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

Abstract

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 2 54 173 toxicogenomic interactions (1 52 173 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/

DISEASES: Text mining and data integration of disease–gene associations

“…The oldest and most famous of databases is Online Mendelian Inheritance in Man (OMIM; http://omim.org/), which we provide hyperlinks to for both genes and diseases. More recent efforts include the Human Gene Mutation Database (HGMD) [30], the Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/) [31,32], and GHR (http:// ghr.nlm.nih.gov/) [5]. In addition to these dedicated disease-gene association databases, UniProtKB also annotates diseases associated with each gene [6].…”

Analysis of expression profile data identifies key genes and pathways in hepatocellular carcinoma

“…The chem- (30). The chem- (30). The chemical-gene interaction data for HCC were obtained from the CTD database to investigate the therapeutic efficacy of several drugs.…”

The Proliferation of “Big Data” and Implications for Official Statistics and Statistical Agencies

“…Thanks to these types of methods, insights were possible which otherwise would have been difficult to discover. One example is the discovery of adverse effects to drugs (Gurulingappa et al, 2013;Davis et al 2013). …”