5. Health Research Board
5
â˘State agency under Department of Health.
â Budget âŹ43m, funding portfolio âŹ150 - âŹ200m, staff of 61
â˘Funding health research.
âInfrastructure, capacity building, specific projects.
â˘Providing evidence for policy.
âPublic Health Alcohol Bill, Food Pyramid, Fluoridation.
â˘Information for service planning.
âDrug use, disability, mental health.
7. Oncotype DX Public Usage Oct 2011-Sept 2012 Analysis carried out by GHI and presented at St Gallen 2013
7
0
10
20
30
40
50
60
70
St Vincents
Mater
UCHG/ West
St James's
Waterford
Beaumont
CUH
Midwestern
Patients Tested
Patients Receiving Chemo
Over 4 year clinical trial: âŹ5M saving i.e. âŹ3M in avoided chemotherapy and âŹ2M in free oncotype DX tests for the 690 participating patients.
8. Personalised Medicine â in future
8
â˘Moving from Personalised Medicine Care to Personalised Medicine Research
10. Mutation analysis
Sequencing
Kinase activation
RPPA assays
Copy number analysis
SNP arrays
Transcriptomcs
Expression arrays
(RNAseq also)
Bioinformatics/Cross âomicâ analysis
Target / biomarker validation
Discovery
Data
Analysis
Biomarkers
Impact
Validated companion diagnostic Clinical trial in stratified patient population
Tissue sample collection
Tissue
Discovery
Data
Analysis
Biomarkers
Drug Targets
Impact Tissue
Drug Targets
Difficult-To-Treat Breast
Cancers
Representative cell lines
6 million euro
European programme
8 partners (2 SMEs, 6
academic groups)
Project
Management:
OncoMark
10
11. +
PI3K inhibitor
Tamoxifen
Placebo
Tamoxifen
+
OR
Ductal & lobular
Lobular only
Phase II Clinical Trial
n=180
n=110
www.ratherproject.com
11
12. 6 million euro
European programme
10 partners
(4 SMEs,
6 academic groups)
www.angiopredict.com
12
13. Wagle N et al. Journal of Clinical Oncology (2011)
Before treatment
After 15 weeks
After 23 weeks
A 38-year-old man with BRAF-mutant melanoma and subcutaneous metastatic deposits, treated with Vemurafenib
(BRAF inhibitor)
Predicting Response and Resistance
Approx 50- 55k euro/6 months treatment
13
14. RHO-adRP
â˘Inherited form of retinitis pigmentosa
âcaused by >150 mutations in the rhodopsin gene
âeach affected family has a single dominantly inherited mutation
â˘Affects ~1 in 30,000 people worldwide
â>30,000 patients in developed economies
â˘Patients suffer from visual dysfunction losing sight progressively & completely by middle age
âdeath of the both rod and cone photoreceptor cells as a consequence of the processing / translation of faulty rhodopsin
â˘Treatment options
âno treatments currently available for patients with retained sight
âretinal chips approved in the US for end-stage blind patients
14
15. â˘Single dose, sub-retinal injection containing two independent AAV 2/5 vectors encoding:
âRNAi to down-regulate mutant rhodopsin
âreplacement rhodopsin gene with a modified nucleotide sequence to escape suppression & provide functional rhodopsin
â˘25+ patents granted across all key territories
âother key IPR licensed from Spark, Benitec (shRNAi) and NIH (AAV)
â˘US & EU orphan drug designation granted
15
GT038 â lead therapeutic
For the treatment of rhodopsin linked autosomal dominant retinitis pigmentosa (RHO-adRP)
16. 16
GT038 â overcomes RHO mutational diversity
RHO-adRP
The challenge: >200 different RHO mutations
adRP patient: single, simple 220Îźl subretinal injection of GT038 to the back of the eye
GT038
2 x AAV 2/5 vectors in fixed proportions containing:
RHO RNAi suppression, and an
Excess of a special RHO gene replacement gene
RHO mutant mRNA destroyed
RHO replacement survives due to âwobbleâ at certain 3rd base pairs
+
GT038: a unique single solution for RHO-adRP caused by multiple RHO mutations
17. GT038 Development
â˘Proof of concept established in mouse and rat models
â˘Toxicology & biodistribution studies to commence in Q1 2015
â˘Manufacturing agreements signed with Spark Therapeutics (US) and 1st GLP batches expected in Q12015
â˘Phase I clinical trial to commence in 2017
17
19. 40 s
120 s
200 s
280 s
360 s
40 s
120 s
200 s
280 s
360 s
CVD Patients
Control
Percentage Coverage
Time (sec) Control
Time (sec) Patients
2.5
20
165
5
40
245
10
90
390
15
145
450
Attenuated thrombus formation in CVD patients on Aspirin and Clopidogrel versus normal controls
Healthy Control
CVD Patient
19
20. Economics of Personalised Health (PH)
â˘Professor John Forbes
â˘First HRB Research Leader.
