Transcript Name of the meeting

The PROTECT project
An Innovative Public-Private Partnership for New Methodologies in
Pharmacovigilance and Pharmacoepidemiology
Progress Status: November 2011
PROTECT is receiving funding from the
European Community's Seventh
Framework Programme (FP7/2007-2013)
for the Innovative Medicine Initiative
(www.imi.europa.eu).
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PROTECT Goal
To strengthen the monitoring of benefit-risk
of medicines in Europe by developing
innovative methods
to enhance early detection and
assessment of adverse drug
reactions from different data
sources (clinical trials,
spontaneous reporting and
observational studies)
to enable the integration
and presentation of data
on benefits and risks
These methods will be tested in real-life situations.
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Data collection from consumers – WP4
Clinical trials
Observational
studies
Benefits
Electronic
health records
Spontaneous
ADR reports
Risks
Signal detection
WP3
Benefit-risk integration and
representation – WP5
Signal evaluation
WP2
Validation
studies
WP6
Training and
education
WP7
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Partners
Public
Private
Regulators:
EMA (Co-ordinator)
DKMA (DK)
EFPIA companies:
AEMPS (ES)
MHRA (UK)
Sanofi- Aventis
GSK (Deputy Coordinator)
Roche
Novartis
Academic Institutions:
University of Munich
FICF (Barcelona)
Pfizer
Amgen
Genzyme
INSERM (Paris)
Mario Negri Institute (Milan)
Poznan University of Medical
Sciences
University of Groningen
Others:
WHO UMC
GPRD
IAPO
University of Utrecht
Imperial College London
University of Newcastle
CEIFE
Merck Serono
Bayer
Astra Zeneca
Lundbeck
NovoNordisk
Takeda
SMEs:
Outcome Europe
PGRx
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WP 1: Project Management and
Administration
Objectives:
To create and maintain the conditions needed to
achieve the objectives and deliverables of the
PROTECT project.
Scientific steer
towards the
overall project
objectives and
strategy
Quality
control and
assurance
measures
Knowledge
management
tools and
strategies
Administrative, Track of work
organisational progress in line
with the work
and financial
programme
support
Financial
monitoring and
accountancy
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WP 2: Framework for
pharmacoepidemiological studies
Objectives:
To:
•
develop
•
test
•
disseminate
methodological standards for the:
•
design
•
conduct
•
analysis
of pharmacoepidemiological studies applicable to:
•
different safety issues
•
using different data sources
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Art is made to disturb. Science reassures.
Georges Braque
Is it always true ?
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Two studies on the use of statins and the risk of fracture done
in GPRD around the same period by two different groups.
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Why such a difference ?
• Different patients (source population, study period, exclusion criteria)
• Study design (e.g. matching criteria for age)
• Definition of current statin use (last 6 months vs. last 30 days)
• Possibly different outcomes (mapping)
• Possibly uncontrolled/residual confounding
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Work Package 2
Work plan
• Three Working Groups (WG1-WG3)
• Databases
• Confounding
• Drug Utilisation
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Work Package 2 – WG1: Databases
Work Plan
• Conduct of adverse event - drug pair studies in different
EU databases
• Selection of 5 key adverse event - drug pairs
• Development of study protocols for all pairs
• Compare results of studies
• Identify sources of discrepancies
Databases
• Danish national registries
• British THIN databases
• Dutch Mondriaan database
• Spanish BIFAP project
• British GPRD database
• German Bavarian claims database
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Work Package 2 – WG1: Databases
Progress status (1/3)
Selection of key adverse events and drugs
• Selection criteria:
• Adverse events that caused regulatory decisions
• Public health impact (seriousness of the event,
prevalence of drug exposure, etiologic fraction)
• Feasibility
• Range of relevant methodological issues
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Work Package 2 – WG1: Databases
Progress status (2/3)
 Selection of 5 key adverse events and drugs
• Initial list of 55 events and >55 drugs
• Finalisation based on literature review and consensus meeting
Antidepressants (incl. Benzodiazepines) - Hip Fracture
Antibiotics - Acute liver injury
Beta2 Agonists - Myocardial infarction
Antiepileptics - Suicide
Calcium Channel Blockers - Cancer
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Work Package 2 – WG1: Databases
Progress status (3/3)
Development of study protocols
•
Descriptive studies for the Drug AE pairs in all databases
•
