**35**

**Guidance for Industry**

**BIOEQUIVALENCE GUIDANCE**

**(THIS VERSION OF THE GUIDANCE REPLACES THE VERSION TITLED
"BIOEQUIVALENCE GUIDANCE" THAT WAS MADE AVAILABLE ON OCTOBER 10, 2000)**

*Section III.A. of this guidance has been superceded by CDER\'s guidance
entitled \"Bioanalytical Method Validation\". Any general questions
regarding the application of the Bioanalytical Method Validation
guidance to new animal drugs should be directed to Marilyn Martinez,
Center for Veterinary Medicine, Food and Drug Administration, 7500
Standish Pl., Rockville, MD 20855, (301)827-7577,
<mmmartin1@cvm.fda.gov>. Any questions regarding analytical methods for
tissue residues should be directed to Valerie Reeves, 7500 Standish Pl.,
Rockville, MD 20855, (301)827-6973, <mvreeves@cvm.fda.gov>.*

This document is intended to provide guidance for the design and
analysis of in vivo bioequivalence studies. This revision to the version
that was made available in April 1996 adds an illustrative example of
how to calculate confidence bounds when log transformed data are used.

Comments and suggestions regarding this guidance document should be
submitted to the Dockets Management Branch (HFA-305), Food and Drug
Administration, 5630 Fishers Lane, rm. 1061, Rockville, MD 20852. All
comments should be identified with the Docket Number (94D-0401).
Additional information on the 1996 guidance document can be found in the
Federal Register (Vol. 61, No. 102, May 24, 1996). Comments will be
accepted at any time.

For questions regarding this guidance document, contact Lonnie Luther,
Center for Veterinary Medicine (HFV-100), Food and Drug Administration,
7500 Standish Place, Rockville, MD 20855.

**U.S. Department of Health and Human Services\
Food and Drug Administration\
Center for Veterinary Medicine (CVM)\
October 9, 2002**

BIOEQUIVALENCE GUIDANCE

CENTER FOR VETERINARY MEDICINE\
FOOD AND DRUG ADMINISTRATION\
7500 STANDISH PLACE\
ROCKVILLE, MD 20855

Docket No. 94D-0401

***This guidance document represents the agency\'s current thinking on
this matter. It does not create or confer any rights for or on any
person and does not operate to bind the FDA or public. An alternative
approach may be used if such approach satisfies the requirements of the
applicable statute, regulations or both.***

**PREAMBLE**

In 1996, the Center for Veterinary Medicine (CVM) revised a document
entitled "April 1990 Bioequivalence Guideline." The revised document,
"Bioequivalence Guidance (Final) 1996", was issued in final form
following notice and comment.

Many of the changes in the "Bioequivalence Guidance (Final) 1996" were
based upon reports from panel presentations at the 1993 Veterinary Drug
Bioequivalence Workshop in Rockville, Maryland, sponsored by the Center
for Veterinary Medicine (CVM), the Animal Health Institute (AHI), the
American Academy of Veterinary Pharmacology and Therapeutics (AAVPT),
and the Animal Drug Alliance**^1^**. Some new topics were introduced
into the guidance as a result of issues identified in the review of
bioequivalence protocols and studies.

The major new topics in the guidance were as follows:

1.  Higher than approved dose bioequivalence studies.

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1.  Bioequivalence testing for multiple strength solid oral dosage
    forms.

2.  Assay considerations for bioequivalence studies.

3.  AUC and CMAX as the pivotal parameters for bioequivalence
    determination.

4.  Blood level bioequivalence studies to be accompanied by tissue
    residue depletion studies for generic products for food-producing
    animals.

    CVM has revised the "1996 Bioequivalence Guidance" to add an
    illustrative example of how to calculate confidence bounds when log
    transformed data are used. The guidance has also been revised in
    accordance with FDA's Good Guidance Practices (GGPs, found in the
    Federal Register of February 27, 1997, 62 FR 8961). With the
    exception of the addition of information on how to calculate
    confidence bounds when log transformed data are used, minor
    revisions made to comply with the GGPs (e.g., addition of a cover
    sheet), and revisions to the Preamble, the document is the same as
    the document issued in 1996. In September 2000, FDA revised the
    guidance to clarify sources of information more clearly.

A person may follow the guidance or may choose to follow alternate
procedures or practices. If a person chooses to use alternate procedures
or practices, that person may wish to discuss the matter further with
the agency to prevent an expenditure of money and effort on activities
that may later be determined to be unacceptable to FDA. Although this
guidance document does not bind the agency or the public, and it does
not create or confer any rights, privileges, or benefits for or on any
person, it represents FDA\'s current thinking on bioequivalence testing
for animal drugs. When a guidance document states a requirement imposed
by statute or regulation, the requirement is law and its force and
effect are not changed in any way by virtue of its inclusion in the
guidance.

**TABLE OF CONTENTS**

I. INTRODUCTION [6](#__RefHeading___Toc489327080)

II\. GENERAL CONSIDERATIONS [7](#GENCON)

A. Selection of Reference Product for Bioequivalence Testing
[7](#__RefHeading___Toc489327082)

B. Criteria for Waiver of In Vivo  Bioequivalence Study
[7](#__RefHeading___Toc489327083)

C. Selection of Blood Level, Pharmacologic End-point, or Clinical
End-point Study [8](#__RefHeading___Toc489327084)

D. Species Selection [8](#__RefHeading___Toc489327085)

E. Dose Selection [8](#__RefHeading___Toc489327086)

F. Multiple Strengths of Solid Oral Dosage Forms
[9](#__RefHeading___Toc489327087)

G. Manufacturing of Pilot Batch (\"Biobatch\")
[9](#__RefHeading___Toc489327088)

III\. BLOOD LEVEL STUDIES [9](#__RefHeading___Toc489327089)

A. Assay Consideration [9](#__RefHeading___Toc489327090)

B. General Experimental Design Considerations
[12](#__RefHeading___Toc489327091)

1\. Dosing by Labeled Concentration [12](#__RefHeading___Toc489327092)

2\. Single Dose vs Multiple Dose Studies
[12](#__RefHeading___Toc489327093)

3\. Subject Characteristics [12](#__RefHeading___Toc489327094)

4\. Fed vs Fasted State [13](#__RefHeading___Toc489327095)

C. Pharmacokinetic and Statistical Considerations in Study Design
[13](#__RefHeading___Toc489327096)

1\. Sampling Time Considerations [13](#__RefHeading___Toc489327097)

2\. Protein Binding [14](#__RefHeading___Toc489327098)

3\. Subject Number [14](#__RefHeading___Toc489327099)

4\. Cross-over and Parallel Design Considerations
[14](#__RefHeading___Toc489327100)

5\. Duration of Washout Time for Cross-over Study
[15](#__RefHeading___Toc489327101)

6\. Pivotal Parameters for Blood Level Bioequivalence
[16](#__RefHeading___Toc489327102)

D. Statistical Analysis [17](#__RefHeading___Toc489327103)

IV\. PHARMACOLOGIC END-POINT STUDIES [22](#__RefHeading___Toc489327104)

A. General Design Aspects [22](#__RefHeading___Toc489327105)

B. Statistical Analysis [22](#__RefHeading___Toc489327106)

V. CLINICAL END-POINT STUDIES [23](#__RefHeading___Toc489327107)

A. General Design Aspects [23](#__RefHeading___Toc489327108)

B. Subject Characteristics and Data Collection
[23](#__RefHeading___Toc489327109)

C. Statistical Analysis [23](#__RefHeading___Toc489327110)

VI\. HUMAN FOOD SAFETY CONSIDERATIONS [24](#__RefHeading___Toc489327111)

APPENDIX: Confidence Bounds on the Logarithm Scale
[26](#__RefHeading___Toc489327112)

Bibliography/Endnotes [28](#__RefHeading___Toc489327113)

[]{#__RefHeading___Toc489327080 .anchor}I. INTRODUCTION

This document is intended to provide guidance for the design and
analysis of *in vivo*  bioequivalence studies. The guidance is an update
of the April 12, 1990 Bioequivalence Guideline. Many of the changes in
the guideline are based upon reports from panel presentations at the
1993 Veterinary Drug Bioequivalence Workshop**^1^.**

Two products are considered to be bioequivalent when they are equally
bioavailable; that is, equal in the rate and extent to which the active
ingredient(s) or therapeutic ingredient(s) is (are) absorbed and
become(s) available at the site(s) of drug action.

