**[Note: The document below
was previously provided to the Committee as background material for the April
2004 ACPS meeting]**

**Background
Information for Advisory Committee Meeting
**

**On ****April 14, 2004**

**Bioequivalence Requirements for Highly Variable Drugs and Drug Products
**

**Introduction**

Bioequivalence studies are generally conducted by
comparing the *in vivo* rate and extent
of drug absorption of a test and a reference drug product in healthy
subjects. In a standard *in vivo* bioequivalence study design,
participants receive a single dose of test and reference products on separate
occasions with random assignment to the two possible sequences of product
administration. Samples of an accessible
biologic fluid such as blood or urine are analyzed for drug concentrations, and
pharmacokinetic measures such as area under the curve (AUC) and peak
concentration (C_{max}), are obtained from the resulting
concentration-time profiles. To evaluate
bioequivalence, the U.S. Food and Drug Administration (FDA) has employed a testing
procedure termed *the two one-sided tests
procedure *to determine whether the average values for the pharmacokinetic
measures from the test and reference products are comparable[1]. This procedure involves the calculation of a
confidence interval for the ratio between the average values of the test and
reference product[2]. In the _{max} between the
test and reference fall within 80-125%.
Currently, the bioequivalence limits of 80-125% have been applied to
almost all drug products by the FDA[3].

Concerns have been expressed at times regarding the
difficulty of meeting the standard bioequivalence criteria for highly variable
drugs and/or drug products[4]^{,[5]}. To date, there is no regulatory definition
for these drugs or drug products. In the
context of bioequivalence, however, drugs and drug products exhibiting
intra-subject variability greater than 30% C.V. (coefficient of variation) in
the pharmacokinetic measures, AUC and/or C_{max} are considered highly
variable^{4,5}. To pass the
conventional “goalposts”, the number of subjects required for a study of these
drugs or drug products can be much greater than normally needed for a typical
bioequivalence study. Thus, the resource
implications coupled with the ethical concern of exposing a large number of
healthy subjects to a test drug further challenges the appropriateness of the
conventional bioequivalence criteria for highly variable drugs/products. Examples exist of a highly variable reference
product failing to demonstrate bioequivalence with itself using the standard
design/sample size for a bioequivalence study[6].

The issue of highly variable drugs/products in bioequivalence has been
discussed in many conferences and meetings, nationally and internationally. However, there is no universal consensus or
solution at this time. The objectives of
this paper are to: a) explore the need for applying alternative bioequivalence
limits for highly variable drugs/products; b) review the available proposals
for alternative criteria and/or limits; and c) discuss possible regulatory
approaches for resolution of the issue for these drugs/products.

**Background
**

Although global harmonization is a general goal, to date, bioequivalence has not been accepted as a topic by the International Conference on Harmonization (ICH). Nonetheless, the resource and ethical concerns for highly variable drugs/products in bioequivalence are generally recognized by international regulatory agencies. It is thus useful to review the differing regulatory approaches before an informed recommendation is made on the topic. The following outlines the bioequivalence standards used in different regions:

In _{max}[7]. As a result of random
variation or a larger than expected relative difference, the sponsor may add
more subjects. If this option is chosen,
it must be stated in the study protocol. In addition, two criteria must be met
before combining is acceptable:

1)
The same protocol must be
used; and

2)
Consistency tests must be
met at an alpha error rate of five percent.

The European Agency for the
Evaluation of Medicinal Products (EMEA)* *has
similar bioequivalence standards to those in the FDA, *i.e*., 90% confidence limits of 80-125% on AUC and C_{max},
with the qualification that these limits may be expanded in certain cases for C_{max}
(*e.g.*, 75-133%) provided that there
are no safety or efficacy concerns[8].

In _{max},
although wider limits are allowed for less potent drugs. Additionally, if the confidence limits are
outside of 80-125%, bioequivalence may be claimed on the grounds that the study
meets all three conditions listed below[9].

1) The total number of subjects in the initial bioequivalence study is no less
than 20 (n=10/group), or pooled sample size of the initial and add-on studies
is no less than 30;

2) The differences in average values of logarithmic AUC and C_{max}
between two products are between log(0.9) – log(1.11); and

3) Dissolution rates of test and reference products are determined to be
equivalent under all dissolution testing conditions specified.

_{max}, except for narrow
therapeutic range drugs, when an acceptance interval of 80-125% applies[10]. For highly variable drugs, a wider interval or
other appropriate measure may be acceptable, but should be stated a priori and
justified in the protocol.

