David Neasham Practical Use Pharmacoepi Drug Dev
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David Neasham Practical Use Pharmacoepi Drug Dev

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DIA Clinical Forum conference speaker: Practical use of Pharmacoepidemiology in Clinical Drug Development (Ljubljana Oct 20-23, 2008).

DIA Clinical Forum conference speaker: Practical use of Pharmacoepidemiology in Clinical Drug Development (Ljubljana Oct 20-23, 2008).

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David Neasham Practical Use Pharmacoepi Drug Dev David Neasham Practical Use Pharmacoepi Drug Dev Presentation Transcript

  • 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
    • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Example 1
    • 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
  • 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
  • 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
  • Example 2
    • 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
  • 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
  • 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
  • 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
  • Pharmacoepidemiology program overview
    • Table: Study parameters
    • Numbers and incidence rates of study outcomes
    • Results from McAfee et al. (2006) (Ingenix)
    Safety evaluation studies
  • 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)
  • Dr David Neasham [email_address] +44 (0)1628 408442 Questions & answers Company logo here Practical use of Pharmacoepidemiology in clinical drug development