The Generic Animal Drug and Patent Term Restoration Act (GADPTRA) signed
into law on November 16, 1988, permits sponsors to submit an Abbreviated
New Animal Drug Application (ANADA) for a generic version of any
off-patent approved animal drug (with certain exceptions noted in the
law) regardless of whether the drug was approved prior to 1962 and
subject to the National Academy of Sciences / National Research Council
/ Drug Effectiveness Study Implementation (NAS/NRC/DESI) review.

Bioequivalence studies are used in a variety of situations, most often
when a sponsor proposes manufacturing a generic version of an approved
off-patent product. A bioequivalence study may also be part of a new
animal drug application (NADA) or supplemental NADA for approval of an
alternative dosage form, new route of administration, or a significant
manufacturing change which may affect drug bioavailability.

The Center has concluded that the tissue residue depletion of the
generic product is not adequately addressed through bioequivalence
studies. Therefore, sponsors of ANADA\'s for drug products for
food-producing animals will generally be asked to include bioequivalence
and tissue residue studies (21 USC 360 b (n) (1) (E)). A tissue residue
study should generally accompany clinical end-point and pharmacologic
end-point bioequivalence studies, and blood level bioequivalence studies
that can not quantify the concentration of the drug in blood throughout
the established withdrawal period (21 USC 360 b (n) (1) (A) (ii)).

Bioequivalence studies (*i.e.*, blood level, pharmacologic end-point,
and clinical end-point studies) and tissue residue depletion studies
should be conducted in accordance with good laboratory practice (GLP)
regulations (21 CFR Part 58).

Whereas the focus of the guidance is bioequivalence testing for ANADA
approval, the general principles also apply to relative bioavailability
studies conducted for NADA\'s.

Sponsors should consult with the Center early in the product development
process to facilitate the design of studies adequate for drug approval.
The Center urges sponsors to submit protocols for review prior to
conducting studies.

[]{#GENCON .anchor}

II\. GENERAL CONSIDERATIONS

[]{#__RefHeading___Toc489327082 .anchor}A. Selection of Reference
Product for Bioequivalence Testing

As a general rule, the proposed generic product should be tested against
the original pioneer product.

If the original pioneer product is no longer marketed, but remains
eligible to be copied, then the first approved and available generic
copy of the pioneer should be used as the reference product for
bioequivalence testing against the proposed new generic product.

If several approved NADA\'s exist for the same drug product, and each
approved product is labeled differently (*i.e.*, different species
and/or claims), then the generic sponsor must clearly identify which
product label is the intended pioneer. Bioequivalence testing should be
conducted against the single approved product which bears the labeling
that the generic sponsor intends to copy.

The generic sponsor should consult with CVM regarding selection of the
appropriate reference product before conducting the bioequivalence
study.

[]{#__RefHeading___Toc489327083 .anchor}B. Criteria for Waiver of *In
Vivo*  Bioequivalence Study

The requirement for the in vivo bioequivalence study may be waived for
certain generic products (21 USC 360 b (n) (1) (E)). Categories of
products which may be eligible for waivers include, but are not limited
to, the following:

1.  Parenteral solutions intended for injection by the intravenous,
    subcutaneous, or intramuscular routes of administration.

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1.  Oral solutions or other solubilized forms.

2.  Topically applied solutions intended for local therapeutic effects.
    Other topically applied dosage forms intended for local therapeutic
    effects for non-food animals only.

3.  Inhalant volatile anesthetic solutions.

In general, the generic product being considered for a waiver contains
the same active and inactive ingredients in the same dosage form and
concentration and has the same pH and physico-chemical characteristics
as an approved pioneer product.

However, the Center will consider bioequivalence waivers for non-food
animal topical products with certain differences in the inactive
ingredients of the pioneer and generic products.

If a waiver of the *in vivo*  bioequivalence and/or the tissue residue
study/studies is granted for a food animal drug product, then the
withdrawal period established for the pioneer product will be assigned
to the generic product.

Sponsors may apply for waivers of *in vivo*  bioequivalence studies
prior to submission of the ANADA\'s.

[]{#__RefHeading___Toc489327084 .anchor}C. Selection of Blood Level,
Pharmacologic End-point, or Clinical End-point Study

*In vivo*  bioequivalence may be determined by one of several direct or
indirect methods. Selection of the method depends upon the purpose of
the study, the analytical method available, and the nature of the drug
product. Bioequivalence testing should be conducted using the most
appropriate method available for the specific use of the product.

The preferred hierarchy of bioequivalence studies (in descending order
of sensitivity) is the blood level study, pharmacologic end-point study,
and clinical end-point study. When absorption of the drug is sufficient
to measure drug concentration *directly*  in the blood (or other
appropriate biological fluids or tissues) and systemic absorption is
relevant to the drug action, then a blood (or other biological fluid or
tissue) level bioequivalence study should be conducted. The blood level
study is generally preferred above all others as the most sensitive
measure of bioequivalence. The sponsor should provide justification for
choosing either a pharmacologic or clinical end-point study over a
blood-level (or other biological fluids or tissues) study.

When the measurement of the rate and extent of absorption of the drug in
biological fluids can not be achieved or is unrelated to drug action, a
pharmacologic end-point (*i.e.*, drug induced physiologic change which
is related to the approved indications for use) study may be conducted.
Lastly, in order of preference, if drug concentrations in blood (or
fluids or tissues) are not measurable or are inappropriate, and there
are no appropriate pharmacologic effects that can be monitored, then a
clinical end-point study may be conducted, comparing the test (generic)
product to the reference (pioneer) product and a placebo (or negative)
control.

[]{#__RefHeading___Toc489327085 .anchor}D. Species Selection

A bioequivalence study generally should be conducted for each species
for which the pioneer product is approved on the label, with the
exception of \"minor\" species (as defined in section 514.1 (d) (1) of
Title 21 of the Code of Federal Regulations) on the label.

[]{#__RefHeading___Toc489327086 .anchor}E. Dose Selection

Dose selection will depend upon the label claims, consideration of assay
sensitivity, and relevance to the practical use conditions of the
reference product. A blood level bioequivalence study should generally
be conducted at the highest dose approved for the pioneer product.

However, the Center will consider a bioequivalence study conducted at a
higher than approved dose in certain cases. Such a study may be
appropriate when a multiple of the highest approved dose achieves
measurable blood levels, but the highest approved dose does not. In
general, the study would be limited to 2-3x the highest dose approved
for the pioneer product. The pioneer product should have an adequate
margin of safety at the higher than approved dose level. The generic
sponsor should also confirm (*e.g.*, through literature) that the drug
follows linear kinetics. A higher than approved dose bioequivalence
study in food animal species would be accompanied by a tissue residue
withdrawal study conducted at the highest approved dose for the pioneer
product.

For products labeled for multiple claims involving different
pharmacologic actions at a broad dose range (*e.g.,*  therapeutic and
production claims), a single bioequivalence study at the highest
approved dose will usually be adequate. However, multiple bioequivalence
studies at different doses may be needed if the drug is known to follow
nonlinear kinetics. The sponsor should consult with CVM to discuss the
bioequivalence study or studies appropriate to a particular drug.

[]{#__RefHeading___Toc489327087 .anchor}F. Multiple Strengths of Solid
Oral Dosage Forms

The generic sponsor should discuss with CVM the appropriate *in vivo* 
bioequivalence testing and *in vitro*  dissolution testing to obtain
approval for multiple strengths (or concentrations) of solid oral dosage
forms.