**Proposals from the
Literature
**

As indicated, the bioequivalence criteria in the

Various authors have explored the use of replicate designs or group-sequential
designs. If a subject-by-formulation
interaction is negligible, the sample size required for a replicate design
study can be reduced up to 50% of that for a non-replicate design study[11].
The number of study periods is the same since approximately half the usual
number of subjects is used but they are each studied for twice as many
periods. Therefore, it takes a longer
time to complete a replicate design study, resulting in an increased chance of
subject dropout from the trial. A
group-sequential design may be useful in cases where there is uncertainty about
the estimates of variability.
Nonetheless, the total number of subjects employed with this design may
be the same as that used for a study without the group-sequential design if the
interim analysis does not indicate bioequivalence^{11}. Also, to preserve the overall Type I error
rate of 5%, a higher level of confidence interval has to be used at each stage
of the interim analysis^{11}.

Several proposals are available in the literature to modify the existing
bioequivalence criteria for highly variable drugs and drug products[12]^{,[13]}. In general, these various criteria are based
on either the reduction of the level of the confidence interval or an increase
of the width of the equivalence limits, or both.

The level of confidence interval reflects the degree of consumer risk (Type I
error in statistical terms) that can be tolerated by the regulatory
agencies. A reduction in the level of
confidence interval, for example, from 90% to 85%, implies a possible increase
in the consumer risk, which would not be in the best interests of public
health. In contrast, the width of
equivalence limits represents the allowable boundary for the ratio (or difference)
of the means between products in comparison.
Any adjustment of these limits should be based on consideration of the
statistical properties of the data as well as on the clinical characteristics
of the individual drug. Statistically, widening the bioequivalence limits can
be accomplished through expansion of the allowable boundary or by scaling the
criteria based on the high variability of the reference product.

Discussed below are the proposals available to date for
widening the acceptable limits of pharmacokinetic measures in bioequivalence
studies.

A. *Direct Expansion of Bioequivalence Limits*

Sample size in bioequivalence studies is determined in large part by the
bioavailability parameter with the highest variability. In most cases, C_{max }has higher
variability than AUC. Thus, widening of
the bioequivalence limits for C_{max} has been proposed to reduce the
sample size needed in the evaluation of bioequivalence for highly variable
drugs/products. The greater variability
observed with C_{max} may result from the fact that this parameter is a
single point measurement, which is highly dependent on the sampling
time/frequency and elimination rate of the drug.

The EMEA currently allows for
expanded limits (*e.g*., 75-133%) for C_{max}
in certain cases where no safety or efficacy concern arises, based on the
consideration of higher variability for this measure as compared to AUC^{8}.

B.* Expansion of Bioequivalence Limits
Based on Fixed Sample Size
*

This method was proposed based on
the notion that only a reasonable number of subjects should be required for a
bioequivalence study^{4,12}.

The number of subjects is fixed by a standard two-period, crossover study comparing the reference product with itself where the study fails to meet the 80-125% limit. The confidence interval obtained from the reference product in this study would become the “goalposts” for the subsequent studies comparing the test with reference product, using the same number of subjects.

C.** ***Widening
of Bioequivalence Limits Based on Reference Variability
*

The
bioequivalence limits for these methods are not determined by the sample
size. Rather, they will be scaled based
on the within-subject variability of the reference product. For both Methods 2 and 3 below, a side
condition to constrain the mean difference between the test and reference
products has also been proposed (see **Discussion**).

Method 1:

The rationale for this approach is that a mean difference of 25% is considered
small relative to the range of values an individual may experience when the
within-subject variability is high, *e.g*.,
40%. Therefore, the acceptable limits
may be scaled in relation to the size of within-subject variability as follows^{12}:

[U, L] = Exp [±
ks_{WR}] (Eq.
1)

where U and
L are the upper and lower limits, respectively; k represents the pth percentile
of the standard normal distribution, Zp; and s_{WR} is the
estimated within-subject standard deviation (obtained from the ANOVA on the log
scale) for the reference. When k = 1, ~
67% of the pharmacokinetic measures (such as AUC) experienced by an individual
will be within the range of [U, L].
Table I lists the choices of limits at k = 1.

Table I

CV (%) |
SD (s |
Lower Limit |
Upper Limit |

30 |
0.294 |
0.75 |
1.34 |

35 |
0.340 |
0.71 |
1.40 |

40 |
0.385 |
0.68 |
1.47 |

45 |
0.429 |
0.65 |
1.54 |

50 |
0.472 |
0.62 |
1.60 |

Different k values could be chosen for different drugs depending on their therapeutic windows.