5 different study designs in selected databases
•
Cohort design
•
Case crossover
•
Nested case control design
•
Self controlled case series
•
Population based case control
•
Harmonised approach across the 5 drug-event pairs (common
standards, processes and template)
•
Blinding of results procedure
 6 Final protocols in Nov 2011
(separate protocols for antidepressants
and benzodiazepines versus hip fracture)
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Work Package 2 – WG1: Databases
Next steps
• Data request and approval by scientific committee (i.e ISAC for
GPRD studies)
• Priorities for conducting PE studies agreed:
• Descriptive and cohort studies:
• Mar 2012: preliminary results
• Jun 2012: first publications
• Other designs:
• Oct 2012: preliminary results
• Feb 2013: first publication
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Work Package 2 – WG2: Confounding
Work Plan
• Objective
• To evaluate and improve innovative methods to control
confounding
• Method
• Creation of simulated cohorts
• Use of methods to adjust for observed and unobserved
confounding
e.g. time-dependent exposure, propensity scores, instrumental
variables, prior event rate ratio (PERR) adjustment, evaluation of
measures of balance in real-life study
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Work Package 2 – WG2: Confounding
Progress status
• 2010: Final protocol to conduct simulation studies
• Propensity score methods
• Instrumental variable methods
• Time-dependent confounding
• First results on propensity scores (PS)/balance measures
• Usefulness of measures for balance for reporting of the amount of
balance reached in PS analysis and selecting the final PS model
• Recommendation of methods to quantify balance of confounder
distributions when applying PS methods:
• standardised difference
• Kolmogorov-Smirnov distance, or
• overlapping coefficient
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Work Package 2 – WG2: Confounding
Next steps
• Multi-database studies
• Evaluate the impact of different left and right censoring
mechanisms on estimates of exposure effects when pooling the
data.
• March 2012: First results expected
• Analysis of instrumental variables (IV) in Drug AE pairs
• Evaluate the potential for IV analysis on the selected Drug AE pairs
in the databases that are available within PROTECT
• Feb 2012: Identify potential IV for each of the 5 Drug AE pair and
in each WG1 database
• Aug 2013: Results of IV studies in databases (if an appropriate IV
can be identified & measured)
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Work Package 2- WG3: Drug Utilisation
Work Plan
• Use of national drug utilisation data (incl IMS)
• Inventory of data sources on drug utilisation data
for several European countries
• Evaluation and dissemination of methodologies for
drug utilisation studies in order to estimate the
potential public health impact of adverse drug
reactions
• Collaboration with EuroDURG agreed
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Work Package 2- WG3: Drug Utilisation
Progress Status
 Inventory on Drug Use data “Drug consumption
databases in Europe”
(last version August 2011: http://www.imi-protect.eu/results.html)
• 11 research working groups across Europe identified
• Databases heterogeneous, administrative focus and
influenced by the national health system structure
• Collecting DU data (in/out hospital)
• from public databases (for 6 selected drugs)
• from IMS (Antibiotics, Antidepressants and Benzodiazepines.
Explored for other drugs)
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Work Package 2- WG3: Drug Utilisation
Next steps
• Literature Search on Randomized Controlled Trials (RCT)
• Search for existing meta-analyses or syntheses available in the
literature (avoid duplication of work already done).
• Dec 2011: Development of specific protocols for literature search
Jan 2012: Start of literature search starts.
• Dec 2012: Results of the literature search on RCTs expected.
• Public health impact of selected Drug AE pairs
• Evaluate validity of drug use data
• Estimate the exposed population to drugs and calculate population
attributable risk
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Work Package 3: Signal Detection
Objective:
To improve early and proactive signal detection from
spontaneous reports, electronic health records, and
clinical trials.
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Work Package 3: Signal Detection
Scope
• Develop new methods for signal detection in Individual Case
Safety Reports.
• Develop Guidelines for signal detection and strengthening in
Electronic Health Records.
• Implement and evaluate concept-based Adverse Drug Reaction
terminologies as a tool for improved signal detection and
strengthening.
• Evaluate different methods for signal detection from clinical
trials.
• Recommendations for good signal detection practices.
