Transcript ProSanos Products and Services for Drug Safety

Systematic Analysis of
Observational Data to Augment
Current Pharmacovigilance
Practices
Stephanie Reisinger, SVP
ProSanos Corp.
Harrisburg, PA
ProSanos Corporation Confidential and Proprietary
1
Today’s Agenda
 Primary Sources of Safety Data Today
– What are the Gaps?
 Systematic Analysis of Observational Data
–
–
–
–
What is it?
Why now?
What Are the Challenges?
How can Systematic Analysis Augment and Inform
Current Practices?
Objective: Framework for thinking about the
use of observational data in the context of of
current pharmacovigilance practices
2
ProSanos Corporation Confidential and Proprietary
Case Study and Disclosure
 In 2005, GlaxoSmithKline (GSK) initiated a largescale R&D project (SafetyWorks) to research and
develop methodologies to enable the systematic
use of observational data
 GSK and ProSanos® worked in partnership to
implement the methodologies in web based
software for access by GSK Safety Scientists, which
was implemented in 2008
 ProSanos markets a commercial version of this
software
3
ProSanos Corporation Confidential and Proprietary
Primary Sources Safety Data
Phase I – III Clinical Trials
Clinical Trials
Patient Data
Strengths
Phase
IV
Safety
Reports
Spontaneous
Safety Reports
Strengths
Population
Analysis
Observational
Data
Strengths
Very large population
Large population
High Quality Data
Data comprises
suspected Adverse
Events
Real-world medical
practice
Provides strongest
evidence of safety
issues
Analysis is relatively
quick and inexpensive
(compared to trial or
post approval study)
Longitudinal record of
exposure, dose,
diagnoses
Randomized / controlled
patient population
Clinical Trial Safety Data Weaknesses
Typically 500 – 3,000 carefully selected patients exposed
in Phase III testing:
500 patients
3,000 patients
30,000 patients
Event rate / year
Number
of Events
1 in
1,000
0.607
0.303
0.076
0.013
0
1
2
3
…
1 in
10,000
…
0.951
0.048
0.001
0.000
…
1 in
1,000
0.050
0.149
0.224
0.224
…
1 in
10,000
1 in
10,000
0.050
0.149
0.224
0.224
0.741
0.222
0.033
0.003
…
…
Probability of Detecting one or more cases:
1 in 1,000: 40%
1 in 10,000: 5%
5
1 in 1,000: 95%
1 in 10,000: 26%
ProSanos Corporation Confidential and Proprietary
$$
1 in 10,000: 95%
Spontaneous Report Data Weaknesses
 Voluntary reports
 Databases maintained by companies
and regulatory agencies
 Medical review of cases and data
mining analysis used for detecting
signals
 Issues with Spontaneous Safety Data
–
–
–
–
–
6
Uncertain causal association
Data quality issues
Underreporting (only 10%?)
Not good at detecting events that also occur in background
population
False positive rate high
ProSanos Corporation Confidential and Proprietary
Observational Data for Drug Safety
Insurance Claims
Electronic Health Record
Health-care data collected for
payment for medical services
(medical and pharmacy
claims)
Data collected for billing,
limited to time patient is
enrolled, biased towards
persons w/ private insurance
Reflects a medical record of a
patient held by the provider
Data can be comprehensive
within the point-of-care, but
may miss services outside
EHR system
Pharmacoepidemiology: assess relationship
between exposure and outcome
– Bias (not sample size) is main analytic concern
Weaknesses of Current Observational
Data Paradigm
 “One –off” studies analyses not reusable
on disparate databases due data format
differences
 Expensive  cost of individual pharmacoepidemiology studies*:
– $30K-50K for a feasibility/protocol assessments
– $80K-150K (<$200K) for a descriptive study
– $200K-400K for a single case-control/analytical
studies (no validation)
 Time-consuming  6 weeks to over 6
months depending on study
*Internal estimate based on studies at GSK
8
ProSanos Corporation Confidential and Proprietary
Opportunities for Observational Data
 Cost-effective approaches to address
current drug safety gaps:
– Detection of rare adverse events, especially
those occurring in background population
(MI, stroke, etc.)
– Rapid evaluation of potential safety signals
– Information to inform and focus expensive
pharmaco-epidemiology studies
9
ProSanos Corporation Confidential and Proprietary
Today’s Agenda
 Primary Sources of Safety Data Today
– What are the Gaps?
