Personalised medicine     Medicine for the Quantum age….    Dr. Patrick Gladding, MBChB, PhD            Cardiologist, WDHB
Problems with contemporary medicine• Clinical decisions are based on historical clinical  trial data• Clinical trials have...
Current medicine is reductionistic“The whole is more than the sum of itsparts.”       – Aristotle, Metaphysica  Interconne...
4
5
Personalised Medicine                        • Its about:                           – populations as                      ...
Genomic and Personalised Medicine• Genomics,  proteomics,  metabolomics• Information  technology• Artificial intelligence•...
• Superconvergence  – “Ubiquitous” Cloud    supercomputing  – Low-cost genome sequencing  – Pervasive connectivity  – Digi...
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10101011010010101010001010010101001010010000100011001010010101010010100100000101111100101010100110100100101000101010001010...
Genome sequencing   > Exponential reduction in cost                                           NEJM 2012
Handheld minisequencing                • $1 per test                • 20mins for result                • Not yet available
Feedback loops – giving patients theirinformation       Action                 Personalised data                          ...
• An integrated EMR &  biobank is essential• Semantic  interoperability• Machine processable  databases
Gladding et al. Personalized Medicine (2010) 7(4)
Deep computing – data mining
Network    Medicine•   Nodes, hubs•   Topology•   Scale free•   Self-organising•   Small world•   Holographic
Social networks and molecular medicine
Reclassifying disease – “diseasome”                            • Linking                              diseases            ...
Drug repositioning
Non-profitSocially responsible Virtual companyJobs and NZ knowledge economy
Disclosures - IP                                                                CardioMPO                                 ...
Pharmacogenomics  Tailoring drug treatment to genotype
0.75c/d   $4/d                         Prasugrel$550k     $2.9 million                              Ticagrelor            ...
Ethnic rates of nonresponders                                *2/wt = higher dose                                *2/*2 = al...
Rapid low-cost genotyping - NSH1    • MALDI-TOF MS         • Cheap <$30                                • Nanosphere2      ...
Mass Spectrometry MALDI-TOF
Mass Spectrometry MALDI-TOF                         2                             • Pattern recognition of spectral output...
Cardiac mHealth projects        Community focused                            31
Cardiac ultrasound project• Modeling astronaut’s hearts on ISS• NZ Employment
ICMA v 1.0    User ID   Password
Jagir Hussan                     ICMA v 1.0Auckland 03 Sept 12 10:31 NZST
Jagir Hussan                                     ICMA v 1.0Auckland 03 Sept 12 10:47 NZSTMR. John E. Dtotu                ...
Advanced ECG for general practice                                  Digital ECG e.g. XML                                   ...
Advanced ECG• Standard 12L snap-shot resting ECG from  Mortara, Philips, Cardiax machine• 12L ECG is ‘spectralised’ into m...
Enhancement of exercise treadmill testing • Problem: Limited access to exercise   treadmill testing for patients with ches...
A-ECG results                •   Lime - Healthy                •   Red – CAD                •   Blue - HCM                ...
Enhancement of exercise treadmill testing -Results • A-ECG sensitivity of 97.2% and specificity   of 53% for the angiograp...
Screening programs                                              Population GenomicsTargeted resource allocation           ...
Conclusion• Personalised medicine is emerging as the  fastest evolving aspect of medicine• Ushering in a new era in preven...
Personalised medicine: Medicine for the Quantum age
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Personalised medicine: Medicine for the Quantum age

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Patrick Gladding
Cardiologist
Waitemata District Health Board
(Friday, 9.00am, Form Room)

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Personalised medicine: Medicine for the Quantum age

  1. 1. Personalised medicine Medicine for the Quantum age…. Dr. Patrick Gladding, MBChB, PhD Cardiologist, WDHB
  2. 2. Problems with contemporary medicine• Clinical decisions are based on historical clinical trial data• Clinical trials have traditionally taken heterogeneous populations and demonstrated results only for the “average patient”• Personalized risk predictions could be made using databases containing heterogeneous data, e.g. genomic data, imaging data (EMR) and cardiac models
