Fighting Neurodegenerative Diseases
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To learn more visit: https://insidescientific.com/webinar/cutting-edge-conversations-fighting-neurodegenerative-diseases/ Evelyn Pyper, MPH discusses how a patient-centered approach to real-world data collection and evidence generation can transform research in neurodegeneration. Neurodegenerative diseases often affect both motor and cognitive function, produce emotional and social changes, and require significant caregiver support, all while stretching across a fragmented healthcare ecosystem. Participatory research that directly obtains patient consent, empowers patients, and simplifies the task of linking multiple data sources, can lead to a more comprehensive capture of medical histories. This presentation briefly explores ways in which patient-centered research can improve understanding of disease diagnoses, symptomatology, and progression.
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Fighting Neurodegenerative Diseases
- 1. Copyright 2022. All Rights Reserved. Contact Presenter for Permission Fighting Neurodegenerative Diseases Evelyn Pyper, MPH Life Sciences Partnerships PicnicHealth Senior Evidence Strategy Manager
- 2. 2 Cutting Edge Conversations: Fighting Neurodegenerative Diseases Evelyn Pyper, Senior Evidence Strategy Manager, PicnicHealth June 28, 2022
- 3. 3 Fighting Neurodegenerative Diseases A patient-centered approach to real-world data collection and evidence generation for neurodegeneration research
- 4. 4 Session Objectives Objective 1: Demonstrate the need for real-world data in neurodegeneration research Objective 2: Describe patient-centric research and the unique value it brings within this therapeutic area Objective 3: Highlight the importance of using unstructured data to construct a complete picture of patient health Objective 4: Provide disease-specific challenges & opportunities of RWD collection
- 5. 5 An Introduction to Real-World Data (RWD) Sources of Real-World Data What is Real-World Data? Real-world data is data derived from outside the context of traditional RCTs. Research questions Appropriate analytics Real-World Evidence Medical records Prescription data Registries Lab data Hospital visits Claims data Patient- generated data
- 6. 6 Real-World Evidence (RWE) Evolution Evolving Landscape Evolving Stakeholders Evolving Applications Increasing number and complexity of players Public-private partnerships Growing role of data organizations (collection, linkage, analysis, etc.) Patient/consumer empowerment Growing recognition and acceptance by regulators and HTA bodies • FDA RWE Framework & Draft Guidance Documents • NICE RWE Framework • EMA’s Data Analysis and Real World Interrogation Network (DARWIN EU) Proliferation of RWE assets Uses across the product life cycle, starting from trial design and execution Value based contracting with payors Both assessment and reassessment by HTA bodies Personalized healthcare
- 7. 7 RWE Across the Product Life Cycle Earlier, more proactive RWE strategies enable greater depth and breadth of data serving research objectives across the product lifecycle ✔ More data, throughout the patient journey ✔ Ability to identify important trends and subpopulations ✔ Sustained engagement with hard-to-reach patient groups What are life sciences researchers looking for? Pre-Clinical Phase I Phase II Phase III Phase IV Ongoing Surveillance PicnicHealth partners with life sciences researchers to provide connected, complete patient-level data Unmet need / disease burden Natural history of disease Post-marketing safety Value-based reassessment Patient journey mapping Economic burden/HCRU Treatment patterns Cost saving with treatment PRO development/ identification Patient finding & recruitment Comparative effectiveness Monitor prescribing/use Subpopulation studies Patient experience External control arm Trial design
- 8. 8 Innovative approaches are needed for progress in neurodegenerative diseases We recognize the urgent need for new treatments… To face that challenge and to accelerate drug development, we need innovative approaches to better understand these diseases while also building on current scientific and research capabilities. - FDA Commissioner Robert M. Califf, M.D. “ ”
- 9. 9 The Case for RWE in Neurodegenerative Diseases Complexity of disease Clinical considerations Time and cost of trials Burden on patients and caregivers Diversity/heterogeneity of patients
- 10. 10 Why Patient-Centric RWD for Neurodegeneration? • Routinely-collected data can miss key information • What happens between clinic visits matters • Neuropathological change ≠ clinical significance • Bringing together data from disparate sources begins with the patient • Important insights come from cognitive, functional, and patient-reported measures • Direct-to-patient channels can extend to caregivers • Empowering patients and caregivers in their care journey & research inputs • Minimizing burden There’s more to the story Data spans sites & specialists Caregivers are involved
- 11. 11 What does patient-centric RWD look like in practice? PicnicHealth works directly with patients to create patient-centered RWE
- 12. 12 With patients at the center, we capture comprehensive and longitudinal data All Providers Average 23 providers per patient ● Primary Care ● Specialist Care ● Emergency Care All Care Sites Average 8 sites per patient ● Academic & Community ● Inpatient & Outpatient All Medical Records Average 91 clinical documents per patient ● Structured data (e.g., EMR) ● Narrative text (e.g., doctor’s notes) Leveraging unstructured narrative text can identify more patients vs. using only ICD codes in structured sections of the EHR. For LN, we were able to identify 95% more patients!1 1. Tierney M, Rowe C Addition of narrative next abstraction to ICD-based abstraction significantly improves identification of lupus nephritis in real-world data [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 10).
