# Presentation: 379860

## Clinical bioequivalence

## Specific example

*A manufacturer wishes to change its approved  production method for standardized fedweed pollen extract*
*CBER acknowledges that existing in vitro potency tests will be applicable to the new preparation. However, CBER requests that the manufacturer demonstrate compositional similarity by parallel line bioassay *

## Clinical bioequivalence

## Clinical bioequivalence

## Clinical bioequivalence

*“**At least 4 subjects are required per assay. One or more test  extracts are to be compared against a reference in each subject. *
*For evaluating compositional differences, subjects...selectively sensitive to specific allergens in the crude mix should be selected...[I]n order to enhance detection of compositional differences, subjects can be selected who differ widely (10-10,000 fold) in their allergen skin test sensitivity...to the test of reference extracts.”*

## Slide 6

## Slide 7

**Notes:**
[Space for Tony’s talk]

## Manufacturer proposal

*Based on the 1993 protocol, the manufacturer proposes to enlist 4 to 6 study subjects of varying levels of sensitivity to fedweed pollen*
*If the slopes of the two preparations are not significantly different in these study subjects, the manufacturer will conclude that the two preparations are compositionally similar*

## Initial assumptions

*Mean slope: 10 *
*Acceptable difference: 10% = 1*
*SD slope: 1*
* **** **= 0.05*
* **** **= 0.2*

**Notes:**
You will recall that Dr. Lachenbrich provided you with these formulas for determining significance using the two one-sided testing approach.

To give you a sense of what this approach means in terms of study size, we need to solve these equations for n, which is the number of study subjects.  Let us assume for this illustration that n1 = n2. Rearrangement [and replacement of some of the values] yields the following:

- To determine the number of subjects (*n*) required to detect a difference  with confidence  and power 1 -  with the two one-sided test approach, use the following:

**Notes:**
Note that, for any given  and  ,  n is a function of the square of the ratio of , which is the standard deviation of the quantity being measured, and  , which is the acceptable difference between the reference and test values.

If we solve this equation for the default  and  values of 0.05 and 0.2, respectively, we get the following:

- For  = 0.05 and  = 0.2:

**Notes:**
Again, n is a function of the square of the ratio of , which is the standard deviation of the quantity being measured, and  , which is the acceptable difference between the reference and test values.

**Notes:**
Let’s plug in some numbers and see what values of n we obtain.

In all of these examples we accept the default values of  and. In the top panel we accept the default value of  , the acceptable difference between the reference and test values, as 10%.  In the top row, we use the given  of 2 (about 20% of the measured quantity), and we find that we need at least 50 study subjects to make a statistically valid comparison.

Even if the standard deviation of the test is smaller - say, equal to  - we still need at least 13 subjects in the study. It is only when becomes quite small, just a little over half of , that four study subjects would be adequate.

In the lower panel, we look at the consequences of liberalizing . If we double it - that is we say that a 20% difference is acceptable - then, in this example,  and again we need to study 13 subjects. It is only when we increase to 3.5 - that is, 35% of the measured quantity - that four study subjects would be adequate.

## Conclusions

*The failure to demonstrate a difference is not sufficient to demonstrate equivalence*
*Given that, for the parallel line bioassay, ****, the number of study subjects required to demonstrate equivalence will usually exceed 4-6*

## Advisory Committee discussion

*CBER requests that the Committee discuss CBER’s current approach to clinical bioequivalence studies as it applies to allergen extract studies.*