Method 2:

A scaled
average bioequivalence criterion has been proposed^{13,[14],[15]}:

(m_{T}
- m_{R})^{2
}/s^{2}_{WR }__<__ q (Eq.
2)

where m_{T}
and m_{R}
are the averages of the log-transformed measure for the test and reference
products, respectively; and q is the bioequivalence limit. Comparing Methods 1 and 2, it can be seen
that k = q^{
-1/2} = (ln1.25)/s_{W0 }where s_{W0 }is the
cutoff within-subject standard deviation for scaling. Table II shows the relationship of k and s_{W0}.

Table II

s |
k |

0.20 |
1.116 |

0.223 |
1.0 |

0.25 |
0.893 |

0.294 |
0.759 |

Method 3:

Derived
from the comparison of the distance measure between the test and reference
products, the following individual bioequivalence criterion has a reference
variance in the denominator, and thus is scaled to the reference variability
(10, 12):

[(m_{T}
- m_{R})^{2
}+ (s^{2}_{WT}
- s^{2}_{WR})
+ s^{2}_{D}]
/ s^{2}_{WR }__<__ q_{I
}(Eq.
3)

where s_{WT}
is the estimated within-subject standard deviation for the test product; s^{2}_{D
}is the subject-by-formulation interaction variance component; and q_{I}
is individual bioequivalence limit.

Although theoretically sound, the individual bioequivalence criterion requires
replicate designs and inclusion of target population in the study. Because of these resource implications, the
FDA has recommended the continued use of an average criterion to compare
bioavailability measures^{3}.

D. *Expansion of Bioequivalence Limits Based on
Sample Size and Scaling
*

In addition to fixing the sample
size, this method takes into consideration the producer’s risk (Type II error)
and reference variability^{12}.
The equation for the allowable limits is:

[U, L] = Exp [±
(t_{a}
+ t_{b/2})
n ^{-1/2} s_{WR}]
(Eq.
4)

where a
and b
are the consumer and producer risks, respectively; 2n is the number of subjects
desired in the study; and t is the percentile of the t-distribution with 2n-2
degrees of freedom.

The current regulatory standard has kept the consumer risk at a level of no
more than 5% while allowing the drug applicant or sponsor to control its own
producer risk. Based on Eq. 4, for example,
assuming a 5% consumer risk and 10% producer risk, the proposed bioequivalence
limits for a typical sample size of 24 subjects will be

(0.74,
1.35) at s_{
WR} = 0.3

(0.61,
1.65) at s_{
WR} = 0.5

**Discussion**

The impact of C_{max} variability on the determination of
bioequivalence, as well as the possible approaches to resolving this issue, has
been discussed extensively in the published literature. Major regulatory agencies have provisions in
their regulations which can accommodate the effect of higher variability
associated with C_{max} on the design of bioequivalence studies. For example, *Health Canada* does not require any limits on the confidence
interval for C_{max}, although limits are placed on the point estimates
for this parameter. The EMEA and Medicines
Control Council of South Africa both allow for expanded limits for C_{max}
in certain cases provided that there are no safety or efficacy concerns^{8}. The expanded limits are not defined, although
they cite 75-133% as an example.
Similarly, the Japanese Division of Drugs accepts limits greater than 80
– 125%, “for drugs with pharmacologically mild actions”^{9}. Additionally, a failed bioequivalence study
can utilize additional subjects to increase power and the likelihood of meeting
BE criteria, provided other conditions are met.

Tothfalusi *et al*.,
compared scaling bioequivalence limits for highly variable drugs to widening of
the limits around C_{max}^{15}.
Scaling may involve widening of the confidence interval limits as a
function of the variability of the reference drug product. The authors’ conclusion was that scaling
would significantly reduce the sample size needed for bioequivalence studies of
highly variable drugs. Additionally,
they concluded that the same result could be achieved by simple expansion of
the regulatory limits to 75-133% or even 70-143% for C_{max}.

Simple expansion of the regulatory limits may lead to
acceptance of BE for drug products with mean ratios for C_{max}
exceeding 125%. This possibility was discussed
by Hauck *et al*.[16]. The authors reported that widening the
confidence limits to 70-143% could allow acceptance of C_{max} ratios
of 128%. A difference of this magnitude
in the point estimate may not be acceptable for many drugs. This possibility, however, may be eliminated
by placing an additional regulatory constraint on the point estimate for C_{max},
which would accompany any expanded limits of the confidence interval.