 Systematic Analysis of Observational Data
–
–
–
–
What is it?
Why now?
What Are the Challenges?
How can Systematic Analysis Augment and Inform
Current Practices?
Objective: Framework for thinking about the
use of observational data in the context of of
current pharmacovigilance practices
10
ProSanos Corporation Confidential and Proprietary
What is Systematic Observational
Analysis?
Library of analytic routines that can be
applied to any observational database
without requiring custom programming
for each data source
Interface between medical
knowledge,
statistics/epidemiology, and
computer science
11
ProSanos Corporation Confidential and Proprietary
Why Now?
Increased
Data
Availability
Technology
Advances
Industry &
Regulatory
Focus
A Perfect Storm
12
ProSanos Corporation Confidential and Proprietary
Regulatory / Industry Focus
Recent high-profile safety issues have focused
attention on gaps in drug safety process
 FDA’s Sentinel Network
– National, integrated, electronic system for monitoring medical product
safety using observational data
 OMOP
– Partnership between FDA, FNIH, PhRMA
– Two-year methods research initiative using observational data to detect
and evaluate drug safety issues
 EU-ADR
– European collaboration to detect adverse drug reactions (ADRs) using
clinical data from electronic healthcare records (EHRs) of 30 million
patients from several European countries
13
ProSanos Corporation Confidential and Proprietary
Technology Advances
Approximate size of 31 Million Patient Claims
Database: 500 GB*
1992
2007
Worlds First GB
Hard Drive (IBM)
1 GB Hard
Drive for PC
(Seagate)
First Terabyte
Hard Drive for
PC (Hitachi)
Cost: ~$100,000
$100,000 per GB
Cost: ~$2,000
$2,000 per GB
Cost: $399
$.40 per GB
$500,000,000
$1,000,000
$200
1980
Similar advances in
ProSanos Corporation Confidential and Proprietary
processing speed…
*Pharmetrics Choice patient-centric database, IMS
14
What are the Major Technical
Challenges?
Goal: Develop library of analytic routines that
can be applied by a drug safety scientist to any
observational database without requiring
custom programming for each data source
 Challenge  “normalize” disparate
vocabulary, data format and structures
– Create standardized vocabulary for drugs
and conditions
– Develop a common data format
15
ProSanos Corporation Confidential and Proprietary
Considerations for a Selection of a
Common Vocabulary
 Correct and uniform classification for drugs
and conditions
 Comprehensive with respect to source data
 Hierarchical structure to adequately
represent relationships
– Drugs, ingredients, classes
– Conditions and related condition groups
 Easy to view and navigate graphically
Standardized Drug Vocabulary
Map drugs found in source data into SNOMEDCT “reference vocabulary”
Enables analysis of:
Permax, Pergolide
(generic), Autonomic
agent (class)….
References to drug “Permax”
from source data mapped into
SNOMED-CT hierarchy.
17
ProSanos Corporation Confidential and Proprietary
Standardized Condition Vocabulary
Map conditions found in source data into a
MedDRA “reference vocabulary”
References to ICD-9 code
395.1 (Insufficiency,
Rheumatic Aortic)
mapped to MedDRA
Preferred Term “Aortic
Valve Incompetence”
Considerations for a Common Data
Model for Observational Data
 Data from different data providers in different
physical and organizations formats
– Analysis via common analysis routines (don’t require custom code
for each analysis)
– Normalize, but don’t integrate
 Data on all conditions no matter what the cause
– Adverse events not explicitly coded
 Data is longitudinal
– One patient can have more than one drug exposure
– After exposure, there is a follow-up time where the patient is “at
risk”
– Patient information is available for the period prior to exposure
Common Data Format for Observational
Data: Patient Timeline
Drug A,
Condition X
Drug A,
Condition Y
Drug Exposure 1
Drug A
Drug B
Persistence Window
Drug Exposure 2
Drug B,
Condition X
Drug
Exposure 3
Drug B,
Condition Y
Drug B,
Condition YPersistence Window
Risk Period
Condition Episode 1
Condition X
Persistence Windows
Condition Y
Condition Episode 2
Persistence Window
Prescription
Diagnosis
Drug-Condition Pair
Condition Episode 3
Example of Scope: Normalization
Metrics for a Claims and EHR Database
EHR
Claims
8,901,057
31,404,941
Drug Eras
85,405,889
282,406,557
Condition Eras
52,987,151
743,765,883
Raw Import DB
100 GB
500 GB
Normalized
Database Size
10 GB
65 GB
Persons
21
ProSanos Corporation Confidential and Proprietary
Conceptual Overview of Systematic
Observational Analysis at GSK
Normalized EHR
Database
Observational
Screening
Exploratory Framework for
assessing all drugs and
conditions in a cohort
Patient Natural
History
Signal
Detection
Normalized
Claims Database
Observational
Evaluation
Potential
Signals from
elsewhere
Evaluation of
a Signal
Standard epidemiology
methods applied in a semiautomated fashion to
assess drug / condition pair
Analytic Considerations for
Observational Screening
Exploratory Framework for assessing all
drugs and conditions in a cohort
 Spontaneous data uses disproportionality
analysis
– Is the condition being disproportionately reported for a
particular drug compared to all other drugs?
