Drug Target Database Research Articles

Network Pharmacological Study of Achyranthis bidentatae Radix Effect on Bone Trauma.

Purpose Bone trauma is a clinical condition that afflicts the majority of the world's population. For the management of bone trauma, the underlying mechanisms of the drugs effective for bone healing are deemed necessary. Achyranthis bidentatae Radix (ABR) is a popular alternative medicine recommended in the treatment of bone trauma and injury, yet its mechanism of action persists to be vague. This study was conducted for the evaluation of the mode of action of ABR through network pharmacology in treating bone trauma. Methods An extensive survey of published works led to the development of a drug-target database, after which multiple protein targets for bone trauma were discerned. The protein-protein interaction network was developed by utilizing the STITCH database and gene ontology (GO) enrichment analysis using Cytoscape and ClueGO. Moreover, docking studies were performed for revealing the affinity of various ingredients with IL6. Results The extensive literature survey yielded the presence of 176 components in ABR, and 151 potential targets were acquired. Scrutinization of these targets revealed that 21 potential targets were found to be associated with bone trauma. Out of which, some remarkable targets such as IL6, MAPK14, MAPK8, SRC, PTGS2, and MMP2 were observed to be associated in the functional interaction of ABR. According to docking results, several ingredients of ABR such as Baicalien, Copistine, Epiberberine, Kaempferol, and Palmatine have the lowest docking scores (range between -6 and -7). Conclusions The results of the study elucidated that ABR can positively be utilized for the management of bone trauma, which can be mediated by multiple molecular mechanisms such as ERBB2 signaling pathway, positive regulation of oxidoreductase activity, JNK cascade pathway, multicellular organism metabolic process, T cell costimulation, and the positive regulation of MAPK activity. The findings also suggest that several ingredients of ABR such as Baicalien, Copistine, Epiberberine, Kaempferol, and Palmatine have good affinity with IL6, suggesting the promising potential of ABR in treating bone trauma, likely through IL6.

DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation.

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

A newly identified small molecular compound acts as a protein kinase inhibitor to suppress metastasis of colorectal cancer

BackgroundTargeted therapy has demonstrated high efficacy in the treatment of advanced cancer, and protein kinase inhibitors are a major focus of that therapy; therefore, our study aimed to identify a new protein kinase inhibitor that could be used in the treatment of advanced cancers. MethodsWe analyzed the expression profile of colorectal cancer (CRC), combined the driver gene and drug target databases, and identified protein kinase kalirin RhoGEF kinase (kalirin/KALRN) which is related to CRC metastasis. Based on the structure of kalirin, we screened for the small molecular compound ZINC65387069. We first compared the kinase inhibitory activities and molecular properties of ZINC65387069 and tyrosine kinase inhibitors (TKIs). We then determined the effects of ZINC65387069 on the phosphorylation of protein kinase B-Raf. Finally, we determined the effects of ZINC65387069 on migration and apoptosis of HCT116 cells as well as RKO cells. The cell cytoskeleton was also determined. ResultsCompared with traditional TKIs, ZINC65387069 had stronger kinase inhibitory activity, a simpler structure, higher water solubility, a smaller polar surface area, and lower molecular weight and volume. In CRC cells, ZINC65387069 could significantly inhibit the phosphorylation of B-Raf as well as inhibit cell migration, destroy the cell cytoskeleton, and promote cell apoptosis. ConclusionZINC65387069 is a newly identified protein kinase inhibitor that deserves additional research as a lead compound for drug development to help create targeted therapy against CRC.

