Abstract: BACKGROUND: There are presently hundreds of online databases hosting millions of chemical compounds and associated data. As a result of the number of cheminformatics software tools that can be used to produce the data, subtle differences between the various cheminformatics platforms, as well as the naivety of the software users, there are a myriad of issues that can exist with chemical structure representations online. In order to help facilitate validation and standardization of chemical structure datasets from various sources we have delivered a freely available internet-based platform to the community for the processing of chemical compound datasets. RESULTS: The chemical validation and standardization platform (CVSP) both validates and standardizes chemical structure representations according to sets of systematic rules. The chemical validation algorithms detect issues with submitted molecular representations using pre-defined or user-defined dictionary-based molecular patterns that are chemically suspicious or potentially requiring manual review. Each identified issue is assigned one of three levels of severity - Information, Warning, and Error - in order to conveniently inform the user of the need to browse and review subsets of their data. The validation process includes validation of atoms and bonds (e.g., making aware of query atoms and bonds), valences, and stereo. The standard form of submission of collections of data, the SDF file, allows the user to map the data fields to predefined CVSP fields for the purpose of cross-validating associated SMILES and InChIs with the connection tables contained within the SDF file. This platform has been applied to the analysis of a large number of data sets prepared for deposition to our ChemSpider database and in preparation of data for the Open PHACTS project. In this work we review the results of the automated validation of the DrugBank dataset, a popular drug and drug target database utilized by the community, and ChEMBL 17 data set. CVSP web site is located at http://cvsp.chemspider.com/. CONCLUSION: A platform for the validation and standardization of chemical structure representations of various formats has been developed and made available to the community to assist and encourage the processing of chemical structure files to produce more homogeneous compound representations for exchange and interchange between online databases. While the CVSP platform is designed with flexibility inherent to the rules that can be used for processing the data we have produced a recommended rule set based on our own experiences with the large data sets such as DrugBank, ChEMBL, and data sets from ChemSpider.

Abstract: Quorum sensing (QS) plays a vital role in regulating the virulence factor of many food borne pathogens, which causes severe public health risk. Therefore, interrupting the QS signaling pathway may be an attractive strategy to combat microbial infections. In the current study QS inhibitory activity of quercetin and its anti-biofilm property was assessed against food-borne pathogens using a bio-sensor strain. In addition in-silico techniques like molecular docking and molecular dynamics simulation studies were applied to screen the quercetin's potentiality as QS inhibitor. Quercetin (80 mug/ml) showed the significant reduction in QS-dependent phenotypes like violacein production, biofilm formation, exopolysaccharide (EPS) production, motility and alginate production in a concentration-dependent manner. Synergistic activity of conventional antibiotics with quercetin enhanced the susceptibility of all tested pathogens. Furthermore, Molecular docking analysis revealed that quercetin binds more rigidly with LasR receptor protein than the signaling compound with docking score of -9.17 Kcal/mol. Molecular dynamics simulation predicted that QS inhibitory activity of quercetin occurs through the conformational changes between the receptor and quercetin complex. Above findings suggest that quercetin can act as a competitive inhibitor for signaling compound towards LasR receptor pathway and can serve as a novel QS-based antibacterial/anti-biofilm drug to manage food-borne pathogens.

Abstract: BACKGROUND: Predicting novel drug-target associations is important not only for developing new drugs, but also for furthering biological knowledge by understanding how drugs work and their modes of action. As more data about drugs, targets, and their interactions becomes available, computational approaches have become an indispensible part of drug target association discovery. In this paper we apply random walk with restart (RWR) method to a heterogeneous network of drugs and targets compiled from DrugBank database and investigate the performance of the method under parameter variation and choice of chemical fingerprint methods. RESULTS: We show that choice of chemical fingerprint does not affect the performance of the method when the parameters are tuned to optimal values. We use a subset of the ChEMBL15 dataset that contains 2,763 associations between 544 drugs and 467 target proteins to evaluate our method, and we extracted datasets of bioactivity =1 and =10 muM activity cutoff. For 1 muM bioactivity cutoff, we find that our method can correctly predict nearly 47, 55, 60% of the given drug-target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 muM dataset in top 50 rank positions. For 10 muM bioactivity cutoff, we find that our method can correctly predict nearly 32.4, 34.8, 35.3% of the given drug-target interactions in the test dataset having more than 0, 1, 2 drug target relations for ChEMBL 1 muM dataset in top 50 rank positions. We further examine the associations between 110 popular top selling drugs in 2012 and 3,519 targets and find the top ten targets for each drug. CONCLUSIONS: We demonstrate the effectiveness and promise of the approach-RWR on heterogeneous networks using chemical features-for identifying novel drug target interactions and investigate the performance.