- 13. 13 Confidential Multiple Sclerosis journeys necessitate longitudinal & complete data PicnicHealth provides: 2,500 patient cohort 7+ years of data per patient 5 providers per patient 3 sites of care per patient
- 14. 14 Confidential Assistive Devices Wheelchair use survival plot 0 10 20 30 40 Time in Years Survival Probability 0.00 0.25 0.75 1.00 0.50 Number at risk 100 55 23 4 0 Assistive Device Use Progression to Wheelchair Use 52% 38%
- 15. 15 PicnicHealth has a strong track record in MS evidence generation. Manuscript, JAMIAOpen, Volume 5, Issue 1 Research Poster, ACTRIMS 2021Virtual Forum Research Poster, ACTRIMS- ECTRIMS Meeting, MSVirtual2020 Research Poster, ECTRIMS Congress 2019
- 16. 16 Parkinson’s Disease 1,500+ patients Evolving understanding of Parkinson’s Disease (PD) • PD is increasingly recognized as a complex condition with both motor and non-motor clinical features, incl. neuropsychiatric manifestations • Past registries or chart reviews may not have captured the right elements to describe the natural history of PD as we now understand it Common data limitations • Generalizability • Recency • Focus on incident or untreated populations • Dependence on patient recall Key data elements • Treatment histories • UPDRS parts I and II • Subjective and objective measures of disability • Motor & non-motor symptoms Insights from Unstructured Data Unrestricted access to narrative text improves captures of motor & non-motor symptoms for assembling clinical phenotypes of PD across multiple stages of disease.
- 17. 17 PD Cohort: Disease and Treatment Burden Medication discontinued 73 year old, female Medication restarted at increased dose Identify unmet needs and explore the reasons for treatment switches and discontinuations Explore the presence of psychiatric comorbidities and association with poorer PD outcomes TREATMENTS MENTAL HEALTH Can prospectively capture UDPRS and/or extract data on symptoms and disability from medical record PROGRESSION
- 18. 18 Looking at the whole patient (motor and non- motor symptoms) is critical Mental Health Burden in PD 68% 58% 35% 31% 14% 5.60% 0% 10% 20% 30% 40% 50% 60% 70% 80% Any mental health disorder Sleep disorders Anxiety Depression Cognitive deficiency Psychosis
- 19. 19 Alzheimer’s Disease We are building a cohort of 2,500patients Learnings from our journey in Alzheimer’s disease to date : • Industry interest in MCI and patients who are yet to have formal diagnosis • Complexity from specific imaging needed for diagnosis • Reimbursement decisions around diagnostic tests can influence therapeutic landscape Common data limitations • Disease staging not available • Long disease course requires sufficient lookback/forward • Recency – given competitive and evolving Tx landscape Key data elements • Onset of cognitive impairment • Diagnosis, symptoms & comorbidities • Treatment & dosages • Treatment-emergent AEs • Biomarkers Insights from Unstructured Data We can use narrative text abstraction to access scores of diagnostic exams (MMSE, MoCA) and other mentions of physician/neurologist assessment of cognitive impairment.
