A Rare International Dialogue (Sunday, May 12, 2019) Theme Six: Orphan Drug Pricing for Innovation and Access Project Hercules: A UK Duchenne Global Collaboration - Josie Godfrey, JG Zebra Consulting

1. Project HERCULES: A
Duchenne UK Global
Collaboration for
evidence
Josie Godfrey
A Rare International Dialogue
Toronto, May 2019

3. Workshop programme
• Introductions
• Aims of the workshop Introduction
• Requirements for HTA, challenges for rare diseases
• DISCUSSION:
HTA challenges in
rare diseases
• Introduction to Project HERCULES' paradigm
• Emerging findings
• DISCUSSION: benefits and challenges for collaboration in your
disease area/country
Project HERCULES
• Challenges and successes in data sharing
• FAIR principles for data sharing
• DISCUSSION: data sharing challenges and state of data in different
disease areas.
Data sharing

4. Have you been involved in a Health Technology Assessment?
Have you had experience of collaborations for data?

5. Aims of the workshop
1. Learn about Duchenne UK’s Project HERCULES, a ground-breaking international project
creating a new paradigm for collaboration for Health Technology Assessment in Duchenne
Muscular Dystrophy (DMD) and related projects that aim to improve every step of the drug
development pathway.
2. Understand what is needed for Health Technology Assessment
3. Understand the challenges for rare diseases and the potential benefits of collaborating in
your disease area.
4. Explore the potential challenges and barriers to collaboration for evidence in your disease
area.
5. Consider the challenges of data sharing and the potential benefits of ensuring research
you fund aligns with FAIR principles for data – making them findable, accessible,
interoperable and reusable.

6. HTA for rare diseases

7. Summary of Drug Development Process

8. Different roles, different questions
Regulators: Licence
• Is the treatment safe (enough)?
• Does the treatment work?
• Is the treatment be produced to a
consistently high quality?
Payers & Health Technology
Assessment
• How effective is the treatment?
• Is the treatment good value for
money?
• Is the treatment affordable?

9. Health Technology Assessment
HTA is a multidisciplinary process that summarises information about
the medical, social, economic and ethical issues related to the use of a
health technology in a systematic, transparent, unbiased, robust
manner. *
*EUNetHTA definition

10. In many countries, new treatments for DMD and other rare diseases will likely be
appraised by Health Technology Assessment bodies such as NICE or CADTH
HTA bodies look at the costs and benefits of a new treatment.
Some look at the cost-effectiveness of a new treatment and, increasingly the
affordability or budget impact is taken into account.
Fore example, NICE is interested in the impact of a new treatment on Quality of Life
§ How many Quality Adjusted Life Years (QALYs) will a new treatment provide
compared to existing treatment options? How will the clinical trial results translate to
the real world?
§ How much do these additional QALYs cost?
§ Is the new treatment cost-effective within NICE thresholds?
§ What is the budget impact? Is that affordable to the NHS?
The Health Technology Assessment Hurdle

11. Evidence challenges for rare diseases
Many new treatments for rare and very rare conditions are not cost-effective based on HTA
methods designed for more common diseases or even in programmes such as NICE’s HST.
There is often limited evidence available. Challenges include:
• Small, heterogeneous populations
• Short duration of follow-up studies compared to anticipated long term benefits
• Limited scientific understanding/consensus on clinical endpoints
• Limited hard clinical outcomes such as survival
• Limited natural history data, globally spread
• Lack of consensus/data on comparators
• Limited tools for measuring paediatric Health related Quality of Life
• Caregiver burden not adequately measured

12. What do we need to prepare
for HTA?
In order to prepare for HTA companies will need:
• An understanding of treatment pathways
• Systematic reviews of the evidence of
• Comparator treatment efficacy / effectiveness
• Utility data
• Resource use data
• Data on the burden of illness to inform discussions / populate an
economic model
• Evidence to fill any gaps e.g. utility mappings, registry analysis,
etc.
• An economic model
• A publication of the model with product specific data
• Clinicians and patient organisations will contribute evidence to
inform these and may also be expected to provide additional
evidence to HTA agencies.

