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Nikhil anmol pres_092014_1.0_final_2222

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Biomarkers, Social Media and Personalized Medicine: Security and Integration Challenges in Managing the Data Deluge and Data Scarcity

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Nikhil anmol pres_092014_1.0_final_2222

  1. 1. Biomarkers, Social Media and Personalized Medicine: Security and Integration Challenges in Managing the Data Deluge and Data Scarcity: Nikhil Kumar, President & Founder, ApTSi Anmol Limaye, Research Intern, ApTSi 1
  2. 2. Content • Overview • Personalized Medicine – a context • Information and Personalized Medicine • Security and Personalized Medicine • Q & A
  3. 3. Why Personalized Medicine? • Some Food for Thought – Which is one of the greatest killers in the US today? – What percentage of new drugs have serious, undetected adverse effects at the time of approval? – How many of the recently FDA approved medications were subsequently withdrawn from the market or given a black box warning? – What percentage of Americans have gene-based variations that significantly increases the risk of having an ADR? – What percentage of rare diseases are genetic in origin?
  4. 4. Key Guidances / Reflection Papers • The FDA: – FDA Guidance - Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products - Dec’12 – FDA report - Paving the way for Personalized Medicine, Oct 13 • The EMA – EMA - Reflection paper on methodological issues with pharmacogenomic biomarkers (for consultation until 25 Nov 2011) – EMA - Approving oncology drugs in the era of personalized medicines, Dec’11 • The Industry – AMS, MHRA, Industry - Realizing the potential of stratified medicine. Jul 13
  5. 5. "When I use a word," Humpty Dumpty said in rather a scornful tone, "it means just what I choose it to mean -- neither more nor less." "The question is," said Alice, "whether you can make words mean so many different things." Figure from https://www.cs.cmu.edu/~rgs/alice-VII.html
  6. 6. So let’s define what we mean.. • Personalized medicine.. ‘a medical model that proposes the customization of healthcare using molecular analysis - with medical decisions, practices, and/or products being tailored to the individual patient, often employing diagnostic testing to personalize the treatment’ OR • “the right patient with the right drug at the right dose at the right time.”(EU) OR • “Health care that is informed by each person’s unique clinical, genetic, and environmental information” (AMA) – The prevailing person-centric holistic model of modern Personalized Medicine, where Omics play a part Personalized medicine is more than pharmacogenomics, covering the space of the omics and including biotic, chemical, physical and genomic aspects
  7. 7. Some more terms.. • A companion diagnostic test – essentially a biomarker test that enables better decision making on the use of a therapy (and usually accompanies the PM drug) • Biomarker.. – ‘An indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention.’ • Stratification.. – ‘a medical model that proposes the customization of healthcare using molecular analysis - with medical decisions, practices, and/or products being tailored to the individual patient, often employing diagnostic testing to personalize the treatment’ .. PM is more than pharmacogenomics Biomarkers and patient involvement play a growing role in Personalized Medicine
  8. 8. Data Analytics Biomarkers… • The Pharma industry and regulators are further emphasizing the role of biomarkers in drug development • For example – Personalized Medicine Coalition Report, 2014 – A 57% increase in personalized drugs/ treatments from 2006 – 2014 – 30% of biopharma require all developing compounds to have a biomarker – 50% of all clinical trials collect DNA from patients for use in biomarker development – Today, 137 FDA-approved drugs have pharmacogenomic information in their labeling, and 155 total pharmacogenomic biomarkers are included on FDA-approved drug labels • Starting with Herceptin for breast cancer – to Vectibix recently for metastatic colorectal cancer – we are moving ahead 1 Salter et al, 2014 OMICS IOT, Social Media mHealth Wellness
  9. 9. PM Today.. Kumar1 OMICS IOT, Social Media Data Analytics mHealth Wellness
  10. 10. Analytics A Genomic System Model OMICS IOT, Social Media Data mHealth Wellness Methylomics Transcrip-tomics Proteomics Methylation Transcription Genomics Metablomics De- Methylation -mRNA Expression/ Splicing - Alternative Splicing - Allele specific expression - microRNA Expression and Discovery Synthesis, Degradation, Transportation, Translation Etc.