- 20. 20 Huntington’s Disease The current HD landscape • Large, established research base through long-standing registries; but much of this data is siloed and incomplete. • No cure or treatment for HD disease progression. However, Tx’s exist to help patients manage movement, cognitive, and psychiatric symptoms. Common data limitations • Hard to connect cognitive assessments to costs, treatment patterns, or HCRU • Limited access to patients for collection of PROs • Linked genetic information Key data elements • Age of onset, Date of diagnosis • PROs (e.g. UHDRS-TFC, EQ- 5D, WPAI, HiDEF, DSST) • Functional assessments • Cognitive & behavioral assessments Insights from Unstructured Data To inform overall understanding of Huntington's disease, we can do customized abstraction of assistive device use, weight loss, cognitive impairment, and onset/progression. We are building a cohort of 400+patients
- 21. 21 Targeting Patients across Neurodegenerative Diseases: Challenges & Opportunities Challenges Opportunities • Inconsistent scales for severity staging • Difficult to define early-stage vs. late- stage patients at a given time point • Progression differs across patients • Challenge of differentiating between symptoms (e.g. MCI), dementia, and formal diagnosis (e.g. Alzheimer’s) • Capture measures of motor function through wearables, mobile apps • Capture cognitive, emotional, and social changes through PROs • Capture diverse spectrum of disease stages, severity, symptoms, treatment status and more complete medication information through unstructured data
- 22. 22 What patient-centric research means for patients Empowering patients and their caregivers Inclusion of the patient experience, not just outcomes More data = better understanding of disease Better characterize quality of life, not just quantity of life 01 02 03 04
- 23. 23 Confidential Patients sign up, consent, and get access to their records picnichealth.com/hemophilia
- 24. 24 Confidential PicnicHealth sets a new standard for RWE Traditional RWE Data Aggregators Aggregate siloed de-identified data sets Work directly with patients All sites of care for each patient Single site of care for each patient Abstraction from doctors notes and reports Structured data fields only Retrospective and prospective Retrospective only Customizable data model to answer research questions Fixed data model Enrichment with imaging files, PROs, and claims data Stand-alone data De-centralized enrollment from anywhere in ~5 minutes Limited to patients in existing data set
- 25. 25 Confidential PicnicHealth Cohorts Fully Enrolled PD Parkinson’s Disease 1,500+ Patients SCD Sickle Cell Disease 900+ Patients MG Myasthenia Gravis 500+ Patients Enrolling in 2022 Hem Hemophilia A + B 450+ Patients Pilots Launched Amyloidosis Atopic Dermatitis Berger’s Disease Cystic Fibrosis Duchenne’s Muscular Dystrophy Eosinophilic Esophagitis Focal Segmental Glomerulosclerosis Hepatitis B Human Immunodeficiency Virus Migraine Non-alcoholic Steatohepatitis Pulmonary Arterial Hypertension Scleroderma A AD BD CF DMD EE FSGS Hep HIV M NASH PAH S T1D Type 1 Diabetes PBC Primary Biliary Cholangitis 300+ Patients LN Lupus Nephritis 250+ Patients MS Multiple Sclerosis 2,500+ Patients IBD Inflammatory Bowel Disease 1,000+ Patients ALS Amyotrophic Lateral Sclerosis 500+ Patients AD Alzheimer’s Disease 2,500+ Patients HD Huntington’s Disease 400+ Patients ITP Immune Thrombocytopenic Purpura 300+ Patients PNG Paroxysmal Nocturnal Hemoglobinuria 150+ Patients Pom Pompe Disease 75+ Patients
- 26. 26 Confidential How to work with PicnicHealth 1: License an Existing Cohort PicnicHealth has a cohort of patients or subset of patients ready-built that meets your needs. 2: Request a New Cohort The data you need doesn’t exist, but PicnicHealth can build a cohort that we all benefit from. 3: Build A Virtual Registry Have patients you wants to study? Use our platform to manage enrollment, data collection, and future engagement and/or linkage. Custom Curated Deep Dataset
- 27. 27 Looking Ahead A challenging, but hopeful future for neurodegeneration research An ever-growing patient population in need ofTx A large, promising pipeline for thisTA
- 28. Watch the Cutting Edge Conversations Series On Demand CLICK HERE to register
Hinweis der Redaktion
- Thank you for joining this session today - and thank you to all of you who play a role the pursuit of treatment for these devastating conditions. So many (myself included) have been personally impacted by neurodegenerative diseases, whether first-hand witnessing its impact on a friend or relative, through serving a role as a caregiver, or from working in this space as a researcher or clinician. The challenging, non-linear journey from where we are today to future breakthroughs for neurodegenerative conditions is made up of many connected components – from expanding our basic scientific understanding of neurodegenerative diseases and their underlying causes, to identifying better prognostic and diagnostic biomarkers, to finally developing treatment to fight or even cure these diseases. And whether we are considering epidemiological studies, innovative clinical trial design, or clinical effectiveness of treatments, data – collected from patients in the real world – has a role to play.