CVM will consider the ratio of active to inactive ingredients and the
*in vitro*  dissolution profiles of the different strengths, the water
solubility of the drug, and the range of strengths for which approval is
sought.

One *in vivo*  bioequivalence study with highest strength product may
suffice if the multiple strength products have the same ratio of active
to inactive ingredients and are otherwise identical in formulation.

*In vitro*  dissolution testing should be conducted, using an FDA
approved method, to compare each strength of the generic product to the
corresponding strength of the reference product.

[]{#__RefHeading___Toc489327088 .anchor}G. Manufacturing of Pilot Batch
(\"Biobatch\")

A pilot batch or \"biobatch\" should be the source of the finished drug
product used in the pivotal studies (*i.e*., bioequivalence studies and
tissue residue studies), stability studies and the validation studies
for the proposed analytical and stability indicating methods (refer to
CVM\'s guidance number 42, \"Animal Drug Manufacturing Guidelines\").

[]{#__RefHeading___Toc489327089 .anchor}III. BLOOD LEVEL STUDIES

Blood level bioequivalence studies compare a test (generic) product to a
reference (pioneer) product using parameters derived from the
concentrations of the drug moiety and/or its metabolites, as a function
of time, in whole blood, plasma, serum (or in other appropriate
biological fluids or tissues). This approach is particularly applicable
to dosage forms intended to deliver the active drug ingredient(s) to the
systemic circulation (*e.g*., injectable drugs and most oral dosage
forms). Generally, the study should encompass the absorption,
distribution, and depletion (elimination) phases of the drug
concentration *vs* time profiles.

[]{#__RefHeading___Toc489327090 .anchor}A. Assay Consideration

A properly validated assay method is pivotal to the acceptability of any
pharmacokinetic study. Sponsors should discuss any questions or problems
concerning the analytical methodology with CVM before undertaking the
bioequivalence studies. The ANADA submission should contain adequate
information necessary for the CVM reviewer to determine the validity of
the analytical method used to quantitate the level of drug in the
biological matrix (*e.g.*, blood).

The following aspects should be addressed in assessing method
performance:

1.  Concentration Range and Linearity

    The quantitative relationship between concentration and response
    should be adequately characterized over the entire range of expected
    sample concentrations. For linear relationships, a standard curve
    should be defined by at least 5 concentrations. If the concentration
    response function is non-linear, additional points would be
    necessary to define the non-linear portions of the curve.
    Extrapolation beyond a standard curve is not acceptable.

2.  Limit of Detection (LOD)

    The standard deviation of the background signal and LOD should be
    determined. The LOD is estimated as the response value calculated by
    adding 3 times the standard deviation of the background response to
    the average background response.

3.  Limit of Quantitation (LOQ)

    The initial determination of LOQ should involve the addition of 10
    times the standard deviation of the background response to the
    average background response. The second step in determining LOQ is
    assessing the precision (reproducibility) and accuracy (recovery) of
    the method at the LOQ. The LOQ will generally be the lowest
    concentration on the standard curve that can be quantified with
    acceptable accuracy and precision (see items 5. and 6. below).

4.  Specificity

    The absence of matrix interferences should be demonstrated by the
    analysis of 6 independent sources of control matrix. The effect of
    environmental, physiological, or procedural variables on the matrix
    should be assessed. Each independent control matrix will be used to
    produce a standard curve, which will be compared to a standard curve
    produced under chemically defined conditions. The comparison of
    curves should exhibit parallelism and superimposability within the
    limits of analytical variation established for the chemically
    defined standard curve.

5.  Accuracy (Recovery)

    This parameter should be evaluated using at least 3 known
    concentrations of analyte freshly spiked in control matrix, one
    being at a point 2 standard deviations above the LOQ, one in the
    middle of the range of the standard curve (\"mid-range\") and one at
    a point 2 standard deviations below the upper quantitative limit of
    the standard curve. The accuracy of the method, based upon the mean
    value of 6 replicate injections, at each concentration level, should
    be within 80-120% of the nominal concentration at each level (high,
    mid-range, and LOQ).

6.  Precision

    This parameter should be evaluated using at least 3 known
    concentrations of analyte freshly spiked in control matrix, at the
    same points used for determination of accuracy. The coefficient of
    variation (CV) of 6 replicates should be ± 10% for concentrations at
    or above 0.1 ppm (0.1 µ g/mL). A CV of ± 20% is acceptable for
    concentrations below 0.1 ppm.

7.  Analyte Stability

    Stability of the analyte in the biological matrix under the
    conditions of the experiment (including any period for which samples
    are stored before analyses) should be established. It is recommended
    that the stability be determined with incurred analyte in the matrix
    of dosed animals in addition to, or instead of, control matrix
    spiked with pure analyte. Also, the influence of 3 freeze-thaw
    cycles at 2 concentrations should be determined.

    Stability samples at 3 concentrations should be stored with the
    study samples and analyzed through the period of time in which study
    samples are analyzed. These analyses will establish whether or not
    analyte levels have decreased during the time of analysis.

8.  Analytical System Stability

    To assure that the analytical system remains stable over the time
    course of the assay, the reproducibility of the standard curve
    should be monitored during the assay. A minimal design would be to
    run analytical standards at the beginning and at the end of the
    analytical run.

9.  Quality Control (QC) Samples

    The purpose of QC samples is to assure that the complete analytical
    method, sample preparation, extraction, clean-up, and instrumental
    analysis perform according to acceptable criteria. The stability of
    the drug in the text matrix for the QC samples should be known and
    any tendency for the drug to bind to tissue or serum components over
    time should also be known.

    Drug free control matrix, *e.g.*, tissue, serum, etc. that is
    freshly spiked known quantities of test drug, should be analyzed
    contemporaneously with test samples, evenly dispersed throughout
    each analytical run. This can be met by the determination of
    accuracy and precision of each analytical run (Items 5 and 6).

10. Replicate and Repeat Analyses

    Single rather than replicate analyses are recommended, unless the
    reproducibility and/or accuracy of the method are borderline.
    Criteria for repeat analyses should be determined prior to running
    the study and recorded in the method SOP.

11. Summary of Samples to Be Run With Each Analysis

    a\. Accuracy estimate (Item 5)\
    b. Precision estimate (Item 6)\
    c. Analytical system stability (Item 8)\
    d. Analyte stability samples (Item 7)

[]{#__RefHeading___Toc489327091 .anchor}B. General Experimental Design
Considerations

1.  []{#__RefHeading___Toc489327092 .anchor}Dosing by Labeled
    Concentration

> The potency of the pioneer and generic products should be assayed
> prior to conducting the bioequivalence study to ensure that FDA or
> compendial specifications are met. The Center recommends that the
> potency of the pioneer and generic lots should differ by no more than
> ±5% for dosage form products.
>
> The animals should be dosed according to the labeled concentration or
> strength of the product, rather than the assayed potency of the
> individual batch (*i.e.*, the dose should not be corrected for the
> assayed potency of the product). The bioequivalence data or derived
> parameters should not be normalized to account for any potency
> differences between the pioneer and generic product lots.

2.  []{#__RefHeading___Toc489327093 .anchor}Single Dose vs Multiple Dose
    Studies

    A single dose study at the highest approved dose will generally be
    adequate for the demonstration of bioequivalence. A single dose
    study at a higher than approved dose may be appropriate for certain
    drugs (refer to the section on Dose Selection).

    A multiple dose study may be appropriate when there are concerns
    regarding poorly predictable drug accumulation, (*e.g.* ,a drug with
    nonlinear kinetics) or a drug with a narrow therapeutic window. A
    multiple dose study may also be needed when assay sensitivity is
    inadequate to permit drug quantification out to 3 terminal
    elimination half-lives beyond the time when maximum blood
    concentrations (CMAX) are achieved, or in cases where prolonged or
    delayed absorption**^2^** exist. The determination of prolonged or
    delayed absorption (*i.e.*, flip-flop kinetics) may be made from
    pilot data, from the literature, or from information contained with
    FOI summaries pertaining to the particular drug or family of drugs.

