[The FDA Process for Approving Generic Drugs]{.underline}

[SLIDE 1]{.underline}

Hi. I\'m Gary Buehler, Director of FDA's Office of Generic Drugs, and
today we are going to discuss the FDA process for approving generic
drugs.

[SLIDE 2]{.underline}

Do you know that generic drugs are safe and effective alternatives to
brand name prescriptions? They can help both consumers and the
government reduce the cost of prescription drugs. Currently, about 50%
of all prescriptions dispensed are generic and they save an average of
about \$50.00 for every prescription sold.

[SLIDE 3]{.underline}

Tracing back to the beginnings of the generic drug program, the
Hatch-Waxman Amendments of the Federal Food, Drug, and Cosmetic Act were
passed in 1984. This was considered one of the most successful pieces of
legislation ever passed and, in fact, created the generic drug industry
as we know it today. It increased the availability of generics from
about 12% in 1984 to 44% in the year 2000, and close to 50% today. This
was a compromise legislation benefiting both the brand and the generic
firms.

[SLIDE 4]{.underline}

For the generic firms, it allowed them to rely on the findings of safety
and efficacy of the innovator drug after the expiration of certain
patents and exclusivities. In other words, the generic drug firms did
not have to repeat the expensive clinical and pre-clinical trials that
have to be done for a new drug application. For the innovator industry,
it allowed certain patent extensions and exclusivities that were not
available previously.

[SLIDE 5]{.underline}

Going over a comparison of the review required for a new drug
application or an NDA to the review required for an abbreviated new drug
application or ANDA, many of the points are the same. Chemistry,
Manufacturing, and Controls are rigorously reviewed by both the new drug
reviewers and the generic drug reviewers. Labeling is also reviewed and
has to be identical in most aspects. Testing is identical for the new
drug applications and the generic drug applications. The same FDA field
inspectors inspect the manufacturing facilities for generics and for the
innovator products. These facilities must be up to date with respect to
good manufacturing practices or GMPs and must have documentation that
they are able to manufacture the products for which they have
applications. The differences in the applications, as previously stated,
are the clinical studies and animal studies required for the new drug
application and the bioavailability studies that have to be conducted to
define the drug's interactions and adverse events. A surrogate for these
studies is the bioequivalence study that has to be submitted for the
ANDA. Showing in the bioequivalence study that the active ingredient is
absorbed at the same rate and extent as the reference product allows the
generic to rely on the findings of safety and efficacy of that product.

[SLIDE 6]{.underline}

The general requirements for a generic drug application or ANDA are
labeling, chemistry and microbiology, bioequivalence, and legal. We will
go into these in a little more detail.

[SLIDE 7]{.underline}

How do we assure the quality of a generic drug? As stated above, we have
five steps to the review process that are identical to the NDA process.
Bioequivalence for complicated products is discussed with the same staff
who have reviewed the brand name product.

FDA has extensive experience with the innovator drug product. Many times
this product has been on the market for many years. So in tapping from
the experience from the new drug review, we have experience in reviewing
adverse events and clinical experiences with the drug product. There is
also a great amount of scientific literature on the drug product that we
are able to access. Lastly, the product is known to be safe. Again, with
its years of marketing history, there is no question about the safety of
the drug product.

[SLIDE 8]{.underline}

This is a schematic of the generic drug review process from receipt of
application until its final approval. The applicant submits the
application to us and it goes through an initial filing review to make
sure the application has all the requisite pieces to be reviewed. With
respect to any patents or exclusivities, this group also assures that
all initial legal requirements have been addressed. From the filing
review, it is sent to chemistry and microbiology if necessary for
sterile products. It is sent for a labeling review and to bioequivalence
for a review of the bioequivalence study. We also request certain plant
inspections and manufacturing inspections to make sure that the
manufacturing sites and all other ancillary sites are in compliance with
good manufacturing practices. Each of these disciplines completes their
review and once everyone has resolved any deficiencies identified in the
application, the product can be approved.

[SLIDE 9]{.underline}

What are the requirements for a generic drug? A generic drug has to have
the same active ingredient(s), the same route of administration, the
same dosage form (tablet, capsule or injectable), and the same strength,
and the same conditions of use when compared to its reference listed
drug or corresponding brand name product.

[SLIDE 10]{.underline}

The labeling has to be the \"same\" as the brand name labeling. The
generic applicant may delete portions of the labeling protected by
patent or exclusivity. The labeling may differ in certain excipients,
pharmacokinetic data, and how supplied section. The generic does not
have to have the same bottle sizes as the innovator product and the
generic can also differ in product-specific characteristics.