  3. 3. Current medicine is reductionistic“The whole is more than the sum of itsparts.” – Aristotle, Metaphysica Interconnected and Interdependent Systems 3
  4. 4. 4
  5. 5. 5
  6. 6. Personalised Medicine • Its about: – populations as much as individuals – Increasing efficiency and resource utilisation – In a world of limited resources only option is to personalise
  7. 7. Genomic and Personalised Medicine• Genomics, proteomics, metabolomics• Information technology• Artificial intelligence• Supercomputing• Molecular imaging• Nanomedicine• Biosensors
  8. 8. • Superconvergence – “Ubiquitous” Cloud supercomputing – Low-cost genome sequencing – Pervasive connectivity – Digital medicine – mHealth
  9. 9. tgaagccctctttttctctccttctatttctctctagagcactcaagactttactgacgaaaactcaggaaatcctctatcacaaagaggtttggcaactaaactaagacattaaaaggaaaataccacatgccactctgcaggttgcaataactactacttactggatacattcaaaccctccagaatcaacagttatcaggtaaccaacaagaaatgcaagccgtcgacaacctcacctctgcgcctggtaacaccagtctgtgcaccagagactacaaaatcacccaggtcctcttcccactgctctacactgtcctgttttttgttggacttatcacaaatggcctggcgatgaggattttctttcaaatccggagtaaatcaaactttattatttttcttaagaacacagtcatttctgatcttctcatgattctgacttttccattcaaaattcttagtgatgccaaactgggaacaggaccactgagaacttttgtgtgtcaagttacctccgtcatattttatttcacaatgtatatcagtatttcattcctgggactgataactatcgatcgctaccagaagaccaccaggccatttaaaacatccaaccccaaaaatctcttgggggctaagattctctctgttgtcatctgggcattcatgttcttactctctttgcctaacatgattctgaccaacaggcagccgagagacaagaatgtgaagaaatgctctttccttaaatcagagttcggtctagtctggcatgaaatagtaaattacatctgtcaagtcattttctggattaatttcttaattgttattgtatgttatacactcattacaaaagaactgtaccggtcatacgtaagaacgaggggtgtaggtaaagtccccaggaaaaaggtgaacgtcaaagttttcattatcattgctgtattctttatttgttttgttcctttccattttgcccgaattccttacaccctgagccaaacccgggatgtctttgactgcactgctgaaaatactctgttctatgtgaaagagagcactctgtggttaacttccttaaatgcatgcctggatccgttcatctattttttcctttgcaagtccttcagaaattccttgataagtatgctgaagtgccccaattctgcaacatctctgtcccaggacaataggaaaatagaacaggatggtggtgacccaaatgaagagactccaatgtaaacaaattaactaaggaaat
  10. 10. tgaagccctctttttctctccttctatttctctctagagcactcaagactttactgacgaaaactcaggaaatcctctatcacaaagaggtttggcaactaaactaagacattaaaaggaaaataccacatgccactctgcaggttgcaataactactacttactggatacattcaaaccctccagaatcaacagttatcaggtaaccaacaagaaatgcaagccgtcgacaacctcacctctgcgcctggtaacaccagtctgtgcaccagagactacaaaatcacccaggtcctcttcccactgctctacactgtcctgttttttgttggacttatcacaaatggcctggcgatgaggattttctttcaaatccggagtaaatcaaactttattatttttcttaagaacacagtcatttctgatcttctcatgattctgacttttccattcaaaattcttagtgatgccaaactgggaacaggaccactgagaacttttgtgtgtcaagttacctccgtcatattttatttcacaatgtSingle Nucleotide Polymorphisms (SNPs)atatcagtatttcattcctgggactgataactatcgatcgctaccagaagaccaccaggccatttaaaacatccaaccccaaaaatctcttgggggctaagattctctctgttgtcatct ~1 letter every 1,200gggcattcatgttcttactctctttgcctaacatgattctgaccaacaggcagccgagagacaagaatgtgaagaaatgctctttccttaaatcagagttcggtctagtctggcatgaaatagtaaattacatctgtcaagtcattttctggattaatttcttaattgttattgtatgttatacactcattacaaaagaactgtaccggtcatacgtaagaacgaggggtgtaggtaaagtccccaggaaaaaggtgaacgtcaaagttttcattatcattgctgtattctttatttgttttgttcctttccattttgcccgaattccttacaccctgagccaaacccgggatgtctttgactgcactgctgaaaatactctgttctatgtgaaagagagcactctgtggttaacttccttaaatgcatgcctggatccgttcatctattttttcctttgcaagtccttcagaaattccttgataagtatgctgaagtgccccaattctgcaacatctctgtcccaggacaataggaaaatagaacaggatggtggtgacccaaatgaagagactccaatgtaaacaaattaactaaggaaat
  11. 11. 101010110100101010100010100101010010100100001000110010100101010100101001000001011111001010101001101001001010001010100010100101001001001010111111111111111111111111110000001010010001001000001000100111000101001010100101001010010101001100101000010011111111111111111111100000100101101111110011001010101011010111101010100101010100101011010010100100101010010111111110010101010101010101010010101001010010100101111111111111111110010010101010100101010100101010Single Nucleotide Polymorphisms (SNPs)0111111111111111111101010100101001010100101010010010100101010010100100101001010101001010010101001001 ~1 letter every 1,20000101001001010010010101001010100101011111111111111111111111111001010101010010101010010100100101010100000101011111111111111111101010010100100000000000000000001010100101001010100101010100101010101100100001010101001010100101010001010101000101010010100100010101011110100010001000111110000010011111000101010001010111100100101010010101001010011111001010101010010100100100010001111111111100010101010000010111111100010111010101000101010100010111110010010100111110010100101110100101111001