13. DISCUSSION: Evidence checklist
Availability Coverage Quality
An understanding of treatment pathways and natural
history
Systematic reviews of the evidence of
Comparator treatment efficacy / effectiveness
Utility data
Resource use data
Data on the burden of illness to inform discussions /
populate an economic model
Evidence to fill any gaps e.g. utility mappings, registry
analysis, etc
An economic model
A publication of your model with your data (product
specific)

14. What does it cost to go it alone?
£200,000 to £400,000 per company per product for the basic suite of materials
BUT
May not have access to best data and best expertise
Limited pool of patients and clinical experts in rare diseases
§ difficult for them to engage with all companies
§ Participation fatigue?

15. How could working together get past
some of these issues?
• It would be possible to have larger overall budgets for a more thorough study (and a lower cost per
company) versus a basic study undertaken by a company independently
• Cost for one company = £100,000
• Cost for four companies = £50,000 each, £200,000 in total
• This is also a benefit given as of the four drugs, not all are likely to make it to market
• Credibility is increased by broader review, and being more impartial versus a single company study
• As a collective, companies are able to access leading experts who may be reluctant to connect with a
single company
• Patient groups are able to more easily and willingly engage with a collaboration
• By working together, repetition of efforts may be avoided and materials
may be made available in advance of when they are required

16. And then there’s the ethics …
• To collect data from patients and then not let it be used it is difficult to justify
• Patients enter trials and risk their own health, to help patients like them
• Placebo arm data and data from products that will no longer be developed
• Data on non-sensitive areas such as patient height and weight should be able to be shared
• This has been implicitly recognised by the pharma industry with initiatives like Project Data
Sphere (https://www.projectdatasphere.org/)
• Transparency is also valued by HTA agencies, patients and the public

17. Examples of collaboration in practice
• Diabetes
• The CORE diabetes model has many companies involved
• Mount Hood meetings are an example of joint working
• Rheumatoid arthritis
• By the use of a broadly standardised model (the BRAM – Birmingham Rheumatoid
Arthritis Model), input values can be used in competitor models
• Although companies keep independently rebuilding the model framework, at least it saves
having to conceptualise it each time
• Open source modelling
• A small movement, but growing
• Various models are now available freely, particularly in RA
• Duchenne Muscular Dystrophy
• Project HERCULES

18. When is collaboration likely to happen?
• Previously under studied areas (rare diseases) where substantial investment is needed in
developing the evidence base for HTA
• Well established diseases (e.g. diabetes, hypertension, depression), but not many
companies are investing here
• Where multiple companies are developing products i.e. at an early stage
• With smaller companies who have fewer internal people
• General epidemiology studies
• Maybe literature reviews (may also need bespoke SLRs)
• Registries
• Mapping different stages of disease
• Finite patient populations – competition for patients limits opportunity to gather
evidence
• Understanding broader definitions of value – Quality of Life, Burden of Illness
• Strong patient organisations able to help drive work

19. When is collaboration less likely?
• Where there are marketed products in direct competition
• Companies will be in competition for market share, with data an a tool
to do this
• When there is a monopoly
• No companies available for collaboration
• Where companies are far apart in timings
• Companies entering Phase II will have different needs to those finishing
Phase III which may not be compatible with collaboration
• Where there are competition concerns
• Companies must tread carefully where there are legal ramifications – a
formal collaboration
should be set up to avoid any accusations of collusion / price fixing

20. Patient involvement in HTA
20

21. What can patients bring to HTA?
21
“Without the patient’s
voice, it’s easier to be a
little bit more dismissive if
you’re looking at clinical
data… rather than
hearing what effect it had
on the individual patient.”

22. What can patients bring to HTA?
• Evidence and experience
• Patient group submissions can include a combination of qualitative and quantitative
evidence and experiential knowledge about:
• The condition in question – particularly aspects of the disease not well captured by standard tools
• The treatment in question
• Patient stories describing the impact of the condition
• Individual patients can bring experiential knowledge which can provide a fresh perspective
on the evidence. Subjective stories of personal experience can help committee members
better understand the real impact of a condition.
22