[SLIDE 11]{.underline}

Chemistry is an important aspect of the generic product review. The
chemist looks at the components, composition of the generic product, to
make sure that the formulation is stable and in compliance with our
regulations and standards. The chemist looks at all the manufacturing
and controls of the product, carefully reviews the batch formulation and
records, and the description of the facilities to make sure they are in
compliance with good manufacturing practices. The chemist carefully
looks at the specifications and tests to make sure that the impurities
are within our certain limits and that the tests are appropriate for the
particular product. Packaging is reviewed to make sure it assures the
stability of the product and the stability is assessed to assure that
the shelf life of the product is appropriate.

[SLIDE 12]{.underline}

We have manufacturing and compliance programs to assure the quality of
the marketed drug products. We have routine surveillance of marketed
drug products to make sure they are, in fact, what they say they are. We
have routine inspections of all manufacturing sites to make sure that
they are in compliance with current good manufacturing practices. The
pre-approval manufacturing and testing plant inspections also assure
that the actual manufacturing site is capable of manufacturing a
particular product.

[SLIDE 13]{.underline}

Approved Drug Products with Therapeutic Equivalence Evaluations is the
official name of what we affectionately call the Orange Book.

[SLIDE 14]{.underline}

The Orange Book lists all FDA approved drug products, that is, NDAs,
over-the-counter products, and abbreviated new drug applications or
generic products. The Orange Book also contains therapeutic equivalence
codes. Any product that has an A prefix is considered a substitutable
product by FDA. Any product that has a B prefix has not been proven to
be equivalent and, therefore, is not substitutable, although it is a
safe and effective product for use. The Orange Book also has the
expiration dates of certain patents and exclusivities and it denotes the
reference listed drug for each particular brand name product. The
generic companies usually use this information to determine what
reference product they have to do their bioequivalence tests on.

And now I would like you to hear from my colleague who will discuss the
bioequivalence requirements for generic drug products. Thank you.

[SLIDE 15]{.underline}

Hi. I\'m Dale Conner, Director of the Division of Bioequivalence, Office
of Generic Drugs, and I am going to discuss bioequivalence and how we
apply that science to assure therapeutic equivalence of generic drugs.
Bioequivalence on its face seems like a very simple concept where you
compare two formulations containing the exact same amount of an active
drug in the same dosage-form; you simply determine whether that
formulation supplies that drug to the body in an equivalent manner. As
you will see, how we go about doing that and assuring that equivalence
is often misunderstood. It is not only confusing to laymen and consumers
but to medical professionals as well. Even people within the FDA
sometimes get confused about this.

I\'m going to start out by giving a simple definition of bioequivalence.
There are, perhaps, many definitions and this one, I believe, is adapted
from the regulations: \"Pharmaceutical equivalents whose rate and extent
of absorption are not statistically different when administered to
patients or subjects at the same molar dose under different similar
experimental conditions.\" This encompasses a lot of explanation in
that, under similar conditions, when we give these two pharmaceutically
equivalent products to the same individual patient, the patient should
absorb the same amount of drug, or very close to the same amount of
drug, and absorb it at the same rate.

[SLIDE 16]{.underline}

What is the purpose of bioequivalence? From the Generic Drugs
perspective, in the end, we would like a generic drug that is
substitutable for a reference or brand name drug. This substitution
could be implemented without any additional prescriber intervention.
That means that a patient could walk into their pharmacy one day, having
received and been very well controlled on the brand name drug, or
perhaps even another generic drug, and the pharmacist could substitute
an A-rated FDA approved generic drug to that patient for their next
month or so of drug therapy, and we would effectively see no objective
difference in the therapeutic outcome for that patient. There would be
both no change in efficacy and no greater incidence of side effects. So
we look at both the efficacy side and the side effect side.

In effect what we are aiming for by doing bioequivalence is to assure
therapeutic equivalence of these products. Bioequivalent products and
therapeutically equivalent products can be substituted for each other
without any adjustment in dose or other additional therapeutic
monitoring other than what you would ordinarily do for that patient on
that particular medication. The most efficient method of assuring
therapeutic equivalence in the end is to assure that the formulations
perform in an equivalent manner.

This brings up one of the important points that are often misunderstood
about bioequivalence. Bioequivalence is really a test of in-vivo
formulation performance. What we are interested in is that two
manufacturers or sometimes the same manufacturer has made two separate
formulations containing exactly the same amount of exactly the same
active drug and they want to test or be sure that those products perform
in the in-vivo situation in a close to identical manner.

[SLIDE 17]{.underline}

This slide is a simplified schematic of what happens when one gives an
oral dosage form. I\'m going to spend a little bit of time on this
because this lays out the areas of concern or what happens when you give
an oral product. It also serves to explain why we do certain things to
assess sameness or bioequivalence and why certain other options at our
disposal are not the best choice.

