovery program. Sadly, many of the tropical forests of the world are under severe environmental pressure, and deforestation is a serious problem in most tropical countries. One way to combat this loss is to demonstrate their value as potential sources of new pharmaceutical or agrochemical products.
As part of an effort to integrate biodiversity conservation and drug discovery with economic development, we initiated an International Cooperative biodiversity Group (ICBG) to discover potential pharmaceuticals from the plant biodiversity of Suriname and Madagascar. The Group, established with funding from agencies of the United States government, involved participants from the USA, Suriname, and Madagascar. The basic approach was to search for bioactive plants in the Suriname and Malagasy flora, and to isolate their bioactive constituents by the best available methods, but the work included capacity building as well as research. Progress on this project will be reported, drawing on results obtained from the isolation of bioactive natural products from Suriname and Madagascar. The benefits of this general approach to biodiversity and drug discovery will also be discussed.
doi:10.1080/13880200902988629
PMCID: PMC2746688  PMID: 20161050
Suriname; Madagascar; biodiversity conservation; bioactive compounds; alkaloids; cardenolides; terpenoids; marine metabolites
8.  Traditional Medicine Collection Tracking System (TM-CTS): A Database for Ethnobotanically-Driven Drug-Discovery Programs 
Journal of ethnopharmacology  2011;135(2):590-593.
Aim of the study.
Ethnobotanically-driven drug-discovery programs include data related to many aspects of the preparation of botanical medicines, from initial plant collection to chemical extraction and fractionation. The Traditional Medicine-Collection Tracking System (TM-CTS) was created to organize and store data of this type for an international collaborative project involving the systematic evaluation of commonly used Traditional Chinese Medicinal plants.
Materials and Methods.
The system was developed using domain-driven design techniques, and is implemented using Java, Hibernate, PostgreSQL, Business Intelligence and Reporting Tools (BIRT), and Apache Tomcat.
Results.
The TM-CTS relational database schema contains over 70 data types, comprising over 500 data fields. The system incorporates a number of unique features that are useful in the context of ethnobotanical projects such as support for information about botanical collection, method of processing, quality tests for plants with existing pharmacopoeia standards, chemical extraction and fractionation, and historical uses of the plants. The database also accommodates data provided in multiple languages and integration with a database system built to support high throughput screening based drug discovery efforts. It is accessed via a web-based application that provides extensive, multi-format reporting capabilities.
Conclusions.
This new database system was designed to support a project evaluating the bioactivity of Chinese medicinal plants. The software used to create the database is open source, freely available, and could potentially be applied to other ethnobotanically-driven natural product collection and drug-discovery programs.
doi:10.1016/j.jep.2011.03.029
PMCID: PMC3096074  PMID: 21420479
Database; Traditional Chinese Medicine; High Throughput Screening; Ethnobotany; Drug Discovery
9.  An update on Ayurvedic herb Convolvulus pluricaulis Choisy 
Convolvulus pluricaulis Choisy (C. pluricaulis) is a perennial herb that seems like morning glory. All parts of the herb are known to possess therapeutic benefits. The plant is used locally in Indian and Chinese medicine to cure various diseases. It is used in Ayurvedic formulation for chronic cough, sleeplessness, epilepsy, hallucinations, anxiety etc. Based on the comprehensive review of plant profile, pharmacognosy, phytochemistry, pharmacological and toxicological data on the C. pluricaulis, there will be more opportunities for the future research and development on the herb C. pluricaulis. Information on the C. pluricaulis was collected via electronic search (using Pub Med, SciFinder, Google Scholar and Web of Science) and library search for articles published in peer-reviewed journals. Furthermore, information also was obtained from some local books on ethnopharmacology. This paper covers the literature, primarily pharmacological, from 1985 to the end of 2012. The C. pluricaulis is an important indigenous medicine, which has a long medicinal application for liver disease, epileptic disease, microbial disease, cytotoxic and viral diseases, central nervous system (CNS) disease in Ayurvedic medicine, traditional Chinese medicine and other indigenous medical systems. The isolated metabolites and crude extract have exhibited a wide of in vitro and in vivo pharmacological effect, including CNS depression, anxiolytic, tranquillizing, antidepressant, antistress, neurodegenerative, antiamnesic, antioxidant, hypolipidemic, immunomodulatory, analgesic, antifungal, antibacterial, antidiabetic, antiulcer, anticatatonic, and cardiovascular activity. A chemical study of this plant was then initiated, which led to the isolation of carbohydrats, proteins, alkaloids, fatty acids, steroids, coumarins, flavanoids, and glycosides as active chemicals that bring about its biological effects. A series of pharmacognostical studies of this plant show that it is a herb, its stem and leaves are hairy, more over it has two types of stomata, anisocytic and paracytic. A herb, C. pluricaulis has emerged as a good source of the traditional medicine for the treatment of liver disease, epileptic disease, microbial disease, cytotoxic and viral diseases, and CNS disease. Pharmacological results have validated the use of this species in traditional medicine. All the parts of the herb are known to possess therapeutic benefits. Expansion of research materials would provide more opportunities for the discovery of new bioactive principles from C. pluricaulis.