3.  []{#__RefHeading___Toc489327094 .anchor}Subject Characteristics

    Ordinarily, studies should be conducted with healthy animals
    representative of the species, class, gender, and physiological
    maturity for which the drug is approved. The bioequivalence study
    may be conducted with a single gender for which the pioneer product
    is approved, unless there is a known interaction of formulation with
    gender.

    An attempt should be made to restrict the weight of the test animals
    to a narrow range in order to maintain the same total dose across
    study subjects.

    The animals should not receive any medication prior to testing for a
    period of two weeks or more, depending upon the biological half-life
    of the ancillary drug.

4.  []{#__RefHeading___Toc489327095 .anchor}Fed vs Fasted State

    Feeding may either enhance or interfere with drug absorption,
    depending upon the characteristics of the drug and the formulation.
    Feeding may also increase the inter- and intrasubject variability in
    the rate and extent of drug absorption. The rationale for conducting
    each bioequivalence study under fasting or fed conditions should be
    provided in the protocol.

    Fasting conditions, if used, should be fully described, giving
    careful consideration to the pharmacokinetics of the drug and the
    humane treatment of the test animals.

    The protocol should describe the diet and feeding regime which will
    be used in the study.

    If a pioneer product label indicates that the product is limited to
    administration either in the fed or fasted state, then the
    bioequivalence study should be conducted accordingly. If the
    bioequivalence study parameters pass the agreed upon confidence
    intervals, then the single study is acceptable as the basis for
    approval of the generic drug.

    However, for certain product classifications or drug entities, such
    as enteric coated and oral sustained release products, demonstration
    of bioequivalence in both the fasted and the fed states may be
    necessary, if drug bioavailability is highly variable under feeding
    conditions, as determined from the literature or from pilot data. A
    bioequivalence study conducted under fasted conditions may be
    necessary to pass the confidence intervals. A second smaller study
    may be necessary to examine meal effects. CVM will evaluate the
    smaller study with respect to the means of the pivotal parameters
    (AUC, CMAX). The sponsors should consult with CVM prior to
    conducting the studies.

[]{#__RefHeading___Toc489327096 .anchor}C. Pharmacokinetic and
Statistical Considerations in Study Design

1.  []{#__RefHeading___Toc489327097 .anchor}Sampling Time Considerations

    The total number of sampling times necessary to characterize the
    blood level profiles will depend upon the curvature of the profiles
    and the magnitude of variability associated with the bioavailability
    data (including pharmacokinetic variability, assay error and
    interproduct differences in absorption kinetics).

    The sampling times should adequately define peak concentration(s)
    and the extent of absorption. The sampling times should extend to at
    least 3 terminal elimination half-lives beyond TMAX. The sponsor
    should consult with CVM prior to conducting the pivotal
    bioequivalence study if the assay is unable to quantify samples to 3
    half-lives.

    Maximum sampling time efficiency may be achieved by conducting a
    pilot investigation. The pilot study should identify the general
    shapes of the test and reference curves, the magnitude of the
    difference in product profiles, and the noise associated with each
    blood sampling time (*e.g*., variability attributable to assay error
    and the variability between subjects, for parallel study designs, or
    within subjects, for crossover study designs). This information
    should be applied to the determination of an optimum blood sampling
    schedule. Depending upon these variability estimates, it may be more
    efficient to cluster several blood samples rather than to have
    samples which are periodically dispersed throughout the duration of
    blood sampling.**^3^**

2.  []{#__RefHeading___Toc489327098 .anchor}Protein Binding

    In general, product bioequivalence should be based upon total (free
    plus protein bound) concentrations of the parent drug (or
    metabolite, when applicable). However, if nonlinear protein binding
    is known to occur within the therapeutic dosing range (as determined
    from literature or pilot data), then sponsors may need to submit
    data on both the free and total drug concentrations for the generic
    and pioneer products.

    Similarly, if the drug is known to enter blood erythrocytes, the
    protocol should address the issue of potential nonlinearity in
    erythrocyte uptake of the drug administered within the labeled
    therapeutic dosing range.

    The bioequivalence protocol or completed study report should provide
    any information available from the literature regarding erythrocyte
    uptake and protein binding characteristics of the drug or drug
    class, including the magnitude of protein binding and the type of
    blood protein to which it binds.

3.  []{#__RefHeading___Toc489327099 .anchor}Subject Number

    Pilot studies are recommended as a means of estimating the
    appropriate sample size for the pivotal bioequivalence study.
    Estimated sample size will vary depending upon whether the data are
    analyzed on a log or linear scale. Useful references for sample size
    estimates include Westlake**^4^**, Hauschke**^5^**, and
    Steinijans**^6^**.

4.  []{#__RefHeading___Toc489327100 .anchor}Cross-over and Parallel
    Design Considerations

    A two-period cross-over design is commonly used in blood level
    studies. The use of cross-over designs eliminates a major source of
    study variability: between subject differences in the rates of drug
    absorption, drug clearance, and the volume of drug distribution.

    In a typical two-period cross-over design, subjects are randomly
    assigned to either sequence A or sequence B with the restriction
    that equal numbers of subjects are initially assigned to each
    sequence. The design is as follows:

    Sequence A Sequence B

    \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

    Period 1 Test Reference

    Period 2 Reference Test

    A crucial assumption in the two-period cross-over design is that of
    equal residual effects. Unequal residual effects may result, for
    example, from an inadequate washout period. Another assumption of
    the cross-over (or extended period) design is that there is no
    subject by formulation interaction. In other words, the assumption
    is that all subjects are from a relatively homogeneous population
    and will exhibit similar relative bioavailability of the test and
    reference products. If there are subpopulations of subjects, such
    that the relationship between product bioavailability is a function
    of the subpopulation within which they are being tested, then a
    subject by formulation interaction is said to exist.

    A one-period parallel design may be preferable in the following
    situations:

    a\. The drug induces physiological changes in the animal (e.g.,
    liver microsomal enzyme induction) which persist after total drug
    clearance and alter the bioavailability of the product administered
    in the second period.

    b\. The drug has a very long terminal elimination half-life,
    creating a risk of residual drug present in the animal at the time
    of the second period dosing.

    c\. The duration of the washout time for the two-period cross-over
    study is so long as to result in significant maturational changes in
    the study subjects.

    d\. The drug follows delayed or prolonged absorption (flip-flop
    kinetics**^2^**), where the slope of the \[\[beta\]\]-elimination
    phase is dictated by the rate of drug absorption rather than the
    rate of drug elimination from one or both products.

    Other designs, such as the two-period design with four treatment
    sequences (Test/Test, Reference/Reference, Test/Reference and
    Reference/Test) or the extended period design may be appropriate
    depending on the circumstances. The use of alternative study designs
    should be discussed with CVM prior to conducting the bioequivalence
    study. Pilot data or literature may be used in support of
    alternative study designs.

5.  []{#__RefHeading___Toc489327101 .anchor}Duration of Washout Time for
    Cross-over Study

    For drugs which follow a one or two compartment open body model, the
    duration of the washout time should be approximately 10x the plasma
    apparent terminal elimination half-life, to provide for 99.9% of the
    administered dose to be eliminated from the body.

    If more highly complex kinetic models are anticipated (*e.g*., drugs
    for which long withdrawal times have been assigned due to prolonged
    tissue binding), or for drugs with the potential for physiologic
    carryover effects, the washout time should be adjusted accordingly.

    The washout period should be sufficiently long to allow the second
    period of the cross-over study to be applicable in the statistical
    analysis. However, if sequence effects are noted, the data from the
    first period may be evaluated as a parallel design study.

