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Open Access Highly Accessed Database

An integrated pharmacokinetics ontology and corpus for text mining

Heng-Yi Wu1, Shreyas Karnik1, Abhinita Subhadarshini1, Zhiping Wang12, Santosh Philips3, Xu Han134, Chienwei Chiang1, Lei Liu5, Malaz Boustani6, Luis M Rocha7, Sara K Quinney39, David Flockhart123489 and Lang Li1247*

  • * Corresponding author: Lang Li lali@iupui.edu

  • † Equal contributors

Author Affiliations

1 Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA

2 Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN, USA

3 Department of Pharmacology and Toxicology, School of Medicine, Indiana University, Indianapolis, IN, USA

4 Division of Clinical Pharmacology, School of Medicine, Indiana University, Indianapolis, IN, USA

5 Shanghai Center for Bioinformation and Technology, Shanghai, 200235, China

6 Regenstrief Institute, Indianapolis, IN, USA

7 Informatics and Cognitive Science Center for Complex Networks and Systems Research, School of Informatics & Computing, Indianapolis, IN, USA

8 Indiana Institute of Personalized Medicine, Indianapolis, IN, USA

9 Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis, IN, USA

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BMC Bioinformatics 2013, 14:35  doi:10.1186/1471-2105-14-35

Published: 1 February 2013

Abstract

Background

Drug pharmacokinetics parameters, drug interaction parameters, and pharmacogenetics data have been unevenly collected in different databases and published extensively in the literature. Without appropriate pharmacokinetics ontology and a well annotated pharmacokinetics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection from the literature and pharmacokinetics data integration from multiple databases.

Description

A comprehensive pharmacokinetics ontology was constructed. It can annotate all aspects of in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. It covers all drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK-corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK-corpus was demonstrated by a drug interaction extraction text mining analysis.

Conclusions

The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK-corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.