This webinar will teach critical scientific principles related to the regulatory framework as they pertain to drug and medical device litigation for seasoned in house and outside counsel alike. Examples of topics that will be covered include safety signaling and pharmacovigilance, epidemiological and randomized controlled trial study design, risk management principles, causality assessment, and the strategic role of regulatory guidelines and compliance.

1. Working the Science and
Regulations Harder to Win Your
Drug and Device Cases
John A. Clark, MD, MSPH
April Zambelli-Weiner, Ph.D., M.P.H.
David H. Schwartz, Ph.D.

2. BRIEF INTRODUCTION
David H. Schwartz, Ph.D.
Founding Partner, Innovative Science
Solutions, LLC

3. Drug and Device Cases
Historical Perspective
Early / Landmark Cases Representative Drug Cases Representative
Device Cases

4. Big Stakes!
Phen-Fen Litigation
• $6.44 Billion
Value of class action
settlement
• $567.67 Million
Plaintiff awards

5. The Science-Based Approach
in Drug and Device Litigation
Craft a Winning
Scientific Strategy
Recruit the Best
Experts
Inherit the Scientific
Facts and Data
Gain Crucial
Admissions from
Opposing Experts
Direct
Align all Facts with
Strategy
Best
Resolution!
Develop Affirmative
Case
Cross

6. Q And it's on this forest plot, along with its odds ratio and confidence
interval, correct?
A Yeah.
Q And if you look at the last study on the forest plot, it's the same
study, Kornum 2010, same odds ratio and same confidence interval,
true?
A You're right.
Q And to paraphrase My Cousin Vinny, no self-respecting
epidemiologist would do a meta-analysis by including the same study
twice, correct?
A Well, that was an error. Yeah, you're right.
5
SSRI Birth Defect Litigation
Testimony of Anick Bérard, Kuykendall v. Forest Labs, at 223:14-17; 238:17-
20; 239:11-240:10; 245:5-12 (Cole County Missouri Nov. 15, 2013)

7. The Causation Question
Drug/device is not capable of
causing the condition (General
Causation defense)
Drug/device can cause the condition
but didn’t in this case (Specific
Causation Defense)
Drug/device can/did cause the
condition, but the warnings were
adequate (product misuse)

8. Clinical Data
Published Case Reports
Epi Studies
Discovery Development Market
Approval /
Launch
Litigation filed
Phase I / II
Phase III
Phase IV Studies (PMS)
S
p
o
n
s
o
r
Spontaneous AEs
I
n
d
e
p
e
n
d
e
n
t
RCTs

9. Presenters
John A. Clark, M.D., M.S.P.H.
President and Chief Medical Officer,
PCSglobal
Making Effective Use of Safety Surveillance
Data in Drug and Device Litigation
April Zambelli-Weiner, Ph.D., M.P.H.
President and Founder, Translational
Technologies International
The Use of Epidemiological Data in Product
Liability Litigation

10. Making Effective Use of Safety
Surveillance Data in Drug and
Device Litigation
John A. Clark, M.D., M.S.P.H.
President and Chief Medical Officer,
PCSglobal

11. KEY DEFENSE COMPONENTS
Traditional Pharmacovigilance
Case-based
reporting since
early 1960’s
Important causality
information in
some individual
cases
Many
interconnections
(ICH E2B
standard)
3 level system –
company, national,
international
(WHO)
Cases come from
studies, literature,
medical practice,
Internet
Epidemiological
study follow-up
Reporting
patterns
(e.g. over
time, by
geography)

12. KEY DEFENSE COMPONENTS
Safety Signaling
• Safety signaling is a process that assesses multiple cases
• Disproportionality – cases occur more often than expected
• Threshold – arbitrary setting that defines a safety signal
• Two phases of safety signaling
o Phase 1: Signal detection (identification of signals)
o Phase 2: Signal evaluation (case series and other analyses)
• Signal detection methods
o Type 1: Individual case information that suggest disproportion
o Type 2: Disproportions from case reporting systems
o Type 3: Statistical disproportions from studies with a control group
• Type 2 methods produce high false positive rates
• Phase 1/2 may or may not be followed by formal studies

