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Changing the Model in Pharma and Healthcare – Can We Afford to Wait Any Longer?

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Book cover Data Integration in the Life Sciences (DILS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 7970))

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Abstract

Innovations in healthcare delivery and Pharma require re-examination of process models at the foundation of our knowledge discovery and clinical practice. Despite real-time availability of ‘big data’ from ubiquitous sensors, mobile devices, 3D printing of drugs, and a mind shift in data ownership, data integration still remains one of the core challenges to innovation. Increasingly persistent, semantic data integration is gaining recognition for its dynamic data model and formalisms which make it possible to infer from and reason over interconnected contextualized data, creating actionable knowledge faster and at lower cost. While such technical advances underpin the successful strategies to drive positive patient outcomes or accelerate drug design, there are equally profound social changes towards the willingness of patients to share their own data - opening doors to new patient-centric, precision-medicine healthcare models. Adding astronomically rising costs in research and healthcare, we have arrived at a critical turning point where it is now well within our reach to change how drugs are developed, how trials are performed and how patients are treated - and we can do this with huge benefits for otherwise unsustainable industries. Examples show that not only is this possible today, but that such approaches already have traction; (i) in Pharma for assessing impact of excipient on drug stability and efficacy; for pre-clinical toxicity assessment and integral systems views on drug safety, (ii) in Government at the FDA’s cross species biomarker initiative to reduce animal testing and (iii) in Health Care for organ transplant rejection assessment and COPD. Using comparative effectiveness and side effect analyses to base treatments on solid prognoses and therapy decision support, we can and must change discovery and healthcare into a data driven and patient centric paradigm. The socio-economic benefits of such a change will be enormous.

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References

  1. Technology survey on LIMS and ELN in Life Sciences. Project Share Biotech, University of Nantes, France (2011), http://www.biogenouest.org/sites/default/files/Biogenouest/Fichiers/qualite/lims_eln_sharebiotech_study_report.pdf

  2. OECD health systems 2012 (2012), http://www.oecd.org/health/health-systems/oecdhealthdata2012.htm

  3. Richter, B.G., Sexton, D.P.: Managing and Analyzing Next-Generation Sequence Data. PLoS Comput. Biol. 5(6), e1000369 (2009), doi:10.1371/journal.pcbi.1000369, http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369#s5

  4. Wearable wireless health sensor for remote bio-monitoring. Gizmag (2010)

    Google Scholar 

  5. Sensors facilitate health monitoring. Sensor Magazine (2011), http://www.sensorsmag.com/specialty-markets/medical/sensors-facilitate-health-monitoring-8365

  6. 10 wearable health tech devices to watch. Information Week (2012), http://www.informationweek.com/healthcare/mobile-wireless/10-wearable-health-tech-devices-to-watch/240012613

  7. Mobile ECG monitor via iPhone. AliveCor (2011), http://www.alivecor.com/?gclid=CPP9yqyL-LUCFchaMgodg3QANA

  8. fitbit one – Wireless activity monitor. Fitbit (2011), http://www.fitbit.com/one/gallery

  9. Sanjeev, A., Boaz, B.: Computational Complexity – A Modern Approach, Cambridge (2009) ISBN 978-0-521-42426-4

    Google Scholar 

  10. Zax, D.: MIT Technology Review, November 2011: Is Personal Data the New Currency? (2011), http://www.technologyreview.com/view/426235/is-personal-data-the-new-currency/

  11. Ohlhorst, F.J.: Big Data Analytics: Turning Big Data into Big Money. Wiley (2012) ISBN: 978-1-118-14759-7, http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118147596.html

  12. Dean, M.: Crowdsourcing as Healthcare Policy Pros&Cons. The Nation (2013), http://www.thenation.com/blog/174059/crowdsourcing-health-care-policy-cant-we-do-better

  13. Engelen, L.: Crowdsource your health. Video on TED.com (2012), http://www.ted.com/talks/lucien_engelen_crowdsource_your_health.html

  14. Davis, K.: The $1,000 Genome. The Revolution in DNA Sequencing and the New Era of Personalized Medicine. Free Press (2010) ISBN 9781416569596

    Google Scholar 

  15. Mardis, E.: The $1,000 genome, the $100,000 analysis? Genome Medicine 2, 84 (2010)

    Article  Google Scholar 

  16. Herper, M.: The truly staggering costs of inventing new drugs. Forbes (2012), http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/

  17. PBS, 2012: Healthcare costs: How the US compares with other countries (2012), http://www.pbs.org/newshour/rundown/2012/10/health-costs-how-the-us-compares-with-other-countries.html

  18. Evangelista, E.: Per patient clinical trial costs rise 70% in three years. Marketwire (2011), http://www.marketwire.com/press-release/per-patient-clinical-trial-costs-rise-70-in-three-years-1538269.htm

  19. OECD Health Statistics: Data Dissemination and Results (2012), http://www.oecd.org/els/health-systems/Item%202_OECD%20Health%20Data_Dissemination%20and%20Results_MCC_Final.pdf