Another
approach, using reference scaling for all drugs, will effectively widen the
equivalence limits for highly variable drugs/products. The method, however, should not be used for
drugs exhibiting low intra-subject variability in the reference product since
it may unnecessarily narrow the equivalence limit beyond the public-health
need. The choice of cutoff for reference
scaling (s_{W0})
will have to be made by the regulatory agency.
This approach, however, has a discontinuity at the changeover point (s_{W0})
from no scaling to reference scaling.
For example, if the estimate of the within-subject standard deviation of
the reference is just above the changeover point, the confidence interval will
be wider than just below. In this
context, the confidence interval could pass the predetermined bioequivalence
limit if the estimate is just below the boundary and could fail if just
above. Another question that may be
raised for using the scaling method is the reliability of the estimate for the
reference variability although this may be achieved by increasing the sample
size or setting minimum requirements for the precision of the estimate.

With the exception of direct expansion of bioequivalence limits, it appears that all the reference-scaling approaches either require a study with replicate design or need more than one study to allow determination of reference variability.

**Discussion
Topics for the ACPS Meeting**

**April 14,
2004**

1)
Highly variable drugs or drug products may be defined as those
exhibiting intra-subject variability of 30% CV or greater in AUC or Cmax.

Does the committee concur?

2)
The Advisory Committee is asked to comment on the following approaches
and whether there is promise in developing one or both of these approaches to
improve the bioequivalence assessment of HVDs.

a) Direct
Expansion of Bioequivalence Limits: Change from 80-125%, and restrict the mean
T/R difference, *e.g*., ± 20?
What information is necessary to properly set these new confidence interval limits?

b) Reference Scaling: Scale current bioequivalence criterion based on
the reference variability in each study and restrict the mean T/R difference as
above.

**References
**

1 D.J.
Schuirmann. A comparison of the two one-sided tests procedure and the power
approach for assessing the equivalence of average bioavailability. *J.
Pharmacokinet. Biopharm.* 15: 657-680 (1987).

[2]
W.J. Westlake. Response to *Biometrics*. 37:589-594 (1981).

[3]
*Guidance
for Industry: Bioavailability and Bioequivalence Studies for Orally
Administered Drug Products--General Considerations*, Office of Training and
Communications, Division of Communications Management, Drug Information Branch,
HFD-210, Rockville MD 20857, March 2003.

[4]
H. Blume and K.K. Midha. Report of Consensus Meeting: Bio-international
’92. Conference on Bioavailability,
Bioequivalence and Pharmacokinetic Studies. *Eur. J. Pharm. Sci.* 1:165-171 (1993).

[5]
Bio-international ’94,

[6] Health

[7]
Health *Guidance for Industry: Conduct and Analysis of Bioavailability and
Bioequivalence Studies – Part A: Oral Dosage Formulations Used for Systemic
Effects.* 1992.

[8] Committee for Proprietary Medicinal Products (CPMP),
the European Agency for the Evaluation of Medicinal Products (EMEA). *Note
for Guidance on the Investigation of Bioavailability and Bioequivalence*. 2001.

[9]
Japan National Institute of Health, Division of Drugs. *Guideline
for Bioequivalence Studies of Generic Drug Products*. 1997.

[10]
Medicines Control Council, Department of Health, *Registration
of Medicines: Biostudies*. 2003.

[11] S.D. *Eur. J.
Clin. Pharmacol.* 57:663-670 (2001).

[12] A.W. Boddy, F.C. Snikeris, R.O. Kringle, G.C.G. Wei,
J.A. Oppermann, and K.K. Midha. An
approach for widening the bioequivalence acceptance limits in the case of
highly variable drugs. *Pharm. Res.* 12:1865-1868 (1995).

[13]
L. Tothfalusi, L. Endrenyi, K.K. Midha, M. J. Rawson, and J.W. Hubbard. Evaluation of the bioequivalence of highly
variable drugs and drug products. *Pharm.
Res. *18:728-733 (2001).

[14] L. Tothfalusi and L. Endrenyi. Limits for the scaled average bioequivalence
of highly variable drugs and drug products. *Pharm.
Res. * 20:382-389 (2003).

[15] L. Tothfalusi, L. Endrenyi, and K.K. Midha. Scaling or wider bioequivalence limits for
highly variable drugs and for the special case of C_{max}. *Int. J.
Clin. Pharmacol. Ther. * 41:217-225
(2003).

[16] W.W. Hauck, A. Parekh, L.J. Lesko, M.-L. Chen, and
R.L. Williams. Limits of 80-125% for AUC and 70-143% for C_{max}. *Int. J.
Clin. Pharmacol. Ther. * 39:350-355
(2001).