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Andrew Bate, Senior Director,, Analysis Team Lead,
Epidemiology and Big Data Analytics
BIA Regulatory Innovation Conference on “Innovation and
regulatory science in an evolving environment”
Tuesday 17 September 2019 at RCGP, London
Industry perspective: post-authorisation evidence generation
Disclosures and Potential Conflicts of Interest
• I am a full time employee of Pfizer and hold stocks and stock options
• The views presented in these slides are my own and not necessarily those of
Pfizer
Phase 4Approval
Post-marketing Requirements
Standing Cohorts
Phase 2-3
Real World Evidence Part of Regulatory
Strategies
Rapid Queries for Signal Refinement
Compare safety/effectiveness & detect unexpected signals in real
world clinical practice
Establish dynamic cohorts representative of clinical program and indicated
populations
Electronic Medical Records, Insurance Claims, Hospitals and Registries
Rapidly estimate risks to address ad-hoc questions
In the past two years, some regulatory decisions
on effectiveness have been informed by RWE
MRD = minimal residual disease
SSTR = Somatostatin receptor
GEP-NETs = gastroenteropancreatic
neuroendocrine tumors
HER2 = human epidermal growth factor receptor 2
HR = hormone receptor
FDA EMA
ApprovalLabel
Expansion
Conditional
ApprovalApproval
Pragmatic Schizophrenia P(2018)
External
Comparators
Metastatic merkel cell carcinoma (RW Benchmark) P(2017)
Accelerated*P(2017)
Infantile batten disease (RW Comparator) P(2017)
FullP(2017)
Diffuse large B-cell lymphoma (RW Benchmark)P(2017)
FullP(2018)
Omegaven Parenteral nutrition-associated cholestasis (RW Comparator)P(2018)
Full
B-cell precursor acute lymphoblastic leukemia in 1st / 2nd
complete remission with MRD ≥ 0.1% (RW Comparator)
P(2018)
AcceleratedP(2019)
Observational
Reduce the risk of graft rejection in pediatric class 3 beta-
thalassemia
P(2017)
Full
Somastatin receptor-positive gastroenteropancreatic
neuroendocrine tumors (GEP-NETs)
P(2018)
FullP (2017)
HR+, HER2- advanced /metastatic breast cancer in males P (2019)
Regulators asked for post-approval clinical trial
RWE included in the label
Ref Kraus A. Postmarket Real World Data Perspectives: Oncology
Registration Use Cases. FDA/AACR workshop 19 July 2019
A Selection of Healthcare Databases
Database Country Characteristic Population
Size
THINUK
GP primary care
database10.5 M1
Danish National Health
Service Register Database
Denmark Healthcare
registry of care
5.5 M2
PremierUS
Clinical data from
the hospitals
130 M+ patient
discharges3
Normative Health
Information (NHI)
DatabaseUS
Transactional
claims records of
a commercial
health insurer
60 M+4
Health Insurance Review
and Assessment Service
(HIRA)Korea
Insurance Claims
from near
universal national
system
48 M5
1 Blak et al Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates.
Informatics in Primary Care 2011;19:251–52 Furu K. et. al. The Nordic Countries as a Cohort for Pharmacoepidemiological Research. Basic & Clinical Pharmacology &Toxicology 2009; 106:
86-943 Fisher BT et al. In-hospital databases In Pharmacoepidemiology 5th Edn 2011 pp 244-2584 Seeger J, Daniel GW. Commercial Insurance Databases. In Pharmacoepidemiology 5th Edn 2011 pp 189-208 5 Kimura T et al. Pharmacovigilance systems and databases in Korea, Japan and Taiwan.