  11. 11. Content • Overview • Personalized Medicine – a context • Information and Personalized Medicine • Security and Personalized Medicine • Trends and Advances
  12. 12. The Evolving Healthcare Ecosystem is Person-Centric FHIM focus Payers Healthcare IT Patient Internet of Things (IOT) BRIDG HIPAA Business Associate & Covered Entity Regulatory and Compliance Providers IDNs Labs Analytics HIMMS & Continuaa introduce personal connected care The new world of healthcare is person-centric Pharma Companion Dx ONC Direct Connect PBM Pharmacy Social Media Data Standards / initiatives A Person-Centric model based on seamless interoperability, regulatory compliance and security are the cornerstones of modern healthcare OMICS IOT, Social Media mHealth Wellness
  13. 13. The future of Healthcare… Data Analytics The Modern Clinical World based on Personalized Medicine Aetna accepts 100 genetic tests for genetic testing http://www.aetna.com/cpb/medical/data/100_199/0140.html FDA issues Personalized Medicine guidance – 2013 OMICs Data Clinical Decisions CDx narrows scope Clinical Data IOT, Lab and other data Clinical Decisions Systems Physician Engagement Patient Engagement Outcomes Wellness The physician of the future is going to use Cdx’s and Clinical Decision Systems to take Clinical Decisions.. And a focus on wellness and patient engagement is going to shift the process and the quality OMICS IOT, Social Media mHealth Wellness
  14. 14. The advent of personalized medicine…. • “personalized medicine” is here to stay • There is a deluge of data • Biomarkers, bioinformatics and IT are making this actionable • Companion diagnostics and IT(big data, SOA) facilitate adoption • Limited by business model issues, limited data sources and the ability to analyze and use it reliably • Enabled by support from regulatory bodies • Person-Centric: – Supports the empowered consumer and wellness!! Personalized medicine is here to stay. It IS the future of medicine. And it is data centric & person-centric. Capturing, translating, and interpreting data are key success factors
  15. 15. A deluge of data… Data Analytics “..healthcare is 17 percent of the US economy. It's upwards of $3 trillion. The costs of healthcare are a problem, not just in the United States, but all over the world, and there are a great number of inefficiencies in the way we practice healthcare. ” – Jason Lee, Director, Healthcare Forum, The Open Group • There is an exponential increase in data • ePRO, internet of things and social media add to the variety! • Predictive analytics and Integrative models gaining adoption • Balance this against cost, agility and quality considerations!!!! “..$1000 sequencing …$1,000,000 interpretation” Ken Davies We need to reduce complex data into a model that is accessible for human comprehension Bryn Roberts “All research data at Roche up to 2010 amounted to about 100 TB”. During 2011/12, we ran a project called CELLO, where the genomes from about 300 cancer cell lines were sequenced. Together with other data from the cells, we generated 100 TB of data in this single ‘experiment’—equal to 100 years of Roche research up until 2010!” .. Bryn Roberts The effective capture and interpretation of this information will change the practice of medicine OMICS IOT, Social Media mHealth Wellness
  16. 16. Biomarkers & computational techniques are enablers • …the problem cannot be solved (reasonably) with Data Analytics CDER Biomarker Program traditional brute force techniques. So we must use new ones.. – Biomarkers help reduce complexity and incorporate disease etiology – New computational techniques provide a foundation for supporting integrative models and bench to the bedside – necessary for successful adoption of HIT • Machine learning • Reverse Markov models • The list goes on… – Standards provide a framework for interoperability – Ontologies, vocabularies and metadata link it together The appropriate use of biomarkers, ontologies, metadata and modern computational techniques provide a framework to harness the data FDA Guidance on Biomarker Development (2014)1 1 FDA - Qualification Process for Drug Development Tools OMICS IOT, Social Media mHealth Wellness
  17. 17. Content • Overview • Personalized Medicine – a context • Information and Personalized Medicine • Security and Personalized Medicine • Q & A
  18. 18. The SOA Ecosystem pervades HIT Ecosystem … “Service Orientation” is disruptive and here Enterprise SOA Cloud Computing Modern SOA Ecosystem Legacy APIs (CORBA/DCOM) EAI Business Adoption and Impact of Service Orientation A world of SOAs Micro Service Architectures/ APIs & IOT Low Increasing High SOA RA Kumar 1 Service orientation in its different flavors is creating a HIT fabric for information exchange 1 Derived from Kumar, 2014 … And will be the cornerstone of the HIT world OMICS IOT, Social Media Data Analytics mHealth Wellness
  19. 19. New ways for gathering data... • IOT – do we even know if the device is right? – Who owns the data? – Is it secure? – OK now I have it – what does it mean? • ePro and Social Media – It really works in the world of wellness – It really works in the world of drug adherence – So how do we capture it and interpret it? The coalescing of SOA and Business requires stakeholders from both IT and the business to think Service Oriented. This presentation should provide an introduction of the concepts involved. 0110 0110 01001 0110 1110 0110101 Unstructured data is 80% of data (Seth Grines)… and growing
