Pk studydesign en
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WHO Prequalification Program Workshop, Kiev, Ukraine, June 25-27,2007
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Visit FDA website: www.fda.gov
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Requires a ComparatorProduct?
SAMENESSVARIABILITY
Statistical Test
IndustryRisk
ConsumerRisk
Highly Variable DrugsFood effectsNarrow Therapeutic IndexDrugs with Long T50
Factors to Consider for Bioequivalence
Parent CompoundMetabolite(s)?P’dynamic Response
Single DoseMultiple Dose
Set at 5%
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• Innovator Pharmaceutical Product ( Safety and efficacy)
• A generic product should not be a comparator as long as an innovator product is available.
• Selection should be made at the national level by the drug regulatory agency
– National Innovator– WHO comparator product ( quality-safety-efficacy and
has reference to manufacturing site)– ICH or associated country comparator product
Comparator Product
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In The Case that Innovator Product cannot be identified
• Important Criteria for Selection– Product is in the WHO list– Approval in an ICH – Associate Country- Pre-qualified by WHO– Extensive documented use in clinical trials
reported in peer-reviewed scientific journals– Long unproblematic post-market surveillance (“well selected
comparator”)
A product approved based on comparison with A non domestic comparator product may not be
interchangeable with currently marketed domestic products
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Set at 5%
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Stavchansky’s Recommendation: FDA should pressure the InnovatorCompanies to put forward a Confidence Interval for their HVP
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GE = PE + TE
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Therapeutic Equivalence can be assured when the multisource product is:
pharmaceutically equivalent and
bioequivalent.
TE = PE + BE
Therapeutic Equivalence of Multisource Product
The concept of interchangeability applies to:
1. - the dosage form and
2. - the indications and instruction for use.
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AVERAGE BIOEQUIVALENCEA GLOBAL STANDARD OF PHARMACEUTICAL QUALITY
?
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Origin of ABE
• A survey of physicians suggested that for most drugs, a difference of up to 20% in dose between two treatments would have no clinical significance
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Average Bioequivalence
• two drug products are Bioequivalent ‘on the average’ when the (1-2α) confidence interval around the Geometric Mean Ratio falls entirely within 80-125% (regulatory control of specified limit)
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AVERAGE BIOEQUIVALENCEComapre the population average response of the log-transformed Bioavailability
Parameters after administration of the Test and Reference products.
Test
Reference
The same Mean differentVariances ? What to do?
Pf RT 1)(Pr 2,1
Confidence Interval
Which BA metrics and which distributionparameters must meet criteriaThe width of the intervalThe assigned assurance probability
Average Responsetest within 80 -125% 25.1)(8.0 LnLn RT
80 12590 111NTI
digoxin, phenytoin, warfarin, theophylline, lithium
67 150
Who decides the goal post?Clinical JudgementCMSVariability of Reference ProductPopulation vs Individual Dose-Response curves
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Some International Criteria
Country/Region AUC 90% CI Criteria
Cmax 90% CICriteria
Canada (most drugs) 80 – 125% none (point estimate only)
Europe (some drugs) 80 – 125% 75 – 133%
South Africa (most drugs) 80 – 125% 75 – 133% (or broader if justified)
Japan (some drugs) 80 – 125% Some drugs wider than 80 – 125%
Worldwide (WHO) 80 – 125% “acceptance range for Cmax may be wider
than for AUC”
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)( 05.0,exp.100%90 abdfbA SEtLSMLSMCIGeometric
Least SquareMeans from ANOVA
t-statistic with0.05 in one
tail
StandardError
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Limitations of 2-Period Designs
• The intra subject variance associated with the Test and Ref products may not be the same
• A poor pharmaceutical product may have inflated intrasubject variance because of high within formulation variability
• The residual variance in 2-period designs averages the intrasubject variance of the two products– The Test and Ref intrasubject variance cannot be
separated
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REPLICATED CROSSOVER DESIGNS FOR TWOFORMULATIONS
OPTIMAL FOR CARRYOVER ESTIMATION
PERIODSEQUENCE 1 2 1 2 3 1 2 3 4
1 T T T R R T T R R2 R R R T T R R T T3 T R T R R T4 R T R T T R
SWITCHBACK DESIGNS
SEQUENCE 1 2 3 1 2 3 41 T R T A B A B2 R T R B A B A
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Replicate Designs
• Yields information on the Intrasubject Variance
• Ideally, intrasubject variance of the Test product should be similar to the intrasubject variance of the Reference product
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What do we learn from ANOVA Analysis
• The sources of variance in the model are– Product– Period– Sequence– Subject (Sequence)
– Residual variance
These accountfor all the inter-subjectvariability
This estimatesIntra-subject variability
Source: Modified from K. Midha
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‘Fixed Effects” in ANOVA
• Product• Period• Sequence
• Subject nested within sequence is usually significant (f-test) because of large variability between subjects
These fixed effects usually are notsignificant in the f-test
Source: Modified from K. Midha
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The Residual Variance (SW2)
• Sources of Variability– Intra-subject variance in Pharmacokinetics– Analytical variability – Subject by formulation interaction– Unexplained random variation
WSVCVANOVAVariance ResidualCVANOVA
%100
Source: Modified from K. Midha
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T, obs = 24.7 ng/mlR, obs = 23.7 ng/ml
v = 22t0.95(v) = 1.7171
s = 5.693 n = 24 s*sqrt 2/n = 1.543
24.7 – 23.7 +/- 1.717 (1.643) ng/ml
1 +/- 2.82 ng/ml
-1.82 ng/ml; 3.82 ng/ml
The lower CI limit = 23.7 – 1.82 / 23.7= 92.3 %
The Upper CI limit = 23.7 + 3.83 / 23.7= 116%
NStobsRobsT
2)(95.0,,
Example using ANOVA results
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The ‘ANOVA-CV’
• The ANOVA-CV which is easily calculated from the residual variance is an estimate of WSV
)(%100
WSVeectVariancWithinSubjCVANOVAVariance ResidualCVANOVA
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Variability
• It is well known the Between Subject Variance (BSV) can be very high– Biological variation– Within Subject Variance (WSV) contributes to
BSV• WSV can also be high e.g. highly variable
drugs and highly variable drug products• Drugs with an ANOVA-CV 30% are
defined as ‘highly variable drugs’
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Thank you
Muchas Gracias