PDS. 2011; 20: 1237–1245
US FDA Sentinel Initiative
• Large Claims and EHR databases for analysis of drug
outcomes, linked in “distributed network”
• Mandated by Congress: FDA Amendments Act of 2007
• Full Sentinel System now in routine use
– Sole FDA use Mini-Sentinel Pilot project ran from 2009-2014
• Distributed database: data from 18 health plan data
partners that retain physical and operational control
over its own data
• Data on 193 million members
• Rapid analysis capability
Sources: 8th Annual Sentinel Initiative Public Workshop 2016 accessed 22nd February 2016
6
FDA’s Sentinel InitiativePartner Organizations
Institute for
Health
Lead – HPHC Institute
Data and
scientific
partners
Scientific
partners
Common Data Model in Distributed Network (e.g., OMOP)
Source 1 Source 2 Source 3
OMOP
Analysis
results
Analysis
method
Transformation to a common data model e.g. OMOP
Diagram reference: OMOP
Use of a Common Data Model facilitates fast
analysis of multiple databases, and allows
analyses across a distributed network. Use of
data converted to common denominator can
be problematic
‘Three tiered’ Real World Data Strategy needed for supporting a wide portfolio
“Ad-hoc” use data sets
Remote access databases
Centralized licensed in-house data
Suitability of RWD source to address the question of interestData capture and its structureAccessibilityDemonstrability of data and analysis integrityRecency of data available for analysisStakeholder needs
‘Three tiered’ data strategy
Secured appropriate efficient governance
Imperfections in any RWD coupled with huge inter-source heterogeneity requires situation specific RWD solutions
A ‘smorgasbord’ styledata strategy
Rapid Cycle Analysis across databases from 8 countries and 685 million patients –days not weeks/months/years
Ref Zhou X et al. Big Data and Real World Evidence: Rapid Cycle Analysis Capability via Emerging Analytic Tools – Insights in
Atopic Dermatitis and Lessons for Wider Adoption. Pharmacoepidemiology and Drug Safety. In Press
Examples of EU biological registries11
BIOBADASER
ARTIS
RABBIT
DANBIO
BSRBR
Harnessing the Power of Real World Evidence: Pharmacoepidemiology strategy for tofacitinib
Ref Gatto NM et al. The Role of Pharmacoepidemiology in Industry in Pharmacoepidemiology (6th Edition). In Press
Structured (“Coded”) Unstructured (“Free Text”)
Natural Language Processing (NLP) in Electronic Medical Records
Demographics. diagnoses,
procedures, Rx, lab orders &/or
results, billing, operations data
For an applied example (acute liver injury) see Walker A et al.. Int J Medical Informatics 86: 62-70, 2016; Zhou X et al.. PDS 23(S1): S397, 2016
Emerging RWE capabilitiesHarnessing the Value of Unstructured RWD
Prediction Model for Advanced Stage ER+/HER2-Breast Cancer
• Predictive models developed from clinical knowledge and empirically from claims data using logistic and lasso regression. • Female breast cancer cases in Anthem's Cancer Care Quality Program served as gold standard validation sample• Model applied to HealthCore Integrated Research Database (Claims) to identify cohort of women with ER+/HER2−
Ref Beachler DC et al 2019. Predictive model algorithms identifying early and advanced stage ER+/HER2− breast cancer in claims data. Pharmacoepidemiology and drug safety, 28(2), pp.171-178.
Can we have clarity on how evidence was generated?
Do we have confidence in the scientific approach?
Is the benefit-harm profile acceptable?
TRANSPARENCY & REPRODUCIBILITY
ROBUSTNESS DECISION
The 3 hurdles for healthcare decision making with RWE
Slide courtesy of Sebastian Schneeweiss, Harvard Medical School
Multiple, multiple database initiatives around the world with different access approaches
Different approaches, different results/insights, we take a smorgasbord approach
Emerging Guidance supports the Robust Use of RWD
International Society for
Pharmacoeconomics and
Outcomes Research
RECORD-PE
EMA Registries Initiative
1. https://www.ema.europa.eu/human-regulatory/post-authorisation/patient-registries2. https://www.ema.europa.eu/documents/report/report-haemophilia-registries-workshop_en.pdf
Conclusions
• Post-approval credible Real World Evidence generation is a multiple data stream problem. Access to data is critical
• A smorgasbord approach to RWD use is essential to support a varied product portfolio post-approval – Registers, claims databases, EHRs, distributed data networks and data linkage all being important– Continually consider emerging data sources and technologies
• Ongoing efforts are required to maintain a regulatory/legislative environment that fosters research on RWD
• Standardization and harmonisation initiatives including private-public partnerships essential• Guidance from governmental and other bodies on the use of Real World Evidence to ensure
consistency how, what and when it is used and interpreted • Need agreement on standards of RWD sharing and transparency with “data stewards” and
researchers within and outside industry
Recommended