Transcript Document 7205881

Comments on
FDA’s Pharmacovigilance and
Pharmacoepidemiologic
Assessment Concepts
Testimony April 11, 2003
Gerald Faich MD MPH
Pharmaceutical Safety Assessments
610-664-1088
[email protected]
General Comments

Thoughtful, useful
 Addresses the
need to balance
PE (structured
studies) and PV
(particularly use of
spontaneous
reports)
RESPONSES TO 4 of 6 QUESTIONS FOR THE
PUBLIC WORKSHOP
1. Spontaneous report quality
2. Data mining -What are possible advantages or
disadvantages for signaling?
3. Causality Assessments- advantages or
disadvantages ?
4. Registries-when useful?
Comments about Large Simple Safety Studies
1. Quality of case reports
We have concentrated on reporting
elements, standardized coding and
requirements. Now need to look at
content.
 Most information will be obtained
during first encounter, resistance
after this.
 Need a trained health care provider
for initial intake, not an MD.

1.

Quality of case reportsImproving content and completeness
Intake with a central
computer-assisted interview
process
– built-in structure, logic and
range checks (not merely word
processing)
– e.g.. prespecify part of anatomy
then give choices

Automatically generate
missing information request
Traditional Paper Approach vs. Technology
Paper Based Process
Call
Fax > Log > Write up > Code > Enter > Verify > Review
Mail
Electronic Process, e.g., SafetyTrak®
International Society of
Pharmavigilance (ISoP):
Draft Points to consider
Paris Jan. 2003
Panel-Ralph Edwards,Gerry Faich,
Hugh Tilson
ISoP POINT TO CONSIDER
Pharmacovigilance signal detection

Report volume for a drug is affected by, volume of use,
publicity, type and severity of the event and other
factors, therefore the reporting rate is not a true
measure of the rate or the risk

Spontaneous reporting is a method with a high
sensitivity and low specificity.

An observed event may be due to the indication
for therapy rather than the therapy itself (e.g.
suicide after anti-depressant ); therefore
observed associations should be viewed as
signal, and causal conclusions drawn with
caution
2. Data mining
The database has
evolved over time—volume, quality,
requirements, waivers
Numerous nonbiologic influences affect
reporting-labeling, publicity
Reported AEs are greatly affected by the
indication and patient population
Thus false signals are inevitable
Data mining –Limitation
Examples and a Suggestion
Claritin and arrhythmias (channeling and
need for detailed data not in data base)
Increased number of reports due to
preexisting condition. Selection of high
risk patients for the drug deemed safest
for them.
Prozac and suicide (confounding by
indication) Large increase in reports
following publicity and stimulated
reporting
Data Mining
Largely restrict within therapeutic
class
 Yogi Berra was right “If I hadn’t
believed it, I wouldn’t have seen it”

3. Causality assessment

No standardized method that is
reproducible (Koch-Weser 1968).

Time and resource consumptive
– particularly distinguishing between probable
and possible.
– Routine assessment dropped at FDA in 1983
because of its limited value and because it was
a major source of delay in entry and
contributed to backlogging.

Information often lacking
3. Causality assessment



For spontaneous reports, do whenever a signal is
under investigation, but not be routine
Whenever causality assessed, always note results
are preliminary in order to assist in interpreting
patterns of reporting and do not represent definitive
conclusions (secondary incomplete records, legal
implications)
For serious trial ADR reports, causality assessments
need to be done. The Tome (new requirements) calls
for submission to FDA when causality cannot be
ruled out. This could clog the system and
undermining the value of expedited reporting.
4. Registries
Follow large number of exposed
patients often for extended periods
of time for particular outcomes
 Recent examples- lymphoma and
infections after exposure to drugs
potentially affecting the immune
system
 Issues-sizing (power), feasibility,
data collection

4. Registries-Issues-sizing (power),
duration, feasibility, data collection

Sizing-need to recognize that will need 10s
of thousands over many years to rule out
risk of 3-fold. If one insists on ruling out a
2- fold risk, study may become infeasible
 Duration-drop outs are a fact of life.
Anticipate 20% per year. Thus very long
follow-up (except NDI) may not be
feasible—5 years yes, 10 years maybe
 Data collection –direct-to-patient calls to
find sentinel events (e.g. hospitalization or
biopsy)
Other Suggestions/responsesLSSTs

LSSTs done in the periapproval period must
draw upon pharmacoepidemiology (like
cohort studies-broad inclusion criteria, simple
focused data collection)
 Traditional epi studies, even using automated
databases, will be delayed until enough
penetration of the drug of interest into
populations which can be studied
 Both LSSTs and registries need to consider
apply technology and direct-to-patient
methods.
Smoking Cessation Study:
10,000 pt. Registered/7000 pts surveyed in <5 months.
Patients
Investigators
Inbound call or website
access to register
Study
Coordinators
Menu-Driven
SafetyTrak®
Pharmacists
Physicians
Consumers
Outbound calls to conduct
interview or investigate safety
Easy Data-Tables
Thesauri
Drop-Downs
Standardizations
Auto-QC
Auto-Narrative
Global Access
Multilingual
Post-NDA tools
External
Databases:
Utilization
Passive
surveillance
(AERS)
Background
rates
Active
Surveillance
Summary
1.
2.
3.
4.
5.
6.
For spontaneous case reports, the means to
improve content is to standardize and improve
intake
Data mining likely will generate many false
positives and affirmations of what was
previously known
Causality assessments should largely be
reserved refining important signals
Registry challenges of size, duration and data
collection are markedly intertwined with
feasibility issues
Large safety trials need epidemiologic input
PE-PV concept paper is excellent
GAF Background
FDA 1983-1990—Office Director
safety reporting, epi programs
 Since FDA

– Senior positions in CROs, safety
consultant to >20 companies and >40
projects/issue
– IDMCs
– U Penn