  12. 12. Genome sequencing > Exponential reduction in cost NEJM 2012
  13. 13. Handheld minisequencing • $1 per test • 20mins for result • Not yet available
  14. 14. Feedback loops – giving patients theirinformation Action Personalised data Relevance ChoicesA Learning system 14 TED talks: Thomas Goetz
  15. 15. • An integrated EMR & biobank is essential• Semantic interoperability• Machine processable databases
  16. 16. Gladding et al. Personalized Medicine (2010) 7(4)
  17. 17. Deep computing – data mining
  18. 18. Network Medicine• Nodes, hubs• Topology• Scale free• Self-organising• Small world• Holographic
  19. 19. Social networks and molecular medicine
  20. 20. Reclassifying disease – “diseasome” • Linking diseases through networks, closest neighbour based on genomics http://diseasome.eu/
  21. 21. Drug repositioning
  22. 22. Non-profitSocially responsible Virtual companyJobs and NZ knowledge economy
  23. 23. Disclosures - IP CardioMPO Australasia Clopidogrel Pharmacogenomics PCT phase US, China, Japan, Europe, Australia, New Zealand• Founder of non-profit Theranostics Laboratory• USPTO issued patent; Licensing fees and royalties
  24. 24. Pharmacogenomics Tailoring drug treatment to genotype
  25. 25. 0.75c/d $4/d Prasugrel$550k $2.9 million Ticagrelor $6/d $4.4 million 26 Pharmgkb
  26. 26. Ethnic rates of nonresponders *2/wt = higher dose *2/*2 = alternative drug 27
  27. 27. Rapid low-cost genotyping - NSH1 • MALDI-TOF MS • Cheap <$30 • Nanosphere2 o Rapid 2hrs o Blood->result o Clopidogrel/warfarin PGx o Norovirus o Hypervirulent C. diff
  28. 28. Mass Spectrometry MALDI-TOF
  29. 29. Mass Spectrometry MALDI-TOF 2 • Pattern recognition of spectral output • ‘Machine learning’ 3 • Dendrogram: assignment of species pattern1• Culture specimen• Ionise• Analyse 4 • Species identification based on probabilistics
  30. 30. Cardiac mHealth projects Community focused 31
  31. 31. Cardiac ultrasound project• Modeling astronaut’s hearts on ISS• NZ Employment
  32. 32. ICMA v 1.0 User ID Password
  33. 33. Jagir Hussan ICMA v 1.0Auckland 03 Sept 12 10:31 NZST
  34. 34. Jagir Hussan ICMA v 1.0Auckland 03 Sept 12 10:47 NZSTMR. John E. Dtotu Referring Physician: Dr James Thomas5543AT751071, Kna street, Male 163 cm 04-971-3343 90 kgsKapiti Coast 10-12-1975Scans Models 4Views Stress14 Pretrial P4LVX Created on: 31 Sept 2012 Created by: Dr. Patrick Gladding Status: Validated (Dr James Thomas) Analysis Workshee t APLAX Normal Fiber 4CH 2CH SAX Surface Lines Epi Endo CLEAR ALL CLEAR ALL SAVE SAVE
  35. 35. Advanced ECG for general practice Digital ECG e.g. XML Internet • Spectralised, digital ECG • Artificial intelligence • Metric for health
  36. 36. Advanced ECG• Standard 12L snap-shot resting ECG from Mortara, Philips, Cardiax machine• 12L ECG is ‘spectralised’ into multiple parameters• Pattern recognition, artificial intelligence applied to resulting parameters• Higher diagnostic yield than standard 12L ECG for CAD, HCM, NICM, ICM
  37. 37. Enhancement of exercise treadmill testing • Problem: Limited access to exercise treadmill testing for patients with chest pain • ETT Sensitivity 67%, specificity 78% • n=58 patients (10 normal, 48 severe disease on coronary angio) • All with positive treadmill “coronary disease” • One excluded due to noise and six due to other cardiac abnormalities on echo 38
  38. 38. A-ECG results • Lime - Healthy • Red – CAD • Blue - HCM • Aqua - LVH • Purple - NICM • Orange - ICM 39
  39. 39. Enhancement of exercise treadmill testing -Results • A-ECG sensitivity of 97.2% and specificity of 53% for the angiography results • 8 healthy patients with abnormal ETT would have been identified correctly with A-ECG • Theoretically reducing unnecessary invasive coronary angiograms by 53% • Cost saving $528/A-ECG 40
  40. 40. Screening programs Population GenomicsTargeted resource allocation ModelingCommunity oriented Disease SimulationPatient-centric Cost-effectiveness Analysis Diagnostics Lab Wireless Biosensor Genebank High risk individual Digital Avatar Electronic Healthcare Database Population 4.2 million
  41. 41. Conclusion• Personalised medicine is emerging as the fastest evolving aspect of medicine• Ushering in a new era in preventative medicine with low cost molecular diagnostics, advanced informatics• Will be applicable to every specialty within medicine• Need for industry partnerships (e.g. IBM, Orion) & HINZ members• Needs your help! – Acknowledgements: Innovation Hub, North Shore hospital laboratory staff, CEO

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