13. SAFETY SIGNALING
US Regulations
Clinical
(312.32)
Assess safety
related
information from
all sources
Certain cases
sent in 15 days
(7 days if fatality)
Case series
analysis for
expedited
case reports
Postmarketing
(314.80)
Assess safety
related
information
from all sources
Certain cases
sent in 15 days
Case-based
reporting
High false
positive rate,
establishes
expected
reporting
patterns
Non-case
based
Disproportions
subject to
interpretation

14. SAFETY SIGNALING
Generally Accepted Practices
• Derive from CIOMS and ICH consensus conferences
o CIOMS = Conference of International Organizations of the Medical
Sciences (sponsored by WHO; Headquarters is in Geneva)
o ICH = International Conference on Harmonisation (sponsored by US,
Japan, and EU; Headquarters is in Geneva)
• CIOMS consensus conferences bring together thought
leaders from agencies and industry on risk topics
• ICH considers CIOMS recommendations and creates
international guidelines
o May or may not be enacted into law by countries, BUT
o Is used by agencies as a basis for auditing

15. SAFETY SIGNALING
Trends and Updates
• Case-based signaling using “designated
medical events”
o Low incidence
o Can be caused by drugs, biologics, or devices
• FDA’s sentinel system
o Available only to FDA
o Applies signaling techniques to non-spontaneous datasets
• Combining adverse event and product
complaint data
• Signaling within risk management programs

16. Safety Signaling
Comments
• Both case processing and safety signaling are
extensively described by CIOMS and ICH
• Safety signaling is not “one size fits all”
• Signals come from multiple sources: individual
cases, groups of cases, studies, and non-human
data
• Develop objective case definitions
• Look for confounding factors
• Assess case series over time to identify effects due
to the information system (rather than the product)
• Don’t overemphasize epidemiological study results

17. SAFETY SIGNALING
Client Performance?
• Signaling is a process; Responsibility =
establishing appropriate processes?
o Processes clearly defined
o Processes carried out appropriately
o Results of internal and agency audits or inspections
addressed by CAPAs
• When did the signal first occur? (company
reaction time)
• Training and education? (e.g., medical
expertise)
• Pharmacovigilance plan in place?
• Systems appropriate for the monitoring plan?

18. SAFETY SIGNALING
Data Source Checklist

Was a comprehensive case series
evaluation done?

Regulatory requirement

Clear criteria for when a case series
evaluation must be done

Frequently exonerates the product

Literature screening procedures

Internet case screening?

FDA’s Adverse Event Reporting
System database (FAERS)

FDA’s Manufacturer and User
Facility Device Experience database
(MAUDE)

Product complaint/product quality
database (especially for biologics) help
to monitor for:

Counterfeit product

Contract manufacturing issues

Difficult manufacturing processes
(e.g., complex biologics)

Integrated clinical trial data

Specialized datasets (registries, risk
management compilations)

Claims or electronic medical record
data

19. HANDLING OF SAFETY ISSUES
What Should the Jury Know?
• Client company has good processes in place
for finding, evaluating, and conveying safety
signals
• Client company responded quickly
• Clear, supportable case definition
• Other causative (confounding) factors
• Information system factors (e.g., publicity)
• Study design factors

20. LESSONS LEARNED
EXAMPLES
Vioxx and vascular events
• Product pulled from market before an assessment of all data had been performed
• Sales force and publication strategy misinformed the public about study results
• Most vascular event cases had numerous confounding factors
• Epidemiological studies are consistent with a slight association of the same
magnitude for ibuprofen
• Advisory committees in multiple countries have voted to reintroduce the product
Do a thorough analysis of all
sources of data, including cases
Represent potential safety
problems accurately to the public
Don’t panic and pull a drug off
the market prematurely
Lesson 1
Lesson 2
Lesson 3

21. LESSONS LEARNED
EXAMPLES
Metal-on-metal hip replacement and early hip joint
damage
• Monitoring systems at the company were rudimentary
• Many so-called cases did not qualify as cases
• Study design and data sources for epidemiological studies that showed a
low level increase in joint damage were not well done
• Much of the increase in reporting was publicity induced
• There was not an accepted biological mechanism
Make sure case reporting
processes are robust
Critically evaluate
epidemiological study results
Always look at the timing of case
reporting
Lesson 1
Lesson 2
Lesson 3