doi:10.1016/S2221-1691(14)60240-9
PMCID: PMC3868798  PMID: 25182446
Convolvulus pluricaulis Choisy; Pharmacognosy; Microscopy; Macroscopy; Phytochemistry; Pharmacology
10.  New Perspectives on How to Discover Drugs from Herbal Medicines: CAM's Outstanding Contribution to Modern Therapeutics 
With tens of thousands of plant species on earth, we are endowed with an enormous wealth of medicinal remedies from Mother Nature. Natural products and their derivatives represent more than 50% of all the drugs in modern therapeutics. Because of the low success rate and huge capital investment need, the research and development of conventional drugs are very costly and difficult. Over the past few decades, researchers have focused on drug discovery from herbal medicines or botanical sources, an important group of complementary and alternative medicine (CAM) therapy. With a long history of herbal usage for the clinical management of a variety of diseases in indigenous cultures, the success rate of developing a new drug from herbal medicinal preparations should, in theory, be higher than that from chemical synthesis. While the endeavor for drug discovery from herbal medicines is “experience driven,” the search for a therapeutically useful synthetic drug, like “looking for a needle in a haystack,” is a daunting task. In this paper, we first illustrated various approaches of drug discovery from herbal medicines. Typical examples of successful drug discovery from botanical sources were given. In addition, problems in drug discovery from herbal medicines were described and possible solutions were proposed. The prospect of drug discovery from herbal medicines in the postgenomic era was made with the provision of future directions in this area of drug development.
doi:10.1155/2013/627375
PMCID: PMC3619623  PMID: 23634172
11.  Cyperus rotundus, a substitute for Aconitum heterophyllum: Studies on the Ayurvedic concept of Abhava Pratinidhi Dravya (drug substitution) 
In the absence of a desired first choice medicinal herb, classical Ayurveda recommends use of a functionally similar substitute. Post 16th century Ayurvedic texts and lexicons give specific examples of possible substitutes. Here we report a preliminary study of one such Ayurvedic substitution pair: Musta (Cyperus rotundus L., Cyperaceae), a common weed, for the rare Himalayan species, Ativisha (Aconitum heterophyllum Wall. ex Royle; Ranunculaceae). The study's strategy was to use modern phytochemical and pharmacological methods to test the two herbs for biochemical and metabolic similarities and differences, and literary studies to compare their Ayurvedic properties, a novel trans-disciplinary approach. No previous scientific paper has compared the two herbs’ bioactivities or chemical profiles. Despite being taxonomically unrelated, the first choice, but relatively unavailable (Abhava) plant, A. heterophyllum, and its substitute (Pratinidhi) C. rotundus, are not only similar in Ayurvedic pharmacology (Dravyaguna) profile, but also in phytochemical and anti-diarrheal properties. These observations indicate that Ayurveda may attach more importance to pharmacological properties of raw drugs than to their botanical classification. Further research into the nature of raw drugs named could open up new areas of medicinal plant classification, linking chemistry and bioactivity. Understanding the logic behind the Ayurvedic concept of Abhava Pratinidhi Dravya (drug substitution) could lead to new methods of identifying legitimate drug alternatives, and help solve industry's problems of crude drug shortage.
doi:10.4103/0975-9476.59825
PMCID: PMC3149390  PMID: 21829299
Abhava Pratinidhi Dravya; Ayurveda; anti-diarrheal; drug substitution
12.  PA02.24. Importance of microscopic techniques for the identification/authentication of herbal medicines 
Ancient Science of Life  2013;32(Suppl 2):S69.