– Exposure not considered: MI after 1 day of exposure
treated the same MI after 100 days of exposure
 Observational data is different in that it
includes exposure, and a patient denominator
– Metrics such as rates and proportions can be produced
using exposure and denominator information
Calculation of a “Screening Proportion”
• Screening Proportion: The proportion of exposed
persons who have had at least one occurrence of a
condition in a given timeframe
Screening Proportion =
# persons with >= 1 occurrence in time (t) prior to exposure /
total number of persons exposed
Natural History: Screening Proportion
Prior to Exposure
Drug B
Condition X
1 Occurrence of
Condition X
2 Occurrences
of Condition X
3 Occurrences of
Condition X
Within 7 days Prior
Within 30 days Prior
Within 90 days Prior
Calculate Screening Proportion for varying
“surveillance windows”
Multiple “surveillance windows” prior to
exposure provide different information
Person Timeline
Relations prior to exposure:
7 -> 0
30 -> 0
90 -> 0
6mo -> 0
1yr -> 0
start -> 0
Drug Era A
7 days prior to exposure
30 days prior to exposure
90 days prior to exposure
6 months prior to exposure
1 year prior to exposure
Anytime prior to exposure
Analysis question determines appropriate
surveillance window to use:
• Prior conditions: use ‘6 months prior to exposure’
• Co-morbidities: use ‘7 days prior to exposure’
Example: Comparison of co-morbidities
in two Parkinson’s drugs
Screening Proportion within
7 days of exposure
Condition
Condition Ancestor
Pergolide
Pramipexole
Top 10 co-morbid conditions for patients exposed to Pergolide
versus Pramipexole
How Can Patient Natural History Inform
Current Pharmacovigilance Practices?
Normalized EHR
Database
Observational
Screening
• Better
understand
Normalized
Claims
Database
• Better understand
Patient Natural
History
the disease
how the drug is used, in whom,
and what other drugs/conditions occur
• Use to document compliance for a risk
management plan (who is taking it, co-morbid
conditions)
• Evaluate off-label use
• Identify important covariates for a future study
Calculation of a Screening Rate
• Screening Rate (SR): The approximate rate of
occurrence of a condition per 1,000 years of
exposure
Screening Rate (SR) = (# of events during time window) /
(person-time exposure)
No adjustment for confounding
Confidence Intervals use Poisson confidence limit approximation by Begaud
et el to calculate lower and upper bounds of 95% confidence interval:
UB95: (Z1-α / 2 + √(x + 0.96))2 / t
LB95: (Z1-α / 2 + √(x + 0.02))2 / t
Signal Detection: Screening Rates Post
Exposure
Drug B
Person time exposure
Condition X
During Exposure
7 days Post Exposure
90 days Post Exposure
1 Occurrence of
Condition X
2 Occurrences of Condition X
4 Occurrences of Condition X
Calculate Screening Rate for varying “at risk periods”
Screening time windows following
exposure
Person Timeline
Drug Era A
Relations following exposure:
Exposure (t): (end – start)
Post 0d
0 -> t
Post 7d
Exposure risk
windows
0 -> t+7
Post 30d
0 -> t+30
Post 90d
0 -> t+90
Post xd
After 7d
After 30d
Initiation risk
windows
After 90d
After anytime
0 -> t+x
0 -> 7
0 -> 30
0 -> 90
0 -> end
Comparison of Screening Rates
Screening Rate Ratio (SRR) = Screening Rate(1) /
Screening Rate(2)
Confidence intervals assume ratio of two rates following Poission
Distribution Graham, PL, Mergenson K, Morton AP, Confidence
limit for the ratio of two rates based on likelihood scores: non
iterative method: Statist. Med 2008 22-2071-2083:
Comparison of Screening Rates
Screening Rate Ratio (SRR) = Screening Rate(1) / Screening
Rate(2)
• Within a single observational database
– Within a cohort of interest: Compare screening rate post exposure
to screening rate prior to exposure for a condition of interest
– Cohort of interest versus a comparator: Compare pre- and postexposure screening rates for a conditions of interest
– Cohort of interest versus background: Compare pre- and postexposure screening rate for a condition of interest to the screening
rate of the background population
• Between observational databases
– Comparisons of screening rates between databases (do we see it
both places?)