In silico identification and characterization of promising drug targets in highly virulent uropathogenic Escherichia coli strain CFT073 by protein-protein interaction network analysis

Abstract Urinary tract infections (UTIs) are one of the commonest bacterial infections worldwide. The most common etiological agent is uropathogenic Escherichia coli. The occurrence of high rates of antimicrobial resistance among UPEC has complicated therapeutic management and emphasized the need for new drug targets or newer approaches to tackle the increasing trend of antimicrobial resistance. The use of OMICS technologies provides an attractive alternative to experimental methods to provide momentum in the discovery of drugs and decrease the chances of drug failure in later stages of drug development. The availability of the full genome sequence of many disease-causing organisms and humans in public databases has significantly increased the search for promising drug targets. The present study was performed to identify and characterize promising drug targets against a highly virulent UPEC strain CFT073 by utilizing a novel hierarchical in silico approach. We conducted the study in four phases. In phase 1, three sets of proteins were mined through a chokepoint, virulence, and resistance genes analysis. The proteins selected from phase 1 were further subjected to the nonhomology analysis in phase 2. In phase 3, co-evolution analysis was done to find non-homologous proteins which interact with virulence and resistance genes at high correlation values (≥0.9). We found eight putative drug targets (MurB, MurC, HisC, HyaB, HybO, FliN, FliH, and MotB) that interacted with five virulence (chuU, chuW, iucA, fepA, and hylD) and two resistance genes (arnA and acrB). Most of the identified drug targets are part of distinct metabolic pathways (viz., peptidoglycan biosynthesis, histidine biosynthesis etc). In phase 4, qualitative characterization of the above drug targets including protein location in the bacterial cell, broad-spectrum analysis, interactome analysis, evaluation of druggability by similarity search against drug-target database, and essentiality analysis was performed. PCR-based confirmation of identified drug targets in clinical isolates of UPEC was also carried out. The findings of this study may aid in the discovery of new antibacterial agents for better treatment of E. coli UTIs.

Metabolic Drug Survey Highlights Cancer Cell Dependencies and Vulnerabilities

Here we assembled a novel metabolic drug library covering 243 compounds allowing to systematically identify metabolic dependencies in high-throughput phenotypic screens. The CeMM Library of Metabolic Drugs (CLIMET) was compiled in a stepwise fashion, starting from 8000 candidate compounds, after a survey of public drug-target databases, and ending with 243 highly-curated compounds including extensive crosschecking for approval status, structural information, compound's potency and selectivity for the intended target, pathway/target redundancy, and commercial availability. To assess the potency of the compounds in CLIMET, we screened the full collection against a panel of 15 diverse myeloid leukemia cell lines. Each compound was tested for its effect on cell growth and survival in a 10,000-fold concentration range (1nM to 10uM) enabling the generation of dose response curves and calculation of area under the curve values (AUC) for each drug. We, further, functionally grouped the cell lines and drugs based on metabolic drug efficacy patterns and associated them with distinct genomic and metabolic attributes. Analysis of the metabolic drug response profiles revealed that 77 compounds (32%) affected cell viability with the top effective compounds targeting nucleotide metabolism, oxidative stress, and the PI3K/mTOR pathway. Unsupervised hierarchical clustering of the drug sensitivity profiles stratified the cell lines in 5 functional taxonomic groups, with the activity of 19 compounds significantly contributing to the cell line grouping (e.g. PF-02545920, GW 4064, mTOR inhibitors, daporinad). Comparison of the oxygen consumption rate and extracellular acidification rate showed that the examined cell lines have analogous baseline metabolic phenotypes, suggesting that the mitochondrial function of the cells as assessed by Seahorse analysis did not significantly influence the clustering. Genotype to phenotype associations were identified between FLT3mutations and sensitivity to 5-FU, lestaurtinib, and PF-02545920. Moreover, RAS mutations negatively correlated to mTOR and mitochondrial respiration inhibitor sensitivity, whereas TP53 mutations conferred a resistance phenotype to PI3K pathway inhibitors and antineoplastic agents. Selective sensitivities were detected to the lactate transporter (SLC16A1) inhibitor AZD3965, the PI3K inhibitor pictilisib, and the fatty acid synthase inhibitor GSK2194069, which could be explained by varied gene expression in sensitive cell lines and target/process dependency. CLIMET allows for identification of metabolic susceptibilities, grouping of cancer cells based on metabolic dependencies, as well as understanding of context-dependent mechanism of action of drugs. Functional drug testing may provide a rapid and robust approach to identify metabolic vulnerabilities, responding patients, and prioritize compounds for clinical evaluation as illustrated with our study. Disclosures Staber: Janssen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Astra Zeneca: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Celgene/ BMS: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; msd: Consultancy, Honoraria.

Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework.

Paracoccidioidomycosis (PCM) is the most prevalent endemic mycosis in Latin America. The disease is caused by fungi of the genus Paracoccidioides and mainly affects low-income rural workers after inhalation of fungal conidia suspended in the air. The current arsenal of chemotherapeutic agents requires long-term administration protocols. In addition, chemotherapy is related to a significantly increased frequency of disease relapse, high toxicity, and incomplete elimination of the fungus. Due to the limitations of current anti-PCM drugs, we developed a computational drug repurposing-chemogenomics approach to identify approved drugs or drug candidates in clinical trials with anti-PCM activity. In contrast to the one-drug-one-target paradigm, our chemogenomics approach attempts to predict interactions between drugs, and Paracoccidioides protein targets. To achieve this goal, we designed a workflow with the following steps: (a) compilation and preparation of Paracoccidioides spp. genome data; (b) identification of orthologous proteins among the isolates; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Paracoccidioides essential targets using validated genes from Saccharomyces cerevisiae; (e) homology modeling and molecular docking studies; and (f) experimental validation of selected candidates. We prioritized 14 compounds. Two antineoplastic drug candidates (vistusertib and BGT-226) predicted to be inhibitors of phosphatidylinositol 3-kinase TOR2 showed antifungal activity at low micromolar concentrations (<10 μM). Four antifungal azole drugs (bifonazole, luliconazole, butoconazole, and sertaconazole) showed antifungal activity at low nanomolar concentrations, validating our methodology. The results suggest our strategy for predicting new anti-PCM drugs is useful. Finally, we could recommend hit-to-lead optimization studies to improve potency and selectivity, as well as pharmaceutical formulations to improve oral bioavailability of the antifungal azoles identified.

Hemap: An Interactive Online Resource for Characterizing Molecular Phenotypes across Hematologic Malignancies.

Large collections of genome-wide data can facilitate the characterization of disease states and subtypes, permitting pan-cancer analysis of molecular phenotypes and evaluation of disease context for new therapeutic approaches. We analyzed 9,544 transcriptomes from more than 30 hematologic malignancies, normal blood cell types, and cell lines, and showed that disease types could be stratified in a data-driven manner. We then identified cluster-specific pathway activity, new biomarkers, and in silico drug target prioritization through interrogation of drug target databases. Using known vulnerabilities and available drug screens, we highlighted the importance of integrating molecular phenotype with drug target expression for in silico prediction of drug responsiveness. Our analysis implicated BCL2 expression level as an important indicator of venetoclax responsiveness and provided a rationale for its targeting in specific leukemia subtypes and multiple myeloma, linked several polycomb group proteins that could be targeted by small molecules (SFMBT1, CBX7, and EZH1) with chronic lymphocytic leukemia, and supported CDK6 as a disease-specific target in acute myeloid leukemia. Through integration with proteomics data, we characterized target protein expression for pre-B leukemia immunotherapy candidates, including DPEP1. These molecular data can be explored using our publicly available interactive resource, Hemap, for expediting therapeutic innovations in hematologic malignancies. SIGNIFICANCE: This study describes a data resource for researching derailed cellular pathways and candidate drug targets across hematologic malignancies.

Network pharmacology exploration reveals the bioactive compounds and molecular mechanisms of Li-Ru-Kang against hyperplasia of mammary gland.

