Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug--drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug--ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7--0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.

1. Maulik R. Kamdar, Mark A. Musen
Center for Biomedical Informatics Research
Stanford University
Twitter: @maulikkamdar
Mechanism-Based Pharmacovigilance Over the
Life-Sciences Linked-Open-Data Cloud
Applications of Informatics to Improve Patient Safety

2. What this talk is about ..
• Introduction to the Life-Sciences Linked-Open-Data cloud and
Semantic Web technologies to query data sources in the Cloud.
• Application of Semantic Web technologies and apriori algorithm
for mechanism-based pharmacovigilance.
• Evaluation against two baseline pharmacovigilance methods over
three datasets on drug-adverse reactions associations.
2AMIA 2017 | amia.org

3. What this talk is about ..
• Introduction to the Life-Sciences Linked-Open-Data cloud and
Semantic Web technologies to query data sources in the Cloud.
• Application of Semantic Web technologies and apriori algorithm
for mechanism-based pharmacovigilance.
• Evaluation against two baseline pharmacovigilance methods over
three datasets on drug-adverse reactions associations.
3AMIA 2017 | amia.org

4. Pharmacovigilance
4AMIA 2017 | amia.org Jane P.F. Bai and Darrell R. Abernethy. Annual review of pharmacology and toxicology 53 (2013)
Post-marketing
surveillance for detecting
drug–drug interactions and
adverse reactions
US FDA
Adverse Event
Reporting System

5. Mechanism-Based Pharmacovigilance
5AMIA 2017 | amia.org Jane P.F. Bai and Darrell R. Abernethy. Annual review of pharmacology and toxicology 53 (2013)

6. 6AMIA 2017 | amia.org
The Walled
Garden of
Biomedical
Dataspace
Isolated databases
and knowledge bases

7. Linked-Open-
Data Cloud
7AMIA 2017 | amia.org
www.lod-cloud.net

8. Life-Sciences
Linked-Open-Data Cloud
8AMIA 2017 | amia.org

9. Life-Sciences
Linked-Open-Data Cloud
9AMIA 2017 | amia.org
…

10. Life-Sciences
Linked-Open-Data Cloud
10AMIA 2017 | amia.org
…

11. Resource Description Framework (RDF)
11AMIA 2017 | amia.org

12. Resource Description Framework (RDF)
12AMIA 2017 | amia.org
Gleevec (Mol. Wt.: 589.25 g/mol,
Half-Life: 18 hours) inhibits PDGFR,
involved in signal transduction.

13. Resource Description Framework (RDF)
13AMIA 2017 | amia.org
Gleevec (Mol. Wt.: 589.25 g/mol,
Half-Life: 18 hours) inhibits PDGFR,
involved in signal transduction.
v

14. Resource Description Framework (RDF)
14AMIA 2017 | amia.org
Gleevec (Mol. Wt.: 589.25 g/mol,
Half-Life: 18 hours) inhibits PDGFR,
involved in signal transduction.
v

15. SPARQL Graph Query Language
15AMIA 2017 | amia.org

16. SPARQL Graph Query Language
16AMIA 2017 | amia.org
What are the half-lives of drugs that have
Mol. Wt < 1000 g/mol and inhibit proteins
involved in signal transduction?

17. SPARQL Graph Query Language
17AMIA 2017 | amia.org
What are the half-lives of drugs that have
Mol. Wt < 1000 g/mol and inhibit proteins
involved in signal transduction?

18. SPARQL Graph Query Language
18AMIA 2017 | amia.org
What are the half-lives of drugs that have
Mol. Wt < 1000 g/mol and inhibit proteins
involved in signal transduction?

19. The story so far …
• Systems Pharmacology networks for mechanism-based
pharmacovigilance requires integration of entities and
relations from multiple sources.
• Semantic Web technologies and Linked-open-data can
be used to develop methods for querying and integrating
data and knowledge from isolated sources.
19AMIA 2017 | amia.org

20. What this talk is about ..
• Introduction to the Life-Sciences Linked-Open-Data cloud and
Semantic Web technologies to query data sources in the Cloud.
• Application of Semantic Web technologies and apriori algorithm
for mechanism-based pharmacovigilance.
• Evaluation against two baseline pharmacovigilance methods over
three datasets on drug-adverse reactions associations.
20AMIA 2017 | amia.org

21. PhLeGrA: Linked
Graph Analytics in
Pharmacology
21AMIA 2017 | amia.org Kamdar MR, et al. International Conference on World Wide Web (WWW) (2017)
Life Sciences Linked
Open Data Cloud
PhLeGrA Query Federation
Mapping
Rules
Data
Model
Queries

22. Systems
Pharmacology
network of
drugs, proteins,
pathways and
phenotypes
22AMIA 2017 | amia.org
Life Sciences Linked
Open Data Cloud
PhLeGrA Query Federation
Mapping
Rules
Data
Model
Drug Protein Pathway
Adverse
Reaction
Queries