22. LESSONS LEARNED
EXAMPLES
GLP-1 anti-diabetic drugs and adenocarcinoma of the pancreas

The link to adenoCA of the pancreas is largely based on case reporting

AdenoCA of the pancreas is a co-morbid condition of diabetes

Confounder: adenoCA of the pancreas occurs at the same anatomical location as the site of
activity; reporters can easily draw a conclusion of possible relationship based on this
observation

Subsequent studies have not clearly demonstrated this link

Incidence and prevalence of adenoCA of the pancreas is increasing
Look carefully at cases for
sources of bias
Design epi studies to minimize
case level biases
Beware whenever the event is
becoming more common
Lesson 1
Lesson 2
Lesson 3

23. LESSONS
LEARNED EXAMPLES
Over-the-counter products and gastrointestinal events

Musty smell and GI events found in reports for OTC products

Company initiated multiple recalls and posted GI events on their website

Incidence and prevalence of the GI events were extremely high

Comparison of product (odor) and adverse event (GI events) databases showed
no relationship

Eventual toxicity studies done with a malodorous contaminant showed that it was
very low level and was biologically inert
Create process that minimize publicity until
all data has been evaluated
Product complaint processes that continue
indefinitely until the root cause of an
important finding is clear
Lesson 1
Lesson 2

24. The Use of Epidemiological Data in
Product Liability Litigation
April Zambelli-Weiner, Ph.D., M.P.H.
President and Founder
Translational Technologies
International

25. Types of Studies and Sources of Data
PRIMARY
RESEARCH
 Randomized
Clinical Trials
 Observational
Studies
 Post-Market
Registries
SECONDARY
RESEARCH
 Comparative
Effectiveness
 Health
Economics
and
Outcomes
Research
 Systematic
Reviews
 Meta-Analyses
 Pooled Analyses

26. Analytic Study Designs
Randomized Control Trials
(experimental)
Cohort studies (observational)
Case-control studies (observational)
Cross-sectional studies (observational)

27. Randomized Clinical Trials (RCTs)
Conducted in 4 Phases, each of which
answer different research questions
Phase I
• Testing in humans
• Small sample size
• Preliminary testing
of safety, dosage,
and side effects
Phase II
• Testing in humans
• Larger sample
size
• Additional testing
of efficacy and
safety
Phase III
• Testing in humans
• Large sample size
and follow-up to
support
registration
• “Pivotal” Studies:
compare efficacy
and safety against
placebo and
comparator
Phase IV
• Post-market
studies
• Provides
information
regarding safety
and efficacy of
drug within real-world
context
Pivotal Trials

28. Strengths and Limitations of Clinical Trials
STRENGTHS
 Can be used to
evaluate causation
Gold standard for
obtaining evidence
of a treatment effect
Randomization
protects against
most forms of bias
LIMITATIONS
 Do not reflect real-world
use scenarios
Narrow Focus
Expensive
Only possible
where there is
“intervention” that
people are willing to
be randomized to

29. Observational Studies
Used to study a wider range of exposures
than experimental studies
“Natural” experiments
Mitigate many issues which are not
feasible in experimental studies

30. Strengths and Limitations of Observational
Studies
STRENGTHS
 Provide information
on “real world” use and
practice
Larger sample sizes
Longer follow-up
periods
Less costly
Different study
designs
Efficient use of
available data
LIMITATIONS
 Subject to many
biases
Limited control over
composition of the
control groups
Standardization of
exposures and
outcomes varies
Data more likely to be
incomplete and of
poorer quality

31. Post-Market Registries
• Subset of observational studies
•Follows subjects forward in time and collects
information on well-defined outcomes of
interest for analysis and reporting
•Registry participants are recruited on a disease
basis or exposure/treatment basis