Li-Ru-Kang (LRK) has been commonly used in the treatment of hyperplasia of mammary gland (HMG) as a cipher prescription and achieved obvious therapeutic effects. However, the bioactive compounds and underlying pharmacological mechanisms remain unclear. This study aims to decipher the bioactive compounds and potential action mechanisms of LRK in the treatment of HMG using an integrated pharmacology approach. The ingredients of LRK and the corresponding drug targets were retrieved through drug target databases and were used to construct the "compound-target-disease" network and function-pathway network. Ultimately, 89 compounds and 2150 drug targets were collected. Gene ontology enrichment analysis revealed that mammary gland alveolus development and mammary gland lobule development were the key biological processes and were regulated simultaneously by three direct targets, including androgen receptor (AR), estrogen receptor (ER) and cyclin-D1. Moreover, 14 compounds of LRK were directly involved in the regulation of the three aforementioned targets. KEGG pathway enrichment analysis found that five signaling pathways and seven direct targets were closely related with HMG treatment by LRK. The results of animal experiments showed that LRK significantly improved the histopathological status of HMG in rats. Additionally, LRK markedly regulated the protein expressions of AR, cyclin-D1, MMP2, MMP3 and MMP9. But interestingly, the effect of LRK on ER was not obvious. This study demonstrated that LRK exerted its therapeutic efficacy based on multi-components, multi-targets and multi-pathways. This research confirms the advantages of network pharmacology analyses and the necessity for experimental verification.

Discovery of High Affinity Receptors for Dityrosine through Inverse Virtual Screening and Docking and Molecular Dynamics.

Dityrosine is the product of oxidation that has been linked to a number of serious pathological conditions. Evidence indicates that high amounts of dityrosine exist in oxidized milk powders and some milk related foodstuffs, further reducing the nutritional value of oxidized proteins. Therefore, we hypothesize that some receptors related to special diseases would be targets for dityrosine. However, the mechanisms of the interaction of dityrosine with probable targets are still unknown. In the present work, an inverse virtual screening approach was performed to screen possible novel targets for dityrosine. Molecular docking studies were performed on a panel of targets extracted from the potential drug target database (PDTD) to optimize and validate the screening results. Firstly, two different conformations cis- and trans- were found for dityrosine during minimization. Moreover, Tubulin (αT) (−11.0 kcal/mol) was identified as a target for cis-dityrosine (CDT), targets including αT (−11.2 kcal/mol) and thyroid hormone receptor beta-1 (−10.7 kcal/mol) presented high binding affinities for trans-dityrosine (TDT). Furthermore, in order to provide binding complexes with higher precision, the three docked systems were further refined by performing thermo dynamic simulations. A series of techniques for searching for the most stable binding pose and the calculation of binding free energy are elaborately provided in this work. The major interactions between these targets and dityrosine were hydrophobic, electrostatic and hydrogen bonding. The application of inverse virtual screening method may facilitate the prediction of unknown targets for known ligands, and direct future experimental assays.

Multifunctional Proteins: Involvement in Human Diseases and Targets of Current Drugs

Multifunctionality or multitasking is the capability of some proteins to execute two or more biochemical functions. The objective of this work is to explore the relationship between multifunctional proteins, human diseases and drug targeting. The analysis of the proportion of multitasking proteins from the MultitaskProtDB-II database shows that 78% of the proteins analyzed are involved in human diseases. This percentage is much higher than the 17.9% found in human proteins in general. A similar analysis using drug target databases shows that 48% of these analyzed human multitasking proteins are targets of current drugs, while only 9.8% of the human proteins present in UniProt are specified as drug targets. In almost 50% of these proteins, both the canonical and moonlighting functions are related to the molecular basis of the disease. A procedure to identify multifunctional proteins from disease databases and a method to structurally map the canonical and moonlighting functions of the protein have also been proposed here. Both of the previous percentages suggest that multitasking is not a rare phenomenon in proteins causing human diseases, and that their detailed study might explain some collateral drug effects.

Regular aspirin use and gene expression profiles in prostate cancer patients.

Pharmacoepidemiology studies suggest prognostic benefits of aspirin in prostate cancer. We hypothesized that aspirin induces transcriptional changes in tumors or normal prostate tissue. We analyzed the prostatic transcriptome from men diagnosed with prostate cancer during follow-up of the Physicians' Health Study 1 (PHS, n = 149), initially a randomized controlled trial of aspirin. Aspirin target genes were identified through systematic literature review and a drug target database. We compared target gene expression according to regular aspirin use at cancer diagnosis and used whole-transcriptome gene set enrichment analysis to identify gene sets associated with aspirin use. Results were validated in the Health Professionals Follow-up Study (HPFS, n = 254) and in Connectivity Map. Of 12 target genes identified from prior studies and 540 genes from the drug target database, none were associated with aspirin use. Twenty-one gene sets were enriched in tumor tissue of aspirin users, 18 of which were clustered around ribosome function and translation. These gene sets were associated with exposure to cyclooxygenase inhibitors in Connectivity Map. Their association with cancer prognosis was U-shaped in both cohorts. No gene sets were enriched in normal tissue. In HPFS, neither the target genes nor the gene sets were associated with aspirin use. Regular aspirin use may affect ribosome function in prostate tumors. Other putative target genes had similar expression in tumors from aspirin users and non-users. If results are corroborated by experimental studies, a potential benefit of aspirin may be limited to a subset of prostate cancer patients.

Complex network theory for the identification and assessment of candidate protein targets

In this work we use complex network theory to provide a statistical model of the connectivity patterns of human proteins and their interaction partners. Our intention is to identify important proteins that may be predisposed to be potential candidates as drug targets for therapeutic interventions. Target proteins usually have more interaction partners than non-target proteins, but there are no hard-and-fast rules for defining the actual number of interactions. We devise a statistical measure for identifying hub proteins, we score our target proteins with gene ontology annotations. The important druggable protein targets are likely to have similar biological functions that can be assessed for their potential therapeutic value. Our system provides a statistical analysis of the local and distant neighborhood protein interactions of the potential targets using complex network measures. This approach builds a more accurate model of drug-to-target activity and therefore the likely impact on treating diseases. We integrate high quality protein interaction data from the HINT database and disease associated proteins from the DrugTarget database. Other sources include biological knowledge from Gene Ontology and drug information from DrugBank. The problem is a very challenging one since the data is highly imbalanced between target proteins and the more numerous nontargets. We use undersampling on the training data and build Random Forest classifier models which are used to identify previously unclassified target proteins. We validate and corroborate these findings from the available literature.

Identification of ST3AGL4, MFHAS1, CSNK2A2 and CD226 as loci associated with systemic lupus erythematosus (SLE) and evaluation of SLE genetics in drug repositioning

ObjectivesSystemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic component in its pathogenesis. Through genome-wide association studies (GWAS), we recently identified 10 novel loci associated with...

The evolution of regulators of G protein signalling proteins as drug targets - 20 years in the making: IUPHAR Review 21.

Regulators of G protein signalling (RGS) proteins are celebrating the 20th anniversary of their discovery. The unveiling of this new family of negative regulators of G protein signalling in the mid-1990s solved a persistent conundrum in the G protein signalling field, in which the rate of deactivation of signalling cascades in vivo could not be replicated in exogenous systems. Since then, there has been tremendous advancement in the knowledge of RGS protein structure, function, regulation and their role as novel drug targets. RGS proteins play an important modulatory role through their GTPase-activating protein (GAP) activity at active, GTP-bound Gα subunits of heterotrimeric G proteins. They also possess many non-canonical functions not related to G protein signalling. Here, an update on the status of RGS proteins as drug targets is provided, highlighting advances that have led to the inclusion of RGS proteins in the IUPHAR/BPS Guide to PHARMACOLOGY database of drug targets.

