Practical use of Pharmacoepidemiology in clinical drug development Presenter: Dr David Neasham Company logo here

Contents Background, contributing areas & reasons to perform pharmacoepidemiology studies Study design Database studies Two practical examples: YASMIN (ethinylestradiol with drospirenone) CRESTOR (rosuvastatin) Summary Discussion / Q & A

Background, contributing areas & reasons to perform pharmacoepidemiology studies

Study design

Database studies

Two practical examples:

YASMIN (ethinylestradiol with drospirenone)

CRESTOR (rosuvastatin)

Summary

Discussion / Q & A

Background Pharmacoepidemiology Application of epidemiological techniques to the content area of clinical pharmacology Population level Study of adverse drug effects Many areas of contribution

Pharmacoepidemiology

Application of epidemiological techniques to the content area of clinical pharmacology

Population level

Study of adverse drug effects

Many areas of contribution

Contributions Better quantification of incidence of known adverse and beneficial effects Higher precision In patients excluded in pre-marketing studies – e.g., children, elderly, pregnant women Interaction by other drugs or illnesses Relative to other drugs used for same indication

Better quantification of incidence of known adverse and beneficial effects

Higher precision

In patients excluded in pre-marketing studies – e.g., children, elderly, pregnant women

Interaction by other drugs or illnesses

Relative to other drugs used for same indication

Reasons Regulatory e.g., obtain earlier approval for marketing Clinical e.g., hypothesis testing or generating Marketing e.g., assist in repositioning of drug Legal e.g. In anticipation of product liability litigation

Regulatory

e.g., obtain earlier approval for marketing

Clinical

e.g., hypothesis testing or generating

Marketing

e.g., assist in repositioning of drug

Legal

e.g. In anticipation of product liability litigation

Study design

© Imperial College London Slide

Study design Case-cohort design Selecting all cases in cohort – i.e. all subjects with adverse event Randomly selecting a sample of pre-determined size of subjects from the cohort Reduced version of cohort, with all cases added

Case-cohort design

Selecting all cases in cohort – i.e. all subjects with adverse event

Randomly selecting a sample of pre-determined size of subjects from the cohort

Reduced version of cohort, with all cases added

Study design Large Simple trial (LST) Large randomized trials made simple by reducing data collection to minimum needed to test specific hypotheses Randomization of treatment assignment Controls of confounding by known or unknown risk factors Sufficient statistical power  small risks of common events + large risks of rare events

Large Simple trial (LST)

Large randomized trials made simple by reducing data collection to minimum needed to test specific hypotheses

Randomization of treatment assignment

Controls of confounding by known or unknown risk factors

Sufficient statistical power  small risks of common events + large risks of rare events

Study design Efficiency of LSTs could be enhanced using health delivery systems databases Relevant outcomes (e.g., hospitalizations for gastrointestinal bleeding) could be captured electronically which would eliminate need to contact patients for follow-up Still necessary to identify eligible subjects, obtain consent and randomize treatment Could be a very effective hybrid method

Efficiency of LSTs could be enhanced using health delivery systems databases

Relevant outcomes (e.g., hospitalizations for gastrointestinal bleeding) could be captured electronically which would eliminate need to contact patients for follow-up

Still necessary to identify eligible subjects, obtain consent and randomize treatment

Could be a very effective hybrid method

A typical scenario: identifying risks INCIDENCE OF COMMONLY OCCURRING EVENT FROM CLINICAL TRIALS Background Epidemiology Risk management strategy  INCIDENCE OF EVENT IN GENERAL POPULATION IDENTIFICATION OF RISK FACTORS POTENTIAL SIGNALS OF RARE EVENTS Spontaneous Reports Observational Studies Other Activities 1/1,000 1/500 1/100 1/10,000 1/5,000 1/1,000 1/100,000 1/50,000 1/10,000 HYPOTHESIS TESTING 1/1,000,000 1/500,000 1/100,000, Clinical trial Data Insufficiently powered Evidence base Self-report bias Best study design….

Safety challenges in pre-approval phase Challenges of safety signal identification during pre-approval phase: pre-approval studies cannot usually be statistically powered to identify readily low frequency safety events of concern samples often narrowly defined – therefore may not be truly representative of final treatment population short time frames: mid to long-term effects cannot be thoroughly evaluated

Challenges of safety signal identification during pre-approval phase:

pre-approval studies cannot usually be statistically powered to identify readily low frequency safety events of concern

samples often narrowly defined – therefore may not be truly representative of final treatment population

short time frames: mid to long-term effects cannot be thoroughly evaluated

Post-marketing pharmacovigilance Limitations of post-marketing spontaneous reporting systems: patterns of spontaneous reporting vary by country, increase during early post-launch period and are affected by publicity -> s pontaneous reporting rates biased limited population level denominator information for common events (e.g., MI) difficult to separate actual signals from background noise