Abstract: BACKGROUND: Pharmacogenomics (PGx) as an emerging field, is poised to change the way we practice medicine and deliver health care by customizing drug therapies on the basis of each patient's genetic makeup. A large volume of PGx data including information among drugs, genes, and single nucleotide polymorphisms (SNPs) has been accumulated. Normalized and integrated PGx information could facilitate revelation of hidden relationships among drug treatments, genomic variations, and phenotype traits to better support drug discovery and next generation of treatment. METHODS: In this study, we generated a normalized and scientific evidence supported cancer based PGx network (CPN) by integrating cancer related PGx information from multiple well-known PGx resources including the Pharmacogenomics Knowledge Base (PharmGKB), the FDA PGx Biomarkers in Drug Labeling, and the Catalog of Published Genome-Wide Association Studies (GWAS). We successfully demonstrated the capability of the CPN for drug repurposing by conducting two case studies. CONCLUSIONS: The CPN established in this study offers comprehensive cancer based PGx information to support cancer orientated research, especially for drug repurposing.

Abstract: Peripheral arterial disease (PAD) results from atherosclerosis that leads to blocked arteries and reduced blood flow, most commonly in the arteries of the legs. PAD clinical trials to induce angiogenesis to improve blood flow conducted in the last decade have not succeeded. We have recently constructed PADPIN, protein-protein interaction network (PIN) of PAD, and here we combine it with the drug-target relations to identify potential drug targets for PAD. Specifically, the proteins in the PADPIN were classified as belonging to the angiome, immunome, and arteriome, characterizing the processes of angiogenesis, immune response/inflammation, and arteriogenesis, respectively. Using the network-based approach we predict the candidate drugs for repositioning that have potential applications to PAD. By compiling the drug information in two drug databases DrugBank and PharmGKB, we predict FDA-approved drugs whose targets are the proteins annotated as anti-angiogenic and pro-inflammatory, respectively. Examples of pro-angiogenic drugs are carvedilol and urokinase. Examples of anti-inflammatory drugs are ACE inhibitors and maraviroc. This is the first computational drug repositioning study for PAD.

Abstract: Relation extraction typically involves the extraction of relations between two or more entities occurring within a single or multiple sentences. In this study, we investigated the significance of extracting information from multiple sentences specifically in the context of drug-disease relation discovery. We used multiple resources such as Semantic Medline, a literature based resource, and Medline search (for filtering spurious results) and inferred 8,772 potential drug-disease pairs. Our analysis revealed that 6,450 (73.5%) of the 8,772 potential drug-disease relations did not occur in a single sentence. Moreover, only 537 of the drug-disease pairs matched the curated gold standard in Comparative Toxicogenomics Database (CTD), a trusted resource for drug-disease relations. Among the 537, nearly 75% (407) of the drug-disease pairs occur in multiple sentences. Our analysis revealed that the drug-disease pairs inferred from Semantic Medline or retrieved from CTD could be extracted from multiple sentences in the literature. This highlights the significance of the need of discourse-level analysis in extracting the relations from biomedical literature.