23. What can patient organisations actually
do?
• Drive evidence generation e.g. Project HERCULES, MPS Society UK,
• Produce a detailed written statement describing the most important aspects of the condition and the
treatment
• Nominate clinical and patient experts
• Gather evidence and represent the views of patients – survey members, use previous research
• Support patient experts in preparing for and attending committee meetings
• Follow up after the committee meeting on behalf of the patient experts – raise any issues that were
not covered and comment on the meeting
• Represent patients in any negotiations with NHS England
• Work with other patient organisations, particularly on any awareness campaigns – a united voice is
stronger
23

24. What can individual patients actually
do?
• Respond to questionnaires and contribute to patient organisation submissions
• Consider giving broad consent to the use of your anonymised data
• Volunteer to attend committee meetings and submit personal statements
• Submit responses to consultation documents
• Participate in awareness raising activities
24

25. Patient organisation checklist
25
• Do you understand the formal and informal mechanisms they can engage with HTA? Are
there special processes for rare diseases?
• Do you understand the requirements of the role you could play and have the skills needed?
• Do you want to be involved?
• If there is more than one relevant patient organisation, how aligned are they?
• Do clinicians understand the HTA processes? Are clinical and patient perspectives aligned?
• Are there evidence gaps that need to be addressed? How much time do you have to address
these gaps?
• How willing are you to work with industry? Are there multiple companies working in the
disease area that might make collaboration easier?
• Do you have the skills and the capacity to be effective?

26. Project HERCULES

27. Duchenne Muscular
Dystrophy
• Duchenne muscular dystrophy (DMD) is a genetic
muscle wasting disease caused by the lack of the
protein dystrophin. It affects the entire body.
• DMD is the most common fatal genetic disease
diagnosed in childhood. The disease almost always
affects boys, and they tend to be diagnosed before
the age of 5.
• Children will typically be wheelchair bound by the
age of 12 and will be totally paralysed by their
teens and they usually wont live beyond their 20s.
• There an an estimated 2,500 patients in the UK and
an estimated 300,000 sufferers worldwide.
• Duchenne muscular dystrophy is classified as a rare
disease.
• There are some licenced treatments and many in
development

28. Timeline for Translarna (UK)

29. About Duchenne
UK
Duchenne UK is an ambitious and highly
focused charity with a clear vision: to fund and
accelerate treatments and a cure for
Duchenne muscular dystrophy (DMD) for this
generation of patients.
Duchenne UK is a parent led charity. Co-
founders Alex Johnson and Emily Crossley met
when their sons were diagnosed with DMD.
They both set up charities, which joined forces
in 2016 to become Duchenne UK.

30. Unique multi-
stakeholder
collaboration
• To allow pharmaceutical companies, charities,
academics, patient organisations and experts to
work together to build the evidence base for DMD
required by Health Technology Assessment
Agencies, such as the National Institute of Health
and Care Excellence (NICE).
• To generate, align and share high quality disease-
level evidence across an entire condition to enable
an informed Health Technology Assessment (HTA)
process for more transparent and consistent
reimbursement decisions.
30

32. Working with stakeholders

33. Project
HERCULES:
key work
streams
33

34. Project Hercules’ deliverables
• Mapping clinical trial endpoints and natural history to clinical
outcomes
• Peer reviewed article
Data analysis
• Critique of existing QoL metrics
• Bespoke metric for DMD
Quality of Life
Metric
• Broad measure of burden of DMD to inform economic model
Burden of Illness
study
• Template economic model for companies to adapt for individual
products Economic model

35. Key Project HERCULES events/milestones
2019 • Burden of Illness study starts – March 2019
• Critique of QoL metrics complete – January 2019
• EURORDIS Black Pearl Award dinner - 12 February 2019
• Stage 2 Quality of Life survey launched – January/February 2019
• Data analysis complete – May 2019
• Draft QoL metric available – May 2019
• Economic model complete – June 2019
• Quality of Life metric workstream completed – August 2019
• UK Parliamentary event – to be confirmed (autumn 2019)
• Project report – December 2019
• ISPOR – November 2019
• Final project event – November 2019
• Burden of Illness study reports – early 2020 (date tbc)

36. Benefits
• Cost effective
• Charity and Academic leads
• Access data more easily
• Connect with HTA bodies
• Obtain Pro Bono support
• Close collaboration with patients
• Sharing capability
• No repetition - duplication - conflicting
results
• Access to world leading capability
• Gold standard outputs
• Recognised by HTA bodies
• Changing understanding and
perceptions of DMD

37. Patient and clinician participation has
been essential
• Clinical validation at every stage:
• Quality of Life metric
• Disease model
• Burden of Illness on patients and families
• Clinical and patient advisory groups
• Bringing perspectives together to improve understanding of what matters most
• Highlighting diverse patterns of care in UK despite a set of well established
guidelines
• Understanding and adoption
• Raising level of understanding of what NICE needs to know and how to represent patient
needs in HTA format

38. Challenges
In the absence of a blueprint challenges
are all new.
• Data: Identifying potential data
sources and accessing this data
• Contracts: Moving from concept to
sign up of 9 industry partners
• Paediatric interviews for Quality of
Life
• Burden of illness complexity
• The need for tailored work ex EU
• ICER review timing
• Scientific Advice from regulators and
HTA agencies can be costly and time
consuming

39. Some emerging findings
• Patient and clinician led natural history model
• Quality of life and cost impacts of losing ability to weight bear
• ‘New” disease state – transfer stage between ambulatory and non ambulatory states
• We need to better measure what is important to patients and families – it can’t
count if we don’t count it!
• Family/caregiver quality of life and burden of illness is poorly measured
• Considering developing a measure of carer quality of life that could include other
paediatric progressive conditions

40. DISCUSSION: Planning for success
Find technical experts to help you assess and articulate the need and develop the project scope
Raise awareness of the evidence gap for HTA and market access
Meet with industry to explore their interest in collaborating for HTA
Ensure common
understanding of
the need
Project governance: a steering group is recommended to ensure the views of all stakeholders are
able to contribute. A chair with experience of HTA is helpful in ensuring this group works effectively
Project team who have credibility with key stakeholders and the expertise and experience to
deliver the project
Project planning
and governance
Engagement with payers and HTA agencies, clinicians, patient industry and others
Develop a communications and engagement plan that will ensure awareness of the project and the
key outputs. This will maximise sign up to the project and adoption of any outputs.
Create opportunities for getting advice from HTA agencies, clinicians, patients and others
Publish methods and results
Build stakeholder
engagement in to
the project
40

41. DISCUSSION
• Could collaboration for HTA tools and evidence work in your disease area/
country?
• Is there clarity on the priorities in your disease area/country?
• How could you start?

42. Data sharing

43. Searching for data
We brought the experts together
surely the data would follow?
Unfortunately not.
Finding and accessing data has been
the biggest challenge for Project
HERCULES.

44. Data challenges
Project HERCULES has struggled to find and access suitable data
• Some of the most significant challenges for Project HERCULES have been around difficulties
accessing data and poor data management of prior studies, registries etc. These have
included:
• Lack of clarity over what data may have been collected to inform publications, what data are included in
registries and data sets
• Data that has transferred ownership and is effectively missing
• Legal and other challenges in setting up arrangements for access to data even when all parties are keen
to share data
• Variability in quality and consistency of data
• This has had an impact on timelines for the burden of illness study as well as the natural
history model and economic model.

45. Burden of Illness: Limitations of
published data
Hits
Adequate
quality?
Adequate
quantity?
Burden of illness studies 61 - -
Incidence & prevalence - Yes Yes
Healthcare resource use 308 No Yes
Other medical costs 496 No No
Broader governmental
costs
23 No No
Cost of living impact 128 No No
Productivity losses 437 No No
Impact on families 62 No No
Quality of life of family
and carers
- - -
The evidence on each area was
searched, and key studies
extracted, with quality assessed.
In general the quality was poor for
use in economic modelling. Even
where good studies were available,
these did not report results in a
useful format.
• Either results were given as
costs for a given year without
disaggregated results being
presented, or
• Results were not given by
disease stage – preventing an
understanding of how resource
use changed as the disease
progressed

46. Searching for solutions
• Think global
• Include any data you can get – brining small data sets together
• We have relied heavily but not exclusively on US data
• Collaborate
• University of Leicester natural history informing two collaboratives (Project HERCULES and D-RSC)
• Be persistent
• We are slowly unlocking more and more data sets and will use these for validation and future iterations
of the natural history and economic model
• Look forward
• Identify evidence gaps and propose solutions for future data collection
• Work with stakeholders to improve data quality and accessibility
• FAIR principles (findable, accessible, interoperable, reusable)
46