Limitations of post-marketing spontaneous reporting systems:

patterns of spontaneous reporting vary by country, increase during early post-launch period and are affected by publicity -> s pontaneous reporting rates biased

limited population level denominator information

for common events (e.g., MI) difficult to separate actual signals from background noise

Product life-cycle and safety studies Approval Product life-cycle FIM Ph I Ph II Ph III Ph IV Toxicology (e.g. genotoxicity assays) In silico analysis/ structural alerts Margin of safety & no observed adverse effect level defined Ph I & II: dose ranging, efficacy & toxicity Phase III: demonstration of efficacy and safety signal monitoring Ph IV: spontaneous reporting systems Postmarketing pharmacovigilance Clinical Exposure in humans (Potential Denominator) Preclinical Sufficiently powered Easy study replication Large database studies No self-reporting bias

Toxicology (e.g. genotoxicity assays)

In silico analysis/ structural alerts

Margin of safety & no observed adverse effect level defined

Ph I & II: dose ranging, efficacy & toxicity

Phase III: demonstration of efficacy and safety signal monitoring

Ph IV: spontaneous reporting systems

Example 1 The YASMIN (ethinylestradiol with drospirenone) study

The YASMIN (ethinylestradiol with drospirenone) study

Case Study - Yasmin and the Safety of DRSP Background Yasmin is an OC that contains drospirenone (DRSP) DRSP has antimineralocorticoid activity and can raise serum potassium Phase IV commitment To obtain safety data related to: Complications related to hyperkalemia (elevated serum potassium) Physician prescribing and patient monitoring Breakthrough pregnancy and birth outcomes In 25,000 users of Yasmin and 50,000 users of other OCs Wider business implications Sponsor planned to introduce DRSP into menopausal therapy Needed strong basis for later safety claims

Background

Yasmin is an OC that contains drospirenone (DRSP)

DRSP has antimineralocorticoid activity and can raise serum potassium

Phase IV commitment

To obtain safety data related to:

Complications related to hyperkalemia (elevated serum potassium)

Physician prescribing and patient monitoring

Breakthrough pregnancy and birth outcomes

In 25,000 users of Yasmin and 50,000 users of other OCs

Wider business implications

Sponsor planned to introduce DRSP into menopausal therapy

Needed strong basis for later safety claims

Cohort Formation INGENIX Research Database Women (10-59 yrs) n = 959,482 Propensity score matching of 12 initiator cohorts (n = 22,429) New dispensing of Yasmin (n = 31,149) New dispensing of other OCs (n = 360,505) ≥ 6 months continuous enrollment (n = 22,887) ≥ 6 months continuous enrollment (n = 227,596) Propensity score matching of 12 initiator cohorts (n = 44,858) 1:2 Ratio

Data Collection • Create matched cohorts of Yasmin and other OC users Collect comorbidities, demographics, healthcare utilization, exposure to oral contraceptives • Identify women with hepatic, renal or adrenal insufficiency • Identify potassium-sparing meds New OC or New type of OC OC Rx 6 Months Before Dispensing Prospective Data Collection Identify clinical outcomes based on claims data (prescription, diagnoses, procedures and service) Hospitalizations for new conditions, clinical events potentially related to hyperkalemia, electrolyte abnormality, inappropriate prescribing, potassium monitoring, pregnancy/fetal malformations

• Create matched cohorts of Yasmin and

other OC users

Collect comorbidities, demographics,

healthcare utilization, exposure to oral

contraceptives

• Identify women with hepatic, renal or

adrenal insufficiency

• Identify potassium-sparing meds

Identify clinical outcomes based on

claims data (prescription, diagnoses,

procedures and service)

Hospitalizations for new conditions,

clinical events potentially related to

hyperkalemia, electrolyte abnormality,

inappropriate prescribing, potassium

monitoring, pregnancy/fetal malformations

Yasmin Launch Q3 Time Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2001 2002 2003 2004 Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow-Up Follow-Up Yasmin Control P Follow Follow Yasmin Control P 2005 Q1 Q2 Q3 Q4 Claims-Based Outcomes Chart-Based Outcomes Apr. Report Oct. Report Apr. Report Oct. Report Apr. Report Oct. Report Apr. Report Final Report

Blinded Chart Reviews to Confirm Outcomes Claims Based Abstraction Medical Chart Abstraction Screen Claims Data Medical Review

