Essar
●

2006-01-12 05:47
(6285 d 21:29 ago)

Posting: # 50
Views: 26,176

## BE study designs [Design Issues]

I want to know abt the basic BE designs...can you suggest me some reference (preferably something on the net). i'm pretty much confused...2-way crossover, 2 period, 2 sequence, replicate, parallel...could not understand a word. Suggest me some reference.

The only thing I can understand is fed and fasting (this must give you an idea abt my illeteracy level)
Helmut
★★★

Vienna, Austria,
2006-01-12 19:38
(6285 d 07:38 ago)

@ Essar
Posting: # 51
Views: 24,244

## BE study designs

Hello Essar,

at least the basics of bioequivalence are not that complicated

❝ I want to know abt the basic BE designs...can you suggest me some reference (preferably something on the net). i'm pretty much confused...2-way crossover, 2 period, 2 sequence, replicate, parallel...could not understand a word...

Ok, BE gives us information about the performance (generally extent [AUC] and rate [Cmax/tmax] of bioavailability of a test treatment (e.g., a new formulation of an innovator, a generic drug...) compared to a reference treatment (e.g., an approved formulation of an innovator...). If we accept some assumptions (i.e., constant clearances in cross-over designs, clinical meaningful validity of a chosen acceptance range...] and a formulation was shown to be bioequivalent, we conclude that clinical data used in the registration process for the 'old' formulation is also valid for the 'new' one. In other words bioequivalence is an accepted surrogate for clinical equivalence.

Since generally the intra-subject variability CVintra (the variability of a given PK parameter in a given group of subjects if measured on at least two occasions) is lower than inter-subject variability CVinter (the variability between different subjects), cross-over designs (where both test and reference treatments are given to the same subject) are preferable to parallel designs (where one group of subjects is treated with the test, and another group with the reference) in terms of statistical power. In other words you will need (much) less subjects in a cross-over study compared to a paralled study.

The most simple cross-over design is the 2×2×2 (2 treatment, 2 period, 2 sequence) design:
Half of the subjects are treated in the first period with test, the other half with reference; in the second period the first half is treated with reference, and the second half with test.
Therefore we have two sequences (or groups): T-R for the first half of subjects, and R-T for the second half. Subjects are randomized between these two sequences.

In a parallel design half of the subjects will be treated with test, the other half with reference (only one study period). In the BE setting parallel designs are only rarely used (e.g., for drugs with very long half-lives, or studies in patients).

Before starting the study you have to perform a proper sample-size estimation. You will need the following information beforehand: The acceptance range and confidence level (mainly given by a regulatory authority, e.g., 80% – 125% and 5%), the producer's risk (e.g., 20%), the variability of any PK metrics, and the anticipated deviation of test from reference (e.g., ±5%).

In more statistical terms:
The error type I (alpha-risk, patient's risk of being treated with a product which was erroneously claimed bioequivalent) is set to 5%. Since there is a 5% chance of the bioavailability in a particular patient being either lower or higher than expected, the risk for the entire population of patients is 2×alpha=10% (therefore the confidence interval is set to 90%).
The error type II (beta-risk, producer's risk of getting a formulation rejected, although it is 'bioequivalent') generally is set to 10% – 20%. Since statistical power is 1-beta, 80% or 90% are used. Lower or higher values may raise ethical issues.
Information on variability may come from previous studies, a (not too small!) pilot study, or literature. The reliability decreases in the given order, therefore you should add some 'safety margin'.
The same applies to the expected deviation from the reference.

After analyzing our biosamples we calculate PK metrics from individual plasma profiles, which subsequently may be subjected to an appropriate statistical method, in order to get point estimates (e.g., 103% of the reference) and a 90% confidence interval (e.g., 93% – 113% of the reference). For any particular PK metric an acceptance range must be defined in the protocol (e.g., 80%-125% for AUC). If your confidence interval is entirely included in the acceptance range, you may claim your formulation to be bioequivalent.

The chosen statistical method depends on the assumed distribution of the PK metrics, i.e., ANOVA on log-transformed AUC/Cmax-data and a nonparametric method on untransformed tmax-data.