- Definitions of RWD, RWE Real-world data is data derived from settings outside of randomized controlled trials (RCTs). It can be collected from a variety of real-world sources. Once real-world data is organized, interpreted or analyzed, and used to draw conclusions about a specific research question, that’s when it becomes evidence Sources of RWD Use by different healthcare system stakeholders
- Proliferation of RWE policies, players, etc. FDA RWE Framework & Draft Guidances
- The value of RWE spanning clinical development, market access, reimbursement, surveillance, sustained/expanded value demonstration Right patients being studied Improved probability of success Faster, efficient Right patients receive tx Q re. trial deisgn: Gene therapy example
- Last week, the FDA unveiled its Action Plan for Rare Neurodegenerative Diseases including Amyotrophic Lateral Sclerosis (ALS) – a five-year strategy for improving and extending the lives of people living with rare neurodegenerative diseases by advancing the development of safe and effective medical products and facilitating patient access to novel treatments. “The effects of rare neurodegenerative diseases are devastating, with very few effective therapeutic options available to patients.” This action plan, especially including the use of public-private partnerships and direct involvement of patients, will ensure the FDA is working toward meeting the task set forth by Congress to enhance the quality of life for those suffering by facilitating access to new therapies.”
- Today, we are talking about RWE in the context of neurodegenerative diseases: a heterogeneous group of disorders that are characterized by the progressive degeneration of the structure and function of the central nervous system or peripheral nervous system. In other words, these diseases involve the break down and even death of nerve cells in the brain or peripheral nerves. Certain treatments may help relieve some physical or mental symptoms associated with neurodegenerative diseases, we don’t currently have treatments that slow progression or cure these diseases. Why RWD is conducive to neurodegeneration research Clinical considerations, e.g. symptoms of interest Complexity of disease Complex pathologies Diverse clinical features Multiple factors at play, including cognitive impairment and behavioral health changes (often underdiagnosed) Cost and time to study neurodegenerative processes in clinical trials Minimize burden on patients and caregivers Diversity/heterogeneity of patients (e.g. comorbidities, sociodemographics) Clinical trials often: lack an ethnically, racially or geographically diverse population; exclude those with comorbid conditions or taking concomitant medications
- ** Key slide; Opportunity here to build up the story from the perspective of a patient. There’s more to the story – Routinely-collected data misses key information What happens in between visits matters, especially for this population What is happening at the cellular level does not always translate into clinical significance Caregivers are involved – Direct-to-patient channels can extend to caregivers Data spans sites and specialists – Bringing together disparate data sources starts with the patient Empowering patients and caregivers – With patients part of this process, it not only means better data, but also benefits to patients (empowerment in care journey; contribution to research; recognition of symptoms/experience/QoL beyond outcomes) These diseases also place an immense burden on both patients and caregivers. Patient-centricity means engaging and empowering, while not added additional burden.
