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Mechanism-Based Pharmacovigilance Over the Life-Sciences Linked-Open-Data Cloud

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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.

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Mechanism-Based Pharmacovigilance Over the Life-Sciences Linked-Open-Data Cloud

  1. 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. 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. 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. 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. 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. 6. 6AMIA 2017 | amia.org The Walled Garden of Biomedical Dataspace Isolated databases and knowledge bases
  7. 7. Linked-Open- Data Cloud 7AMIA 2017 | amia.org www.lod-cloud.net
  8. 8. Life-Sciences Linked-Open-Data Cloud 8AMIA 2017 | amia.org
  9. 9. Life-Sciences Linked-Open-Data Cloud 9AMIA 2017 | amia.org …
  10. 10. Life-Sciences Linked-Open-Data Cloud 10AMIA 2017 | amia.org …
  11. 11. Resource Description Framework (RDF) 11AMIA 2017 | amia.org
  12. 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. 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. 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. 15. SPARQL Graph Query Language 15AMIA 2017 | amia.org
  16. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 32. Event-wise thresholds on Network-based RRR statistic generate better AUROCs for certain adverse drug reactions. 32AMIA 2017 | amia.org
  33. 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. 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. 35. Thank you! Email me at: maulikrk@stanford.edu

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