32. Strengths and Limitations of Registries
STRENGTHS
Large number of cases with
long-term follow-up
Reflect “real-world”
experience on diverse patient
population
Can examine issues such
as the impact of clinical
experience or surgical skill
Additional data such as
patient-reported outcomes
LIMITATIONS
Data not 100%
verified
Variability in data
definitions
Under-reporting
Difficulty in
prospective follow-up
Incompleteness of
data
Competing registries

33. THE USE OF
EPIDEMIOLOGICAL DATA IN
LITIGATION

34. Two types of data that are key to
causal assessment
Published
Clinical data
Epidemiological data
Proprietary Studies

35. Know Your Numbers
Accurate analysis of study data can address
important questions about potential safety signals
— Could a safety signal have been detected
earlier than reported?
— Despite the report of adverse events, do
the aggregate data show statistically
meaningful evidence of harm to patients?
— Are results being driven by a single study
or a particular patient subgroup?

36. Example
TABLE 1
Event
All Controlled Studies, n (%) Placebo Controlled Studies, n (%)
DRUG
N=12,581
Control
N=11,214
Rate
Ratio and
95% CI
(p-value)†
DRUG
N=3758
Control
N=2044
Rate
Ratio and 95%
CI
(p-value)†
Deep Vein Thrombosis 15 (0.12) 9 (0.08)
1.49
0.65-3.55
3 (0.08) 4 (0.20)
0.41
0.08-1.98
Venous Thrombosis Limb 0 (0.00) 2 (0.02)
0.20*
(p=0.44)
0 (0.00) 0 (0.00) -
Pulmonary Embolism 4 (0.03) 1 (0.01)
3.57
0.45-88.23
4 (0.11) 1 (0.05)
2.18
0.27-53.86
TABLE 1 REVISED
Event
All Controlled Studies, n (%) Placebo Controlled Studies, n (%)
DRUG
N=15,391
Control
N=13,453
Rate
Ratio and
95% CI
(p-value)†
DRUG
N=5510
Control
N=3093
Rate
Ratio and
95% CI
(p-value)†
Deep Vein Thrombosis 16 9
1.55
(0.69-3.68)
3 4
0.42
(0.08-2.04)
Venous Thrombosis Limb 0 2
0.44*
(p=0.44)
0 0 --
Pulmonary Embolism 5 1
4.37
(0.61-104)
4 1
2.25
(0.28-55.56)

37. Methodological Issues
Outcome reporting (lumping vs. splitting)
 See FDA Guidance for Industry, Premarketing Risk Assessment,
2005
Clinical protocols (monitoring and detection)
 Spontaneous reporting vs. active surveillance
Inclusion and exclusion criteria
 Generalizability of the study populations to general population
Length of follow-up
 Were trials adequate to detect AEs/SAEs

38. Other Important Analyses
Sensitivity Analyses
 Is risk specific to a particular subgroup?
 Are findings robust to choice of control group?
Analysis of registry data
 Are there consequences of long-term use?
 Do we see AEs in previously unstudied populations or
vulnerable populations?
Review/analysis of observational studies
 Do we see replication across studies?
 What is the evidence for pooled analyses or meta-analyses?

39. Example: Subgroup Analyses (Risk/Benefit
Ratio)
Risk of
Adverse
Event
Benefit

41. PUBLISHED DATA

42. The Publication Record
What was level of corporate involvement?
Did all investigators have full access to study
data?
What findings were published and which
findings were not published – and why?
What can be discerned from the published
data?

44. A Current Example: Testosterone Therapy
10/30/2014
43
Industry
Sponsored
Studies
Summary
OR=0.89
95% CI
(0.50-1.60)
Non-
Industry
Funded
Studies
Summary
OR=2.06
95% CI
(1.34-3.17)

46. Excerpted from ISMPP presentation , http://www.ismpp.org/assets/docs/Inititives/GPP2/chris_graf_ismpp_u_may2010.pdf

47. Conclusion
Know the sources of data for your product
Know the strengths and weakness of that
data
Understand the implications of
methodological choices in generating and
analyzing data
Look for data gaps
Review the publication record

48. For more information or to discuss your
needs feel free to contact:
Dr. April Zambelli-Weiner
Translational Technologies International (TTI)
aweiner@transtechint.com
800-580-2990, ext 100

49. Q & A