- Introduce the PicnicHealth approach Working directly with patients; act on their behalf to gather all health records - and information from those records (including unstructured data) Patient consent drives ability to use data to support clinical care, as well as research Near term - Patients benefit from improved continuity of care and lessened burden (e.g. keeping track of disparate medical info) Longer term - This research, enabled by patient consent, can drive improvements in care, availability of innovative treatments, etc. {Slide from standard pitch deck: “PicnicHealth works directly with patients to create patient-centered RWE”}
- PicnicHealth’s strong background in neurodegeneration began with MS We have built a rich cohort of 2,500 patients, representative of the MS population in the US We have also demonstrated the value of a large, longitudinal real-world dataset to life sciences partners- notably, working with Roche as part of the FlywheelMS study uncover novel insights about the clinical profile of individuals prior to disease onset. NOTE: Many of these data elements would not be found in traditional claims data Call out examples ------------- Existing data sources have gaps with real consequences: Claims: 2–3-year window of data capture EMR aggregators: Critical information missing
- Complexity of assistive device use E.g. If they don’t file medical claim to purchase device, it’s not captured in claims Discussing function w/ provider – narrative text use of cane/wheelchair Our platform also enables us to ask further questions through prospectively-collected surveys on our platform.
- Our work in MS can be found in a number of published abstracts/posters/papers. As part of this work, custom abstraction methods were designed to extract MS-specific variables, including disability measurements, neurologic signs related to progression, disease subtype, and brain MRIs.-- All expertise that we can, and have, applied across other neurodegenerative diseases. We are taking the things we learned in MS (progression, assisted devices, treatment switching reasons) and applying to other neurodegenerative diseases, while keeping patients at the centre Which we are now launching
- We have built a cohort of 1,500 PD patients covering 50 U.S. states. Unique evidence generation challenges for PD Evolving understanding of disease: PD is increasingly recognized as a complex condition with both motor and non-motor clinical features, including neuropsychiatric manifestations Limitations of data sources Alternative Data Sources are not generalizable Recency challenges: CMS and VA data sources are not up-to-date and not generalizable. MJFF registries are focused on incident or untreated populations Patient informed registries are dependent on patient recall of symptoms and treatments (6-12 months retrospective coverage) Key data elements – (critical endpoints; but not always done in routine care): Comprehensive treatment histories can be constructed, including treatment-emergent adverse effects (TEAEs) UPDRS parts I and II can be prospectively captured We know that clinicians are gathering this info, but we need to find ways to engage the patients more frequently – can be completed by patient or caregiver Subjective and objective measures of disability Unrestricted access to unstructured text/physician’s notes to better capture motor and non-motor symptoms for assembling clinical phenotypes of PD across multiple stages of disease How use of PH (incl. unstructured data) can overcome these gaps
- TREATMENTS: Can look at longitudinal treatment patterns; Prescriptions, dose, and even reasons for discontinuation MENTAL HEALTH: Can explore mental health burden in PD PROGRESSION: Can prospectively capture UPDRS (unified Parkinson's disease rating scale) parts I and II Part 1 = evaluation of mentation, behavior, and mood (non-motor behaviours of daily living) Part 2 = self-evaluation of the activities of daily life (ADLs) including speech, swallowing, handwriting, dressing, hygiene, falling, salivating, turning in bed, walking, and cutting food Claims would only tell you – billed another prescription – not onset of symptoms, mental illness/comorbidities;
- just show as percentage of cohort. with a title to the point of "looking at the whole patient (motor and non-motor symptoms) is important). May or may not be formal diagnoses or ICD-codes; Can take a number of approaches (unstructured + coded data)