47. The Duchenne Registry is part of the TreatNMD Network

48. D-RSC:
A non-profit consortium to support collaborative research and regulatory acceptance of new drug development
tools (DDTs) for Duchenne muscular dystrophy, to enable the earliest possible patient access to new treatments.
Collaborative approaches:
Duchenne Regulatory Science Consortium @Critical Path Institute

49. 49
Companies:
Biophytis
Catabasis
Mallinkrodt
Pfizer
Santhera
Sarepta
Wave
Academics
Yetrib Hathout, Binghamton University
Hank Mayer, Children’s Hospital of Philadelphia
Heather Gordish-Dressman, Children's National
Health System
Cuixia Tian, Cincinnati Children’s Hospital MC
Ray Hu, Cincinnati Children’s Hospital MC
Jean Bange, Cincinnati Children’s Hospital MC
Annemieke Aartsma-Rus, Leiden University MC
Pietro Spitali, Leiden University MC
Tina Duong, Stanford
Erik Henricson, UC Davis
Craig McDonald, UC Davis
Kathleen Rodgers, University of Arizona
Brenda Wong, U Mass Memorial
Keith R. Abrams, University of Leicester, UK
Michael Crowther, University of Leicester, UK
Micki Hill, University of Leicester, UK
Advocacy:
PPMD
Buddy Cassidy
CDISC data standard – Therapeutic Area User Guide, published
Sept 2017.
Integrated database – 14 datasets integrated, patients age 3-35,
data that can be shared with the consortium has been shared.
Analysis dataset extracted.
Clinical trial enrichment platform– Letter of intent submitted to
FDA, analysis plan developed.
Other projects– Interest in other drug development tools such as
biomarkers, other models; letter of support from EMA for GLDH as a
safety biomarkers.
Deliverables:
Government:
FDA
NIH

50. FAIR data principles
• In 2016, in recognition of the urgency of improving data management to support research, a
paper was published in Data Science that was intended to provide guidelines to improve the
findability, accessibility, interoperability, and reuse of digital assets.
• The principles emphasise machine-actionability (i.e., the capacity of computational systems
to find, access, interoperate, and reuse data with none or minimal human intervention)
because humans increasingly rely on computational support to deal with data as a result of
the increase in volume, complexity, and creation speed of data.
• The World Duchenne Organisation is one of many organisation looking to implement the FAIR
principles to facilitate research. Duchenne UK and Project HERCULES are involved in this
work.
• For more information see www.nature.com/articles/sdata201618

51. Disclosure: Sue Fletcher, Perth, Western Australia
SF acts as a consultant to Sarepta Therapeutics and is named on IP licensed
to Sarepta by The University of Western Australia
*

52. Research outputs and contributions
• Neuromuscular disease models
• Molecular therapies
• Research governance and ethics
• RDRF- rare disease registry framework, data management systems
• Muscular Dystrophy Western Australia (since 1987)
Patient and family support, awareness
Research support
Research partnerships
PhD scholarships
Representation (government, state) and (federal -Rare Voices Australia)

53. Collaborative research- West Australian-style
• Funding: Australian National Health and Medical Research Council
"We propose a paradigm shift in which respiratory, sleep and patient reported outcomes are
considered within a unified framework of outcomes to track disease progression with
nocturnal hypoventilation as the hallmark sign of impending respiratory insufficiency.“
PI: Telethon Kids Institute Perth, Western Australia
CI Stanford University
AI Muscular Dystrophy Western Australia
“We have engaged with MDWA to partner in this project, and include support for students, but
also to ensure the research we are doing is meaningful. Matched to that we are running
community events to update the broad community on our research and also give clinical and
other updates. These other updates are informed by MDWA and the community.
The project has a community reference group and they have informed and influenced the
project at every step of the way, including the protocol and the actual research questions we
are asking.”