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Work Package 3: Sub-projects
1. Merits of disproportionality analysis
2. Concordance with risk estimates
3. Structured database of known ADRs
4. Signal detection recommendations
5. Better use of existing ADR terminologies
6. Novel tools for grouping ADRs
7. Other information to enhance signal detection
8. Subgroups and risk factors
9. Signal detection based on integrated clinical trial data analysis
10.Signal detection in Electronic Health Records
11. Drug-drug interaction detection
12. Duplicate detection
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Work Package 3 – SP 3.1
Properties of disproportionality analysis
• Scope
Directly compare different statistical signal detection algorithms:
• Within different databases
• Between databases on same products
• Current status
• All methods coded in SAS
• Implementations validated
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Work Package 3 – SP 3.2
Concordance with risk estimates
• Scope
Investigate relationship between disproportionality statistics and
conventional measures of association from interventional and
observational studies.
• Current status
• Protocol
• Review of well-characterised ADRs for study dataset
• Innovation – Use of newly established EPITT database as source
of research data.
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Work Package 3 – SP 3.3
Structured database of SPC 4.8
• Objective
Making available, in a structured format, already known ADRs to
allow for
• Triaging out known ADRs
• Automatic reduction of masking effects
• Current status
• All coding for SPCs for CAPs completed
• Application of fuzzy text matching at UMC has increased process
efficiency.
• Matching of SPC to MedDRA terms- if not possible through
automatic text matching- was done by expert medical evaluation
at EMA and Bayer.
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Work Package 3 – SP 3.3
Structured database of SPC 4.8
• Fuzzy text matching (automatic algorithm) to match MedDRA
terms from manual extracted ADRs from the SPCs
• Stemming, Stop words, Permutations, Synonyms and Spelling variations
 Sensitivity of verbatim matching increased from 72%  98%
Drug
SPC Term
Aclasta
FLU-LIKE SYMPTOMS
Verbatim match
Fuzzy matching algorithm
Flu symptoms
Advagraf
OTHER ELECTROLYTE ABNORMALITIES
-
Advagraf
PAIN AND DISCOMFORT
-
Electrolyte abnormality
Pain and discomfort NEC
Advagraf
PRIMARY GRAFT DYSFUNCTION
-
Primary graft dysfunction*
Advagraf
PRURITUS
PRURITUS
Pruritus*
Advagraf
PSYCHOTIC DISORDER
PSYCHOTIC DISORDER
Psychotic disorder*
Advagraf
PULSE INVESTIGATIONS ABNORMAL
-
Investigation abnormal
Advagraf
RASH
RASH
Rash*
Advagraf
RED BLOOD CELL ANALYSES ABNORMAL
-
Red blood cell analyses*
Advagraf
RENAL FAILURE
RENAL FAILURE
Renal failure*
Advagraf
RENAL FAILURE ACUTE
RENAL FAILURE ACUTE
Acute renal failure, Renal failure acute*
Advagraf
RENAL IMPAIRMENT
RENAL IMPAIRMENT
Renal impairment*
Advagraf
RENAL TUBULAR NECROSIS
RENAL TUBULAR NECROSIS
Renal tubular necrosis*
Advagraf
RESPIRATORY FAILURES
-
Respiratory failure, Failure respiratory
Advagraf
RESPIRATORY TRACT DISORDERS
-
Respiratory tract disorders NEC
Advagraf
SEIZURES
-
Seizure, Seizures*
Advagraf
SHOCK
SHOCK
Shock*
Better option:
Red blood cell
abnormal
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Work Package 3 – SP 3.4
SD recommendations: Database survey
• Scope
• EudraVigilance (EMA), VigiBase (UMC)
• National data sets: AEMPS, DKMA, MHRA
• Company data sets: AZ, Bayer, GSK
• Focus
• # reports, # drugs and # ADR terms
• Types of reports (AEs or ADRs, Vaccines, Seriousness, ...)
