Evolution of the Pharmaceutical Industry towards Personalised HealthcareHow are companies leveraging digital technology to improve decision making?
Thomas Brookland
International Regulatory Policy Lead
Hoffmann La Roche
There is growing consensus that, across
the healthcare ecosystem, Personalised
Healthcare is the future
• We are at a pivotal moment in healthcare history
• Unprecedented convergence of medical knowledge, tech and data
science is revolutionising patient care
• These advances enable us to arrive at a deeper understanding of
how to treat an individual
• Digital revolution provides new ways to collect high-quality data and
connect it to data from large pools of other patients
• RWD/RWE, Molecular information generated from NGS, Data from
wearable devices/mobile apps, Novel clinical trials
We are at a Transformational Moment for Healthcare
The challenge: increased complexity in all facets of healthcare
Knowledge acceleration Information management
Medical knowledge
doubled every 50 years in
1950
Today, it doubles every 72
days
Disease complexity
There are 200 tumour
types, which can have
up to 1.2 million
mutations
Diagnostic complexity
Only 2% of US cancer patients are exposed to comprehensive diagnostics
90% of patients exposed to comprehensive diagnostics have a treatment option
Processing patient
information from disparate
sources in multi-
disciplinary teams are
highly complex
The greatest challenges healthcare systems face –unsustainable costs, persistent inefficiencies,
uneven progress in improving patient outcomes –will require us to harness the power of data to
drive improvement
The New Data and Technology Environment is
Creating this Opportunity
5
HEALTHCARE DATA1
150
2’300
2012 20162014 2018 2020
Exa
byte
s
1. International Data Corporation, US only; 2. Big data analytics in healthcare: promise and potential (Raghupathi and Raghupathi); 3. ONC/American Hospital Association
(AHA), AHA Annual Survey Information Technology Supplement; 4. Statista.com
Kaiser Permanente, … is believed to have between 26.5
and 44 petabytes of […] rich data from EHRs,
including images and annotations2
In the US, EHR adoption in
oncology clinics has increased
from ~10% to >95%3
The number of health & fitness
tracker sold worldwide has more
than tripled from 26m in 2014 to
87 million in 20174
Pharma are Therefore Investing in
Data and Technology
6
0
2
12
18
34
~60
0
10
20
30
40
50
2012 2013 2014 2015 2016 2017
Deals
, P
art
ne
rsh
ips
, In
vestm
en
ts
Digital Health
Advanced Analytics
RWE
Genomic Data
Comprehensive Diagnostics
De
als
, P
art
ne
rsh
ips, In
ve
stm
en
ts
(Top 20 Pharma)
The generation and analysis of MDAS means we can
develop truly Personalised Healthcare
Digital Technology and Advanced Analytics can Impact on
Decision-making across the Healthcare Pathway
Patients
• Safety
• Convenience
• Clinical
outcomes
• Costs
Clinicians
• Treatment choice
• Disease
progression
• Diagnosis
Regulators
• Licensing
• Outcomes of
post-marketing
surveillance
• eLabelling
Payers
• Investment
• Reimbursement
R&D
• Biomarker
identification
• Target selection
• Assessment of
trial success
• Patient
recruitment
Decision-making in Personalised Healthcare requires input from
stakeholders at every level
ECHAlliance, European Connected Health Alliance; ICPerMed, International Consortium for Personalised Medicine; IMI EAPM,
Innovative Medicines Initiative European Alliance for Personalised Medicine.
Clinical practice, medical services,
academia and patient organisations
Individual companies
Industry/professional associations (e.g.
Pharma, Dia, IT, medical, data processing etc.)
Multilateral Consortia & Alliances (e.g.
ICPerMed, IMI EAPM, ECHAlliance, etc.)