Geriatric information analysis of the molecular properties of mexidole

Objective : by using the pharmacoinformation profiling, to comprehensively assess all possible effects of the molecules of mexidol, choline alfoscerate, piracetam, glycine, and semax in accordance with the anatomical therapeutic and chemical (ATC) classification system. Material and methods. Chemoreactomic, pharmacoinformation, and geriatric information analyses of the properties of the molecules are based on chemoreactomic methodology. The chemoreactomic analysis uses the information from the PubChem, HMDB, and String databases; the pharmacoinformation analysis applies the information from the international ATC classification and a combined sample of data from the Therapeutic Target Database (TTD), SuperTarget, Manually Annotated Targets and Drugs Online Resource (MATADOR), and Potential Drug Target Database (PDTD); geriatric information analysis employs the data on the geroprotective effect of individual substances from the PubChem database and the data available in the literature data on geroprotection from the PubMed database, which have been collected through the artificial intelligence system. Results and discussion. Mexidol is characterized by the maximum set of positive effects (the drug is used to treat CNS and cardiovascular diseases and metabolic disorders and has anti-inflammatory and anti-infective properties, etc.). Mexidol and glycine are predicted to cause the lowest frequency of adverse reactions, such as itching, constipation, paresthesia, vomiting, etc. Geriatric information assessments of changes in the life span of model organisms have shown that mexidol contributes to the higher life expectancy of C. elegans (by 22.7±10%), Drosophila (by 14.4±15%), and mice (by 14.6±3%); the control drugs do by no more than 6.1%. Conclusion. The results of the study indicate that mexidol has a high potential to be used as a geroprotector.

Esculentoside A suppresses lipopolysaccharide-induced pro-inflammatory molecule production partially by casein kinase 2

Ethnopharmacological relevanceEsculentoside A (EsA) is a saponin isolated from the root of Phytolacca esculenta, an herb which has long been used in Traditional Chinese Medicine for various inflammatory diseases. EsA has been reported to have potent anti-inflammatory properties both in vitro and in vivo. Aim of the studyThe present study focused on the molecular mechanism of EsA for its anti-inflammatory effects in RAW264.7 cells stimulated with lipopolysaccharide (LPS). Methods and resultsEnzyme Linked Immunosorbent Assay (ELISA) showed EsA dose dependently inhibited the production of tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6) and nitric oxide in RAW264.7 cells. Real-time quantitative reverse-transcription polymerase chain reaction (RT-PCR) assay further confirmed the suppression of LPS-induced TNF-α, IL-6 and iNOS gene expression by EsA on a transcriptional level. Moreover, EsA treatment markedly suppressed LPS-stimulated IκB phosphorylation and degradation as well as LPS-stimulated luciferase reporter construct driven by κB response elements in RAW264.7 cells. In addition, EsA significantly reduced LPS-induced stimulation of p38 and JNK, but not ERK1/2, phosphorylation. Furthermore, we used a computational method called “reverse docking” to search the possible binding proteins of EsA from the potential drug target database (PDTD), and focused on CK2 as the primary binding protein of EsA. Afterward, we further tested EsA directly interacts with recombinant CK2 using SPR assay. In CK2 kinase activity assay, EsA inhibited recombinant CK2 holoenzyme activity obviously in a dose-dependent manner. In addition, TBB (4, 5, 6, 7-tetrabromo-2-benzotriazole, a pharmacological inhibitor of CK2) blocked IL-6 release in a dose-dependent manner, whereas co-treatment of cells with EsA and TBB did not have an additive effect. ConclusionsTaken together, these results indicate that EsA blocks the LPS-induced pro-inflammatory molecules expression, at least in part, by impediment of LPS-triggered activation of NF-κB and p38/JNK MAPK pathways in macrophages. Furthermore, we discovered for the first time EsA as a ligand for CK2, which was involved in the inhibition of EsA to the expression of inflammatory cytokines. These findings extended our understanding on the cellular and molecular mechanisms responsible for the anti-inflammatory activity of EsA.