If we go from left to the right of this slide, we see that we start with
a dosage form. It could be a tablet or a capsule. If it\'s an oral
product, we give that tablet to a patient, they swallow it, and the
drug, which is in the solid form in the GI track, goes into solution.
That is a critical step. How that solid form falls apart, releases its
drug, and goes into solution is a critical step as far as formulation
performance.

In effect, as you follow this schematic further on, it\'s the only step
in which we as regulators and the pharmaceutical industry as
manufacturers really have any control. The rest of the steps in the
process are patient-related. In other words, the drug is absorbed and
the natural disposition of the patient or the group of patients controls
how the drug is distributed and how eventually it gets to the site of
action. The thing that we really have control over and the thing that we
as a regulatory agency want to assure is that the first step is
equivalent between these two products so that the two tablets transition
from the solid to the solution state and become available for absorption
in the same manner. That\'s why bioequivalence as we use it is a test of
formulation performance. The formulation performance we are talking
about is in that very first step in the transition from a solid dosage
form to a drug in solution, which is then available in the GI track for
absorption.

As we follow this through, that\'s what we\'re really concentrating on;
making sure that two products by two manufacturers or even two products
by the same manufacturer do that step in an equivalent manner.

As we see, the drug gets into solution and it\'s then available for
absorption through the gut wall and absorbed into the blood. This is
over simplified because there are a number of steps in between. From the
gut wall to the blood, there is often passage through the liver and
I\'ve simplified that here. However, it appears in the blood directly
after absorption. Then the blood carries it to the site of activity and
subsequently the drug does what it is designed to do at the site of
activity and we eventually see a therapeutic effect or some other
pharmacologic effect as well, even an adverse event or a side effect. We
would also see that as well after the drug got to its site or sites of
activity being carried there by the blood. Thus we have described in a
very simple way what happens after we give a solid dosage form to the
point where we would actually see an effect.

The question is what would be the best way of assuring that these two
products perform in the same way in vivo? I can\'t simply give this
solid dosage form to people and look down in the GI track and see how
it\'s dissolving or becoming available. That is really not exactly a
very practical way to approach things. I want to go down the schematic
and say what\'s the most sensitive point that I can actually measure
drug leaving the dosage form and entering into the body and then
subsequently arriving at the site of activity and causing a
pharmacologic effect.

The first step where I can reasonably do that is the blood. The blood
acts as a carrier, an intermediate between the drug being absorbed and
the eventual effect. The blood, as we all know from clinical practice,
can be easily sampled. It is used for a variety of different types of
clinical monitoring and quite a few blood samples can be taken over time
if you really need to. The drug appearance in blood and the
pharmacokinetics of that drug in blood has some very favorable
characteristics for answering the questions we\'re posing about the
dosage form performance.

Often when I speak to clinicians, the question comes up that it\'s all
very nice to measure things in the blood, but what I\'m really
interested in is the eventual therapeutic effect. Why don\'t you just
measure that directly? There are certain types of products where we have
to do that because the blood is either not relevant or the drug doesn\'t
appear in any measurable quantity in the blood or for other reasons. So
we have to use either pharmacodynamic measures or clinical measures,
which are what was used to assess the drug efficacy originally anyway.
When you look at the characteristic of those measurements, there are
some problems. First, the appearance in the blood is very close to the
event that we\'re looking at. There are not too many steps in between
that add variability, because each step that you pass through increases
the cumulative variability. For blood concentrations, most of the
relationships between blood or plasma concentrations and dose are
linear. If the dose increases or decreases a little, we see a linear
increase or decrease in the plasma or blood concentrations. Even when
it\'s non-linear elimination pharmacokinetics, in effect it actually
becomes overly sensitive to telling the differences between products. In
that situation, a small change in delivered dose results in
disproportionally large increase in plasma concentrations. So the
measurement of drug appearing in blood allows us to fairly accurately
tell what the relative bioavailability of the dosage form is between two
or more formulations.

If we consider clinical effects, the pattern of a clinical response, if
we remember our pharmacology, is usually an S-shaped or sigmoidal dose
response curve. What we\'re really looking for when we look at
differences in bioequivalence is if two pharmaceutically equivalent
products, containing the same amount of a drug, effectively deliver a
different dose. What we\'re looking for ideally for a bioequivalent
product is that the product should deliver the same dose at the same
rate to the body. So putting it on a dose-response curve is valid. We
see that the response that we\'re getting in a therapeutic response is
not linear related to a change in dose. It\'s more S-shaped.

[SLIDE 18]{.underline}

If we go to the next slide and see that relationship blown up, in effect
you will see two different situations. What should be evident from the
slide is that the dose that you actually pick to do your study and to
study these two products is extremely critical if you\'re using this
type of relationship to infer differences in dosage form performance,
differences in release from the dosage form, and availability to the
body.