Acceptance of Ayurveda is increasing in the society because of disclosure of its strong fundamental concepts and holistic approach. As a result, demand of Ayurvedic medicines are increasing day by day which results in the unprecedented requirement and depletion of raw materials, especially those of herbal origin. This depletion of herbs along with ignorance of herbal drug collectors from wild sources causes adulteration of drugs. Misinterpretation of Sanskrit slokas from classical texts also plays an important role. Adulteration of raw materials adversely affects the safety and efficacy of Ayurvedic preparations. Therefore standardization and documentation is essential to ensure the genunity of ayurvedic drugs. Identification and Authentication of medicinal plants are normally done by different methods like organoleptic, macroscopic, microscopic and chemical characters. Among them, microscopic techniques are one of the most important methods. The present work reveals the various microscopic characters to determine their authentication, genuity etc.
doi:10.4103/0257-7941.123892
PMCID: PMC4147542
13.  Discovery and resupply of pharmacologically active plant-derived natural products: A review 
Biotechnology advances  2015;33(8):1582-1614.
Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a “screening hit” through a “drug lead” to a “marketed drug” is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis.
While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.
doi:10.1016/j.biotechadv.2015.08.001
PMCID: PMC4748402  PMID: 26281720
Natural products; Plants; Drug discovery; Phytochemistry; Pharmacology; Medicine; Ethnopharmacology; Computer modeling; Organic synthesis; Plant biotechnology
14.  Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana 
A protein interactome focused towards cell proliferation was mapped comprising 857 interactions among 393 proteins, leading to many new insights in plant cell cycle regulation.A comprehensive view on heterodimeric cyclin-dependent kinase (CDK)/cyclin complexes in plants is obtained, in relation with their regulators.Over 100 new candidate cell cycle proteins were predicted.
The basic underlying mechanisms that govern the cell cycle are conserved among all eukaryotes. Peculiar for plants, however, is that their genome contains a collection of cell cycle regulatory genes that is intriguingly large (Vandepoele et al, 2002; Menges et al, 2005) compared to other eukaryotes. Arabidopsis thaliana (Arabidopsis) encodes 71 genes in five regulatory classes versus only 15 in yeast and 23 in human.
Despite the discovery of numerous cell cycle genes, little is known about the protein complex machinery that steers plant cell division. Therefore, we applied tandem affinity purification (TAP) approach coupled with mass spectrometry (MS) on Arabidopsis cell suspension cultures to isolate and analyze protein complexes involved in the cell cycle. This approach allowed us to successfully map a first draft of the basic cell cycle complex machinery of Arabidopsis, providing many new insights into plant cell division.
To map the interactome, we relied on a streamlined platform comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, TAP adapted for plant cells, and matrix-assisted laser desorption ionization (MALDI) tandem-MS for the identification of purified proteins (Van Leene et al, 2007, 2008Van Leene et al, 2007, 2008). Complexes for 102 cell cycle proteins were analyzed using this approach, leading to a non-redundant data set of 857 interactions among 393 proteins (Figure 1A). Two subspaces were identified in this data set, domain I1, containing interactions confirmed in at least two independent experimental repeats or in the reciprocal purification experiment, and domain I2 consisting of uniquely observed interactions.
Several observations underlined the quality of both domains. All tested reverse purifications found the original interaction, and 150 known or predicted interactions were confirmed, meaning that also a huge stack of new interactions was revealed. An in-depth computational analysis revealed enrichment for many cell cycle-related features among the proteins of the network (Figure 1B), and many protein pairs were coregulated at the transcriptional level (Figure 1C). Through integration of known cell cycle-related features, more than 100 new candidate cell cycle proteins were predicted (Figure 1D). Besides common qualities of both interactome domains, their real significance appeared through mutual differences exposing two subspaces in the cell cycle interactome: a central regulatory network of stable complexes that are repeatedly isolated and represent core regulatory units, and a peripheral network comprising transient interactions identified less frequently, which are involved in other aspects of the process, such as crosstalk between core complexes or connections with other pathways. To evaluate the biological relevance of the cell cycle interactome in plants, we validated interactions from both domains by a transient split-luciferase assay in Arabidopsis plants (Marion et al, 2008), further sustaining the hypothesis-generating power of the data set to understand plant growth.
With respect to insights into the cell cycle physiology, the interactome was subdivided according to the functional classes of the baits and core protein complexes were extracted, covering cyclin-dependent kinase (CDK)/cyclin core complexes together with their positive and negative regulation networks, DNA replication complexes, the anaphase-promoting complex, and spindle checkpoint complexes. The data imply that mitotic A- and B-type cyclins exclusively form heterodimeric complexes with the plant-specific B-type CDKs and not with CDKA;1, whereas D-type cyclins seem to associate with CDKA;1. Besides the extraction of complexes previously shown in other organisms, our data also suggested many new functional links; for example, the link coupling cell division with the regulation of transcript splicing. The association of negative regulators of CDK/cyclin complexes with transcription factors suggests that their role in reallocation is not solely targeted to CDK/cyclin complexes. New members of the Siamese-related inhibitory proteins were identified, and for the first time potential inhibitors of plant-specific mitotic B-type CDKs have been found in plants. New evidence that the E2F–DP–RBR network is not only active at G1-to-S, but also at the G2-to-M transition is provided and many complexes involved in DNA replication or repair were isolated. For the first time, a plant APC has been isolated biochemically, identifying three potential new plant-specific APC interactors, and finally, complexes involved in the spindle checkpoint were isolated mapping many new but specific interactions.