Payers/HTA decision makers
Medicines and devices regulators
Healthcare data, infrastructure policy
makers
Data sharing policy makers
Influencers Decision-makers
Personalised Medicine will involve:
• Data from Digital Wearables
• Use of External Controls from RWD/RWE
• E-labeling
Personal mobile device apps
and wearables
• The development of digital technology, including
personal mobile device apps and wearables, is
providing new sources of data
• Objective measurement of data that otherwise would
not have been possible or would have been subject to
patient-reporting bias
• Complement more traditional clinical trial and registry
data
Clinical decision-making: monitoring disease progression
through smartphone apps
Clinical decision-making: monitoring disease progression
through smartphone apps
Clinical decision-making: monitoring disease progression
through smartphone apps
Floodlight
• Active tests of hand, motor
function and cognition
• Passive monitoring of gait
and mobility
• Combining active and
passive data provides a
unique signature for
Multiple Sclerosis (MS)
progression and prognosis
Sensors
Magnetometer
Sound
Light
Touch
Connectivity
Accelerometer
GPS
Gyroscope
Smartphone technology may be used to track patient activity and disease progression and help identify novel digital endpoints for use in clinical trials
Availability of e-Labeling
Decision-making for regulators: e-Labelling
• AI-supported regulatory decision making will play an important part in the e-labelling process in the future
– simplify decision-making in the process of label updates
– enabling medical insights to rapidly impact patient care
• Current global efforts towards e-Labelling are at different stages:
e-Labelling is the process of making the approved product labelling available in a digital format and is of importance for ensuring the most current prescribing
information is available for the safe and effective use of prescription drugs
Draft principles on ePI were
published for comment in
January 2019. EU survey
(2017-18) identified 14
established ePI-related
projects
2014 FDA proposed
requiring electronic
prescribing information
Product e-labels are
available on Health Authority
websites in Japan, Korea,
Singapore, Taiwan and
Malaysia
ROCHE PERSONALISED HEALTHCARE
The Use of Real World
Data (RWD) in External
Control Arms
RWD Definitions • FDA definition*:
• “Data relating to patient health status and/or the delivery of health care routinely
collected from a variety of sources.
• “Examples of RWD include data derived from electronic health records (EHRs);
medical claims and billing data; data from product and disease registries; patient-
generated data, including from in-home-use settings; and data gathered from other
sources that can inform on health status, such as mobile devices”
• EMA definition**
• “Routinely collected data relating to a patient's health status or the delivery of health
care from a variety of sources other than traditional clinical trials”
• “We specifically exclude traditional clinical trials even if single arm but would
incorporate data from pragmatic clinical trials if data were collected remotely through
an electronic health record or other observational data source and solely under
conditions of normal clinical care”
*FDA RWE Framework December 2018
**EMA publication “Real-World Data for Regulatory Decision Making: Challenges and Possible Solutions for Europe” April 2019 Publication in Clinical Pharmacology & Therapeutics
Why has RWD become of interest to stakeholders in
the Healthcare space?
19
• 1) RWD as a concept is not new , nor are methodologies!
• 2) What is new
– A) Newer sources RWD
– B) Availability of highly sophisticated and advanced electronic tools to analyze, integrate and link data
sources
– C) Awareness of RCT limitations:
• Only ~4% of all patients take part in clinical trials
• RCT populations rarely reflect “real world” populations
– Occur within a limited time frame
– Not large enough to detect rare treatment effects
– RCTs may not be generating evidence on endpoints useful to patients, providers, or payers
• Not always ethical to have patients on placebo or not enough patients to sufficiently power a trial
• Multitude of questions remain unanswered at the time of Regulatory approval
Tom Brookland RWD Training 2019
20
These shortcomings of RCTs highlight
the need to:
- Complement (or even replace) RCT data
with sources of data outside RCT
- Evolve/redefine trials where appropriate
and conduct “pragmatic” trials
Why has RWD become of interest to stakeholders in
the Healthcare space?
Q: Where could we use RWD?
A: Across Entire Product Life Cycle
21
Ref: IMI GetReal
High Interest in Use of External Controls
→ For discussion and consideration throughout today’s session!
