Impact of Exploratory Analysis on Drug Approval

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Impact of Exploratory Analysis on Drug Approval

• Joga Gobburu
• Pharmacometrics
• Office Clinical Pharmacology, CDER, FDA

jogarao.gobburu_at_fda.hhs.gov
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Take Home Message

• Exploratory (e.g., pharmacometric) analyses are
  often used to make regulatory decisions
• Decisions are not entirely driven by the
  pre-specified statistical analysis
• Need for change
• Integrate strengths of both approaches
• Think How exploratory analyses can help drug
  development?
• Opportunities for collaboration between
  pharmacometricians and statisticians are abundant
• Think about How can I facilitate this
  collaboration?

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Pharmacometrics (or Quantitative Experimental
Medicine?)

• Science that deals with quantifying disease and
  pharmacology
• Applications
• Benefit/Risk, dose individualization, trial
  design
• Diverse expertise
• Clinical pharmacologists, Pharmacometricians,
  Clinicians, Statisticians, Bioengineers
• Tools
• Linear/Nonlinear Mixed effects models,
  Longitudinal data analysis, Biological models,
  Stochastic simulations

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Impact of Exploratory Analyses 2000-2004
Pivotal Regulatory decision will not be the same
without PM reviewSupportive Regulatory decision
is supported by PM review
Impact Approval Labeling
Pivotal 54 57
Supportive 46 30
No Contribution 0 14
Bhattaram et al. AAPS Journal. 
2005 7(3) Article 51. DOI  10.1208/aapsj070351
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Pivotal Regulatory decision will not be the same
without PM reviewSupportive Regulatory decision
is supported by PM review
Impact of Exploratory Analyses 2005-2006
Impact ? Discipline Approval Labeling
PM Reviewer 95 100
DCP Reviewer 95 100
DCP TL 90 94
Medical Reviewer 90_at_ 90_at_
DCPDivision of Clinical Pharmacology _at_survey
pending in 1 case
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NDA Case Study

• Drug is proposed for a rare debilitating, fatal
  disease with no approved treatment.
• One trial successful and other failed
• Failure likely due to trial execution errors
• Potential miscommunication about dose timing
• Primary variable Change in symptom score
• Key question
• Is there adequate evidence for the effectiveness?

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Equivocal Evidence of EffectivenessPivotal
Studies
DB1 Dbl-blind (DB) Randomized PBO
Controlled Dose Titration N75 Plt0.051 (withdrawal
)
Agency at this point can ask for more evidence
(one or more studies) OR Investigate
further across the clinical trial database
whether there is a consistent signal of
effectiveness or not
DB2 Dbl-blind (DB) Randomized PBO
Controlled Dose Withdrawal N30 Pgt0.051
1change in score at the end of study
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Equivocal Evidence of EffectivenessPivotal
Other Studies
OL-1 Open label (OL) Withdrawal Dose
Titration N75
DB1 Dbl-blind (DB) Randomized PBO
Controlled Dose Titration N75 Plt0.05 (withdrawal)
OL-2 Open label (OL) Continue old dose N30
DB2 Dbl-blind (DB) Randomized PBO
Controlled Dose Withdrawal N30 Pgt0.05
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Significant Dose-Response Relationship DB1, OL1
Parameter Mean (Confidence Interval) Between-Patient Variability (CI)
Slope of dose- response, per mg 4.3 (3.7, 4.6) 56 (46, 66)
Within-Patient Variability 26 (23, 29)
plt0.001
Linear mixed effects model employed
Estimate of dose-response slope is similar for
individual and combined analyses. Results from
combined shown here.
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Significant and Consistent Drug Effects Across
Studies
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Drug in OL1 beat Placebo in DB1 Cross-over
comparison
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Value of Exploratory Analysis

• To Patients/FDA
• Availability of drug sooner, especially given no
  approved treatments (debilitating disease)
• Efficient solution to challenging patient
  enrollment
• Fewer review cycles (because of this issue alone)
• Ultimately might lead to lower drug costs
• To Sponsor
• Alleviated the need for additional trial(s) to
  demonstrate effectiveness
• Save and time
• Pharmacometrics analyses can and do influence
  approval decisions!

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Why did the sponsor not consider making a similar
case?
Unlikely
Unlikely

• Unanticipated concern
• Lack of expertise (both technical, strategic)
• Prescriptive behavior on analysis
• Unclear expectations from FDA

Likely
Likely
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Parkinsons DiseaseCollaboration between
Statistics and Pharmacometrics
Dr. Bhattaram and Dr. Siddiqui are the project
leads with the following team members FDA Stat
istics, Clinical, Policy Makers External Statist
ician, Disease experts
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Symptomatic or Protective?
Placebo
Drug A
Drug B
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Symptomatic or Protective?
Placebo
Drug A
Drug B
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Discern Symptomatic vs. Protective Effects
Delayed Start Design

• Key Questions
• Endpoint ?
• Analysis ?
• Handling missing data?

Placebo Phase
Active Phase
If drug is protective then patients who received
drug longer will have lower scores compared those
who receive drug late.
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Parkinsons Disease Database
Data Source Patients Trial Duration
Trial1 NDA 400 1yr 3yr follow-up
Trial2 NIH 400 1yr follow-up
Trial3 NDA 900 9mo follow-up
Trial4 NDA 200 9mo follow-up
Trial5 IND 300 1.5yr
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Selegiline ( 5 years)
Published Data
Mean (SD) of Total UPDRS scores for patients with
Parkinsons disease treated with levodopa alone
or in combination with selegiline for 5 years and
during the one-month washout period
Eur.J.Neurology, 1999, 6 539-547
The vertical line represents 2 months
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Patients with slower progression remain longer in
clinical trials (TEMPO)
Fraction Remaining
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Value of Collaboration between Pharmacometrician,
Statistician

• Statisticians Contribution
• Primary statistical analysis
• Drop-outs
• Trial design
• Power calculations
• Pharmacometricians/Disease Experts Contribution
• Biological/Mechanistic Interpretation
• Disease Progression
• Drug Effects
• Drop-outs
• Trial design, alternative analysis

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Value of Exploratory Analyses

• Collected a large database of clinical trials
• Extracted patient population, placebo/disease
  progression, drug effect (not shown) and drop-out
  information.
• Simulations to answer the key questions mentioned
  earlier are in progress
• Directly useful to advice sponsors
• Conference planning is underway

  Disease Models Background http//www.fda.gov/
ohrms/dockets/ac/06/briefing/2006-4248B1-04-FDA-to
pic20320replacement.pdf  
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Take Home Message

• Exploratory (e.g., pharmacometric) analyses are
  often used to make regulatory decisions
• Decisions are not entirely driven by the
  pre-specified statistical analysis
• Need for change
• Integrate strengths of both approaches
• Think How exploratory analyses can help drug
  development?
• Opportunities for collaboration between
  pharmacometricians and statisticians are abundant
• Think about How can I facilitate this
  collaboration?

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