6.  []{#__RefHeading___Toc489327102 .anchor}Pivotal Parameters for Blood
    Level Bioequivalence

    The sponsor is encouraged to calculate parameters using formulas
    which involve only the raw data (*i.e.*, so-called model independent
    methods).

    a\. Area Under the Curve (AUC) Estimates

    The extent of product bioavailability is estimated by the area under
    the blood concentration *vs* time curve (AUC). AUC is most
    frequently estimated using the linear trapezoidal rule. Other
    methods for AUC estimation may be proposed by the sponsor and should
    be accompanied by appropriate literature references during protocol
    development.

    For a single dose bioequivalence study, AUC should be calculated
    from time 0 (predose) to the last sampling time associated with
    quantifiable drug concentration AUC(0-LOQ). The comparison of the
    test and reference product value for this noninfinity estimate
    provides the closest approximation of the measure of uncertainty
    (variance) and the relative bioavailability estimate associated with
    AUC(0-INF), the full extent of product bioavailability**^7^**.

    The relative AUC values generally change very little once the
    absorption of both products has been completed**^8,\ 9^**. However,
    because of the possibility of multifunctional absorption kinetics,
    it can not always be determined when the available drug has been
    completely absorbed. Therefore, CVM recommends extending the
    duration of sampling until such time that AUC(0-LOQ)/AUC(0-INF) \> =
    0.80. Generally, the sampling times should extend to at least 3
    multiples of the drug\'s apparent terminal elimination half-life,
    beyond the time when maximum blood concentrations are achieved.

    AUC(0-INF)should be used to demonstrate that the concentration-time
    curve can be quantitated such that AUC(0-LOQ)/AUC(0-INF) \>= 0.80.
    The method for estimating the terminal elimination phase should be
    described in the protocol and the final study report. The
    AUC(0-LOQ)/AUC(0-INF) is calculated to determine whether AUC(0-LOQ)
    adequately reflects the extent of absorption.

    The sponsor should consult with CVM if AUC(0-LOQ)/AUC(0-INF) is
    determined to be \< 0.80. If AUC(0-LOQ)/AUC(0-INF) is \<0.80, then a
    multiple dose study to steady state may be needed to allow an
    accurate assessment of AUC(0-INF) (where AUC(0-INF) = AUC(0-t) at
    steady state and t is the dosing interval).

    In a multiple dose study, the AUC should be calculated over one
    complete dosing interval AUC(0-t). Under steady state conditions,
    AUC(0-t) equals the full extent of bioavailability of the individual
    dose AUC(0-INF) assuming linear kinetics. For drugs which are known
    to follow nonlinear kinetics, the sponsor should consult with CVM to
    determine the appropriate parameters for the bioequivalence
    determination.

    b\. Rate of Absorption

    The rate of absorption will be estimated by the maximum observed
    drug concentration (CMAX) and the corresponding time to reach this
    maximum concentration (TMAX).

    When conducting a steady-state investigation, data on the minimum
    drug concentrations (trough values) observed during a single dosing
    interval (CMIN) should also be collected. Generally, three
    successive CMIN values should be provided to verify that
    steady-state conditions have been achieved. Although CMIN most
    frequently occurs immediately prior to the next successive dose,
    situations do occur with CMIN observed subsequent to dosing. To
    determine a steady state concentration, the CMIN values should be
    regressed over time and the resultant slope should be tested for its
    difference from zero.

    c\. Determination of Product Bioequivalence

    Unless otherwise indicated by CVM during the protocol development
    for a given application, the pivotal bioequivalence parameters will
    be CMAX and AUC(0-LOQ) (for a single dose study) or AUC(0-t) (for a
    multiple dose study). To be indicative of product bioequivalence,
    the pivotal metrics should be associated with confidence intervals
    which fall within a set of acceptability limits (see Statistical
    Analysis section of this Guidance.

    The sponsor and CVM should agree to the acceptable bounds for the
    confidence limits for the particular drug and formulation during
    protocol development.

    If studies or literature demonstrate that the pioneer drug product
    exhibits highly variable kinetics, then the generic drug sponsor may
    propose alternatives to the generally acceptable bounds for the
    confidence limits.

    TMAX in single dose studies and CMIN in multiple dose studies will
    be assessed by clinical judgment.

[]{#__RefHeading___Toc489327103 .anchor}D. Statistical Analysis

> CVM advocates the use of 90% confidence intervals, as the best
> available method for evaluating bioequivalence study data. Papers by
> Schuirmann**^10^** and Westlake**^4^** compare various methodologies
> for assessing drug product equivalence and describe the confidence
> interval approach.
>
> The confidence interval approach should be applied to the individual
> parameters of interest (*e.g.*, AUC and CMAX). The sponsor may use
> untransformed or log transformed data. However, the choice of
> untransformed or log transformed data should be made by the sponsor
> with concurrence by the Center prior to conducting the study.
>
> 1\. Untransformed Data

A discussion of how the confidence interval approach should be applied
to (normally distributed) untransformed data from a two-period crossover
design is given below.

> $$If we let ${\overline{X}}_{T1}$ be the mean for the test drug in
> period 1, ${\overline{X}}_{T2}$the mean for the test drug in period 2,
> and ${\overline{X}}_{R1}$and ${\overline{X}}_{R2}$ the respective
> means for the reference drug, then the estimates for the drugs
> averaged over both periods are
> ${\overline{X}}_{T} = (\frac{1}{2})\left( {\overline{X}}_{T1} + {\overline{X}}_{T2} \right)$
> for the test drug and
> ${\overline{X}}_{R} = (\frac{1}{2})\left( {\overline{X}}_{R1} + {\overline{X}}_{R2} \right)$
> for the reference drug. Although both sequence groups usually start
> with the same number of animals, the number of animals in each
> sequence group (n~A~ and n~B~) that successfully finish the study may
> not be equal. The formulas above utilize the marginal or least squares
> estimates of ~T~ and ~R~, the corresponding means in the target
> population. These means are not a function of the sample size in each
> sequence.

An analysis of variance is needed to obtain the estimate of ^2^, the
error variance. The estimator, s^2^, which will be used in the
calculation of the 90% confidence interval should be obtained from the
\"error\" mean square term found in the following ANOVA table.

Source Degrees of Freedom

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Sequence 1

Animal (Sequence) n~A~+n~B~-2

Period 1

Formulation 1

Error n~A~+n~B~-2

Total 2n~A~+2n~B~-1

Lower and upper 90% confidence intervals are then found by formulas
based on Student\'s t-distribution.

> $$L = \left( {\overline{X}}_{T} - {\overline{X}}_{R} \right) - t_{n_{A} + n_{B} - 2;0\text{.}\text{05}}s\sqrt{\frac{1}{2}\left( \frac{1}{n_{A}} + \frac{1}{n_{B}} \right)}$$
>
> $$U = \left( {\overline{X}}_{T} - {\overline{X}}_{R} \right) + t_{n_{A} + n_{B} - 2;0\text{.}\text{05}}s\sqrt{\frac{1}{2}\left( \frac{1}{n_{A}} + \frac{1}{n_{B}} \right)}$$

The procedure of declaring two formulations bioequivalent if the 90%
confidence interval is completely contained in some fixed interval, is
statistically equivalent to performing two one-sided statistical tests (
= .05) at the end-points of the interval.