  20. 20. Information Reference Model Behavioral Data Interoperability (Rules and Data behavior) Semantic Data Interoperability Syntactic Data Interoperability Physical Data Store Data when assigned st ructure (syntax) and semant icsbecomes information Behavioral Data Interoperability – Data behavior is consistent RDF/OWL Semantic Data Interoperability – Data shares the same semantic implication UDEF Syntactic Interoperability – Data structures are rationalized Canonical Forms & Schemas Data interoperability is a critical success factor in the effective leveraging of data. It is also a key factor in the reduction of the overhead of data mapping and the creation of a virtualized data model. Interoperability Reference Model Structured Data Unstructured Data Characteristics of the interoperability layers Kumar 2009 0110 0110 01001 0110 1110 0110101
  21. 21. Interoperability…. 1. The evolving HIT world involves a plethora of ontologies. 2. Ontologies are controlled vocabularies with relationships between the terms 3. Controlled vocabularies are an accepted list of terms 4. Translation between ontologies is a painstaking but necessary process 5. Metadata is a fundamental base for interoperability 6. In the future communicating processes and services will depend on this interoperability Without interoperability the data deluge is noise. Interoperability must address structure, syntax and semantics. 0110 0110 01001 0110 1110 0110101
  22. 22. The practice of integration • Metadata is key • Assessing completeness for reliable decision • Computational models to manage integration – address the kinds of data • Integration in practice (confidence, traceability, fact vs source of truth) 0110 0110 01001 0110 1110 0110101
  23. 23. A Big Data Model for Personalized Medicine Regulatory (Governance, Security and Monitoring) Acquisition (Landing & Staging) Latency Mediation, MDM, Transformation & Formatting to Enterprise Model Analytic Storage Analysis/ Decision/ Consumption Diverse Data Sources (Structured, Unstructured) at Diverse Velocities Kumar1 Kumar, 2013 0110 0110 01001 0110 1110 0110101
  24. 24. Integrative models and their role • What is an integrative model? • Revisiting – PM is more than pharmacogenomics • Computational implications of integrative models 0110 0110 01001 0110 1110 0110101
  25. 25. Content • Overview • Personalized Medicine – a context • Information and Personalized Medicine • Security and Personalized Medicine • Q & A
  26. 26. CFR-21 NIST The Elephant in the room… • Security • Privacy • Ethics and adoption Security and Privacy can be serious sources for overhead. Use them to establish clarity and plan early PRIVACY Common Controls HIPAA CERT
  27. 27. Compliance • CFR21 Type 11 – access and retention • HIPAA – business associate or covered entity – Privacy implications • Safe Harbor CFR-21 NIST PRIVACY Common Controls HIPAA CERT
  28. 28. Why anonymity Voters list, sales prospects, etc. Healthcare data Ethnicity Address Diagnosis Procedure Sex Birth date CFR-21 NIST PRIVACY Common Controls HIPAA CERT ‒Perfect anonymity can never be guaranteed ‒ But we can make it hard ‒ Regulations require it (HIPAA, CFR-21, Data Protection Act, etc.) !!!
  29. 29. Compliance and realities • Compliance – CFR21 Type 11 – implications – HIPAA –business associate • Trends – Deidentification cannot be complete – ePRO may or may not be private (PatientsLikeMe.com) • What’s involved – Secure your access, Don’t trust … insiders!!! – Address Safe Harbor if you send the data out – Log CFR-21 NIST PRIVACY Common Controls HIPAA CERT
  30. 30. Content • Overview • Personalized Medicine – a context • Information and Personalized Medicine • Security and Personalized Medicine • Q &A
  31. 31. Nikhil Kumar, President & Founder, ApTSi Email: nikhil@ap-tech-solns.com Cell: (248) 797 8143 Anmol Limaye, Research Intern, ApTSi Email: anmolml@ap-tech-solns.com 31
  32. 32. Supporting Materials • Security
  33. 33. Why anonymity Voters list, sales prospects, etc. Healthcare data Ethnicity Address Diagnosis Procedure Sex Birth date CFR-21 NIST PRIVACY Common Controls HIPAA CERT ‒Perfect anonymity can never be guaranteed ‒ But we can make it hard ‒ Regulations require it (HIPAA, CFR-21, Data Protection Act, etc.) !!!
  34. 34. De-id. and annonymity • What is required? CFR-21 PRIVACY – Does not identify a person – No reasonable basis to believe that the information can be used to id. an individual • 2 techniques – Expert determination (obfuscation) – Safe harbor (removal of id. parms) NIST Common Controls HIPAA CERT
  35. 35. CFR-21 With large volumes of data… • Both are used • Expert determination includes: PRIVACY – K-anonymity coupled with t-closeness are well known and normally acceptable – Add obfuscation (one-way) • Once encrypted you can’t identify it NIST Common Controls HIPAA CERT

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