1. PHARMACOGENOMICS DATA TREATMENT AND SOFTWARE FOR INTERPRETATION OCTOBER 8 TH 2015

2. Introduction The promise of “genomic medicine” cannot be achieved unless is is recognized as a discipline of digital medicine. Why? OCTOBER 8, 20152

3. 1000 Facts per Decision 10 100 1990200020102020 Human Cognitive Capacity Source: Daniel R. Masys, M.D., University of Washington Data Data is Overwhelming Cognition OCTOBER 8, 20153

4. Scarcity of informatics resources “It is a lot easier to generate the data than [to] interpret it. You have to adequately resource the informatics function for interpretation. If you don’t, you end up with a very expensive paper weight in your department” The business of genetic testing: a survey of early adopters J. Crawford et al. Genetics in Medicine, Dec 2014 OCTOBER 8, 20154

5. Test Utilization is Still Very Low relative to potential Area2014 Tests US PotentialReference Pharmacogenetics500K to 750K8m4 million new babies, 3.6 million turn 65 Cancer100K5m1.6m new diagnoses 3+ tests per Carrier Screening2.3mCouples married per year Prenatal4mBabies born Hospital Acquired Infections35mHospital Admissions Sexually Transmitted Infections20mNew STIs per year OCTOBER 8, 20155

6. Patient Insurance EMR Interpreters Pharmacogenetics Oncogenomics Cardiology Carrier Screening Others… Labs Decision Support “I want to know the implications of my test in terms that I can understand.” “Does this test represent the standard of care?” “What tests exist, how to interpret them, and who will pay for them?” Diagnostics are more powerful when used in context with clinical data. “We can provide reliable tests but they are not actionable without clinical guidance.” “We need a platform to apply ‘gold standard’ interpretation to tests.” Ecosystem & Opportunities Doctor Platforms OCTOBER 8, 20156

7. Embracing clinical information systems for pharmacogenomics OCTOBER 8, 20157 Successful solutions that can deliver the message to the end user: In a timely fashion In a right format (standard) In a reliable manner In a cost effective manner Within existing medication management programs

8. Software for implementation Translational software, USA Eugonomic, Spain Abomics, Finland HMG System Engineering GmbH, Germany Marand Com, Slovenia OCTOBER 8, 20158

9. Thank You houda.hachad@translationalsoftware.com OCTOBER 8, 20159

10. PRECISION MEDICINE IN PRACTICE

11. Vision To inform every clinical decision affecting every patient everywhere in the world with the best available personalized guidance OCTOBER 8, 201511

12. Why Pharmacogenetics? Good Evidence ◦Phenotypes were observed, then genotypes described ◦FDA required testing during drug approval Few genes affect many drugs Many people take drugs 45% of most prescribed drugs have genetic guidance Including Hydrocodone Metoprolol Simvastatin Omeprazole Sertraline Tramadol Citalopram Oxycodone OCTOBER 8, 201512

13. Requirements for Democratization OCTOBER 8, 201513 The Need: Industrial Revolution for Genomic Analysis

14. Research vs. Clinical Systems OCTOBER 8, 201514

15. Product Characteristics 1. Cheap, commodity assays 2. Clinical Decision Support System to guide care 3. Guidance available in the native language of the recipient 4. Integration of testing and CDSS into every health information system 5. Analysis of outcomes to gain insight from ordinary clinical systems OCTOBER 8, 201515

16. Current Integration OCTOBER 8, 201516

17. Key Partners ABSDiamondLab Track Prolis APEX HealthDOMALabDaqPsyche Systems Apollo eDNA LigoLab SLS Arivium G.Technologies MergeSimple LIMS Antrim Hex Labs MedicusSKYLab Comtron Lab Answer OrchardUniconnect Core LabHexPathagilityVital Axis CSS Avalon Lab Health PathxXIFIN Instruments Supported Laboratory Information Systems OCTOBER 8, 201517

18. Progress Report 28 months since launch ~100 Laboratories 500K+ PGx Results Processed 15,000+ Care Facilities OCTOBER 8, 201518

19. Market Perspective Physicians perceive that precision medicine improves outcomes Current utilization is tragically low Payers do not have enough data to recognize value New business models emerging ◦Self-Pay ◦Accountable Care Organizations accepting reimbursement risk Healthcare organizations beginning to adopt PGx OCTOBER 8, 201519

20. Lessons Learned Many gaps in the data ◦Variant databases are particularly gappy Wide range in evidence quality Real data is really messy A cool solution is moot if labs can’t get paid OCTOBER 8, 201520

21. Medication Usage N = 211,555 OCTOBER 8, 201521

22. Some Genes are “Tricky” OCTOBER 8, 201522 A. Gaedigk, Complexities of CYP2D6 gene analysis and interpretation

23. Multi-Scalar Problem Single SNPOPRM1 118A>G Opoid Efficacy Multi-SNPMTHFR 1298C & 677THyperhomocysteinemia Multi-Gene Factor II 20210A & Factor V Leiden 1691A Thrombosis Risk Multi-Allele rs16947 + rs3892097 + rs1065852 CYP2D6 Haplotype Mixed CYP2C9 Phenotype & VKORC1 -1639G>A Warfarin Sensitivity OCTOBER 8, 201523

24. Analysis Workflow Files Variant Calls Type Analysis Types Metatypes Phenotypes Recommendations OCTOBER 8, 201524

25. Architecture OCTOBER 8, 201525

26. NGS Notes Exomes are not ideal for pharmacogenomics More variants is not always better Structural variation is very important Absence of evidence is not the same as evidence of absence OCTOBER 8, 201526

27. Clinician’s Curiosity is Limited by Time What we want to tell doctors OCTOBER 8, 201527 What doctors want to know Consider alternatives to Codeine #CYP2D6RapidMetabolizer

28. Where we (collectively) Need to Go ReactivePreemptive StaticDynamic Just-in-ChartPortable StandaloneIntegrated OCTOBER 8, 201528

29. Call to Action Use this meeting to collaborate toward more functional solutions Embed genomic medicine into the core of clinical systems Use clinical evidence to provide new evidence Thank You! OCTOBER 8, 201529

30. Thanks from the Team! OCTOBER 8, 201530