• Additional information (presence of data elements available for
stratification and sub-setting, e.g. demographics)
• Supporting systems (analytical methods, medical triages)
• Progress
• Survey deployed and completed by most organisations
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Work Package 3 – SP 3.4
Overview of Databases
EBGM implementations
via external vendor
systems
Lack of
comparability
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Work Package 3 – SP 3.4
Data elements – demography SD
(% data available in all case reports)
DKMA

(unk)
(100%)
O
Country of
case
(100%)
UMC

 (77%)
 (94%)
 (11%)
(100%)
 (>0%)
EMA



O

O
MHRA

 (80%)
 (97%)
O
(100%)
 (57%)
AEMPS

 (96%)
 (99%)
O
(100%)
O
BSP

 (74%)
 (97%)
 (58%)
(100%)
 (83%)
AZ

 (73%)
(92%)
 (26%)
(100%)
(unk)
GSK

 (79%)
(86%)
 (10%)
(96%)
(59%)
db holder Receipt Date Age/DoB Gender Ethnicity
Subject ID
(unk)
High population of some common data elements, e.g. age, gender, country of case
Interim results 2011
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Work Package 3 – SP 3.4
Database size (no of spontaneous reports)
UMC
EMA
GSK
BSP
Serious
Non-serious
AZ
Unknown
MHRA
AEMPS
DKMA
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
Interim results 2011
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Work Package 3 – SP 3.4
DB survey
Others
21%
Top 5 countries by
count of reports used
for signal detection
EMA
(% of total spontaneous reports)
Germany
7%
FRANCE
5%
Others
20%
JAPAN
8%
Canada
5%
USA
53%
France
7%
UK
5%
USA
52%
UMC
France
4%
Canada
5%
Germany
6%
UK
6%
Japan
10%
German
y
12%
Others
26%
Others
27%
AZ
UK
10%
BSP
Brazil
9%
Japan
5%
Interim results 2011
USA
31%
Others
34%
UK
10%
USA
48%
GSK
France
5%
Canada
6%
Germany
USA
49%
UK
10%
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Work Package 3 – SP 3.4
DB survey
Top 5 agents by
count of all
reports
(NB % of total for top 5, not total
db)
Interim results 2011
Sulfamethoxaz
ole/Trimethop
rim Urinary
tract infectious
disease,
58845, 17%
Clozapine
Schizophrenia,
63952, 19%
Etanercept
Reumatoid
artrite, 87385,
26%
UMC
Diphtheria and
tetanus toxoids
and pertussis
Diphtheria/Teta
nus
(prophylaxis),
63405, 18%
Rofecoxib
Pain, 67956,
20%
Metamiz
ol pain
3488
14%
Paraceta
mol pain
4251
17%
Omepraz
ole
prophyla
xis
5979
23%
Methotrexat
e Unknown
indication,
29961, 14%
Prednisolone
Unknown
indication,
30331, 14%
Aspirin
Unknown
indication,
71815, 34%
EMA
Paracetamol
Unknown
indication,
36880, 17%
Furosemide
Unknown
indication,
45261, 21%
AEMPS
Enalapril
essential
hyperten
sion
5072
20%
Tegretol
Antiepile
ptica,
604, 14%
Pandemri
x
Influenza
vaccine,
625, 14%
Sulfotrim
Antiinfect
ive, 635,
14%
Acetylsal
ycilic acid
myocardi
al
ischemia
6603
26%
Pondocell
in
Antiinfect
ive, 1433,
32%
DKMA
Eltroxin
Thyroid
hormon,
1159,
26%
Bupropio
n
Unknown
indication
, 9297,
16%
Fluoxetin
e
Unknown
indication
, 9111,
15%
Clozapine
Unknown
indication
, 15080,
26%
MHRA
Paroxetin
e
Unknown
indication
, 11042,
19%
Neisseria
Meningiti
dis
Unknown
indication
, 14459,
24%
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Work Package 3 – SP 3.5
Better use of existing terminologies
• Scope
• Investigation of established adverse event coding groups for
signal detection
• Proof of concept
• Temozolomide
• Not illustrating timeliness – VigiBase as of Feb 2009
Term
Level of
terminology
# Reports
IC
Erythema Multiforme
PT
13
+0.30
Stevens-Johnson Syndrome
PT
19
+0.68
Toxic Epidermal Necrolysis
PT
6
+0.51
Bullous Conditions
HLT
42
-0.01
Severe Cutaneous Adverse Reactions
SMQ
47
-0.04
WHO-ART HLT
35
+0.46
Erythema Multiforme
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Work Package 3 – SP 3.5
Better use of existing terminologies
• Groups included:
• MedDRA Preferred Terms (PT, HTL, SMQ narrow or broad)
• Ad hoc groupings (developed for the purpose of the study
or existing proprietary groupings of one of the
participating organisations)
• Data sources:
• Medical concepts that are often drug-induced [Trifiro et al]
• EU labeling changes [Alvarez et al]
• WHO ICSR database, VigiBase
37
Work Package 3 – SP 3.5
Better use of existing terminologies
• Tentative findings
• Groupings of PTs slightly outperform predefined
groupings (HLTs, SMQs)
• Little indication that terminology-defined groupings are
effective for screening in signal detection
• Limitations
• Study has been limited largely to reasonably welldefined medical concepts
• Are these results applicable to broader concepts (eg,
bleeding, infection)?