Finally, to get a general view on the complex machinery, modules of interacting cyclins and core cell cycle regulators were ranked along the cell cycle phases according to the transcript expression peak of the cyclins, showing an assorted set of CDK–cyclin complexes with high regulatory differentiation (Figure 4). Even within the same subfamily (e.g. cyclin A3, B1, B2, D3, and D4), cyclins differ not only in their functional time frame but also in the type and number of CDKs, inhibitors, and scaffolding proteins they bind, further indicating their functional diversification. According to our interaction data, at least 92 different variants of CDK–cyclin complexes are found in Arabidopsis.
In conclusion, these results reflect how several rounds of gene duplication (Sterck et al, 2007) led to the evolution of a large set of cyclin paralogs and a myriad of regulators, resulting in a significant jump in the complexity of the cell cycle machinery that could accommodate unique plant-specific features such as an indeterminate mode of postembryonic development. Through their extensive regulation and connection with a myriad of up- and downstream pathways, the core cell cycle complexes might offer the plant a flexible toolkit to fine-tune cell proliferation in response to an ever-changing environment.
Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.
doi:10.1038/msb.2010.53
PMCID: PMC2950081  PMID: 20706207
Arabidopsis thaliana; cell cycle; interactome; protein complex; protein interactions
15.  Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery 
Chromosome 1 of Vibrio vulnificus tends to contain larger portion of essential or housekeeping genes on the basis of the genomic analysis and gene knockout experiments performed in this study, while its chromosome 2 seems to have originated and evolved from a plasmid.The genome-scale metabolic network model of V. vulnificus was reconstructed based on databases and literature, and was used to identify 193 essential metabolites.Five essential metabolites finally selected after the filtering process are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA), which were predicted to be essential in V. vulnificus, absent in human, and are consumed by multiple reactions.Chemical analogs of the five essential metabolites were screened and a hit compound showing the minimal inhibitory concentration (MIC) of 2 μg/ml and the minimal bactericidal concentration (MBC) of 4 μg/ml against V. vulnificus was identified.
Discovering new antimicrobial targets and consequently new antimicrobials is important as drug resistance of pathogenic microorganisms is becoming an increasingly serious problem in human healthcare management (Fischbach and Walsh, 2009). There clearly exists a gap between genomic studies and drug discovery as the accumulation of knowledge on pathogens at genome level has not successfully transformed into the development of effective drugs (Mills, 2006; Payne et al, 2007). In this study, we dissected the genome of a microbial pathogen in detail, and subsequently developed a systems biological strategy of employing genome-scale metabolic modeling and simulation together with metabolite essentiality analysis for effective drug targeting and discovery. This strategy was used for identifying new drug targets in an opportunistic pathogen Vibrio vulnificus CMCP6 as a model.
V. vulnificus is a Gram-negative halophilic bacterium that is found in estuarine waters, brackish ponds, or coastal areas, and its Biotype 1 is an opportunistic human pathogen that can attack immune-compromised patients, and causes primary septicemia, necrotized wound infections, and gastroenteritis. We previously found that many metabolic genes were specifically induced in vivo, suggesting that specific metabolic pathways are essential for in vivo survival and virulence of this pathogen (Kim et al, 2003; Lee et al, 2007). These results motivated us to carry out systems biological analysis of the genome and the metabolic network for new drug target discovery.
V. vulnificus CMCP6 has two chromosomes. We first re-sequenced genomic regions assembled in low quality and low depth, and subsequently re-annotated the whole genome of V. vulnificus. Horizontal gene transfer was suspected to be responsible for the diversification of each chromosome of V. vulnificus, and the presence of metabolic genes was more biased to chromosome 1 than chromosome 2. Further studies on V. vulnificus genome revealed that chromosome 2 is more prone to diversification for better adaptation to the environment than its chromosome 1, while chromosome 1 tends to expand their genetic repertoire while maintaining the core genes at a constant level.