23. Graph analytics
to rank the
mechanisms
- Uses Network-based
Apriori Algorithm
23AMIA 2017 | amia.org
Life Sciences Linked
Open Data Cloud
PhLeGrA Query Federation
Mapping
Rules
Data
Model
Drug Protein Pathway
Adverse
Reaction
Graph
Analytics
Module
Queries

24. Network-based Apriori Algorithm
24AMIA 2017 | amia.org Harpaz, et al. 2010, Inokuchi, et al. 2000
FDA Adverse Event Reporting System: 2013 - 2015
3 million case reports with
Drugs, Adverse Reactions, Indications, Doses etc.

25. Network-based Apriori Algorithm
Association: {Drug}n --> ADR
• Support statistic: Filtering nodes and paths.
• Network-based Relative Reporting Ratio statistic: Predicting if an
association exists.
• Confidence statistic: Ranking underlying mechanisms.
25AMIA 2017 | amia.org Harpaz, et al. 2010, Inokuchi, et al. 2000
FDA Adverse Event Reporting System: 2013 - 2015
3 million case reports with
Drugs, Adverse Reactions, Indications, Doses etc.

26. Evaluation of the approach
“Silver” standard datasets on drug-adverse reaction associations:
• Observational Medical Outcomes Partnership (OMOP)
• Exploring and Understanding Adverse Drug Reactions (EU-ADR)
• Drugs.com and MediSpan Drug-drug interactions (Iyer, et al. 2014)
Methods for comparison:
• Bayesian Confidence Propagation Neural Network (BCPNN)
• Gamma Poisson Shrinkage (GPS)
26AMIA 2017 | amia.org
Dataset Unique Drugs Unique ADRs Positive
Associations
Negative
Associations
OMOP 155 4 137 158
EU-ADR 59 9 44 39
Iyer, et al. 252 9 315 288

27. Evaluation of the approach
“Silver” standard datasets on drug-adverse reaction associations:
• Observational Medical Outcomes Partnership (OMOP)
• Exploring and Understanding Adverse Drug Reactions (EU-ADR)
• Drugs.com and MediSpan Drug-drug interactions (Iyer, et al. 2014)
Methods for comparison:
• Bayesian Confidence Propagation Neural Network (BCPNN)
• Gamma Poisson Shrinkage (GPS)
27AMIA 2017 | amia.org

28. To summarize …
• We use PhLeGrA platform to query four sources –
DrugBank, KEGG, PharmGKB and CTD, to generate a
Systems Pharmacology network.
• We use the FAERS datasets, in conjunction with the
network, to predict drug-adverse reaction associations
and rank the underlying biological mechanisms.
28AMIA 2017 | amia.org

29. What this talk is about ..
• Introduction to the Life-Sciences Linked-Open-Data cloud and
Semantic Web technologies to query data sources in the Cloud.
• Application of Semantic Web technologies and apriori algorithm
for mechanism-based pharmacovigilance.
• Evaluation against two baseline pharmacovigilance methods over
three datasets on drug-adverse reactions associations.
29AMIA 2017 | amia.org

30. 30AMIA 2017 | amia.org
Systems
Pharmacology
Network
http://onto-apps.stanford.edu/phlegra
Entity Type Count
Drug 2,759
Protein 19,903
Pathway 309
Phenotype 3,890

31. 31AMIA 2017 | amia.org
AUROC Statistics
Dataset BCPNN GPS Network-based RRR
OMOP 0.70 0.70 0.72
EU-ADR 0.75 0.76 0.78
Iyer, et al. 0.81 0.83 0.82
Network-based
Apriori method
has comparable
performance
over 3 datasets

32. Event-wise
thresholds on
Network-based
RRR statistic
generate better
AUROCs for
certain adverse
drug reactions.
32AMIA 2017 | amia.org

33. Conclusion
• Life-Sciences Linked-Open-Data Cloud and Semantic Web query federation
methods can generate systems pharmacology networks from multiple
distributed data and knowledge sources.
• Comparable performance on AUROC with existing methods that are used
to detect signals in US FAERS datasets for pharmacovigilance.
• Event-specific thresholds can lead to an AUROC statistic > 0.75 for
predicting more than 146 Adverse reactions.
• Mechanism-based pharmacovigilance with confidence statistics for
underlying mechanisms.
33AMIA 2017 | amia.org

34. Acknowledgments
Musen Lab, Stanford
BMI PhD Program, Stanford
Michel Dumontier
Rainer Winnenberg
Juan Banda
Erik Van Mulligen
US NIH Grant - HG004028
http://onto-apps.stanford.edu/phlegra
34AMIA 2017 | amia.org

35. Thank you!
Email me at:
maulikrk@stanford.edu