> Consider the following example with L = 3, U = 17,
> ${\overline{X}}_{T}$= 110 and ${\overline{X}}_{R}$ = 100. By the
> traditional hypothesis testing approach, the result would be
> considered statistically significant since the confidence interval
> does not include 0. Using the confidence interval approach, the entire
> confidence interval lies within 17% of ${\overline{X}}_{R}$. (The
> lower end of the confidence interval lies within
> L/${\overline{X}}_{R}$ = 3/100 = 3% of ${\overline{X}}_{R}$, while the
> upper end of the confidence interval lies within
> U/${\overline{X}}_{R}$ = 17/100 = 17% of ${\overline{X}}_{R}$ .) If it
> were determined by CVM that only differences larger than 20% were
> biomedically important, then using the confidence interval approach
> the results of this study would be considered adequate to demonstrate
> bioequivalence.
>
> Now consider an example with L = -4, U = 24, ${\overline{X}}_{T}$ =
> 110 and ${\overline{X}}_{R}$ = 100. In this case, by the traditional
> hypothesis testing approach the result would not be considered
> statistically significant since the confidence interval includes 0.
> However, the confidence interval extends as far as 24% from
> ${\overline{X}}_{R}$ . (The lower end of the confidence interval lies
> within L/${\overline{X}}_{R}$ = -4/100 = -4% of ${\overline{X}}_{R}$ ,
> while the upper end of the confidence interval extends to
> U/${\overline{X}}_{R}$ = 24/100 = 24% of ${\overline{X}}_{R}$ .) If it
> were determined by CVM that only differences larger than 20% were
> biomedically important, then the results of this study would be
> considered inadequate to demonstrate bioequivalence, since the entire
> confidence interval is not within 20% of ${\overline{X}}_{R}$ .
>
> 2\. Logarithmically transformed data

This section discusses how the 90% confidence interval approach should
be applied to log-transformed data. In this situation the individual
animal AUC and CMAX values are log-transformed and the analysis is done
on the transformed data. For a two-period crossover study, as described
in D.1, the ANOVA model used to calculate estimates of the error
variance and the least square means are identical for both transformed
and untransformed data. The procedural difference comes after the lower
and upper 90% confidence intervals are found by formulas based on
Student's t-distribution.

> The lower and upper confidence bounds of the log-transformed data will
> then need to be back-transformed in order to be expressed on the
> original scale of the measurement. One thing to keep in mind when
> moving between the logarithm scale and the original scale is that the
> back-transformed mean of a set of data that has been transformed to
> the logarithm scale is not strictly equivalent to the mean that would
> be calculated from the data on the original scale of measurement. This
> back-transformed mean is known instead as the geometric mean.
>
> It may help to see the calculations involved. If the AUC from each
> animal has been transformed to the logarithm scale, we can express the
> transformed AUC as *LnAUC*. Then the mean on the logarithm scale is as
> follows:
>
> $\overline{\text{Ln}}\text{AUC}_{t} = \sum_{i = 1}^{n}\frac{\text{LnAUC}_{\text{it}}}{n}$
> where the subscript *t* represents the AUC determinations for the test
> article, *i* is the AUC of the ith animal, and *n* is the total number
> of animals receiving the text article. When this mean is
> back-transformed, it becomes the geometric mean: $e^{}$. This
> geometric mean will be on the original scale of the measurement. It
> will be close to but not exactly equal to the mean obtained on the
> original scale of the measurement.

The back-transformation of the confidence bounds is accomplished in the
following way:

  --------------------------- ------ -----------------------------------------
  Lower bound (expressed as a =      $$\left( e^{L} - 1 \right)´\text{100}$$
  percentage)                        

  Upper bound (expressed as a =      $$\left( e^{U} - 1 \right)´\text{100}$$
  percentage)                        

  Where                       *L*    is the lower 90% confidence interval as
                                     given in Section III D 1 and calculated
                                     on the log-transformed data;

                              *U*    is the upper 90% confidence interval as
                                     given in Section III D 1 and calculated
                                     on the log-transformed data.
  --------------------------- ------ -----------------------------------------

As an example, consider the data for AUC from a hypothetical crossover
study in the following table:

+-------+--------------+--------------+---------+-----------+---------+
|       |              | Reference    |         | Test      |         |
|       |              | Article      |         | Article   |         |
+-------+--------------+--------------+---------+-----------+---------+
| A     | Crossover    | AUC          | LogAUC  | AUC       | LogAUC  |
| nimal | Sequence     |              |         |           |         |
+-------+--------------+--------------+---------+-----------+---------+
| 1     | 1            | 518.0        | 6.25    | 317.8     | 5.76    |
+-------+--------------+--------------+---------+-----------+---------+
| 2     | 1            | 454.9        | 6.12    | 465.0     | 6.14    |
+-------+--------------+--------------+---------+-----------+---------+
| 3     | 1            | 232.8        | 5.45    | 548.4     | 6.31    |
+-------+--------------+--------------+---------+-----------+---------+
| 4     | 1            | 311.1        | 5.74    | 334.8     | 5.81    |
+-------+--------------+--------------+---------+-----------+---------+
| 5     | 2            | 340.4        | 5.83    | 224.7     | 5.41    |
+-------+--------------+--------------+---------+-----------+---------+
| 6     | 2            | 497.7        | 6.21    | 249.2     | 5.52    |
+-------+--------------+--------------+---------+-----------+---------+
| 7     | 2            | 652.0        | 6.48    | 625.4     | 6.44    |
+-------+--------------+--------------+---------+-----------+---------+
| 8     | 2            | 464.1        | 6.14    | 848.7     | 6.74    |
+-------+--------------+--------------+---------+-----------+---------+
| :::   |              | 433.8        | 6.03    | 451.7     | 6.02    |
| Defin |              |              |         |           |         |
| ition |              |              |         |           |         |
| -Term |              |              |         |           |         |
| Mean  |              |              |         |           |         |
| :::   |              |              |         |           |         |
+-------+--------------+--------------+---------+-----------+---------+
| Sta   |              | 133.3        | 0.33    | 214.3     | 0.47    |
| ndard |              |              |         |           |         |
| devi  |              |              |         |           |         |
| ation |              |              |         |           |         |
+-------+--------------+--------------+---------+-----------+---------+
| Geom  |              |              | 414.7   |           | 410.5   |
| etric |              |              |         |           |         |
| mean  |              |              |         |           |         |
+-------+--------------+--------------+---------+-----------+---------+

> The statistics for AUC will be calculated from the log-transformed
> data. In this example, *L*, the lower 90% confidence interval
> calculated on the log scale is -0.395. *U*, the upper 90% confidence
> interval calculated on the log scale is 0.372. To back-transform these
> intervals and express them as percentages, we do the following:
>
> Back-transformed lower bound:
> $\left( e^{- 0\text{.}\text{395}} - 1 \right) \times \text{100} = \left( 0\text{.}\text{674} - 1 \right) \times \text{100} = \left( - 0\text{.}\text{326} \right) \times \text{100} = - \text{32}\text{.}6\text{\%}$
>
> Back-transformed upper bound:
>
> $\left( e^{0\text{.}\text{372}} - 1 \right) \times \text{100} = \left( 1\text{.}\text{451} - 1 \right) \times \text{100} = \left( 0\text{.}\text{451} \right) \times \text{100} = \text{45}\text{.}1\text{\%}$
>
> Therefore the lower end of the confidence bound lies within -32.6% of
> the geometric mean of the reference article, while the upper end of
> the confidence interval lies within 45.1% of the geometric mean of the
> reference article. If it were determined by CVM that the acceptable
> confidence bound was 80% to125% of the geometric mean of the reference
> article in order to demonstrate bioequivalence, then the
> back-transformed lower bound can be as low as -20% and the
> back-transformed upper bound can be as high as 25%. In this example,
> we would determine that the study had not demonstrated an acceptable
> level of bioequivalence between the test article and the reference
> article.

A more detailed derivation of these expressions for upper and lower
confidence bounds is found in the Appendix.

The width of the confidence interval is determined by the within subject
variance (between subject variance for parallel group studies) and the
number of subjects in the study. In general, the confidence interval for
untransformed data should be 80-120% (the confidence interval should lie
within ± 20% of the mean of the reference product). For logarithmically
transformed data, the confidence interval is generally 80-125% (the
confidence interval should lie within --20% to +25% of the mean of the
reference product). The sponsor and CVM should determine the acceptable
bounds for confidence limits for the particular drug and formulation
during protocol development.

