Abstract: BACKGROUND: A wide range of chemical compound databases are currently available for pharmaceutical research. To retrieve compound information, including structures, researchers can query these chemical databases using non-systematic identifiers. These are source-dependent identifiers (e.g., brand names, generic names), which are usually assigned to the compound at the point of registration. The correctness of non-systematic identifiers (i.e., whether an identifier matches the associated structure) can only be assessed manually, which is cumbersome, but it is possible to automatically check their ambiguity (i.e., whether an identifier matches more than one structure). In this study we have quantified the ambiguity of non-systematic identifiers within and between eight widely used chemical databases. We also studied the effect of chemical structure standardization on reducing the ambiguity of non-systematic identifiers. RESULTS: The ambiguity of non-systematic identifiers within databases varied from 0.1 to 15.2 % (median 2.5 %). Standardization reduced the ambiguity only to a small extent for most databases. A wide range of ambiguity existed for non-systematic identifiers that are shared between databases (17.7-60.2 %, median of 40.3 %). Removing stereochemistry information provided the largest reduction in ambiguity across databases (median reduction 13.7 percentage points). CONCLUSIONS: Ambiguity of non-systematic identifiers within chemical databases is generally low, but ambiguity of non-systematic identifiers that are shared between databases, is high. Chemical structure standardization reduces the ambiguity to a limited extent. Our findings can help to improve database integration, curation, and maintenance.

Abstract: As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

Abstract: Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive and unwilling thoughts (obsessions) giving rise to anxiety. The patients feel obliged to perform a behavior (compulsions) induced by the obsessions. The World Health Organization ranks OCD as one of the 10 most disabling medical conditions. In the class of Anxiety Disorders, OCD is a pathology that shows an hereditary component. Consequently, an online resource collecting and integrating scientific discoveries and genetic evidence about OCD would be helpful to improve the current knowledge on this disorder. We have developed a manually curated database, OCD Database (OCDB), collecting the relations between candidate genes in OCD, microRNAs (miRNAs) involved in the pathophysiology of OCD and drugs used in its treatments. We have screened articles from PubMed and MEDLINE. For each gene, the bibliographic references with a brief description of the gene and the experimental conditions are shown. The database also lists the polymorphisms within genes and its chromosomal regions. OCDB data is enriched with both validated and predicted miRNA-target and drug-target information. The transcription factors regulations, which are also included, are taken from David and TransmiR. Moreover, a scoring function ranks the relevance of data in the OCDB context. The database is also integrated with the main online resources (PubMed, Entrez-gene, HGNC, dbSNP, DrugBank, miRBase, PubChem, Kegg, Disease-ontology and ChEBI). The web interface has been developed using phpMyAdmin and Bootstrap software. This allows (i) to browse data by category and (ii) to navigate in the database by searching genes, miRNAs, drugs, SNPs, regions, drug targets and articles. The data can be exported in textual format as well as the whole database in.sql or tabular format. OCDB is an essential resource to support genome-wide analysis, genetic and pharmacological studies. It also facilitates the evaluation of genetic data in OCD and the detection of alternative treatments.

Abstract: BACKGROUND: Natural products (NPs) are important to the drug discovery process. NP research efforts are expanding world-wide and South Africa is no exception to this. While freely-accessible small molecule databases, containing compounds isolated from indigenous sources, have been established in a number of other countries, there is currently no such online database in South Africa. DESCRIPTION: The current research presents a South African natural compound database, named SANCDB. This is a curated and fully-referenced database containing compound information for 600 natural products extracted directly from journal articles, book chapters and theses. There is a web interface to the database, which is simple and easy to use, while allowing for compounds to be searched by a number of different criteria. Being fully referenced, each compound page contains links to the original referenced work from which the information was obtained. Further, the website provides a submission pipeline, allowing researchers to deposit compounds from their own research into the database. CONCLUSIONS: SANCDB is currently the only web-based NP database in Africa. It aims to provide a useful resource for the in silico screening of South African NPs for drug discovery purposes. The database is supported by a submission pipeline to allow growth by entries from researchers. As such, we currently present SANCDB the starting point of a platform for a community-driven, curated database to further natural products research in South Africa. SANCDB is freely available at https://sancdb.rubi.ru.ac.za/.