The acceptance ranges should be based on clinical grounds, but since generally no 'hard data' supporting such a range exist for the majority of drugs, 80%-125% is used in many legislations for AUC and Cmax. You must check your local guidelines, since exceptions exist (e.g., in some countries these 'goalpost' may be narrower or wider, or even 95% confidence intervals instead of 90% CIs should be used for some drugs).

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
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Essar
●

2006-01-13 05:40
(6284 d 21:37 ago)

@ Helmut
Posting: # 52
Views: 23,401

## BE study designs

Thanks, Helmut...things are much more clearer now. Would get back with more questions.

Essar
Essar
●

2006-01-16 14:20
(6281 d 12:56 ago)

@ Helmut
Posting: # 53
Views: 23,500

## BE study designs

❝ The most simple cross-over design is the 2×2×2 (2 treatment, 2 period, 2 sequence):

❝ Half of the subjects are treated in the first period with test, the other half with reference; in the second period the first half is treated with reference, and the second half with test.

❝ Therefore we have two sequences (or groups): T-R for the first half of subjects, and R-T for the second half.

Dear Helmut,
Taking your explanations ahead, I have tried to prepare a study design for a three treatment BE study, i.e., a BE study comparing:
1. Two formulations of a generic product T1 and T1, and
2. A reference product, R.
The aim of the above study is:
To evaluate, which of the two formulations (T1 or T2), prepared by a generic company is close to the reference product R, in a single BE-study

So, first one-third of the patients (Gp A) are treated in the first period with Test T1, the next one-third (Gp B) with Test T2 and the last one-third with Reference R (Gp C).

In period 2, first one-third of the patients (Gp A) are treated in the first period with Test T2, the next one-third (Gp B) with Reference R and the last one-third with Test T1 (Gp C).

In Period 3, first one-third of the patients (Gp A) are treated in the first period with Reference R, the next one-third with (Gp B) Test T1 and the last one-third with Test T2 (Gp C).

The above periods are summarized in the table below:

Group A B C
Period 1 T1 T2 R
Period 2 T2 R T1
Period 3 R T1 T2

Now, please let me know whether my design is right? Is it approprirate to name this study as "3-treatment, 3-period, 3-sequence crossover Bioequivalence study"?

Can there be other designs to compare T1 and T2 with R in a single BE study?

Also, please let me know how you calculate the no of subjects required for this type of a study.

Regards,
Essar
Helmut
★★★

Vienna, Austria,
2006-01-16 16:39
(6281 d 10:37 ago)

@ Essar
Posting: # 54
Views: 24,899

## BE study designs

Dear Essar,

in order to continue with the nomenclature generally used in BE studies I have re-aranged your table. Periods (P1-P3) in columns, and Sequences/Groups (S1-S3) in rows:
┌────┬────────────┐ │    │ P1  P2  P3 │ ├────┼────────────┤ │ S1 │ T1  T2  R  │ │ S2 │ T2  R   T1 │ │ S3 │ R   T1  T2 │ └────┴────────────┘

❝ Now, please let me know whether my design is right? Is it approprirate to name this study as "3-treatment, 3-period, 3-sequence crossover Bioequivalence study"?

Question #1: no
Question #2: yes

❝ Can there be other designs to compare T1 and T2 with R in a single BE study?

Yes, explanations following:
Your design is not balanced in respect to all effects in the model, in other words, 3 combinations of treatments are missing. The chance of regulatory acceptance of a 3×3 is close to zero.
The plainest design you may apply is a Williams' design (3-treatment, 3-period, 6-sequence):
┌────┬────────────┐ │    │ P1  P2  P3 │ ├────┼────────────┤ │ S1 │ T1  T2  R  │ │ S2 │ T2  R   T1 │ │ S3 │ R   T1  T2 │ │ S4 │ T1  R   T2 │ │ S5 │ T2  T1  R  │ │ S6 │ R   T2  T1 │ └────┴────────────┘
If you want to extract paired comparisons (e.g., for the nonparametric method) you will fail with your design (3×3), but succeed with the given one (6×3).

Since we are leaving the novice's level now, I would suggest you some further reading (not cheap, but every cent worth):
• B Jones and MG Kenward
Design and Analysis of Cross-Over Trials
Chapman & Hall, Boca Raton, (2nd Ed 2003)
• S-C Chow and J-p Liu
Design and Analysis of Bioavailability and Bioequivalence Studies
Marcel Dekker, New York, (2nd Ed 2000)

❝ Also, please let me know how you calculate the no of subjects required for this type of a study.