Next, a genome-scale metabolic network VvuMBEL943 was reconstructed based on literature, databases and experiments for systematic studies on the metabolism of this pathogen and prediction of drug targets. The VvuMBEL943 model is composed of 943 reactions and 765 metabolites, and covers 673 genes. The model was validated by comparing its simulated cell growth phenotype obtained by constraints-based flux analysis with the V. vulnificus-specific experimental data previously reported in the literature. In this study, constraints-based flux analysis is an optimization-based simulation method that calculates intracellular fluxes under the specific genetic and environmental condition (Kim et al, 2008). As a result, 17 growth phenotypes were correctly predicted out of 18 cases, which demonstrate the validity of VvuMBEL943.
The main objective of constructing VvuMBEL943 in this study is to predict potential drug targets by system-wide analysis of the metabolic network for the effective treatment of V. vulnificus. To achieve this goal, a set of drug target candidates was predicted by taking a metabolite-centric approach. Metabolite essentiality analysis is a concept recently introduced for the study of cellular robustness to complement conventional reaction or gene-centric approach (Kim et al, 2007b). Metabolite essentiality analysis observes changes in flux distribution by removing each metabolite from the in silico metabolic network. Hence, metabolite essentiality predicts essential metabolites whose absence causes cell death. By selecting essential metabolites, it is possible to directly screen only their structural analogs, which substantially reduces the number of chemical compounds to screen from the chemical compound library. As a result of implementing this approach, 193 metabolites were initially identified to be essential to the cell. These essential metabolites were then further filtered based on the predetermined criteria, mainly organism specificity and multiple connectivity associated with each metabolite, in order to reduce the number of initial target candidates towards identifying the most effective ones.
Five essential metabolites finally selected are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA). Enzymes that consume these essential metabolites were experimentally verified to be essential, which indeed demonstrates the essentiality of these five metabolites. On the basis of the structural information of these five essential metabolites, whole-cell screening assay was performed using their analogs for possible antibacterial discovery. We screened 352 chemical analogs of the essential metabolites selected from the chemical compound library, and found a hit compound 24837, which shows the minimal inhibitory concentration (MIC) of 2 μg/ml and minimal bactericidal concentration (MBC) of 4 μg/ml, showing good antibacterial activity without further structural modification. Although this study demonstrates a proof-of-concept, the approaches and their rationale taken here should serve as a general strategy for discovering novel antibiotics and drugs based on systems-level analysis of metabolic networks.
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.
doi:10.1038/msb.2010.115
PMCID: PMC3049409  PMID: 21245845
drug discovery; drug targeting; genome analysis; metabolic network; Vibrio vulnificus
16.  The use of pharmacokinetic and pharmacodynamic data in the assessment of drug safety in early drug development 
The pharmaceutical industry continues to look for ways to reduce drug candidate attrition throughout the drug discovery and development process. A significant cause of attrition is due to safety issues arising either as a result of animal toxicity testing or in the clinical programme itself. A factor in the assessment of safety during early drug development is the pharmacokinetic profile of the compound. This allows safety data to be considered in the light of systemic drug exposure and therefore permits a quantitative assessment. This is particularly applicable when assessing the risk of a new chemical entity (NCE) in relation to safety parameters such as QT interval prolongation, where free plasma concentrations have been shown to be predictive of this property in relation to potency in preclinical testing. Prior to actual human exposure it is therefore important to be able to predict reliably the pharmacokinetic behaviour of an NCE in order to place such safety findings into a quantitative risk context. The emerging science of pharmacogenetics is likely to further our ability to assess the risk of NCEs to populations and individuals due to genetic variance. The drug metabolizing enzyme CYP2D6 has been recognized as providing the potential to result in widely differing systemic drug exposure in the patient population due to polymorphic expression. Further knowledge is likely to add to our understanding of population differences in exposure and response and aid in the identification of risk factors. One potential strategy for improving the effectiveness of the drug discovery process is to obtain clinical pharmacokinetic data more rapidly in order to assess more accurately the potential for both efficacy and safety of an NCE. Whilst procedures and technologies are available that allow this on the microdose scale, it is important that we recognize potential limitations of these approaches in order that they can be applied beneficially.
doi:10.1111/j.1365-2125.2004.02194.x
PMCID: PMC1884636  PMID: 15563358
drug discovery; drug safety; pharmacodynamics; pharmacokinetics
17.  NATURAL PRODUCTS: A CONTINUING SOURCE OF NOVEL DRUG LEADS 
Biochimica et biophysica acta  2013;1830(6):3670-3695.
1. Background
Nature has been a source of medicinal products for millennia, with many useful drugs developed from plant sources. Following discovery of the penicillins, drug discovery from microbial sources occurred and diving techniques in the 1970s opened the seas. Combinatorial chemistry (late 1980s), shifted the focus of drug discovery efforts from Nature to the laboratory bench.
2. Scope of Review
This review traces natural products drug discovery, outlining important drugs from natural sources that revolutionized treatment of serious diseases. It is clear Nature will continue to be a major source of new structural leads, and effective drug development depends on multidisciplinary collaborations.
3. Major Conclusions
The explosion of genetic information led not only to novel screens, but the genetic techniques permitted the implementation of combinatorial biosynthetic technology and genome mining. The knowledge gained has allowed unknown molecules to be identified. These novel bioactive structures can be optimized by using combinatorial chemistry generating new drug candidates for many diseases.
4
General Significance: The advent of genetic techniques that permitted the isolation / expression of biosynthetic cassettes from microbes may well be the new frontier for natural products lead discovery. It is now apparent that biodiversity may be much greater in those organisms. The numbers of potential species involved in the microbial world are many orders of magnitude greater than those of plants and multi-celled animals. Coupling these numbers to the number of currently unexpressed biosynthetic clusters now identified (>10 per species) the potential of microbial diversity remains essentially untapped.
doi:10.1016/j.bbagen.2013.02.008
PMCID: PMC3672862  PMID: 23428572
Microbial diversity; synthesis; genomics; natural product drugs
18.  Zebrafish Bioassay-Guided Natural Product Discovery: Isolation of Angiogenesis Inhibitors from East African Medicinal Plants 
PLoS ONE  2011;6(2):e14694.
Natural products represent a significant reservoir of unexplored chemical diversity for early-stage drug discovery. The identification of lead compounds of natural origin would benefit from therapeutically relevant bioassays capable of facilitating the isolation of bioactive molecules from multi-constituent extracts. Towards this end, we developed an in vivo bioassay-guided isolation approach for natural product discovery that combines bioactivity screening in zebrafish embryos with rapid fractionation by analytical thin-layer chromatography (TLC) and initial structural elucidation by high-resolution electrospray mass spectrometry (HRESIMS). Bioactivity screening of East African medicinal plant extracts using fli-1:EGFP transgenic zebrafish embryos identified Oxygonum sinuatum and Plectranthus barbatus as inhibiting vascular development. Zebrafish bioassay-guided fractionation identified the active components of these plants as emodin, an inhibitor of the protein kinase CK2, and coleon A lactone, a rare abietane diterpenoid with no previously described bioactivity. Both emodin and coleon A lactone inhibited mammalian endothelial cell proliferation, migration, and tube formation in vitro, as well as angiogenesis in the chick chorioallantoic membrane (CAM) assay. These results suggest that the combination of zebrafish bioassays with analytical chromatography methods is an effective strategy for the rapid identification of bioactive natural products.
doi:10.1371/journal.pone.0014694
PMCID: PMC3040759  PMID: 21379387
19.  A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases 
PLoS Neglected Tropical Diseases  2016;10(1):e0004300.
Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.
Author Summary
Neglected tropical diseases are human infectious diseases that are often associated with poverty. Historically, lack of interest from the pharmaceutical industry resulted in the lack of good drugs to combat the majority of the pathogens that cause these diseases. Recently, the availability of open chemical information has increased with the advent of public domain chemical resources and the release of data from high throughput screening assays. Our aim in this work was to make use of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to prioritize and identify candidate drug targets in neglected pathogen proteomes, and drug-like bioactive molecules to foster drug development against neglected diseases. Our approach to the problem relied on applying bioinformatics and computational biology strategies to model large datasets spanning complete proteomes and extensive chemical information from publicly available sources. As a result, we were able to prioritize drug targets and identify potential targets for orphan bioactive drugs.
doi:10.1371/journal.pntd.0004300
PMCID: PMC4703370  PMID: 26735851
20.  Identification of PPARgamma Partial Agonists of Natural Origin (II): In Silico Prediction in Natural Extracts with Known Antidiabetic Activity 
PLoS ONE  2013;8(2):e55889.
Background
Natural extracts have played an important role in the prevention and treatment of diseases and are important sources for drug discovery. However, to be effectively used in these processes, natural extracts must be characterized through the identification of their active compounds and their modes of action.