[]{#__RefHeading___Toc489327104 .anchor}IV. PHARMACOLOGIC END-POINT
STUDIES

Where the direct measurement of the rate and extent of absorption of the
new animal drug in biological fluids is inappropriate or impractical,
the evaluation of a pharmacologic end-point related to the labeled
indications for use will be acceptable.

[]{#__RefHeading___Toc489327105 .anchor}A. General Design Aspects

Typically the design of a pharmacologic end-point study should follow
the same general considerations as the blood level studies. However,
specifics such as the number of subjects or sampling times will depend
on the pharmacologic end-point monitored. The parameters to be measured
will also depend upon the pharmacologic end-points and may differ from
those used in blood level studies. As with blood level studies, when
pharmacologic end-point studies are used to demonstrate bioequivalence,
a tissue residue study will also be required in food-producing animals.

[]{#__RefHeading___Toc489327106 .anchor}B. Statistical Analysis

For parameters which can be measured over time, a time *vs*  effect
profile is generated, and equivalence is determined with the method of
statistical analysis essentially the same as for the blood level
bioequivalence study.

For pharmacologic effects for which effect *vs*  time curves can not be
generated, then alternative procedures for statistical analysis should
be discussed with CVM prior to conducting the study.

[]{#__RefHeading___Toc489327107 .anchor}V. CLINICAL END-POINT STUDIES

If measurement of the drug or its metabolites in blood, biological
fluids or tissues is inappropriate or impractical, and there are no
appropriate pharmacologic end-points to monitor (*e.g.*, most production
drugs and some coccidiostats and anthelmintics), then well-controlled
clinical end-point studies are acceptable for the demonstration of
bioequivalence.

[]{#__RefHeading___Toc489327108 .anchor}A. General Design Aspects

Generally, a parallel group design with three treatment groups should be
used. The groups should be a placebo (or negative) control, a positive
control (reference/pioneer product) and the test (generic) product. The
purpose of the placebo (or negative) control is to confirm the
sensitivity or validity of the study.

Dosage(s) approved for the pioneer product should be used in the study.
Dosage(s) should be selected following consultation with CVM and should
reflect consideration for experimental sensitivity and relevance to the
common use of the pioneer product.

[]{#__RefHeading___Toc489327109 .anchor}B. Subject Characteristics and
Data Collection

Studies should generally be conducted using the target animal species,
with consideration for the sex, class, body weight, age, health status,
and feeding and husbandry conditions, as described on the pioneer
product labeling. In general, the length of time that the study is
conducted should be consistent with the duration of use on the pioneer
product labeling.

In general, the response(s) to be measured in a clinical end-point study
should be based upon the labeling claims of the pioneer product and
selected in consultation with CVM. It may not be necessary to collect
data on some overlapping claims (*e.g.*, for a production drug which is
added at the same amount per ton of feed for both growth rate and feed
efficiency, data from only one of the two responses need be collected).

[]{#__RefHeading___Toc489327110 .anchor}C. Statistical Analysis

When considering sample size, it is important to note that the pen, not
the individual animal, is often the experimental unit.

As with blood level bioequivalence studies, CVM is advocating the use of
90% confidence intervals as the best method for evaluating clinical
end-point studies. The bounds for confidence limits (*e.g.,* ± 20% of
the improvement over placebo \[or negative\] control) for the particular
drug should be agreed upon with CVM prior to initiation of the study.

The analysis should be used to compare the test product and the
reference product. In addition, a traditional hypothesis test should be
performed comparing both the test and reference products separately to
the placebo (or negative) control. The hypothesis test is conducted to
ensure that the study has adequate sensitivity to detect differences
when they actually occur. If no significant improvement ( = .05) is seen
in the parameter (*i.e.*, the mean of the test and the mean of the
reference products are each not significantly better than the mean of
the placebo \[or negative\] control), generally, the study will be
considered inadequate to evaluate bioequivalence. Assuming that the test
and reference products have been shown to be superior to the placebo (or
negative) control, the determination of bioequivalence is based upon the
confidence interval of the difference between the two products.

Some clinical end-point studies may not include a placebo (or negative)
control for ethical and/or practical considerations. If the placebo is
omitted, then the response(s) to the test and reference products should
each provide a statistically significant improvement over baseline.

If the results are ordered categorical data (*e.g.*, excellent, good,
fair or poor), a non-parametric hypothesis test of no difference between
test product and placebo (or negative) control and between the reference
product and placebo (or negative) control should be performed. As above,
if these tests result in significant differences between the test
product and control and the reference product and control, then a
non-parametric confidence interval on the difference between the test
and reference products is calculated.

Another acceptable approach for categorical data is to calculate the
confidence interval on the odds ratio between the test and reference
products after showing that the test and reference products are
significantly better than the control**^11^**.

[]{#__RefHeading___Toc489327111 .anchor}VI. HUMAN FOOD SAFETY
CONSIDERATIONS

The toxicology and tolerance developed for the pioneer animal drug are
applied to generic copies of the drug.

The Panel on Human Food Safety at the 1993 Veterinary Drug
Bioequivalence Workshop addressed tissue residue depletion studies for
generic animals drugs**^1^**. The Center has concluded that in addition
to a bioequivalence study, a tissue residue depletion study should be
conducted for approval of a generic animal drug product in a
food-producing species. Two drug products may have the same plasma
disposition profile at the concentrations used to assess product
bioequivalence, but may have very different tissue disposition kinetics
when followed out to the withdrawal time for the pioneer product.
Therefore, to show the withdrawal period at which residues of the
generic product will be consistent with the tolerance for the pioneer
product, a tissue residue depletion study is necessary.

The results of a bioequivalence study or tissue residue depletion study
in one animal species can not generally be extrapolated to another
species. Possible species differences in drug partitioning or binding in
tissues could magnify a small difference in the rate or extent of drug
absorbed into a large difference in marker residue concentrations in the
target tissue. Therefore, for a pioneer product labeled for more than
one food-producing species, a bioequivalence study and a tissue residue
depletion study will generally be requested for each major
food-producing species on the label.

A traditional withdrawal study, as described in CVM\'s guidance number
3, \"General Principles for Evaluating the Safety of Compounds Used in
Food-Producing Animals,\" is considered the best design for collecting
data useful for the calculation of a preslaughter withdrawal period for
drugs used in food-producing animals. In the traditional withdrawal
study, twenty animals are divided into four or five groups of four to
five animals each. Groups of animals are slaughtered at carefully
preselected time points following the last administration of the test
product and the edible tissues are collected for residue analysis. A
statistical tolerance limit approach is used to determine when, with 95%
confidence, 99% of treated animals would have tissue residues below the
codified limits.

For purposes of calculating a withdrawal period for a generic animal
drug, only the generic product would be tested (i.e., not the pioneer
product), and only the marker residue in the target tissue would be
analyzed.

Other study designs will be considered on a case-by-case basis. Sponsors
are encouraged to submit the proposed tissue residue depletion protocol
for CVM concurrence before proceeding with the withdrawal study.

The generic animal drug will be assigned the withdrawal time supported
by the residue depletion data, or the withdrawal time currently assigned
to the pioneer product, whichever is the longer.

The generic animal drug sponsor may request a shorter withdrawal period
for the generic product by supplementing the ANADA and providing tissue
residue data necessary to support the shorter withdrawal period request.
Such a supplement will be reviewed under the agency\'s policy for
Category II supplements. For a Category II supplement, a reevaluation of
the safety (or effectiveness) data in the parent application (i.e., the
pioneer NADA) may be required (21 CFR 514.106 (b) (2)). The Center will
ordinarily approve a request for a shorter withdrawal period when the
residue data are adequate and  when no other human food safety concerns
for the drug are evident.

