John A. Clark, MD, MSPH

April Zambelli-Weiner, Ph.D., M.P.H.

David H. Schwartz, Ph.D.

Working the Science and Regulations Harder to Win Your Drug and Device Cases

BRIEF INTRODUCTION

David H. Schwartz, Ph.D.

Founding Partner, Innovative Science

Solutions, LLC

Drug and Device CasesHistorical Perspective

Early / Landmark Cases Representative Drug Cases Representative

Device Cases

Big Stakes!

Phen-Fen Litigation

• $6.44 Billion

Value of class action

settlement

• $567.67 Million

Plaintiff awards

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

Align all Facts with

Strategy

Best

Resolution!

Develop Affirmative

Case

Direct

Cross

5

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.

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)

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)

Clinical Data

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

Epi Studies

Published Case Reports

Presenters

April Zambelli-Weiner, Ph.D., M.P.H.

President and Founder, Translational

Technologies International

The Use of Epidemiological Data in Product

Liability Litigation

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

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

KEY DEFENSE COMPONENTSTraditional 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)

KEY DEFENSE COMPONENTSSafety 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 signalingo 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

SAFETY SIGNALINGUS 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

• Derive from CIOMS and ICH consensus conferenceso 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 guidelineso May or may not be enacted into law by countries, BUT

o Is used by agencies as a basis for auditing

SAFETY SIGNALINGGenerally Accepted Practices

• Case-based signaling using “designated medical events”o Low incidence

o Can be caused by drugs, biologics, or devices

• FDA’s sentinel systemo Available only to FDA

o Applies signaling techniques to non-spontaneous datasets

• Combining adverse event and product complaint data

• Signaling within risk management programs

SAFETY SIGNALINGTrends and Updates

• 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

Safety SignalingComments

• 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?

SAFETY SIGNALINGClient Performance?

SAFETY SIGNALINGData 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

• 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

HANDLING OF SAFETY ISSUESWhat Should the Jury Know?

LESSONS LEARNEDEXAMPLES

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

Don’t panic and pull a drug off

the market prematurely

Represent potential safety

problems accurately to the publicLesson 2

Lesson 3

Lesson 1

LESSONS LEARNEDEXAMPLES

Make sure case reporting

processes are robust

Always look at the timing of case

reporting

Critically evaluate

epidemiological study resultsLesson 2

Lesson 3

Lesson 1

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

LESSONS LEARNEDEXAMPLES

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

Beware whenever the event is

becoming more common

Design epi studies to minimize

case level biasesLesson 2

Lesson 3

Lesson 1

GLP-1 anti-diabetic drugs and adenocarcinoma of the pancreas

LESSONS LEARNED EXAMPLESOver-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 clearLesson 2

Lesson 1

The Use of Epidemiological Data in Product Liability Litigation

April Zambelli-Weiner, Ph.D., M.P.H.

President and Founder

Translational Technologies International

Randomized

Clinical Trials

Observational

Studies

Post-Market

Registries

PRIMARY

RESEARCH

SECONDARY

RESEARCH

Types of Studies and Sources of Data

Comparative

Effectiveness

Health

Economics

and

Outcomes

Research

Systematic

Reviews

Meta-Analyses

Pooled Analyses

Analytic Study Designs

Randomized Control Trials

(experimental)

Cohort studies (observational)

Case-control studies (observational)

Cross-sectional studies (observational)

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

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

Used to study a wider range of exposures

than experimental studies

“Natural” experiments

Mitigate many issues which are not

feasible in experimental studies

Observational Studies

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

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

Strengths and Limitations of Registries

STRENGTHSLarge 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

LIMITATIONSData not 100%

verified

Variability in data

definitions

Under-reporting

Difficulty in

prospective follow-up

Incompleteness of

data

Competing registries

THE USE OF EPIDEMIOLOGICAL DATA IN

LITIGATION

Two types of data that are key to causal assessment

Published

Clinical data

Epidemiological data

Proprietary Studies

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?

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

0.65-3.553 (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

0.45-88.234 (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 91.55

(0.69-3.68)3 4

0.42

(0.08-2.04)

Venous Thrombosis Limb 0 20.44*

(p=0.44)0 0 --

Pulmonary Embolism 5 14.37

(0.61-104)4 1

2.25

(0.28-55.56)

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

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?

Example: Subgroup Analyses (Risk/Benefit Ratio)

Risk of

Adverse

Event

Benefit

PUBLISHED DATA

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?

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)

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

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

For more information or to discuss your needs feel free to contact:

Dr. April Zambelli-WeinerTranslational Technologies International (TTI)

aweiner@transtechint.com

800-580-2990, ext 100

Q & A