Methodology/Principal Findings
From an initial set of 29,779 natural products that are annotated with their natural source and using a previously developed virtual screening procedure (carefully validated experimentally), we have predicted as potential peroxisome proliferators-activated receptor gamma (PPARγ) partial agonists 12 molecules from 11 extracts known to have antidiabetic activity. Six of these molecules are similar to molecules with described antidiabetic activity but whose mechanism of action is unknown. Therefore, it is plausible that these 12 molecules could be the bioactive molecules responsible, at least in part, for the antidiabetic activity of the extracts containing them. In addition, we have also identified as potential PPARγ partial agonists 10 molecules from 16 plants with undescribed antidiabetic activity but that are related (i.e., they are from the same genus) to plants with known antidiabetic properties. None of the 22 molecules that we predict as PPARγ partial agonists show chemical similarity with a group of 211 known PPARγ partial agonists obtained from the literature.
Conclusions/Significance
Our results provide a new hypothesis about the active molecules of natural extracts with antidiabetic properties and their mode of action. We also suggest plants with undescribed antidiabetic activity that may contain PPARγ partial agonists. These plants represent a new source of potential antidiabetic extracts. Consequently, our work opens the door to the discovery of new antidiabetic extracts and molecules that can be of use, for instance, in the design of new antidiabetic drugs or functional foods focused towards the prevention/treatment of type 2 Diabetes Mellitus.
doi:10.1371/journal.pone.0055889
PMCID: PMC3566095  PMID: 23405231
21.  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.
doi:10.4103/0975-9476.113882
PMCID: PMC3737444  PMID: 23930045
Ayurveda; in silico target prediction; mode-of-action; anti-cancer compounds
22.  Partial purification and characterization of an antimicrobial activity from the wood extract of mangrove plant Ceriops decandra 
EXCLI Journal  2016;15:103-112.
The development of resistance towards the antibiotics in use today has been a source of growing concern in the modern healthcare system around the world. To counter this major threat, there is an urgent need for discovery of new antimicrobials. Many plants, like mangroves, possess highly diversified list of natural phytochemicals which are known to have wide range of bioactivities. These phytochemicals can be good sources for the discovery of new drugs. In this study, we report the partial phytochemical characterization and antimicrobial activities of a semi-purified fraction isolated from the wood tissue of Ceriops decandra, a mangrove plant. This fraction named CD-3PM was chromatographically separated from C. decandra wood extract and was subjected to different spectral analyses to determine its partial chemical nature. The structural investigation indicates the presence of two diterpenoids, i) 3β, 13β-Dihydroxy-8-abietaen-7-one and ii) 3β-Hydroxy-8,13-abietadien-7-one in the CD-3PM fraction. The antimicrobial potential of this fraction was evaluated by microdilution-MTT assay against several organisms. Among the nine microorganisms found to be sensitive to the CD-3PM fraction, six organisms are reported to be pathogenic in nature. The CD-3PM fraction with broad spectrum antimicrobial efficacy revealed the presence of two diterpenoids and possesses potential applications in drug discovery process and food processing industries.
doi:10.17179/excli2015-741
PMCID: PMC4822046  PMID: 27065777
antimicrobial activity; bioactivity guided fractionation; Ceriops decandra; diterpenoids; human pathogens; mangroves
23.  Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery 
PLoS Computational Biology  2015;11(4):e1004074.
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at http://graal.ift.ulaval.ca/peptide-design/.
Author Summary
Part of the complexity of drug discovery is the sheer chemical diversity to explore combined to all requirements a compound must meet to become a commercial drug. Hence, it makes sense to automate this chemical exploration endeavor in a wise, informed, and efficient fashion. Here, we focused on peptides as they have properties that make them excellent drug starting points. Machine learning techniques may replace expensive in-vitro laboratory experiments by learning an accurate model of it. However, computational models also suffer from the combinatorial explosion due to the enormous chemical diversity. Indeed, applying the model to every peptides would take an astronomical amount of computer time. Therefore, given a model, is it possible to determine, using reasonable computational time, the peptide that has the best properties and chance for success? This exact question is what motivated our work. We focused on recent advances in kernel methods and machine learning to learn a model that already had excellent results. We demonstrate that this class of model has mathematical properties that makes it possible to rapidly identify and sort the best peptides. Finally, in-vitro and in-silico results are provided to support and validate this theoretical discovery.
doi:10.1371/journal.pcbi.1004074
PMCID: PMC4388847  PMID: 25849257
24.  Natural products as starting points for future anti-malarial therapies: going back to our roots? 
Malaria Journal  2011;10(Suppl 1):S3.
Background
The discovery and development of new anti-malarials are at a crossroads. Fixed dose artemisinin combination therapy is now being used to treat a hundred million children each year, with a cost as low as 30 cents per child, with cure rates of over 95%. However, as with all anti-infective strategies, this triumph brings with it the seeds of its own downfall, the emergence of resistance. It takes ten years to develop a new medicine. New classes of medicines to combat malaria, as a result of infection by Plasmodium falciparum and Plasmodium vivax are urgently needed.
