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