54. Data, data everywhere (the issues)
• Delayed publication (intellectual property)
• Data irreproducibility (particularly translational research)
• Data ‘matching’ (different labs/groups/centres)
• Unpublished data-> unnecessary repeated studies
• Undisclosed negative data
• Missing data

55. Scientific data management and stewardship
• Findability, Accessibility, Interoperability, Reusability
• (2016) awareness of the concept is increasing (researchers, institutes)
• understanding of the concept is becoming confused, different people apply differing
perspectives
• “FAIR” data practices state that the cost of a data management plans <5% of the total
research budget

56. Research outcomes and data ownership?
Academia
• Federal/state funding
• Not-for -profit/special interest group
• Commercial partnership
• Venture capital
Expected outcome
Ø Publish, data depository for ~omics data (usually mandatory)
Ø Patient benefit (publish/ share/ license/ commercialize)
Ø Commercialize (patent)/’shelve’/publish (unlikely….)
Ø Commercialize
What happens to all the negative data?

57. Research / R & D funding: expectations
Funding to academia:
• return on investment
• funding basic research-> usually little
(immediate) return $$$$
• basic research is rarely developed in a
practical way for doctors, hospitals or
pharmaceutical companies
• Investment in translational research
leverages the investments made in
biomedical science
Industry (pharma):
• return a profit (shareholders)
• income from commercial success will
fund ongoing R & D
• early stage success, deliver profit
before generics (or biosimiliars)
capture the market

58. Candidate molecules (from academia)– what next?
• Patent the data/drug
• License/sell IP
• Retain the IP
• Disclose/publish the data
(eg pre-publication)
Ø cost -$300 000 +
Ø $$$ return to your
institution
Ø establish company, raise
capital
Ø manufacture
• toxicology/preclinical
• safety (human)
• clinical trials
• regulatory approval
Ø commercial outcome
Give the drug to not-for-profit
entity
- Industry partner
- Un-registered drug/lower cost
(eg for a rare disease)
Action Outcome Alternative

59. Data ‘out there’
• ~omics data repositories
• Eg GWAS, WES, RNAseq, proteomics, metabolomics data sets for patients, age matched healthy controls
• Access costs, bioinformatics, proprietary analysis pipelines (salary, computing)
• Clinical studies: data management
• Integrating research and clinical data
• Missing data?
• Pre-publication

61. Data
Demographic
Genotypic
Phenotypic
Patient Registry
Information/Data capture
Advocacy
Clinical trial
Knowledge
Capture/management
Clinical Decision
Diagnosis
Treatment
Patient support
Comorbidities
Allied Health
Matt Bellgard, Queensland University of Technology

62. Registry Frameworks
An essential new dimension
Matt Bellgard, Queensland University of Technology

63. Registry
• National
• Regional/State
• Patient Advocates
Information
• Consent
• Diagnosis
• Treatments
Clinical Validation
International Disease Registries
IDR 1 IDR 2 IDR n…
Pharma/biotech/ academic partnership
• Drug design
• Clinical Trials
BioBanks
Samples Consent
ID/Barcode
-omics Platforms
Genomics
Proteomics
Metabolomics
Samples
IDs
Raw
data
store
Data
IDs
Translational Units (NGO/NFP/NIH)
with Technology/Platform Industries
Data
IDs
Processed
data store
Supercomputer
Infrastructure
Analysis
IDs
Cohort
Studies
Natural
Histories
Candidate
Genes
Population Wide
Studies Epidemiology
studies
• Populations Studies
• Disease gene R&D
Precision Medicine
• Genomics
• Proteomics
• Metabolomic
• Systems Biology
Patient
Analytic Validation
Genotype/
Phenotype
eHealth Records
Regulatory Bodies
• Regulatory
framework
• Decision-making
framework
• Bioethics
• Training
Clinical
Utility
Clinical
Validation
Patient
Specialist clinician
Therapies Monitoring
TREATMENT
General
Practitioner
Patient
Clinician
Symptoms
Tests
Results
IN
Genetic Testing/
Phenotyping
Bellgard et al. Rare Disease Roadmp, HLPT, 2014
Integrating
clinical and
research
data: building
on RDRF
Matt Bellgard, Queensland University of Technology

64. DISCUSSION: collaborating for data
• What are your experiences of data sharing?
• How well understood are the priorities for data in your disease area?
• Could you implement FAIR principles? Or alternative approaches to improving ability to share
and reuse data?

65. Thank you
Josie@jgzebra.com
65