Rate Ratios Some outcomes may be continuations of pre-existing conditions * No rate ratio calculated as no case in Yasmin Cohort ** Composite hyperkalemia outcome comprised of chart-confirmed cases of arrhythmia, syncope, electrolyte disturbance, hyperkalemia, and myocardial infarction. Syncope Arrhythmia Hyperkalemia Other Electrolyte Disturbance Dialysis* Myocardial Infarction* Hospitalization with Hyper/Hypokalemia* Death Composite Hyperkalemia** 0 0 0 0 Incidence Rate Ratio –Yasmin versus Other OC (95% CI) 5.0 1.0 2.0 4.0 3.0 0.5 0.25 0.33 0.2 0.1

Does Yasmin lead to more VTE than other OCs? NO. We added a new outcome which we could evaluate both retrospectively and prospectively for the full cohort. Why do physicians prescribe to women with apparent contraindications of renal, hepatic, or adrenal disease? MAY IGNORE NOMINAL CONTRAINDICATION FOR MILD DISEASE. We directly interviewed physicians with apparently contraindicated prescribing patterns. Why do physicians not perform potassium monitoring as recommended in the label? DISAGREE WITH LABEL. We surveyed physicians with patients who by label should have been monitored, but were not. Flexible Data Collection Facilitates Risk Management

Does Yasmin lead to more VTE than other OCs?

NO. We added a new outcome which we could evaluate both retrospectively and prospectively for the full cohort.

Why do physicians prescribe to women with apparent contraindications of renal, hepatic, or adrenal disease?

MAY IGNORE NOMINAL CONTRAINDICATION FOR MILD DISEASE. We directly interviewed physicians with apparently contraindicated prescribing patterns.

Why do physicians not perform potassium monitoring as recommended in the label?

DISAGREE WITH LABEL. We surveyed physicians with patients who by label should have been monitored, but were not.

Example 2 The CRESTOR (rosuvastatin) studies

The CRESTOR (rosuvastatin) studies

Brief background Rosuvastatin Hepato-selective and relatively hydrophilic hydroxy-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor (statin) Approved by FDA on 12 August, 2003 Management of dyslipidemia

Rosuvastatin

Hepato-selective and relatively hydrophilic hydroxy-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor (statin)

Approved by FDA on 12 August, 2003

Management of dyslipidemia

Potential safety issues Added concerns from withdrawal of cerivastatin in 2001 due to excess cases of fatal rhabdomyolysis Comorbidities and concomitant medications may increase risk of myotoxicity, depending on properties of given statin

Added concerns from withdrawal of cerivastatin in 2001 due to excess cases of fatal rhabdomyolysis

Comorbidities and concomitant medications may increase risk of myotoxicity, depending on properties of given statin

Evolution of Lipid Management ATP Guidelines* ATP I (1988) ATP II (1993) ATP III (2001) Diet; low-dose, non-statin monotherapy High-dose statin, combination therapy Low- to moderate-dose statin monotherapy Increasing aggressiveness of cholesterol-lowering therapy * The National Cholesterol Education Program Adult Treatment Panel (ATP)

The CRESTOR study program An international collaborative pharmacoepidemiology safety program involving many groups… AstraZeneca R&D Centro Espa ñol de Investigación Farmacoepidemiológica (CEIFE), Spain PHARMO Institute, Netherlands i3 Drug Safety, US Drug Safety Research Unit (DSRU), UK

An international collaborative pharmacoepidemiology

safety program involving many groups…

AstraZeneca R&D

Centro Espa ñol de Investigación Farmacoepidemiológica (CEIFE), Spain

PHARMO Institute, Netherlands

i3 Drug Safety, US

Drug Safety Research Unit (DSRU), UK

Pharmacoepidemiology program design Program consisted of nine studies grouped into three components: Patient characteristics studies (x 4 databases) Safety evaluation studies (x 4 databases) Prescription-event monitoring study (DSRU) All studies performed according to GPP guidelines and reviewed and approved by ethics committees

Program consisted of nine studies grouped into three components:

Patient characteristics studies (x 4 databases)

Safety evaluation studies (x 4 databases)

Prescription-event monitoring study (DSRU)

All studies performed according to GPP guidelines and reviewed and approved by ethics committees

Pharmacoepidemiology program overview Table: Study parameters

Table: Study parameters

Numbers and incidence rates of study outcomes Results from McAfee et al. (2006) (Ingenix) Safety evaluation studies

Numbers and incidence rates of study outcomes

Results from McAfee et al. (2006) (Ingenix)

Pooled person-time and outcomes

Summary Contributing areas and reasons to perform pharmacoepidemiology studies Study design Database studies Two practical examples: YASMIN (ethinylestradiol with drospirenone) CRESTOR (rosuvastatin)

Contributing areas and reasons to perform pharmacoepidemiology studies

Study design

Database studies

Two practical examples:

YASMIN (ethinylestradiol with drospirenone)

CRESTOR (rosuvastatin)

Dr David Neasham [email_address] +44 (0)1628 408442 Questions & answers Company logo here Practical use of Pharmacoepidemiology in clinical drug development