A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions.

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88,629 articles relating over 1,200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 254,173 toxicogenomic interactions (152,173 chemical-disease, 58,572 chemical-gene, 5,345 gene-disease and 38,083 phenotype interactions). All chemical-gene-disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer's text-mining process to collate the articles, and CTD's curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug-disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades' worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/

Comparing bioassay response and similarity ensemble approaches to probing protein pharmacology.

MOTIVATION: Networks to predict protein pharmacology can be created using ligand similarity or using known bioassay response profiles of ligands. Recent publications indicate that similarity methods can be highly accurate, but it has been unclear how similarity methods compare to methods that use bioassay response data directly. RESULTS: We created protein networks based on ligand similarity (Similarity Ensemble Approach or SEA) and ligand bioassay response-data (BARD) using 155 Pfizer internal BioPrint assays. Both SEA and BARD successfully cluster together proteins with known relationships, and predict some non-obvious relationships. Although the approaches assess target relations from different perspectives, their networks overlap considerably (40% overlap of the top 2% of correlated edges). They can thus be considered as comparable methods, with a distinct advantage of the similarity methods that they only require simple computations (similarity of compound) as opposed to extensive experimental data. CONTACTS: djwild@indiana.edu; eric.gifford@pfizer.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Expression profiling using a hexamer-based universal microarray.

We describe a transcriptional analysis platform consisting of a universal micro-array system (UMAS) combined with an enzymatic manipulation step that is capable of generating expression profiles from any organism without requiring a priori species-specific knowledge of transcript sequences. The transcriptome is converted to cDNA and processed with restriction endonucleases to generate low-complexity pools (approximately 80-120) of equal length DNA fragments. The resulting material is amplified and detected with the UMAS system, comprising all possible 4,096 (4(6)) DNA hexamers. Ligation to the arrays yields thousands of 14-mer sequence tags. The compendium of signals from all pools in the array-of-universal arrays comprises a full-transcriptome expression profile. The technology was validated by analysis of the galactose response of Saccharomyces cerevisiae, and the resulting profiles showed excellent agreement with the literature and real-time PCR assays. The technology was also used to demonstrate expression profiling from a hybrid organism in a proof-of-concept experiment where a T-cell receptor gene was expressed in yeast.

Assessment of the molecular dynamics structure of DNA in solution based on calculated and observed NMR NOESY volumes and dihedral angles from scalar coupling constants.

To assess the accuracy of the molecular dynamics (MD) models of nucleic acids, a detailed comparison between MD-calculated and NMR-observed indices of the dynamical structure of DNA in solution has been carried out. The specific focus of our comparison is the oligonucleotide duplex, d(CGCGAATTCGCG)(2), for which considerable structural data have been obtained from crystallography and NMR spectroscopy. An MD model for the structure of d(CGCGAATTCGCG)(2) in solution, based on the AMBER force field, has been extended with a 14 ns trajectory. New NMR data for this sequence have been obtained in order to allow a detailed and critical comparison between the calculated and observed parameters. Observable two-dimensional (2D) nuclear Overhauser effect spectroscopy (NOESY) volumes and scalar coupling constants were back-calculated from the MD trajectory and compared with the corresponding NMR data. The comparison of these results indicate that the MD model is in generally good agreement with the NMR data, and shows closer accord with experiment than back-calculations based on the crystal structure of d(CGCGAATTCGCG)(2) or the canonical A or B forms of the sequence. The NMR parameters are not particularly sensitive to the known deficiency in the AMBER MD model, which is a tendency toward undertwisting of the double helix when the parm.94 force field is used. The MD results are also compared with a new determination of the solution structure of d(CGCGAATTCGCG)(2) using NMR dipolar coupling data.