Pharmacological Class Data RepICreBsOen2t0a1t4ioPnroicneetdhinegWseb Ontology Language (OWL)

RESULTS

A total of 5,717 ATC drug entities are included in this study, which corresponds to 4,483 distinct ATC terms. That is, one drug can be categorized into multiple therapeutic classes. Of the 48,266 NDF-RT concepts, 34,011 concepts were used in this study, consisting of 15,857 VA products, 486 VA classes, 9,960 Chemical/Ingredients, 7,184 Generic Ingredient Combinations, and 524 EPCs. The PCPO, which can be accessed at https://sourceforge.net/projects/PCPO/, currently contains 58,241 OWL classes, 98,677 subclass axioms, and 21,917 equivalent class axioms. It has defined 178,838 axioms with 120,594 logical axioms.

DISCUSSION AND CONCLUSIONS

We successfully integrated NDF-RT, ATC, RxNorm, and SPL and built PCPO for representing drug and drug class entities. In addition, the ontology was expanded from a chemical structure perspective by introducing chemical similarity calculation. PCPO supports automated reasoning, which can ultimately be applied for drug repositioning by identifying alternative drugs for a particular disease through drug-drug associations inference. To expand the coverage and usage of PCPO, other drug terminological resources and drug interaction information will be integrated in the future. Pharmacological Class Data Representation in the Web Ontology Language (OWL)

INTRODUCTION

Dozens of drug terminologies and resources capture drug and/or drug class information; they range greatly in their coverage and their adequacy of representation.

In this study, we generated a standardized

Pharmacological Class Profile Ontology, named PCPO, which integrates multiple drug resources in the Web Ontology Language (OWL). PCPO will not only present a large volume of drug data in a well-organized formal form, OWL with possible inference capability, but also potentially support computational drug repurposing application development.

ACKNOWLEDGEMENT

WSe thank Pradip P. Kanjamalafor IT support.

This work was partially supported by the

Cancer Prevention & Research Institute of

Texas (CPRIT R1307).

METHODS

A. PCPO Development We generated the PCPO by linking drug concepts from different drug resources in two layers, drug class layer and individual drug layer. For drug concept mappings among RxNorm, SPL, and NDF-RT, we directly extracted these mappings from 2 RxNorm files, RXNCONSO and RXNREL. Figure 1 shows the workflow of the PCPO generation, along with relationships expressed in the PCPO.

B. PCPO Representation in OWL

We defined a new OWL class for each unique drug term in the mappings generated

previously. For mappings between ATC and NDF-RT, we first created a new OWL class with a PCPO URI (Uniform Resource Identifier) for each unique ATC drug entity. This

OWL class will then be defined as an equivalent class of the corresponding mapped

NDF-RT OWL classes of this ATC class to indicate the mapping between ATC and NDF

RT. For NDF-RT to RxNorm mappings, we specified that an RxNorm concept (OWL class) is a subclass of a mapped NDF-RT concept (OWL class) accordingly. This way, the PCPO contains higher-level drug classifications derived from both ATC and NDF-RT and lower-level information about individual clinical drugs from RxNorm. We represented structural similarity between pairs of drugs in the PCPO. Because OWL only supports binary relationships, we followed the World Wide Web Consortium guidelines [ 4 ] and introduced a new class called “Similarity” to represent the target drug and the corresponding similarity score.

RESULTS

DISCUSSION AND CONCLUSION •  We successfully integrated NDF-RT, ATC, RxNorm, and SPL and built PCPO for representing drug and drug class entities. •  PCPO was expanded from chemical perspective by introducing chemical similarity calculation. PCPO supports automated reasoning, which can ultimately be applied for drug repositioning by identifying alternative drugs for a particular disease through drug-drug associations inference. •  To expand the coverage and usage of PCPO, other drug terminological resources and drug interaction information will e integrated in the future.

References

1. Croset S , Hoehndorf R , RebholzSchuhmann D. OWL representation of the anatomical therapeutic chemical classification system and mapping to DrugBank . 4th Workshop on Ontologies in Biomedicine and Life Sciences; 2012 Sep 27-28.

2. NDF-RT: http://evs.nci.nih.gov/ftp1/NDFRT/.

3. Noy NF , Shah NH , Whetzel PL , Dai B , Dorf M , Griffith N , et al. BioPortal: ontologies and integrated data resources at the click of a mouse . Nucleic Acids Res . 2009 .

4. Noy N , Rector A . Defining N-ary relations on the semantic web [Internet] . W3C Working Group Note 12 April 2006 . Available from: http://www.w3.org/TR/swbp-n-aryRelations/.

A.   5 , 717 ATC drug entities are included in this study, which corresponds to 4,483 distinct ATC terms . Of the 48,266 NDF-RT concepts , 34 , 011 concepts were used in this study , consisting of 15 ,857 VA products, 486 VA classes , 9 ,960 Chemical/Ingredients, 7,184 Generic Ingredient Combinations, and 524 EPCs.

B.  The PCPO , which can be accessed at https:// sourceforge.net/projects/PCPO/, currently contains 58 ,241 OWL classes, 98 ,677 subclass axioms, and 21 ,917 equivalent class axioms. It has defined 178 , 838 axioms with 120,594 logical axioms .