Disambiguation of PharmGKB drug-disease relations with NDF-RT and SPL.

PharmGKB is a leading resource of high quality pharmacogenomics data that provides information about how genetic variations modulate an individual's response to drugs. PharmGKB contains information about genetic variations, pharmacokinetic and pharmacodynamic pathways, and the effect of variations on drug-related phenotypes. These relationships are represented using very general terms, however, and the precise semantic relationships among drugs, and diseases are not often captured. In this paper we develop a protocol to detect and disambiguate general clinical associations between drugs and diseases using more precise annotation terms from other data sources. PharmGKB provides very detailed clinical associations between genetic variants and drug response, including genotype-specific drug dosing guidelines, and this procedure will armGKB. The availability of more detailed data will help investigators to conduct more precise queries, such as finding particular diseases caused or treated by a specific drug. We first mapped drugs extracted from PharmGKB drug-disease relationships to those in the National Drug File Reference Terminology (NDF-RT) and to Structured Product Labels (SPLs). Specifically, we retrieved drug and disease role relationships describing and defining concepts according to their relationships with other concepts from NDF-RT. We also used the NCBO (National Center for Biomedical Ontology) annotator to annotate disease terms from the free text extracted from five SPL sections (indication, contraindication, ADE, precaution, and warning). Finally, we used the detailed drug and disease relationship information from NDF-RT and the SPLs to annotate and disambiguate the more general PharmGKB drug and disease associations.

Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: a case study.

The Pharmacogenomics Research Network (PGRN) is a collaborative partnership of research groups funded by NIH to discover and understand how genome contributes to an individual's response to medication. Since traditional biomedical research studies and clinical trials are often conducted independently, common and standardized representations for data are seldom used. This leads to heterogeneity in data representation, which hinders data reuse, data integration and meta-analyses. This study demonstrates harmonization and semantic annotation work for pharmacogenomics data dictionaries collected from PGRN research groups. A semi-automated system was developed to support the harmonization/annotation process, which includes four individual steps, (1) pre-processing PGRN variables; (2) decomposing and normalizing variable descriptions; (3) semantically annotating words and phrases using controlled terminologies; (4) grouping PGRN variables into categories based on the annotation results and semantic types, for total 1514 PGRN variables. Our results demonstrate that there is a significant amount of variability in how pharmacogenomics data is represented and that additional standardization efforts are needed. This represents a critical first step toward identifying and creating data standards for pharmacogenomics studies.

Integrating VA's NDF-RT drug terminology with PharmGKB: preliminary results.

Biomedical terminology and vocabulary standards play an important role in enabling consistent, comparable, and meaningful sharing of data within and across institutional boundaries, as well as ensuring semantic interoperability. The Veterans Affairs (VA) National Drug File Reference Terminology (NDF-RT) is a federally recommended standardized terminology resource encompassing medications, ingredients, and a hierarchy for high-level drug classes. In this study, we investigate the drug-disease relationships in NDF-RT and determine how PharmGKB can be leveraged to augment NDF-RT, and vice-versa. Our preliminary results indicate that with additional curation and analyses, information contained in both knowledge resources can be mutually integrated.