Abstract: Dihydropyrimidinase is a member of the cyclic amidohydrolase family, which also includes allantoinase, dihydroorotase, hydantoinase, and imidase. These metalloenzymes possess very similar active sites and may use a similar mechanism for catalysis. However, whether the substrates and inhibitors of other cyclic amidohydrolases can inhibit dihydropyrimidinase remains unclear. This study investigated the inhibition of dihydropyrimidinase by flavonoids and substrates of other cyclic amidohydrolases. Allantoin, dihydroorotate, 5-hydantoin acetic acid, acetohydroxamate, orotic acid, and 3-amino-1,2,4-triazole could slightly inhibit dihydropyrimidinase, and the IC50 values of these compounds were within the millimolar range. The inhibition of dihydropyrimidinase by flavonoids, such as myricetin, quercetin, kaempferol, galangin, dihydromyricetin, and myricitrin, was also investigated. Some of these compounds are known as inhibitors of allantoinase and dihydroorotase. Although the inhibitory effects of these flavonoids on dihydropyrimidinase were substrate-dependent, dihydromyricetin significantly inhibited dihydropyrimidinase with IC50 values of 48 and 40 muM for the substrates dihydrouracil and 5-propyl-hydantoin, respectively. The results from the Lineweaver-Burk plot indicated that dihydromyricetin was a competitive inhibitor. Results from fluorescence quenching analysis indicated that dihydromyricetin could form a stable complex with dihydropyrimidinase with the K(d) value of 22.6 muM. A structural study using PatchDock showed that dihydromyricetin was docked in the active site pocket of dihydropyrimidinase, which was consistent with the findings from kinetic and fluorescence studies. This study was the first to demonstrate that naturally occurring product dihydromyricetin inhibited dihydropyrimidinase, even more than the substrate analogs (>3 orders of magnitude). These flavonols, particularly myricetin, may serve as drug leads and dirty drugs (for multiple targets) for designing compounds that target several cyclic amidohydrolases.

Abstract: Recent progress in the field of aging has resulted in ever increasing numbers of compounds that extend lifespan in Caenorhabditis elegans. Lifespan extending compounds include metabolites and synthetic compounds, as well as natural products. For many of these compounds, mammalian pharmacology is known, and for some the actual targets have been experimentally identified. In this review, we explore the data available in C. elegans to provide an overview of which pharmacological classes have potential for identification of further compounds that extend lifespan.

Abstract: We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery.

Abstract: Humans are exposed to thousands of chemicals in the workplace, home, and via air, water, food, and soil. A major challenge in estimating chemical exposures is to understand which chemicals are present in these media and microenvironments. Here we describe the Chemical/Product Categories Database (CPCat), a new, publically available (http://actor.epa.gov/cpcat) database of information on chemicals mapped to "use categories" describing the usage or function of the chemical. CPCat was created by combining multiple and diverse sources of data on consumer- and industrial-process based chemical uses from regulatory agencies, manufacturers, and retailers in various countries. The database uses a controlled vocabulary of 833 terms and a novel nomenclature to capture and streamline descriptors of chemical use for 43,596 chemicals from the various sources. Examples of potential applications of CPCat are provided, including identifying chemicals to which children may be exposed and to support prioritization of chemicals for toxicity screening. CPCat is expected to be a valuable resource for regulators, risk assessors, and exposure scientists to identify potential sources of human exposures and exposure pathways, particularly for use in high-throughput chemical exposure assessment.

Abstract: Traditional Chinese Medicine (TCM) doctors always prescribe various herbal formulae tailored to individual patients. However, there is still a lack of appropriate methods to study the rule and potential biological basis underlying the numerous prescriptions. Here we developed an Herb-Compound-Target-Disease coherent network approach to analyze 871 herbal prescriptions from a TCM master, Mr. Ji-Ren Li, in his clinical practice on treatment of rheumatoid arthritis (RA). The core herb networks were extracted from Mr. Li's prescriptions. Then, we predicted target profiles of compounds in core herb networks and calculated potential synergistic activities among them. We further found that the target sets of core herbs overlapped significantly with the RA related biological processes and pathways. Moreover, we detected a possible connection between the prescribed herbs with different properties such as Cold and Hot and the Western drugs with different actions such as immunomodulatory and hormone regulation on treatment of RA. In summary, we explored a new application of TCM network pharmacology on the analysis of TCM prescriptions and detected the networked core herbs, their potential synergistic and biological activities, and possible connections with drugs. This work offers a novel way to understand TCM prescriptions in clinical practice.

Abstract: An histone deacetylase (HDAC) inhibitor database (HDACiDB) was constructed to enable rapid access to data relevant to the development of epigenetic modulators (HDAC inhibitors [HDACi]), helping bring precision cancer medicine a step closer. Thousands of HDACi targeting HDACs are in various stages of development and are being tested in clinical trials as monotherapy and in combination with other cancer agents. Despite the abundance of HDACi, information resources are limited. Tools for in silico experiments on specific HDACi prediction, for designing and analyzing the generated data, as well as custom-made specific tools and interactive databases, are needed. We have developed an HDACiDB that is a composite collection of HDACi and currently comprises 1,445 chemical compounds, including 419 natural and 1,026 synthetic ones having the potential to inhibit histone deacetylation. Most importantly, it will allow application of Lipinski's rule of five drug-likeness and other physicochemical property-based screening of the inhibitors. It also provides easy access to information on their source of origin, molecular properties, drug likeness, as well as bioavailability with relevant references cited. Being the first comprehensive database on HDACi that contains all known natural and synthetic HDACi, the HDACiDB may help to improve our knowledge concerning the mechanisms of actions of available HDACi and enable us to selectively target individual HDAC isoforms and establish a new paradigm for intelligent epigenetic cancer drug design. The database is freely available on the http://hdacidb.bioinfo.au-kbc.org.in/hdacidb/website.