Example: Signal Detection for Pergolide
versus Pramipexole
Condition
Condition
Ancestor
Screening Rate
90 days post exposure
Pergolide
Pramipexole
Overall DB
Signal for cardiac valvulopathy in patients exposed to
Pergolide in a Claims database
34
ProSanos Corporation Confidential and Proprietary
How Can Signal Detection Augment
Current Pharmacovigilance Practices?
Normalized EHR
Database
Signal Detection
Observational
Screening
• “De-novo” signal detection
Normalized Claims Database
• Additional evidence for signals detected
elsewhere
• Early warning system for “Health Outcomes of
Interest”
• Monitor an existing signal over time
Integrating Screening with Observational
Evaluation
Normalized EHR
Database
Observational
Screening
Patient Natural
History
Signal
Detection
Normalized Claims
Database
Observational
Evaluation
Potential
Signals
Evaluation of
a Signal
Rapidly evaluate and triage
Observational Evaluation: Adjusting for
Confusing or mixing Confounding
effect of exposure with
effect of another factor
Drug A
Drug B
Condition X
Condition Y
Person Timeline
Drug A Era
CONFOUNDING
FACTORS
Drug B Era
B-Y
Condition X Era
Screening:
(exploratory) unadjusted
metrics feasible for
crude temporal
associations across all
drug-condition pairs
Condition Y Era
Observational Evaluation: Metrics that
adjust for confounding and reduce bias
for specific drug-condition pair
Examples of Confounding Factors
 Age and Gender: If falls are being studied, then
cohorts should be balanced with respect to age
(perhaps one drug is more often prescribed for
older patients)
 Changes in prescribing practices over time
 Co-morbidities and other therapies
 Channeling Bias: Comparisons of NSAIDs were
complicated by the fact that patients with existing
GI conditions were steered toward Cox-2 inhibitors
and away from traditional NSAIDs
38
Observational Evaluation Controlling for
Confounding
– Semi-automated process involving the following:
 Cohort definition and restriction
 Outcome Definition
 Creation of a Propensity Score Model
 Creation of an Outcome Model
 Produce adjusted Incidence Rate Ratio and
Confidence Limits
– Feedback to user at each step of the modeling
process
39
ProSanos Corporation Confidential and Proprietary
Observational Evaluation Example:
Pergolide and Cardiac Valvulopathy
Assess the relationship between
exposure and outcome
Using a Claims
database, the Incidence
Rate Ratio for Pergolide
vs. Pramipexole is 4.72
(2.78 – 8.02)
40
ProSanos Corporation Confidential and Proprietary
How can Observational Evaluation Augment
Current Pharmacovigilance Practices?
• Rapidly evaluate a potential signals in multiple
databases
Normalized
EHR signals detected in Screening and elsewhere
• Evaluate
Database
Patient Natural
• Rapidly
Assess issues from regulatory authorities
Observational
History
• Provide information to inform
and
focus
individual
Screening
epidemiology studies
Signal
Detection
• Identify risk factors for an Adverse Event
Normalized
Claims
Database
Potential
Signals
Observational
Evaluation
Evaluation of
a Signal
Summary: A Framework for Systematic
Analysis of Observational Data
Normalized EHR
Database
Observational
Screening
Signal
Detection
Normalized
Claims Database
Potential
Signals from
elsewhere
Patient Natural
History
Observational
Evaluation
Evaluation of
a Signal
Objective: Framework for thinking about the
systematic use of observational data in the context
of of current pharmacovigilance practices
[email protected]
43
ProSanos Corporation Confidential and Proprietary