  20. Harvard Journal of Law and Technology: 25(1) 2011: Much Ado about Data Ownership (2011), http://jolt.law.harvard.edu/articles/pdf/v25/25HarvJLTech69.pdf

  21. Resource Description Framework (RDF): Concepts and Abstract Syntax (2004) W3C Recommendation (February 10, 2004), http://www.w3.org/TR/rdf-concepts/

  22. OWL 2 Web Ontology Language - Document Overview (2nd edn.) (2012) W3C Recommendation (December 11, 2012), http://www.w3.org/TR/owl2-overview/

  23. Cyganiak, R., Zhao, J., Alexander, K., Hausenblas, M.: VoID Vocabulary of Interlinked Datasets. DERI, W3C note (2011)

    Google Scholar 

  24. PROV-O: The PROV Ontology. W3C Candidate Recommendation (2012)

    Google Scholar 

  25. Musen, M.A., Noy, N.F., Shah, N.H., Whetzel, P.L., Chute, C.G., Story, M.A., Smith, B.: The National Center for Biomedical Ontology. J. Am. Med. Inform. Assoc. 19(2), 190–195 (2012)

    Article  Google Scholar 

  26. Salvadores, M., Horridge, M., Alexander, P.R., Fergerson, R.W., Musen, M.A., Noy, N.F.: Using SPARQL to Query BioPortal Ontologies and Metadata. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 180–195. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  27. Luciano, J.S., Andersson, B., Batchelor, C., Bodenreider, O., Clark, T., Denney, C.K., Domarew, C., Gambet, T., Harland, L., Jentzsch, A., Kashyap, V., Kos, P., Kozlovsky, J., Lebo, T., Marshall, S.M., McCusker, J.P., McGuinness, D.L., Ogbuji, C., Pichler, E., Powers, R.L., Prud’hommeaux, E., Samwald, M., Schriml, L., Tonellato, P.J., Whetzel, P.L., Zhao, J., Stephens, S., Dumontier, M.: The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside. J. Biomed. Semantics 2(suppl. 2), S1 (2011)

    Google Scholar 

  28. Connecting medical informatics and bioinformatics: Advances in knowledge acquisition and management. Mayo Clinic’s Enterprise Data Trust. JAMIA (2010), http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3000789/

  29. Podgorelec, V., Grašič, B., Pavlič, L.: Medical diagnostic process optimization through the semantic integration of data resources: New opportunities for automated reasoning at patient-data level: Medical diagnostic process optimization through the semantic integration of data resources. Computer Methods and Programs in Biomedicine (2009), http://www.sciencedirect.com/science/article/pii/S0169260709000832

  30. Linked Life Data http://linkedlifedata.com/sources LLD: Instance mapping, Predicate mapping; 1.1: no of statements: 8,740,201,002;EU IST project

  31. Linked Open Data http://linkeddata.org/ LOD Cloud Diagram: http://lod-cloud.net/ LDOW2012 Linked Data on the Web. Bizer, C., Heath, T., Berners-Lee, T., Hausenblas, M.: WWW Workshop on Linked Data on the Web, Lyon, France (April 16, 2012)

  32. Callahan, A., Cruz-Toledo, J., Ansell, P., Klassen, D., Tumarello, G., Dumontier, M.: Improved dataset coverage and interoperability with Bio2RDF Release 2. In: SWAT4LS Workshop, Paris, France, November 30 (2012)

    Google Scholar 

  33. 13 healthcare IT trends in 2013, http://www.cio.com/slideshow/detail/83055

  34. Plasterer, T.N., Stanley, R., Gombocz, E.: Correlation Network Analysis and Knowledge Integration. In: Dehmer, M., Emmert-Streib, F., Graber, A., Salvador, A. (eds.) Applied Statistics for Network Biology: Methods in Systems Biology. Wiley-VCH, Weinheim (2011) ISBN: 978-3-527-32750-8

    Google Scholar 

  35. Is healthcare IT interoperability (almost) here? HIMSS13, http://www.cio.com/article/731757/Is_Healthcare_IT_Interoperability_Almost_Here_

  36. Ernst & Young: The third place: healthcare everywhere – Global Life Sciences Report (2012), http://www.ey.com/Publication/vwLUAssets/Progressions_The_third_place:_health_care_everywhere_-_Global_Life_Sciences_Report_2012/$FILE/Progressions_Global_Life_Sciences_Report_2012_The_third_place_health_care_everywhere.PDF

    Google Scholar 

  37. Gombocz, E.A., Higgins, A.J., Hurban, P., Lobenhofer, E.K., Crews, F.T., Stanley, R.A., Rockey, C., Nishimura, T.: Does network analysis of integrated data help understanding how alcohol affects biological functions? - Results of a semantic approach to biomarker discovery. 2008 Biomarker Discovery Summit 2008, Philadelphia, PA (2008)

    Google Scholar 

  38. Higgins, A.J., Gombocz, E.A., Stanley, R.A.: From Correlation to Biological Understanding: Multi-modal semantic networks for biomarker discovery and qualification. In: Bio-IT World 2008, Boston, MA (2008), http://www.io-informatics.com/news/pdfs/CHI_BioIT2008_Talk.pdf