Abstract: Preoperative chemoradiotherapy is widely used to improve local control of disease, sphincter preservation and to improve survival in patients with locally advanced rectal cancer. Patients enrolled in the present study underwent preoperative chemoradiotherapy, followed by surgical excision. Response to chemoradiotherapy was evaluated according to Mandard's Tumor Regression Grade (TRG). TRG 3, 4 and 5 were considered as partial or no response while TRG 1 and 2 as complete response. From pretherapeutic biopsies of 84 locally advanced rectal carcinomas available for the analysis, only 42 of them showed 70% cancer cellularity at least. By determining gene expression profiles, responders and non-responders showed significantly different expression levels for 19 genes (P < 0.001). We fitted a logistic model selected with a stepwise procedure optimizing the Akaike Information Criterion (AIC) and then validated by means of leave one out cross validation (LOOCV, accuracy = 95%). Four genes were retained in the achieved model: ZNF160, XRCC3, HFM1 and ASXL2. Real time PCR confirmed that XRCC3 is overexpressed in responders group and HFM1 and ASXL2 showed a positive trend. In vitro test on colon cancer resistant/susceptible to chemoradioterapy cells, finally prove that XRCC3 deregulation is extensively involved in the chemoresistance mechanisms. Protein-protein interactions (PPI) analysis involving the predictive classifier revealed a network of 45 interacting nodes (proteins) with TRAF6 gene playing a keystone role in the network. The present study confirmed the possibility that gene expression profiling combined with integrative computational biology is useful to predict complete responses to preoperative chemoradiotherapy in patients with advanced rectal cancer.

Abstract: Oral cancer is one of the main causes of cancer-related deaths in South-Asian countries. There are very limited treatment options available for oral cancer. Research endeavors focused on discovery and development of novel therapies for oral cancer, is necessary to control the ever rising oral cancer related mortalities. We mined the large pool of compounds from the publicly available compound databases, to identify potential therapeutic compounds for oral cancer. Over 84 million compounds were screened for the possible anti-cancer activity by custom build SVM classifier. The molecular targets of the predicted anti-cancer compounds were mined from reliable sources like experimental bioassays studies associated with the compound, and from protein-compound interaction databases. Therapeutic compounds from DrugBank, and a list of natural anti-cancer compounds derived from literature mining of published studies, were used for building partial least squares regression model. The regression model thus built, was used for the estimation of oral cancer specific weights based on the molecular targets. These weights were used to compute scores for screening the predicted anti-cancer compounds for their potential to treat oral cancer. The list of potential compounds was annotated with corresponding physicochemical properties, cancer specific bioactivity evidences, and literature evidences. In all, 288 compounds with the potential to treat oral cancer were identified in the current study. The majority of the compounds in this list are natural products, which are well-tolerated and have minimal side-effects compared to the synthetic counterparts. Some of the potential therapeutic compounds identified in the current study are resveratrol, nimbolide, lovastatin, bortezomib, vorinostat, berberine, pterostilbene, deguelin, andrographolide, and colchicine.

Abstract: Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1 approximately 0.93, MCC approximately 0.74, iAUC approximately 0.99) and sentences (F1 approximately 0.76, MCC approximately 0.65, iAUC approximately 0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.

Abstract: We bring together fifteen, nonredundant, tabulated collections (amounting to 696 separate measurements) of the apparent permeability (P app) of Caco-2 cells to marketed drugs. While in some cases there are some significant interlaboratory disparities, most are quite minor. Most drugs are not especially permeable through Caco-2 cells, with the median P app value being some 16 10(-6) cm s(-1). This value is considerably lower than those (1,310 and 230 10(-6) cm s(-1)) recently used in some simulations that purported to show that P app values were too great to be transporter-mediated only. While these values are outliers, all values, and especially the comparatively low values normally observed, are entirely consistent with transporter-only mediated uptake, with no need to invoke phospholipid bilayer diffusion. The apparent permeability of Caco-2 cells to marketed drugs is poorly correlated with either simple biophysical properties, the extent of molecular similarity to endogenous metabolites (endogenites), or any specific substructural properties. In particular, the octanol:water partition coefficient, logP, shows negligible correlation with Caco-2 permeability. The data are best explained on the basis that most drugs enter (and exit) Caco-2 cells via a multiplicity of transporters of comparatively weak specificity.