Have a look at

RP Qu
Sample Size and Power Calculation for High Order Crossover Design
Bio/Pharma Quarterly 9(1), 9-14 (March 2003)
which is available online (277kB PDF).

Good Luck!
Essar
●

2006-01-17 05:53
(6280 d 21:24 ago)

@ Helmut
Posting: # 55
Views: 23,388

## BE study designs

❝ Your design is not balanced in respect to all effects in the model, in other words, 3 combinations of treatments are missing. The chance of regulatory acceptance of a 3×3 is close to zero.

❝ The simplest design you may apply is a Williams’ design (3-treatment, 3-period, 6-sequence):

Dear Helmut,
Thanks once again for taking the time out.

When you say that chance of regulatory acceptance of 3x3 is close to zero, you mean specifically for 3x3x3, or it is applicable to 3x3x6 design also?

Also, can you please let me know the reason for the same.

Now, I will start hunting for the references you have given me.

Regards,
Essar
Helmut
★★★

Vienna, Austria,
2006-01-17 10:27
(6280 d 16:50 ago)

@ Essar
Posting: # 56
Views: 24,137

## BE study designs

❝ When you say that chance of regulatory acceptance of 3x3 is close to zero, you mean specifically for 3x3x3, or it is applicable to 3x3x6 design also?

You are right, I was not precise: 3×3×6 is acceptable, whereas 3×3×3 is not.

❝ Also, can you please let me know the reason for the same.

The 3×3×3 does not give us unbiased estimates.
Since we are interested in at least two pairwise comparisons (T1 vs. R, T2 vs. R), it is desirable to get estimates with the same degree of precision. However, to achieve this goal, the design must be balanced. A design is said to be balanced, if (acc. to Jones & Kenward):
1. Each formulation occurs only once with each subject.
2. Each formulation occurs the same number of times in each period.
3. The number of subjects who receive formulation i in some period followed by formulation j in the next period is the same for all i # j.
Williams' designs minimize the number of sequences for a given number of treatments and periods.
Unfortunately for three treatments your sample size has to be a multiple of six, which may be inconvenient in terms of practicability.

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
nguyenvo
●

2006-04-30 23:40
(6177 d 04:36 ago)

@ Helmut
Posting: # 98
Views: 23,466

## BE study designs

Dear Dr. Helmut,

RP Qu

Sample Size and Power Calculation for High Order Crossover Design

❝ Bio/Pharma Quarterly 9(1), 9-14 (March 2003)

❝ which is available online (277kB PDF).

I am trying to access the link but there is something wrong. It is no longer valid. Could you please check it again?

Best regards,
Nguyen
Helmut
★★★

Vienna, Austria,
2006-05-01 13:59
(6176 d 14:17 ago)

@ nguyenvo
Posting: # 99
Views: 23,460

## sample size reference

Hello Nguyen,

you are right, the link is broken.

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
olacy
☆

Hungary,
2006-06-23 11:28
(6123 d 16:49 ago)

@ nguyenvo
Posting: # 155
Views: 23,243

## BE study designs

Dear Nguyen,

Best regards,
Laci
Helmut
★★★

Vienna, Austria,
2006-06-24 14:45
(6122 d 13:31 ago)

@ olacy
Posting: # 156
Views: 23,473

Dear Laci,

thanks for your correction (most likely I did not edit the link correctly).
Here is the working link to the article.

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
shiv
●

2006-01-25 07:46
(6272 d 19:31 ago)

@ Helmut
Posting: # 61
Views: 23,816

## BE study designs

Hello Helmut

I am shiv. I quite impressed for your inputs on study design. I would like to update on ANVISA approval process.

Initially ANVISA approval is for the facility and after approval only we have to conduct the studies. Please update us the current process is it pre approval or post study approval.

Regards
Helmut
★★★

Vienna, Austria,
2006-01-25 14:37
(6272 d 12:39 ago)

@ shiv
Posting: # 63
Views: 23,383

## ANVISA?!

Hi shiv!

❝ Initially ANVISA approval is for the facility and after approval only we have to conduct the studies. Please update us the current process is it pre approval or post study approval.

I do not get your question
What do you mean with 'the current process' ?

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes