=Paper=
{{Paper
|id=Vol-538/paper-10
|storemode=property
|title=Enabling Tailored Therapeutics with Linked Data
|pdfUrl=https://ceur-ws.org/Vol-538/ldow2009_paper9.pdf
|volume=Vol-538
}}
==Enabling Tailored Therapeutics with Linked Data==
https://ceur-ws.org/Vol-538/ldow2009_paper9.pdf
Enabling Tailored Therapeutics with Linked Data
Anja Jentzsch Bo Andersson Oktie Hassanzadeh
Freie Universität Berlin AstraZeneca R&D Lund University of Toronto
Web-based Systems Group 221 87 Lund, Sweden Database Group
Garystr. 21 10 King’s College Rd, Toronto, Canada
14195 Berlin, Germany
bo.h.andersson@
astrazeneca.com oktie@cs.toronto.edu
mail@anjajentzsch.de
Susie Stephens Christian Bizer
Eli Lilly and Company Freie Universität Berlin
Lilly Corporate Center Web-based Systems Group
Indianapolis, Indiana 46285, USA Garystr. 21
14195 Berlin, Germany
Stephens_Susie_M@Lilly.com
chris@bizer.de
ABSTRACT that are suitable for preventive and tailored treatment regimes [1,
Advances in the biological sciences are allowing pharmaceutical 2]. This shift requires a more systematic approach to integrating
companies to meet the health care crisis with drugs that are more and interpreting information spanning genes, proteins, pathways,
suitable for preventive and tailored treatment, thereby holding the targets, diseases, drugs, and patients [3]. The amount of publicly
promise of enabling more cost effective care with greater efficacy available data that is relevant for drug discovery has grown
and reduced side effects. However, this shift in business model significantly over recent years [4, 5], and has reached a point
increases the need for companies to integrate data across drug where present tools are no longer effective. Scientists need new
discovery, drug development, and clinical practice. This is a more efficient ways to interrogate data than simply jumping from
fundamental shift from the approach of limiting integration one public data source to another. This is because there are too
activities to functional areas. The Linked Data approach holds many disparate data sources for scientists to conceptualize there
much potential for enabling such connectivity between data silos, relationships and remember that they all exist, let alone mastering
thereby enabling pharmaceutical companies to meet the urgent the different user interfaces and inconsistent terminology. Further,
needs in society for more tailored health care. This paper the prevalence of single query input fields makes it difficult for
examines the applicability and potential benefits of using Linked scientists to retrieve precise information of interest, and to
Data to connect drug and clinical trials related data sources and retrieve data that spans different data sources.
gives an overview of ongoing work within the W3C's Semantic Linked Data has the potential to ease access to these data for
Web for Health Care and Life Sciences Interest Group on scientists and managers by making the connections between the
publishing drug related data sets on the Web and interlinking data sets explicit in the form of data links. This can be
them with existing Linked Data sources. A use case is provided accomplished using RDF as a standardized data representation
that demonstrates the immediate benefit of this work in enabling format, HTTP as a standardized access mechanism, and through
data to be browsed from disease, to clinical trials, drugs, targets the development of algorithms for discovering the links between
and companies. data sets. Such explicit links allow scientists to navigate between
data sets and discover connections they might not have been
Categories and Subject Descriptors aware of previously. The standardized representation and access
H.3.5 [Online Information Services]: Data Sharing mechanisms allow generic tools, such as Semantic Web browsers
and search engines, to be employed to access and process the
data.
General Terms
Experimentation, Languages The Linking Open Drug Data (LODD) task within the W3C's
Semantic Web for Health Care and Life Sciences Interest Group1
gathered a list of data sets that include information about drugs,
Keywords and then determined how the publicly available data sets could be
Linked Data, Semantic Web, Tailored Therapeutics, Drugs, linked together. The review showed that this domain is promising
Clinical Trials, Competitive Intelligence for Linked Data as there are many publicly available data sets,
and they frequently share identifiers for key entities. The
1. INTRODUCTION complete evaluation results are posted on the W3C ESW Wiki2.
The crisis in health care is changing the business model of
Participants of the LODD task have undertaken to demonstrate
pharmaceutical companies to discovering and developing drugs
the value of Linked Data to the health care and life sciences
Copyright is held by the author/owner(s). 1
http://esw.w3.org/topic/HCLSIG/LODD
LDOW2009, April 20, 2009, Madrid, Spain. 2
http://esw.w3.org/topic/HCLSIG/LODD/Data/DataSetEvaluation
domain. This has been achieved by publishing and linking several of disorders, disease genes, and associations between them was
drug related data sets on the Web, and investigating use cases that obtained from the Online Mendelian Inheritance in Man
demonstrate how researchers in life science, as well as physicians (OMIM)7, a compilation of human disease genes and phenotypes.
and patients can take advantage of the connected data sets. The data set is published by Diseasome in a flat file
representation. The flat files were read into a relational database
This paper is structured as follows: Section 2 describes the
and made accessible as Linked Data using D2R server. The
published data sets, their linkage with other published data
Linked Data version of Diseasome contains 88,000 triples and
sources, and the methods that were used to create the links.
23,000 links8.
Section 3 exemplifies how navigating linked data can be utilized
within a competitive intelligence use case. While Section 4 DailyMed9 is published by the National Library of Medicine, and
summarizes our findings and experiences from publishing and provides high quality information about marketed drugs.
navigating the data sets. DailyMed provides much information including general
background on the chemical structure of the compound and its
mechanism of action, details on the clinical pharmacology of the
2. LINKED DATA SETS
compound, indication (disorder) and usage, contraindications,
In this project, data about pharmaceutical companies, drugs in
warnings, precautions, adverse reactions, overdosage, and patient
clinical trials, mechanisms of action of drugs, safety information,
counseling. The data was originally published in Structured
and data about disease gene correlations were added to the Linked
Product Labeling 10 , a XML-based standard for exchanging
Data cloud. This selection of data sets enabled strong connections
medication information that has been recently introduced by the
to existing Linked Data resources, while providing novel data of
Food and Drug Administration in the United States. It was
interest to the pharmaceutical industry. The existing Linked Data
published using the D2R server. The Linked Data version of
of primary interest to this work includes the many bioinformatics
DailyMed contains 124,000 triples and 29,600 links11.
and cheminformatics data sources published by Bio2RDF [6], and
the information on diseases and marketed drugs in DBpedia [7].
The linkage of the newly published data sets to each other and
relevant existing Linked Data is shown in Figure 1.
The Linked Clinical Trials (LinkedCT) data source 3 is derived
from a service provided by U.S. National Institutes of Health,
ClinicalTrials.gov, a registry of more than 60,000 clinical trials
conducted in 158 countries. Each trial is associated with a brief
description, related disorders 4 and interventions, eligibility
criteria, sponsors, locations (investigators), and several other
pieces of information. The data on LinkedCT is obtained by first
transforming the XML data provided by ClinicalTrials.gov to
relational data using the capabilities of a hybrid relational-XML
Relational Database Management System such as IBM DB2. This
transformation requires identification of the entities and facts in
the XML data and storing them in reasonably normalized
relational tables that are appropriate for transformation into RDF. Figure 1. This figure shows the incorporation of
The RDF data is then published using D2R server [8]. The RDF LinkedCT, DailyMed, DrugBank, and Diseasome into the
version of the dataset contains 7,011,000 triples and 290,000 Linked Data cloud. These data are represented in dark
links. gray, while light gray represents other Linked Data from
the life sciences, and white indicates interlinked datasets
DrugBank [9] is a large repository of almost 5000 FDA-approved covering geographic, person-related and conceptual data.
small molecule and biotech drugs. It contains detailed information
about drugs including chemical, pharmacological and
pharmaceutical data; along with comprehensive drug target data There are many commonly used identifiers in the life sciences
such as sequence, structure, and pathway information. The data that can be utilized for making links between data sets explicit.
was originally published as DrugBank DrugCards 5 and was re- Links that were generated based on shared identifiers include the
published as Linked Data using D2R server. The Linked Data connections from LinkedCT to Bio2RDF's PubMed, and from
version of DrugBank contains 1,153,000 triples and 60,300 links6. DrugBank to DBpedia. The connections between bioinformatics
and cheminformatics data sources are already provided by
Diseasome [10] contains information about 4,300 disorders and Bio2RDF allowing us to interlink our drug-related data sets to
disease genes linked by known disorder–gene associations for their work. In cases where no shared identifiers exist, string and
exploring known phenotype and disease gene associations and semantic matching techniques were applied for link discovery
indicating the common genetic origin of many diseases. The list
7
3
http://linkedct.org www.ncbi.nlm.nih.gov/omim
8
4
disorder is used as a synonym for disease and indication, http://www4.wiwiss.fu-berlin.de/diseasome/
9
http://en.wikipedia.org/wiki/Disease#Disorder http://dailymed.nlm.nih.gov/
5 10
http://www.drugbank.ca/fields http://www.fda.gov/oc/datacouncil/SPL.html
6 11
http://www4.wiwiss.fu-berlin.de/drugbank/ http://www4.wiwiss.fu-berlin.de/dailymed/
[11]. Approximate string matching was employed to interlink DrugBank (drug) → drugbank:cas
2,240
LinkedCT and Diseasome, where for instance "Alzheimer's Bio2RDF’s CAS RegistryNumber
disease" in LinkedCT was matched with "Alzheimer_disease" in DrugBank (drug) →
drugbank: hgncId 1,675
Diseasome. Semantic matching is especially useful in matching Bio2RDF’s HGNC
clinical terms as many drugs and diseases have multiple names. DrugBank (drug) → drugbank: kegg
1,331
Drugs tend to have generic names and brand names, for example, Bio2RDF’s KEGG Compound CompoundId
"Varenicline" has the synonym "Varenicline Tartrate" and the DrugBank (drug) → drugbank:kegg
brand names "Champix" and "Chantix". 913
Bio2RDF’s KEGG Drug Drug
DrugBank (drug) →
Table 1. Numbers of outgoing data links from the published drug drugbank: chebiId 736
Bio2RDF’s ChEBI
related data sets.
Diseasome (gene) → diseasome:bio2rdf
9,743
Data set Number of links Bio2RDF’s Symbol Symbol
290,000 links; Diseasome (disease) →
LinkedCT diseasome:omim 2,929
50,000 of them inside the LODD cloud Bio2RDF’s OMIM
23,000 links; Diseasome (gene) →
DrugBank diseasome:hgncId 688
8,500 of them inside the LODD cloud Bio2RDF’s HGNC
29,600 links; Diseasome (gene) →
DailyMed diseasome:geneId 688
all of them inside the LODD cloud Bio2RDF’s GeneID
23,000 links;
Diseasome
8,400 of them inside the LODD cloud
3. COMPETITIVE INTELLIGENCE CASE
STUDY
Table 1 summarizes the number of links from our published data
A use case has been developed that demonstrates the value of
sets to Linked Data within the LODD cloud and beyond. Table 2
Linked Data about drugs to the pharmaceutical industry.
differentiates the number and type of links between data sources
Departments within pharmaceutical companies have typically
and indicates their frequency. A double headed arrow in the first
decided independently which data sets need to be brought into
column indicates that the links are bidirectional, while a single
their organization for integration and interrogation. Access to the
headed arrow indicates unidirectional links.
data is provided to employees based upon their roles. The use
Table 2. Type and frequency of links between the LODD data case describes the value that can be gained by allowing
sets, and between LODD and Bio2RDF. employees to gain access to a more diverse and linked body of
data. This approach enables new and novel questions to be
Source / Target Link Type Count explored. The following use case describes a scenario in
LinkedCT (intervention) ↔ competitive intelligence.
owl:sameAs 27,685
DailyMed (drug)
A neuroscience focused business manager is interested in seeing
LinkedCT (intervention) ↔
owl:sameAs 12,127 an update on new clinical trials that competitors are starting in
DrugBank (drug)
Alzheimer’s Disease (AD). These updates influence future sales
LinkedCT (intervention) ↔
rdfs:seeAlso 8,848 forecasts across geographies, and impact portfolio decisions as
DBpedia (drug)
new drugs needs to demonstrate improved safety and efficacy
LinkedCT (condition) ↔
owl:sameAs 444 compared to the existing pharmacopeia.
DBpedia (disease)
LinkedCT (condition) ↔ Using a Semantic Web browser of choice – for instance
owl:sameAs 301
Diseasome (disease) Tabulator12 or the Marbles data browser13, the manager is able to
LinkedCT (trial) → see all drugs in trials for AD in LinkedCT, including a new phase
foaf:based_near 129,177
Geonames III trial planned by Pfizer for a drug called Varenicline. The
LinkedCT (reference) → business manager can see that more information is available about
owl:sameAs 42,219
Bio2RDF’s PubMed the drug, which is unusual because not much data is typically
LinkedCT (trial) → available for drugs that are under investigation. Following the
foaf:page 61,920
ClinicalTrials.gov data link the manager sees data from DailyMed that shows that
DrugBank (drug) ↔ drugbank:possible the drug is already on the market for nicotine addiction.