Results
Natural product scaffolds have been the basis of the majority of current anti-malarial medicines. Molecules such as quinine, lapachol and artemisinin were originally isolated from herbal medicinal products. After improvement with medicinal chemistry and formulation technologies, and combination with other active ingredients, they now make up the current armamentarium of medicines. In recent years advances in screening technologies have allowed testing of millions of compounds from pharmaceutical diversity for anti-malarial activity in cellular assays. These initiatives have resulted in thousands of new sub-micromolar active compounds – starting points for new drug discovery programmes. Against this backdrop, the paucity of potent natural products identified has been disappointing. Now is a good time to reflect on the current approach to screening herbal medicinal products and suggest revisions. Nearly sixty years ago, the Chinese doctor Chen Guofu, suggested natural products should be approached by dao-xing-ni-shi or ‘acting in the reversed order’, starting with observational clinical studies. Natural products based on herbal remedies are in use in the community, and have the potential unique advantage that clinical observational data exist, or can be generated. The first step should be the confirmation and definition of the clinical activity of herbal medicinal products already used by the community. This first step forms a solid basis of observations, before moving to in vivo pharmacological characterization and ultimately identifying the active ingredient. A large part of the population uses herbal medicinal products despite limited numbers of well-controlled clinical studies. Increased awareness by the regulators and public health bodies of the need for safety information on herbal medicinal products also lends support to obtaining more clinical data on such products.
Conclusions
The relative paucity of new herbal medicinal product scaffolds active against malaria results discovered in recent years suggest it is time to re-evaluate the ‘smash and grab’ approach of randomly testing purified natural products and replace it with a patient-data led approach. This will require a change of perspective form many in the field. It will require an investment in standardisation in several areas, including: the ethnopharmacology and design and reporting of clinical observation studies, systems for characterizing anti-malarial activity of patient plasma samples ex vivo followed by chemical and pharmacological characterisation of extracts from promising sources. Such work falls outside of the core mandate of the product development partnerships, such as MMV, and so will require additional support. This call is timely, given the strong interest from researchers in disease endemic countries to support the research arm of a malaria eradication agenda. Para-national institutions such as the African Network for Drugs and Diagnostics Innovation (ANDi) will play a major role in facilitating the development of their natural products patrimony and possibly clinical best practice to bring forward new therapeutics. As in the past, with quinine, lapinone and artemisinin, once the activity of herbal medicinal products in humans is characterised, it can be used to identify new molecular scaffolds which will form the basis of the next generation of anti-malarial therapies.
doi:10.1186/1475-2875-10-S1-S3
PMCID: PMC3059461  PMID: 21411014
25.  The prince and the pauper. A tale of anticancer targeted agents 
Molecular Cancer  2008;7:82.
Cancer rates are set to increase at an alarming rate, from 10 million new cases globally in 2000 to 15 million in 2020. Regarding the pharmacological treatment of cancer, we currently are in the interphase of two treatment eras. The so-called pregenomic therapy which names the traditional cancer drugs, mainly cytotoxic drug types, and post-genomic era-type drugs referring to rationally-based designed. Although there are successful examples of this newer drug discovery approach, most target-specific agents only provide small gains in symptom control and/or survival, whereas others have consistently failed in the clinical testing. There is however, a characteristic shared by these agents: -their high cost-. This is expected as drug discovery and development is generally carried out within the commercial rather than the academic realm. Given the extraordinarily high therapeutic drug discovery-associated costs and risks, it is highly unlikely that any single public-sector research group will see a novel chemical "probe" become a "drug". An alternative drug development strategy is the exploitation of established drugs that have already been approved for treatment of non-cancerous diseases and whose cancer target has already been discovered. This strategy is also denominated drug repositioning, drug repurposing, or indication switch. Although traditionally development of these drugs was unlikely to be pursued by Big Pharma due to their limited commercial value, biopharmaceutical companies attempting to increase productivity at present are pursuing drug repositioning. More and more companies are scanning the existing pharmacopoeia for repositioning candidates, and the number of repositioning success stories is increasing. Here we provide noteworthy examples of known drugs whose potential anticancer activities have been highlighted, to encourage further research on these known drugs as a means to foster their translation into clinical trials utilizing the more limited public-sector resources. If these drug types eventually result in being effective, it follows that they could be much more affordable for patients with cancer; therefore, their contribution in terms of reducing cancer mortality at the global level would be greater.
doi:10.1186/1476-4598-7-82
PMCID: PMC2615789  PMID: 18947424

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