- PicnicHealth is building a cohort of 2,500 patients In preparing this cohort, we have learned a lot about this space We have learned from industry the research interest in people with MCI, who do not yet have a formal AD diagnosis; greater opportunity for intervention This space is made even more complex by the need for specific imaging (e.g. amyloid PET scans) which may not be covered by payers. Accordingly, reimbursement decisions around diagnostic tests can drive strategies for real world evidence generation. Why RWE? - This is an example of the healthcare system-related factors that are not accounted for in traditional trials, where all patients may receive the same test. Why PH? - This is just one example of why data from different sources (incl. Imaging, claims, etc.) is needed to tell the full story. E.g. Questions of treatment effectiveness may be biased by the fact that certain patients are being systematically missed (missed diagnosis; missed opportunity to receive treatment, stemming from out-of-pocket costs of PET.) Patient-level data capturing claims, health records, imaging, PROs can help unpack these factors at play Limitations of common data sources Disease staging not available Long disease course requires sufficient lookback/forward Recency – given competitive and evolving Tx landscape Key data elements Onset of cognitive impairment*** Esp. given industry interest in early stages of disease; before formal diagnosis; Caregiver may also notice onset; reliable assessment Diagnosis Symptoms & comorbidities Treatment & dosages PRO collection Treatment-emergent AE’s (Critical to research because TEAEs can greatly impact quality of life, follow-on treatment choices, and HCRUs) How use of PH data (incl. unstructured data) can overcome these gaps We can use narrative text abstraction to access scores of diagnostic exams (MMSE, MoCA) and other mentions of physician/neurologist assessment of cognitive impairment. MMSE = Mini-Mental State Exam MoCA = Montreal Cognitive Assessment NOTE: **We can also capture relevant biomarkers. ; E.g. CSF biomarker analysis of content including Beta Amyloid ratio. * Can be captured from a variety of places; Lab, primary care; FLEXIBLE RWE PLAN (whether traditional, blood-based, imaging, or physician notes); PREPARE TO CAST A WIDE NET
- We are excited to also be launching a cohort in Huntington’s Disease, a more rare, inherited disease that causes uncontrolled movements, emotional problems, and cognitive impairment. Limitations of common data sources There is a large, established research base through long-standing registries–however, much of this data ecosystem is siloed and incomplete Key data elements: Age of onset; Date of diagnosis PROs (e.g. UHDRS-TFC, EQ-5D, WPAI, HiDEF, DSST). UHDRS-TFC= Total Functional Capacity Functional Assessments (e.g., UHDRS, SDMT, Stroop); UHDRS=Unified Huntington's Disease Rating Scale; SDMT= Symbol Digit Modalities Test Cognitive assessments (e.g., MMSE, MoCA, PBA-s, PHQ-9) MMSE = Mini-Mental State Exam MoCA = Montreal Cognitive Assessment PBAs = Problem Behaviours Assessment (short form) PHQ-9=Patient Health Questionnaire [depression] +Genetic information; Mutation information not captured With patient consent, we can connect to any data where the patient has data (vs. other data source providers)
- Critical endpoints, but not being done in routine care (not feasible; not consistently captured); Explore alternative ways to get at key endpoints ; Learn nuances of designed endpoint Challenges: Inconsistent scales used for severity staging Scales may only cover one dimension of health–Cognitive/Functional/QoL–but not all Difficult to define early vs. late patients at a given time Progression differs across patients Teasing apart symptoms (e.g. MCI) vs. dementia vs. AD Opportunities: Can capture measures of motor function through wearables, mobile apps Can capture cognitive, emotional, and social changes through PROs Leveraging unstructured data (narrative text abstraction) enables researchers to explore a diverse spectrum of disease stages, severity, symptoms (e.g. onset of MCI, memory loss), treatment statuses and capture more complete medication information.
- Take-away: Working with patients enables us to do all this great research, but what is the benefit for patients themselves? Empowering patients and their caregivers Inclusion of the patient experience, not just hard outcomes Better characterize quality of life, not just quantity of life More data enables better understanding of disease epidemiology, treatment effectiveness, all over a longer time period
- Wouldn’t want you to leave this webinar without showing how we do this … How our studies typically work Patients sign up electronically in 5 - 10 minutes and consent to PicnicHealth collecting medical records on their behalf PicnicHealth collects complete historical and prospective records and creates custom, deidentified datasets Data is enhanced via multi-modal approaches (e.g., ePROs, claims linkage) via patient-centric approach Patients are empowered to own their health history & receive digitized records via their PicnicHealth timeline
- ALS – FDA
- Neurodegeneration research is complex–but the future is promising There is an ever-growing patient population in need of evidence that brings us closer to better diagnosis, management, and treatment of these diseases Elevated recognition and importance by top researchers and pharma innovators Over 100 drug assets in development / in trials Take away: We’re exciting to work in this space and partner with you to generation real world evidence that makes a difference for real patients