Under 21 CFR 514.1(b)(7), applications are required to include a
description of practicable methods for determining the quantity, if any,
of the new animal drug in or on food, and any substance formed in or on
food because of its use, and the proposed tolerance or withdrawal period
or other use restrictions to ensure that the proposed use of the drug
will be safe. For certain drug products, a tissue residue depletion
study is not needed to ensure that residues of the test product will be
consistent with the codified drug tolerance at the withdrawal time
assigned to the reference product. These include but may not be limited
to products for which a waiver of in vivo  bioequivalence testing is
granted, and products for which the assay method used in the blood level
bioequivalence study is sensitive enough to measure blood levels of the
drug for the entire withdrawal period assigned to the reference product.
Other requests for waiver of the tissue residue study will be considered
on a case-by-case basis.

CVM will not request that the assay methodology used to determine the
withdrawal period for the generic product be more rigorous than the
approved methodology used to determine the existing withdrawal period
for the pioneer product. If an analytical method other than the approved
method of analysis is used, the generic sponsor should provide data
comparing the alternate method to the approved method.

[]{#__RefHeading___Toc489327112 .anchor}APPENDIX: Confidence Bounds on
the Logarithm Scale

We want to develop an expression for the confidence bound of the
difference between the pharmacokinetic parameter for the test treatment
and the reference treatment, expressed as a percentage of the reference
treatment. This bound is derived from the 90% confidence interval of the
difference between the mean of the test treatment and the mean of the
reference treatment. This appendix addresses the case when the data
analysis used to calculate the 90% confidence interval has been done
with the natural log of the pharmacokinetic parameter as the dependent
variable.

For purposes of this illustration we will use Area Under the Curve (AUC)
as the pharmacokinetic parameter.

Notation and Distributional Assumptions:

  ------------------------------------------------------------------------------------------- -------------------- -------
  Area under the Curve for Reference Treatment                                                $$\text{AUC}_{R}$$   \(1\)

  Area under the Curve for Test Treatment                                                     $$\text{AUC}_{T}$$   \(2\)

  Natural log of AUC                                                                          $$\text{LnAUC}$$     \(3\)

  $\text{LnAUC}_{T}$ \~ $N(m_{T},s^{2})$ and $\text{LnAUC}_{R}$ \~ $N(m_{R},s^{2})$                                \(4\)

  $\overline{\text{Ln}}\text{AUC}_{T} = \sum_{i = 1}^{n}\frac{\text{LnAUC}_{\text{iT}}}{n}$                        \(5\)
  which estimates $m_{T}$, similarly for $m_{R}$                                                                   

  $e^{}$ is the geometric mean of $\text{AUC}_{T}$ to be denoted by ${\overset{˙}{m}}_{T}$,                        \(6\)
  similarly for ${\overset{˙}{m}}_{R}$                                                                             
  ------------------------------------------------------------------------------------------- -------------------- -------

<table>
<colgroup>
<col style="width: 27%" />
<col style="width: 5%" />
<col style="width: 1%" />
<col style="width: 25%" />
<col style="width: 0%" />
<col style="width: 5%" />
<col style="width: 0%" />
<col style="width: 25%" />
<col style="width: 0%" />
<col style="width: 0%" />
<col style="width: 7%" />
<col style="width: 0%" />
<col style="width: 0%" />
<col style="width: 0%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="12"><u>Calculation of the Confidence Interval:</u></td>
<td colspan="2"></td>
</tr>
<tr class="even">
<td colspan="9"><p>The 90% Confidence Interval of <span
class="math inline">(<em>m</em><sub><em>T</em></sub>−<em>m</em><sub><em>R</em></sub>)</span>
is denoted by (<em><strong>L, U</strong></em>) and is calculated
from</p>
<p><span class="math inline">$L = \left(
\overline{\text{Ln}}\text{AUC}_{T} - \overline{\text{Ln}}\text{AUC}_{R}
\right) + t_{n_{A} + n_{B} -
2}^{};\text{.}\text{05}´s\sqrt{\frac{1}{2}\left( \frac{1}{n_{A}} +
\frac{1}{n_{B}} \right)}$</span></p>
<p><span class="math display">$$U = \left(
\overline{\text{Ln}}\text{AUC}_{T} - \overline{\text{Ln}}\text{AUC}_{R}
\right) - t_{n_{A} + n_{B} -
2}^{};\text{.}\text{05}´s\sqrt{\frac{1}{2}\left( \frac{1}{n_{A}} +
\frac{1}{n_{B}} \right)}$$</span></p></td>
<td colspan="3">(7)</td>
<td colspan="2"></td>
</tr>
<tr class="odd">
<td colspan="12"><u>Manipulating This Expression Gives:</u></td>
<td colspan="2"></td>
</tr>
<tr class="even">
<td><em>L</em></td>
<td colspan="2">&lt;</td>
<td><span
class="math display"><em>m</em><sub><em>T</em></sub> − <em>m</em><sub><em>R</em></sub></span></td>
<td colspan="2">&lt;</td>
<td colspan="2"><em>U</em></td>
<td colspan="3">(8)</td>
<td colspan="3"></td>
</tr>
<tr class="odd">
<td><span
class="math display"><em>e</em><sup><em>L</em></sup></span></td>
<td colspan="2">&lt;</td>
<td><span
class="math display"><em>e</em><sup><em>m</em><sub><em>T</em></sub> − <em>m</em><sub><em>R</em></sub></sup></span></td>
<td colspan="2">&lt;</td>
<td colspan="2"><span
class="math display"><em>e</em><sup><em>U</em></sup></span></td>
<td colspan="3">(9)</td>
<td colspan="3"></td>
</tr>
<tr class="even">
<td><span
class="math display"><em>e</em><sup><em>L</em></sup></span></td>
<td colspan="2">&lt;</td>
<td><span
class="math display">$$\frac{{\overset{˙}{m}}_{T}}{{\overset{˙}{m}}_{R}}$$</span></td>
<td colspan="2">&lt;</td>
<td colspan="2"><span
class="math display"><em>e</em><sup><em>U</em></sup></span></td>
<td colspan="3">(10)</td>
<td colspan="3"></td>
</tr>
<tr class="odd">
<td><span
class="math display"><em>e</em><sup><em>L</em></sup> − 1</span></td>
<td>&lt;</td>
<td colspan="3"><span
class="math display">$$\frac{{\overset{˙}{m}}_{T}}{{\overset{˙}{m}}_{R}}
- 1$$</span></td>
<td colspan="2">&lt;</td>
<td colspan="3"><span
class="math display"><em>e</em><sup><em>U</em></sup> − 1</span></td>
<td colspan="4">(11)</td>
</tr>
<tr class="even">
<td><span
class="math display"><em>e</em><sup><em>L</em></sup> − 1</span></td>
<td>&lt;</td>
<td colspan="3"><span class="math display">$$\frac{{\overset{˙}{m}}_{T}
- {\overset{˙}{m}}_{R}}{{\overset{˙}{m}}_{R}}$$</span></td>
<td colspan="2">&lt;</td>
<td colspan="3"><span
class="math display"><em>e</em><sup><em>U</em></sup> − 1</span></td>
<td colspan="4">(12)</td>
</tr>
<tr class="odd">
<td colspan="13"><u>Expressed As A Percentage:</u></td>
<td></td>
</tr>
<tr class="even">
<td><span
class="math display">(<em>e</em><sup><em>L</em></sup>−1)´100</span></td>
<td>&lt;</td>
<td colspan="3"><span class="math display">$$\left(
\frac{{\overset{˙}{m}}_{T} - {\overset{˙}{m}}_{R}}{{\overset{˙}{m}}_{R}}
\right)´\text{100}$$</span></td>
<td colspan="2">&lt;</td>
<td colspan="3"><span
class="math display">(<em>e</em><sup><em>U</em></sup>−1)´100</span></td>
<td colspan="4">(13)</td>
</tr>
</tbody>
</table>

[]{#__RefHeading___Toc489327113 .anchor}Bibliography/Endnotes

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