38
Work Package 3 – SP 3.6
Novel tools to group ADRs
• Approach
• Automatic generation of groups of MedDRA terms based on
semantic information
• Based on a mapping of MedDRA to SNOMED CT
• Groups MedDRA terms based on semantic distance
• Progress
• Evaluation study completed
• Comparison with standard MedDRA SMQs as gold standard
• Next steps:
• Refinement of methods
• Use in signal detection!
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Work Package 3 – SP 3.7
Other information to enhance signal detection
• Scope
• Evaluation of masking
• Detection of drug-drug pharmacokinetic interactions using
the information on metabolic pathways
• Current status
• Preliminary investigation of masking in EudraVigilance under
review
• Next steps:
• Protocol for drug-drug interactions
• Extension of masking analysis to other datasets
40
Work Package 3 – SP 3.8
Subgroups and risk factors
• Scope
• Investigate utility of adjusting for covariates and subgroup
analyses in signal detection
Current status
• Review document produced on current opinion and evidence
regarding stratification
• Ongoing discussion on product list – linked to SP3.1
• Next steps:
• Review of methodology
• Await completion of SP3.1 analysis
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Work Package 3 – SP 3.9
Signal Detection from clinical trials
• Overall scope
• Which data should be used and which methods are optimal
• Explore novel uses of existing clinical data in ongoing and
completed clinical trials for safety signal detection
• Current status
• Literature searches completed
• Protocol(s) for the retrospective analysis – some
development needed.
• Products/safety topics identified (Bayer/MS) – GSK
ongoing.
• Assessment of skill set, feasibility and availability of data
to do the activities (Bayer/MS)
42
Work Package 3 - SP 3.10
Signal Detection in Electronic Healthcare Records
• Scope
• Electronic Healthcare Records vs Individual Case Safety
Reports for early signal detection
• Confirmatory vs exploratory data analysis
• Current status
• Development of functionality to apply exploratory methods
on THIN data has continued
• Comparison done against previous published results using
the same method but on a different dataset.
• Read-groupings have been refined or created for the 23
different adverse drug events from Trifiro et al (2009).
43
Work Package 3 - SP 3.11
Drug-drug interaction detection
• Scope
• Investigate statistical methods of detecting DDI
• Current status
• Literature search completed and review underway
• Reference set of known DDI and known non-DDIs under
construction
44
Subpackage 3.12 - Duplicate detection
• Scope
• Investgate statistical methods of detecting duplicate
ICSRS
• Assess impact of removal of ICSRS on signal detection
• Current status
• The protocol has been finalised
• Participants have provided descriptions of their duplicate
detection methodologies.
• Evaluation sheet for the national centres duplicate
evaluation has been agreed, as have approaches for
evaluation of the impact of duplicates on signal detection.
UMC are ready to distribute initial data, and evaluation can
begin shortly.
45
Work Package 4: Data collection from
consumers
Objectives:
To assess the feasibility, efficiency and usefulness of modern
methods of data collection including using web-based data
collection and computerised, interactive voice responsive
systems (IVRS) by telephone
46
Work Package 4 - Project Definition
• Prospective, non interventional study which recruits
pregnant women directly without intervention of health care
professional
• Collect data from them throughout pregnancy using either
web based or interactive voice response systems (IVRS):
– medication usage, lifestyle and risk factors for congenital
malformation
• Compare data with that from other sources and explore
differences
• Assess strengths and weaknesses of data collection and
transferability to other populations
47
Work Package 4 - Issues with current
methods
Using health care professionals to capture data
• Expensive and data capture relatively infrequent
• Will miss drug exposure before comes to attention of HCP
• Patients may not tell truth about “sensitive” issues
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Work Package 4 - Issues with current
methods
Using EHR records
• non prescription medicines, homeopathic and herbal
medicines not captured
– ? Women switch to “perceived safer” medicines
• Medicines prescribed/dispensed may not be medicines
consumed – problem with p.r.n. medicines (i.e. dosage as
needed)
• EHR may miss lifestyle and “sensitive” information
49
Work package 4 - Study population
• 4 countries:
Denmark
United-Kingdom
The Netherlands
Poland
• 1400 pregnant women per country
• Self identified as pregnant
• Volunteers may not be “typical” of pregnant population – can
characterise
50
Work Package 4: Patient workflow
overview
Study subject picks up a leaflet in
a pharmacy or browses specific
web sites to find out about the
study in one of 4 countries.