Data from concurrent RCT
Data from Completed RCT
RWD: Individual patient data
RWD: Aggregated patient data
High
Low
Reliab
ility
/Qu
alit
y
Low
High
Bia
ses
“Gold” Standard
RCT
Ext
ern
al C
on
trols Clinical Trial Data
RWD
= control group consists of patients not part of the same clinical study
Potential Use of External Controls
• External controls could be considered to demonstrate:
– Natural history of disease
– Established efficacy from prior trials (e.g. establishing the null hypothesis for a
single arm trial)
– Comparing efficacy across treatment arms by supplementing or replacing
concurrent controls in a prospective trial
– Contribution of components to treatment effect
• Source of data for controls would determine potential use:
– FDA states: “If we want to compare efficacy endpoints, then high-quality and
complete patient-level data is required”
Where Have Regulators Accepted RWD?
“RWE is currently used extensively for evaluation of safety of marketed
products, but there is very little historical use of RW experience in drug
regulatory decisions about effectiveness”
Janet Woodcock, director of CDER
The use of RWD to support regulatory decision making is not new
Decades worth of regulator experience with RWE:
➢ Post Approval
➢ For safety signal evaluation / Pharmacovigilance
What about Efficacy and Effectiveness?
There is lower acceptability of RWD where the
interest is efficacy and effectiveness… but
there are some examples
FDA Approval History using RWD External Controls to
Support Efficacy
2006
= Breakthrough
Designation
= RWE came from data collected under
treatment IND or expanded access protocol
2010 20122014 (AA)
2017 (AA)2016 2015 2015 2014
2017 20182018 2019
These Approvals have things in Common• Cases involved single-arm trial + benchmark against aggregated external data
• Approvals limited to:
➢ Rare/Orphan settings
➢ High unmet medical needs
➢ RCTs not feasible/ethical
➢ No satisfactory treatment
➢ Single arm effect substantial
“The FDA on a scale of 1 to 10 may be at a 9 or 10 for RWD use in rare
diseases but may only at a 1 for RWE use in common diseases, especially if a
traditional clinical trial could be done to show efficacy”
Rajeshwari Sridhara, director of the CDER Office of Biostatistics
Division of Biometrics V
For the FDA, this rare space has provided an
“early testing ground” for FDA’s use of RWE in
effectiveness decisions
What does the FDA RWE Framework Say
about External RWD Controls?
• Typically external control arms use data from past traditional clinical
trials but in some cases uses RWD
• Limitations of external controls:
– Selection of a comparable population
– Lack of standardized diagnostic criteria or equivalent outcome measures
– Variability in follow-up procedures
• Collection of RWD on patients currently receiving other treatments,
together with proper statistical methods, could improve the
quality of the external control data, provided the relevant covariates
are captured
• FDA may issue guidance on the use of RWD for external controls
Tomas Salmonson
(former chair of
CHMP)
DIA interview 2019
“I would prefer to see a RCT with small numbers and not with
same aim of P<0.05, than single arm trials vs. contemporary or
historical controls….”
“…but if we do single arm trials I think we can do better
when it comes to creating the controls - we need to have
the methodological discussions around how to generate
robust data to come to robust conclusions….”
“Randomisation of RWD within registry studies is perhaps
a way forward…”
Ex-Regulator Recent Comments on External Controls
EMA RWD Article Published April 2019
Key Challenges to Broader Acceptance of RWD
• Data Quality
• Lack of Randomisation and introduction of bias
• Consistent statistical methodologies to develop RWE
• Definition and comparability of RWD Endpoints
• Validated methods to aggregate and link RWD sources
• Transferability of RWD across regions
Next steps
Making Personalised Healthcare a reality
• We must take a holistic approach to adapt the entire drug
development and healthcare system
• No one actor in healthcare can do this alone
• Realising the vision of Personalised Healthcare requires
strategic partnerships on several fronts:
• Patients and patient organisations
• Clinicians, healthcare providers and research partners
• Government and payers
• Regulators
Doing now what patients need next