â˘A scheme to address strategic gaps and leadership capacity in Population Health.
20
21. Economics of Personalised Health (PH)
â˘HRB invest âŹ1.4m over 5 years.
â˘Look at costs / benefits of PH.
â˘Provide reliable evidence for decision making around PH.
21
22. Economics of Personalised Health (PH)
Goal: Develop and apply empirical methods for economic analysis of personalised health to support assessment, regulation and translation.
22
23. Economics of Personalised Health (PH)
Potential impact: Raise awareness and develop methods to address the issues associated with using patient information to prevent, diagnose and treat disease or to promote wellness and bridge the gap between research findings and translation into policy/practice.
23
24. Effect of Personalised Health (PH) on patient outcomes
â˘Professor Sean Dineen
â˘Effect of personalised clinical information on outcomes in people with type 2 diabetes.
24
25. Effect of Personalised Health (PH) on patient outcomes
â˘Goal: Establish best level of information to share with patients so they can be more proactive about their care, increase their confidence in managing their diabetes and leads to improvements in their blood sugar control.
25
26. Effect of Personalised Health (PH) on patient outcomes
â˘Potential impact: Personalised health through increasing the patients awareness, knowledge and ability to make decisions regarding prevention, treatment and care options specifically selected and targeted at their risk profiles.
26
27. Moving from Personalised Medicine Research to Policy
27
How did all this happen? By design? By accident?
28. How did it happen?
28
â˘Bottom Up.
â˘Principal Investigator-driven.
â˘Personal interest.
â˘Open funding calls.
29. How did it happen?
29
â˘There is no âtop downâ policy position on Personalised Medicine.
â˘The funding instruments driving all of this are NOT Personalised Medicine-specific.
â˘Which is a great argument for bottom up funding instruments (creates capacity, starts the process)âŚ..BUT
30. How did it happen?
30
â˘The capacity, skills and infrastructure we have built by the bottom up approach is largely by accident (or serendipitous designâŚ)
â˘So, there is a big gap between the Health Policy layer and the Health Research(Care) layer in Ireland (& elsewhere?)
31. How does the Personalise Medicine Revolution avoid being a mainly technical one?
31
32. How does the Personalise Medicine Revolution avoid being a mainly technical one?
â˘The gap between the âtechnical worldâ and the âpolicy / implementationâ world will determine whether this is a real revolution like the Wheel or Fire, or just a technical one.
32
33. IT: Another âtechnologyâ that transformed the world:
33
â˘Information Technology (IT) took quite a while to move from the lab to the real world.
â˘From Turing in the 40s to PCs in the 1980s, it wasnât until the 90s that the real IT revolution took place.
34. IT: Another âtechnologyâ that transformed the world:
34
â˘Its adoption spread exponentially and drove massive economic expansion in the 90s.
â˘The 00s saw this spread to the consumer world and now it is Business & Consumer: Everyone/Everywhere!
â˘Looked at through this lens, Personalised Medicine has a long way to goâŚ
35. Whither Personalised Medicine in Ireland?
35
â˘The key roadblock to large scale adoption (revolution) is persuading key decision makers in Public Administration and Business of the merits & potential of PM.
â˘We still largely talk an insiderâs language.
â˘9 out of 10 people probably donât know what Personalised Medicine means?
36. Whither Personalised Medicine in Ireland?
36
â˘How can you enable a revolution if most people donât know what you are talking about?
â˘A lot more effort is required in âmaking the caseâ if this is to happen!
37. Whither Personalised Medicine in Ireland?
37
â˘The Solution?
â˘As a first step: assemble the arguments (with supporting evidence), in plain language, and priortise existing resources towards influencing key decision makers;
âto enable real top-down policy measures targeting personalised medicine realisation.
âand thus give reality to (..accelerate adoption of) personalised medicine.