  39. Gombocz, E., Stanley, R.: Predictive Toxicology: Applied Semantics with a major impact on drug safety. In: FDA Drug Safety Workshop: Pharmacological Mechanism-Based Drug Safety Assessment and Prediction FDA, White Oak Campus, Silver Spring, MD (2011)

    Google Scholar 

  40. Gombocz, E., Candlin, J.: A Novel Approach to Recognize Peptide Functions in Microorganisms: Establishing Systems Biology-based Relationship Networks to Better Understand Disease Causes and Prevention. In: 8th Annual Conference US Human Proteome Organization: The Future of Proteomics (HUPO 2012), San Francisco, CA (2012)

    Google Scholar 

  41. Gombocz, E., Candlin, J.: How semantic technology helps fighting infectious diseases: Biological systems approach to understand microbial pathogens. In: Semantic Technology & Businesss Conference (SemTech 2012), San Francisco (2012), http://www.io-informatics.com/news/pdfs/SemTech2012_EGombocz_Talk20120606_r3.pdf

  42. Gombocz, E., Candlin, J., Stanley, R., Chiang, D.: Semantically Enhancing Protein Identification: Systems Biology Knowledgebase for Infectious Disease Screening. In: Bio-IT World 2012, Boston, MA (2012), http://www.io-informatics.com/news/pdfs/BioIT2012_Poster.pdf

  43. A new way to predict and diagnose organ rejection. Transplant Foundation Research of British Columbia (2010), http://www.trfbc.org/site/PageServer?pagename=News_Biomarkers

  44. Bio-IT World Best Practices Award 2010 in Personalized & Translational Medicine: PROOF / iCAPTURE Centre of Excellence; Semantic Data Integration, Knowledge Building and Sharing Applied to Biomarker Discovery and Patient Screening for Pre-symptomatic Heart, Lung or Kidney Failure in Transplantation Medicine (2010), http://www.prweb.com/releases/2010/04/prweb3917414.htm

  45. Ng, R.T., Gombocz, E.: Biomarker Development to Improve Decision Support for the Treatment of Organ Failures: How Far Are We Today? In: ADAPT 2010, Arlington, VA (2010), http://www.io-informatics.com/news/pdfs/ADAPT2010_Talk.pdf

  46. Stanley, R., McManus, B., Ng, R., Gombocz, E., Eshleman, J., Rockey, C.: W3C Semantic Web Use Cases and Case Studies Case Study: Applied Semantic Knowledgebase for Detection of Patients at Risk of Organ Failure through Immune Rejection. Joint Case Study of IO Informatics and University British Columbia (UBC), NCE CECR PROOF Centre of Excellence, James Hogg iCAPTURE Centre, Vancouver, BC, Canada (2011)

    Google Scholar 

  47. Global Initiative for Chronic Obstructive Lung Disease, Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease (2009) (updated), http://www.goldcopd.com/Guidelineitem.asp?l1=2&l2=1&intId=2003

  48. Murray, C.J., Lopez, A.D.: Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet. 349(9064), 1498–1504 (1997)

    Article  Google Scholar 

  49. Jemal, A., Ward, E., Hao, Y., Thun, M.: Trends in the leading causes of death in the United States, 1970-2002. JAMA 294(10), 1255–1259 (2005)

    Article  Google Scholar 

  50. Buist, A.S., McBurnie, M.A., Vollmer, W.M.: International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet. 370(9589), 741–750 (2007)

    Article  Google Scholar 

  51. Mathers, C.D., Loncar, D.: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3(11), e442 (2006)

    Google Scholar 

  52. Mittmann, N., Kuramoto, L., Seung, S.J., Haddon, J.M., Bradley-Kennedy, C., Fitzgerald, J.M.: The cost of moderate and severe COPD exacerbations to the Canadian healthcare system. Respir. Med. 102(3), 413–421 (2008)

    Article  Google Scholar 

  53. Gombocz, E.A.: On the road to production: Semantic integration cases indicate successful adoption to improve knowledge-based decisions in Pharma and healthcare NCBO Webinar Series, Stanford, CA. Recording (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. IO Informatics Inc., Berkeley, California, USA

    Erich Alfred Gombocz

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  1. Erich Alfred Gombocz
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Editors and Affiliations

  1. Department of Computer Science and Applied Statistics, University of New Brunswick, E2L 4L5, Saint John, NB, Canada

    Christopher J. O. Baker

  2. Department of Computer Science, Concordia University, H3G 1M8, Montreal, QC, Canada

    Greg Butler

  3. Ontario Cancer Institute, University of Toronto, M5G 1L7, Toronto, ON, Canada

    Igor Jurisica

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Gombocz, E.A. (2013). Changing the Model in Pharma and Healthcare – Can We Afford to Wait Any Longer?. In: Baker, C.J.O., Butler, G., Jurisica, I. (eds) Data Integration in the Life Sciences. DILS 2013. Lecture Notes in Computer Science(), vol 7970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39437-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-39437-9_1

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