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WORKSHOP L: Josie Godfrey, Project Hercules: A UK Duchenne Global Collaboration - JG Zebra Consulting

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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

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WORKSHOP L: Josie Godfrey, Project Hercules: A UK Duchenne Global Collaboration - JG Zebra Consulting

  1. 1. Project HERCULES: A Duchenne UK Global Collaboration for evidence Josie Godfrey A Rare International Dialogue Toronto, May 2019
  2. 2. 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
  3. 3. Have you been involved in a Health Technology Assessment? Have you had experience of collaborations for data?
  4. 4. 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.
  5. 5. HTA for rare diseases
  6. 6. Summary of Drug Development Process
  7. 7. 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?
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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.
  12. 12. 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)
  13. 13. 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?
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. Patient involvement in HTA 20
  20. 20. 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.”
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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?
  25. 25. Project HERCULES
  26. 26. 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
  27. 27. Timeline for Translarna (UK)
  28. 28. 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.
  29. 29. 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
  30. 30. Working with stakeholders
  31. 31. Project HERCULES: key work streams 33
  32. 32. 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
  33. 33. 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)
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. 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?
  40. 40. Data sharing
  41. 41. 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.
  42. 42. 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.
  43. 43. 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
  44. 44. 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
  45. 45. The Duchenne Registry is part of the TreatNMD Network
  46. 46. 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
  47. 47. 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
  48. 48. 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
  49. 49. 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 *
  50. 50. 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)
  51. 51. 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.”
  52. 52. 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
  53. 53. 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
  54. 54. 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?
  55. 55. Research / R & D funding: expectations Funding to academia: • return on investment • funding basic research-> usually little (immediate) return $$$$ • basic research is rarely developed in a practical way for doctors, hospitals or pharmaceutical companies • Investment in translational research leverages the investments made in biomedical science Industry (pharma): •  return a profit (shareholders) •  income from commercial success will fund ongoing R & D •  early stage success, deliver profit before generics (or biosimiliars) capture the market
  56. 56. Candidate molecules (from academia)– what next? •  Patent the data/drug •  License/sell IP •  Retain the IP •  Disclose/publish the data (eg pre-publication) Ø cost -$300 000 + Ø $$$ 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
  57. 57. 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
  58. 58. 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
  59. 59. Registry Frameworks An essential new dimension Matt Bellgard, Queensland University of Technology
  60. 60. 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
  61. 61. 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?
  62. 62. Thank you Josie@jgzebra.com 65