WHO Prequalification Program Workshop, Kiev, Ukraine, June 25-27,2007

Visit FDA website: www.fda.gov

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%

• 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

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

Set at 5%

Stavchansky’s Recommendation: FDA should pressure the InnovatorCompanies to put forward a Confidence Interval for their HVP

GE = PE + TE

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.

AVERAGE BIOEQUIVALENCEA GLOBAL STANDARD OF PHARMACEUTICAL QUALITY

?

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

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)

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

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”

)( 05.0,exp.100%90 abdfbA SEtLSMLSMCIGeometric

Least SquareMeans from ANOVA

t-statistic with0.05 in one

tail

StandardError

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

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

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

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

‘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

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

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

The ‘ANOVA-CV’

• The ANOVA-CV which is easily calculated from the residual variance is an estimate of WSV

)(%100

WSVeectVariancWithinSubjCVANOVAVariance ResidualCVANOVA

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’

Thank you

Muchas Gracias