Using reverse docking to identify potential targets for ginsenosides

BackgroundGinsenosides are the main ingredients of ginseng, which, in traditional Eastern medicine, has been claimed to have therapeutic values for many diseases. In order to verify the effects of ginseng that have been empirically observed, we utilized the reverse docking method to screen for target proteins that are linked to specific diseases. MethodsWe constructed a target protein database including 1,078 proteins associated with various kinds of diseases, based on the Potential Drug Target Database, with an added list of kinase proteins. We screened 26 kinds of ginsenosides of this target protein database using docking. ResultsWe found four potential target proteins for ginsenosides, based on docking scores. Implications of these “hit” targets are discussed. From this screening, we also found four targets linked to possible side effects and toxicities, based on docking scores. ConclusionOur method and results can be helpful for finding new targets and developing new drugs from natural products.

Using DrugBank for In Silico Drug Exploration and Discovery.

DrugBank is a fully curated drug and drug target database that contains 8174 drug entries including 1944 FDA approved small-molecule drugs, 198 FDA-approved biotech (protein/peptide) drugs, 93 nutraceuticals, and over 6000 experimental drugs. Additionally, 4300 non-redundant protein (i.e., drug target/enzyme/transporter/carrier) sequences are linked to these drug entries. DrugBank is primarily focused on providing both the query/search tools and biophysical data needed to facilitate drug discovery and drug development. This unit provides readers with a detailed description of how to effectively use the DrugBank database and how to navigate through the DrugBank Web site. It also provides specific examples of how to find chemical homologs of potential drug leads and how to identify potential drug targets from newly sequenced tumor samples. The intent of this unit is to give readers an introduction to the field of Web-based drug discovery and to show how cheminformatics can be seamlessly integrated into the field of bioinformatics. © 2016 by John Wiley & Sons, Inc.

Implementation of mechanism of action biology-driven early drug development for children with cancer

An urgent need remains for new paediatric oncology drugs to cure children who die from cancer and to reduce drug-related sequelae in survivors. In 2007, the European Paediatric Regulation came into law requiring industry to create paediatric drug (all types of medicinal products) development programmes alongside those for adults. Unfortunately, paediatric drug development is still largely centred on adult conditions and not a mechanism of action (MoA)-based model, even though this would be more logical for childhood tumours as these have much fewer non-synonymous coding mutations than adult malignancies. Recent large-scale sequencing by International Genome Consortium and Paediatric Cancer Genome Project has further shown that the genetic and epigenetic repertoire of driver mutations in specific childhood malignancies differs from more common adult-type malignancies. To bring about much needed change, a Paediatric Platform, ACCELERATE, was proposed in 2013 by the Cancer Drug Development Forum, Innovative Therapies for Children with Cancer, the European Network for Cancer Research in Children and Adolescents and the European Society for Paediatric Oncology. The Platform, comprising multiple stakeholders in paediatric oncology, has three working groups, one with responsibility for promoting and developing high-quality MoA-informed paediatric drug development programmes, including specific measures for adolescents. Key is the establishment of a freely accessible aggregated database of paediatric biological tumour drug targets to be aligned with an aggregated pipeline of drugs. This will enable prioritisation and conduct of early phase clinical paediatric trials to evaluate these drugs against promising therapeutic targets and to generate clinical paediatric efficacy and safety data in an accelerated time frame. Through this work, the Platform seeks to ensure that potentially effective drugs, where the MoA is known and thought to be relevant to paediatric malignancies, are evaluated in early phase clinical trials, and that this approach to generate pre-clinical and clinical data is systematically pursued by academia, sponsors, industry, and regulatory bodies to bring new paediatric oncology drugs to front-line therapy more rapidly.

Data integration to prioritize drugs using genomics and curated data.

BackgroundGenomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature.ResultsWe have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3.ConclusionsOur results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0097-1) contains supplementary material, which is available to authorized users.