Download Pharmacokinetic (PK) study design for establishing bioequivalence

WHO Prequalification Program Workshop, Kiev, Ukraine, June 25-27,2007
Visit FDA website: www.fda.gov
Factors to Consider for Bioequivalence
Requires a
Comparator
Product
Parent Compound
Metabolite(s)?
P’dynamic Response
SAMENESS
VARIABILITY
Single Dose
Multiple Dose
?
Highly Variable Drugs
Food effects
Narrow Therapeutic Index
Drugs w ith Long T50
Statistical
Test
Set at 5%
Industry
Risk
Consumer
Risk
Comparator Product
• 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
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 Innovator
Companies to put forward a Confidence Interval for their HVP
GE = PE + TE
Therapeutic Equivalence of Multisource
Product
Therapeutic Equivalence can be assured when
the multisource product is:
pharmaceutically equivalent and
bioequivalent.
TE = PE + BE
The concept of interchangeability applies to:
1. - the dosage form and
2. - the indications and instruction for use.
AVERAGE BIOEQUIVALENCE
A 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 BIOEQUIVALENCE
Comapre the population average response of the log-transformed Bioavailability
Parameters after administration of the Test and Reference products.
Pr1  f (T , R )   2   1  P
Test
Confidence Interval
Reference
The same Mean different
Variances ? What to do?
Who decides the goal post?
Clinical Judgement
CMS
Variability of Reference Product
Population vs Individual Dose
-Response curves
BA metrics and which distribution
Which
parameters must meet criteria
width of the interval
The
The assigned assurance probability
Average Response
test within 80 -125%
67
Ln0.8  ( T   R )  Ln1.25
80
125
111
NTI
90
digoxin, phenytoin, warfarin,
theophylline, lithium
150
Some International Criteria
Country/Region
AUC 90% CI
Criteria
Cmax 90% CI
Criteria
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”
Geometric90%CI  100. exp
( LSM A  LSM b t d f , 0.0 5SEa b )
Least Square
Means from ANOVA
t-statistic with
0.05 in one
tail
Standard
Error
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
REP L ICAT ED CRO S S O V ER DES IG NS F O R T W O
F O RM UL AT IO NS
O P T IM AL F O R CARRYO V ER ES T IM AT IO N
S EQ UENCE
1
2
3
4
1
T
R
T
R
P ERIO D
2
1 2 3
T
T R R
R
R T T
R
T
1
T
R
T
R
S W IT CHBACK DES IG NS
S EQ UENCE
1
2
1
T
R
2 3
R T
T R
1 2 3 4
A B A B
B A B A
2
T
R
R
T
3
R
T
R
T
4
R
T
T
R
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
Source: Modified from K. Midha
These account
for all the inter-subject
variability
This estimates
Intra-subject
variability
‘Fixed Effects” in ANOVA
• Product
• Period
• Sequence
These fixed
effects usually are not
significant in the f-test
• Subject nested within sequence is usually
significant (f-test) because of large
variability between subjects
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
ANOVA  CV  Residual Variance 100%
ANOVA  CV  WSV
Source: Modified from K. Midha
Example using ANOVA results
 T , obs   R , obs  t 0 . 95 ( ) S
T, obs = 24.7 ng/ml
R, obs = 23.7 ng/ml
v = 22
t 0.95(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%
2
N
The ‘ANOVA-CV’
• The ANOVA-CV which is easily
calculated from the residual variance is
an estimate of WSV
ANOVA  CV  Residual Variance 100%
ANOVA  CV  WithinSubj ectVariance(WSV )
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