Study subject enrolls for
the web or phone (IVRS)
method of data collection.
IVRS
Web
n = 1200 per country
Study subject completes the
surveys online.
n = 200 per country
Study subject completes the surveys
via an outbound reminder or by
inbound call she initiates.
Final outcome survey is completed
at the end of pregnancy.
51
Work Package 4 – current status
Status Nov 2011:
• Finalised protocol for ethical submission (approval received
in PL, waiver in DK and NL, UK tbc)
• Main questionnaires finalised following pilot testing
• Baseline (web)
• Follow up (web)
• Outcome of pregnancy
• Satisfaction
52
Work Package 5: Benefit-Risk Integration
and Representation
Objectives:
• To assess and test methodologies for the benefit-risk
assessment of medicines
• To develop tools for the visualisation of benefits and
risks of medicinal products
 Perspectives of patients, healthcare prescribers, regulatory
agencies and drug manufacturers
 From pre-approval through lifecycle of products
53
Structure of WP5 – Work streams
• Work stream A
• Work stream B
• Work stream C
• Work stream D
• Case studies wave 1
• Case studies wave 2
• Link with WP6
• Management group
54
Work Package 5: Work Plan
1. Review of methodologies used to model effects of medicines,
elucidation of patients’ preferences and integrating effects and
preferences.
Review of methodologies for graphical representation and
visualisation techniques.
2. Selection of case studies (waves 1 and 2)
3. Data selection/requirements for case studies
4. Identification/development of software for B/R.
5. Application of methodology, recommendations, finalisation of
tools, protocols for validation studies.
55
Work Package 5: Workstream A - completed
• Framework for B-R analysis: achieved through a Charter
(SC approved)
‘The overall objective of WP5 is to assess the relevance of various methodologies for B-R
assessment of medicinal products including the provision of usable data and information,
the underpinning modelling and the presentation of the results, with a particular emphasis
on visualisation methods. The overall plan to achieve this task is outlined below.
Consideration will be given to:
•
Submission and post –approval periods, while recognising the relevance of pre - approval
B-R assessment
•
individual and population- based decision making
•
the perspectives of patients, physicians, regulators and other stakeholders such as societal
views needed for Health Technology Assessment (HTA) although specific cost implications
will not be considered
•
possible interdependencies with other PROTECT Work Packages as well as other relevant
external initiatives.’
56
Work Package 5: Overview
• Wave 1: has 4 case studies: Raptiva, Tysabri,
Ketek and Acomplia.
• Drugs which have data readily available from
EPARs.
• Not revisiting EMA decisions, but use to
demonstrate and test methodologies.
Wave 1
WS C
Case studies
Wave 2
WS B
Methods
• Review of existing methods not inventing new
methods.
• Emphasis on graphical representation.
• Methods estimating(1) magnitude / incidence of
events and (2) value elicitation of benefits and
risks, from a patient and regulator perspective
and how combine them into a single measure.
WS D
Framework /
Data
• PrOACT-URL framework for
performing benefit-risk analysis.
• Oversee working parties for
extracting objective measures of
magnitude / incidence of benefits
and risks.
57
Work stream B
Review of benefit risk methodologies
• Completed and circulated for comment within WP5 in June
2011
• Useful feedback received and review is in the process of being
updated to take account of this
• Reviewed and classified 44 approaches
• Made preliminary recommendations of 13 approaches
58
Classifications of B-R approaches
59
Work Package 5: Workstream C
• Objective
• Establishment of criteria and process for selection of ‘initial’,
‘development’ and ‘validation’ case-studies
• Taking into account perspectives of regulators and
prescribers as well as patients and HTA.
• Progress
• Criteria for wave 1 +2 case studies decided
• Drugs for wave 1: Acomplia®, Raptiv®, Tysabri®, Ketek®;
library of possible candidates for wave 2
• Wave 1 studies have commended, wave 2 more challenging
(1 study started)
60
Wave 2 Criteria
• Uncertainty about what the main benefits and risks are.
• Uncertainty about the population who has the disease.
• Different time for Benefit and for Risk (long term risks).
• Individual benefit-risk, or subgroups of benefit risk.
• New drugs vs. long marketed drugs.
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Work Package 5: Workstream C
• Next steps
• Wave 1: Report due by Dec 2011
• Wave 2: Case studies to include aspects of visualisation
and due to finish August 2012
• Discussion with other WP5 members for appropriate data
identification and extraction, applicability of case studies.