8,201
Diseasome (disease) DiseaseTarget
As side effects are better understood for drugs that are already on
DrugBank (drug) ↔ drugbank:branded
1,593 the market, they tend to be more successful in trials. Out of
DailyMed (drug) Drug
curiosity, the manager scrolls down the page to see that side
DrugBank (drug) ↔
owl:sameAs 1,522 effects are listed as constipation, sleeping problems, vomiting,
DBpedia (drug)
nausea, and gas; and that the typical dose is 1mg twice daily. The
DrugBank (drug target) → drugbank: pfam
19,028 dose stated on LinkedCT for the trial was no higher than that, so it
Bio2RDF’s PFAM DomainFunction
is unlikely that this drug will have new safety problems.
DrugBank (drug) → drugbank:enzyme
4,660
Bio2RDF’s UniProt SwissprotId
DrugBank (drug) →
drugbank:iupacId 4,592
Bio2RDF’s IUPAC 12
http://www.w3.org/2005/ajar/tab
DrugBank (drug target) →
drugbank:pdbId 3,379 13
http://beckr.org/marbles
Bio2RDF’s PDB
Given the promising safety profile, the manager is curious to 4. OUTLOOK
discover why a nicotine addiction drug might work for AD. This paper describes the mapping of four drug related data
Linking to DrugBank highlights to the manager that Varenicline sources into the Linked Data cloud, and the ensuing insights that
is an alpha-4 beta-2 neuronal nicotinic acetylcholine receptor can be gained in the area of competitive intelligence. However,
agonist. However, Diseasome indicates that the corresponding this is just the beginning, because more interesting and novel
genes are only important in nicotine addiction, rather than AD. questions will be able to be addressed as additional data sets are
This suggests that there is a more complex relationship between added. As a next step, it would be interesting to incorporate data
the diseases, than just sharing a drug target. Extending the relating to epidemiology, as that could provide information
browsing to the SWAN Knowledgebase14 [12] shows that there relating to geographical areas in which diseases are prevalent, and
are hypotheses relating AD to nicotinic receptors through amyloid where there is a strong need for the development of a drug that
beta [13]. meets the needs of a specific population. It would also be valuable
Using the Linked Data approach a business manager was able to to create links to the AD hypotheses data that is in RDF within the
browse data relating to companies, clinical trials, drugs, diseases SWAN Knowledgebase.
and genetic variation. More specifically, the manager was able to Pharmaceutical companies need to make decisions based upon
determine when extra data was available, gain access to data both internal and external data, it is therefore important that
without needing to map different identifiers and synonyms, and companies begin to make internal data available in a linked
gain additional insights as to interesting questions to ask. representation, both to break down the internal silos and to easily
connect with external data. Such an approach would require
organizations to understand where the linkage points occur across
internal data sets, but this is ongoing work as it is a critical
prerequisite for all data integration efforts relating to the effective
tailoring of drugs.
Currently, when pharmaceutical companies bring copies of data
within their organizations for integration, they each need to have
experts who understand the connectivity across data sets.
However, with the Linked Data approach, this responsibility is
shifted to the data providers. This is a much more efficient
approach, as the data providers are the individuals who
understand the data best. It also means that the integration only
has to happen one time. In addition, it becomes possible for data
providers to incrementally add links to new data sets as they
become aware of their existence, rather than needing to design a
model to do everything in one go. As stated in [14], reasoning and
querying limitations can often be compensated for by integrating
additional data resources.
As the Linked Data cloud grows, focus in pharmaceutical
companies will be moved to approaches for interpretation. One
project with potential to utilize the value from Linked Data is the
Large Knowledge Collider (LarKC), a platform for massive
distributed incomplete reasoning that aims at removing the
scalability barriers of currently existing reasoning systems for the
Semantic Web15.
The Linked Data approach is very promising for the
pharmaceutical industry, and its value will increase as more data
sources become available. However, our technical work as well as
use case experiments revealed various challenges that need to be
mitigated to make this approach robust enough to be deployed
within an enterprise environment:
1. Progress needs to be made in finding links between data
items across data sets where no commonly used identifiers
exist. Discovering such links requires using specific record
Figure 2. Data relating to Varenicline from LinkedCT, linkage [15] and duplicate detection [16] techniques
DrugBank and Diseasome shown within the Marbles data developed within the database community as well as
browser. ontology matching [17] methods from the knowledge
representation literature. Recent work has proposed
frameworks for simplifying this task for RDF data sets [18]
and relational data [11]. In order to benefit from these
14 15
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rules required for finding the links.
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