• Identify potential presentations and publications.
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Work Package 5: Workstream D
• Scope
• Data Collection dependent from Framework used:
•
Using PrOACT-URL (generic framework for decision making), identification of
data sources to be used depend on detailed description of each of the steps of
the framework (see back up slide)
• Lead to a draft “Guidelines for preparing a Case Study Report”
• In addition to EPAR as main source of data/information, additional
data sources for other drugs or for other perspectives will require
• Additional data collection from existing data sets (PSURs, formal B-R
reviews)
• Creation of new data (e.g. questionnaires for patient preferences
elicitation)
• Progress
• Fine-tuned the PROACT-URL generic decision making framework for
regulatory decision-making
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PrOACT of PrOACT-URL*
Example: Efalizumab for psoriasis
•
Benefits – improvement of severe chronic plaque psoriasis
•
Risks – PML, cardiotoxicity, neurotoxicity, serious infections including
tuberculosis
Application to efalizumab example
1. Problems
Could any risk minimisation measures be implemented to bring the BR balance of the drug back to positive?
2. Objectives
Re-evaluation of B-R balance of efalizumab from MA pivotal trials and
post-marketing safety information using known favourable effects and
unfavourable effects (EPAR, SPC, PSUR, literature, etc.)
3. Alternatives
•
Do nothing
•
Restriction: limit duration, restrict indication, suspend
•
Do nothing: B-R balance still considered positive.
•
Restrictions: (i) 2 year treatment duration limitation; (ii) target
population change; (iii) suspension/withdrawal - drug recall
worldwide
4. Consequences
(* See Hammond JS, Keeney RL, Raiffa H. Smart Choices: A Practical Guide to Making Better Decisions.
Boston, MA: Harvard Business School Press; 1999)
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PrOACT of PrOACT-URL*
Example: Efalizumab for psoriasis
Application to efalizumab example
5. Trade-offs
B-R assessment to be reiterated using the same data but with MCDA
quantitative method
6. Uncertainty
•Uncertainty on the extent of off-label use in patients with less severe
conditions, decreasing the benefit part of the balance.
•Uncertainty on the shape of the risk function of PML (probably not
linear), based on only 4 cases.
•No true incidence but only reporting rate.
•Same for all other post-marketing safety data which lead to SPC
Variations
7. Risk tolerance
•No consensus between rapporteurs at the initial MA approval
•Efalizumab has only modest efficacy compared to alternatives
•Psoriasis is not life-threatening but serious impact on social life
•Improvement of the RMP
•Patients’ value judgments are different from regulators
8. Linked decisions
FDA made the same decision as EMA to withdraw efalizumab from the
market
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Work Package 5: Next steps
• Develop the work in WP5 to gather more information from
wider stakeholders – particularly patients and sub groups of
patients
• Likely techniques include exploring different perceptions of
acceptable risk/benefit balance with information presented in
different ways e.g. Different visualisation methods.
• Potentially include web based surveys alongside qualitative
based work
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Work Package 6: Validation
Objectives:
• To validate and test the transferability and feasibility
of methods developed in PROTECT to other data
sources and population groups
• To determine the added value of using other data
sources as a supplement or alternative to those
generally used for drug safety studies, in
order to investigate specific aspects or issues.
Started in September 2010
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Work Package 6 - Inventory of data
sources
• Creating a comprehensive list of data sources (ongoing)
• Review of European databases (electronic healthcare
records, cohorts, registries)
• ENCePP
• EFPIA
• Outcomes of other Work Packages (2-5) will be evaluated
in light of the inventory of data sources (e.g. type of
data, covariate information, mode of collection, type of
prescription data, etc)
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Work Package 6 – WP2 validation studies
• Protocols have been developed and data sources have been
identified, leading to a revised list for the Extended
Audience.
• The objectives of these studies are:
• Objective 1: Replication study in same database
• Objective 2: Replication study in different database
• Objective 3: Negative control study
• Objective 4: Use of alternative outcome definition
• Objective 5: Validation of outcome
• Objective 6: Assessment of confounders
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Draft WP6 research plan: Study objectives,
rationale and design
Defined Study
Objective***
Objective 1:
Replication study in
same database
Scientific Question
Investigator
Database
Study design*
Is the study replicable when conducted
independently in the same database?
Using same design/analysis
1. Takeda , LA-SER
Using different design/analysis
2.Takeda , LA-SER
3. Aarhus University
1. GPRD
2. GPRD
3. Danish Psychiatric, Somatic
Hospital Discharge & Mortality
Registers
1. A; Population case
control
2. A; Population case
control
3. E; Population case
control
Objective 2:
Replication study in
different database
Do the results have external validity?
Using same design/analysis
1. SARD
2. SARD
3. LA-SER
Using different design/analysis
4. GSK
5. LA-SER **
6. LA-SER
7. LA-SER
8. Utrecht University**
1. LabRx/Premier
2. LabRx/Premier
3. PGRx
1. A; Nested case control
2. C; Nested case control
3. C; Population case
control
4. D; Cohort
5. D; Cohort **
6. C; Population case
control
7. E; Population case
control
8. A; Descriptive study**
Objective 3:
Negative control
study
Does a study using the same protocol
provide absence of evidence of an
association where the exposure is such that
the expected result is one of no association?
1. SARD
2. GSK
3. LA-SER
4. MarketScan and Medicare
5. E3N **
6. PGRx
7. PGRx
8. UPOD**
1. LabRx/Premier
2. GPRD
3. PGRx
1. Nested case control
(AMI)
2. Self-controlled-series
(hip fracture)
3. Population case control
(AMI)
*Study design: A=Antibiotics and ALI; B=Antidepressants/BZD and hip/femur fractures; C=Beta2Agonists and AMI; D=Calcium channel blockers and cancer;
E=Antiepileptics and suicidality
** To be confirmed.
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*** Each study may have several objectives
Draft WP6 research plan: Study objectives,
rationale and design (continued)
Defined Study
Objective
Objective 4: Use of
alternative outcome
definition
Scientific Question
Investigator
Database
Study design*
What is the impact of different levels of
certainty of the outcome (e.g. definite,
probable, possible) on the effect estimate?
1. Takeda , LA-SER
2. LA-SER
3. LA-SER
4. Aarhus University
1. GPRD
2. PGRx
3. PGRx
4. Danish Psychiatric, Somatic
Hospital Discharge & Mortality
Registers
Objective 5:
Validation of
outcome
Has the outcome of interest been validated
through clinical record review? What is the
impact of validation on the effect estimate?
1. Takeda , LA-SER
2. SARD
3. Utrecht University**
4. Aarhus University
5. GSK
1. GPRD
2. LabRx/Premier
3. UPOD**
4. Danish Psychiatric, Somatic
Hospital Discharge & Mortality
Registers
5. GPRD
1. A; Population case
control
2. C; Population case
control
3. E; Population case
control
4. E; Population case
control
1. A; Population case
control
2. A; Nested case control
3. A; Descriptive study
4. E; Population case
control
5. E; Descriptive study/
Population case control
Objective 6:
Assessment of
confounders
Has confounding been adequately taken into
consideration? Are there additional
confounders that need to be assessed? How
does better control for confounding impact
the effect estimate?
1. Utrecht University**
2. LA-SER
3. LA-SER
4. Aarhus University
1. UPOD**
2. PGRx
3. PGRx
4. Danish Psychiatric, Somatic
Hospital Discharge & Mortality
Registers
1. A; Descriptive study**
2. C; Population case
control
3. E; Population case
control
4. E; Population case
control
*Study design: A=Antibiotics and ALI; B=Antidepressants/BZD and hip/femur fractures; C=Beta2Agonists and AMI; D=Calcium channel blockers and cancer;
E=Antiepileptics and suicidality
** To be confirmed.
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*** Each study may have several objectives
Work Package 6 – WP5 validation studies
• To be explored based on wave 1 studies (1st choice: Tysabri)
• Scope potential areas of variability and draft a protocol
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Work Package 7: Training & communication
Objective:
To identify training opportunities and support training
programmes to disseminate the results achieved in
PROTECT.
73
Work Package 7: Scope
• Development of a Platform of Training Opportunities
 Launched.
• Regular interaction with Eu2P Consortium
 Mechanism in place to ensure timely input from PROTECT WPs 2-5
into Eu2P training programmes.
• Communication Plan
 Draft list of conferences and other international forums suitable for
the presentation of the results of PROTECT.
 Search of communication expert initiated.
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Work Package 7: Training Platform
•
Available at https://w3.icf.uab.es/trainingopp (or through link from
PROTECT homepage)
•
Launched in July 2011
•
Extended to EU2P in July
2011
•
Extension to ENCePP as of
Nov 2011
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More information?
Website:www.imi-protect.eu
Email: [email protected]
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