We see at the top of the plateau where we\'re getting maximal effect,
where we simply can give a lot more of the drug and we\'ve maxed out the
pharmacologic effect that we\'re going to get. You can have a very large
difference between products. On this scale, since it\'s a log scale, you
might see a ten times or one hundred times difference and see absolutely
no difference in the therapeutic response. Obviously, if you went up to
a big enough difference in dose, other effects perhaps unrelated to this
might come into play, such as toxic effects. But in this effect that
we\'re measuring, we see that we can have a very, very large difference
in the performance of those products and see absolutely no difference in
our clinical response.

If, however, we were to study it at a lower dose that was in this
increasing part of the dose response curve, we would see a very nice
difference in response with even a fairly small relative difference in
dose between these two products. If we\'re going to do this, one of the
problems is we have to have some idea of the dose response relationship.
We have to do our study at a dose that is on that rising portion of the
curve if we hope to be able to have that test show any existing
difference. So that\'s critical and often we really don\'t know that. We
don\'t have a good idea of the correct and most sensitive dose range so
it\'s very hard when we have to do this type of study to actually pick
the dose that will give us sensitivity and actually tell us if there is
a difference between the products. That\'s really critical and for that
reason this doesn\'t have properties that are as easy to deal with as
blood.

As you know from looking at clinical responses, either in patient
treatment or in studies, the variability of these measurements is quite
high for most clinical responses. In addition to the problem that I
mentioned, that yields studies that are very large and often it\'s very
easy to misjudge the power and not put enough patients in your trial.
Therefore, you end up doing a large trial without any definitive results
saying that the product is or is not bioequivalent. So this type of
study, although it appears to be exactly what you want to see, is
fraught with problems and actually ends up being fairly insensitive if
not done perfectly.

[SLIDE 19]{.underline}

Again, this is a repeat of the straight plasma concentration curve. No
matter what dose you pick, you still get the same relative response. A
doubling of the dose would give a comparable increase in your outcome,
which is plasma concentration, if you study it at a lower or a higher
dose. That\'s a very nice property of plasma in addition to the fact
that it has lower variability and needs fewer subjects to get at the
same answer.

[SLIDE 20]{.underline}

The Regulations tell us that we have a number of options or approaches
to determining bioequivalence and this regulation is cited on this slide
and that is from 21 CFR 320.24. It gives us a list of approaches to
determine bioequivalence in order of preference.

The first is an in vivo measure of active moiety or moieties in the
biological fluid. That is at the top of the list for the reasons I just
cited.

Other choices when that is not available or feasible are in-vivo
pharmacodynamic comparisons. These are pharmacologic effects that
hopefully can be quantitated and studied in a controlled environment.
They don\'t necessarily have to be done in patients but we look at an
actual pharmacologic endpoint that can be quantitated. Sometimes there
is no appropriate pharmacodynamic response available for that either.

Then we have to go to in vivo limited clinical comparisons. We use the
same type of endpoints that were originally used to approve the product
to begin with to prove safety or efficacy. We adapt that type of
approach to do a comparative study. Obviously the study size, given the
variability of clinical responses and the fact that often with the new
drug products to begin with in order to show efficacy against a placebo
you needed several thousand patients. Now we\'re trying to show
differences or sameness between two products designed to perform in the
same manner so that the need for patient numbers may go up considerably
from the original trial.

If we can\'t do that, we have the option of doing valid in vitro
comparisons. On this slide a couple of examples of each one are listed.
Under in vitro comparisons, for example, we\'ve done in vitro
comparisons on cholestyramine, which is non-absorbable resin that binds
bile salts in the GI track, to look at the ability of these different
products to bind in an equivalent manner. In the laboratory in a very
controlled manner investigators look at the ability of the two products
to bind bile salts. They have to calculate all of the binding curve
characteristics and those characteristics are compared in an equivalence
fashion to make sure that the binding characteristics of these products
are the same. That\'s an example of an in vitro comparison where we
don\'t really study the bioequivalence in vivo because it would be
extremely difficult to do that or at least it would be a trial that
would probably involve the study of many, many thousands of patients;
whereas, this gets at the function of these two products very
efficiently.

The Regulations allow us to be creative if none of the above work and
find other approaches that are appropriate and scientifically valid to
show equivalence.

[SLIDE 21]{.underline}

The usual study designs that we do for bioequivalence are single-dose,
two-way crossover fasted or fed studies. This means that all of the test
subjects receive both products. It is done in a crossover manner and
products are given once to each subject on different occasions and then
the bioequivalence and pharmacokinetic data and parameters are compared
between the two treatments within each subject. Subjects serve as their
own control.

We have alternatives to those studies when they are either not practical
or not indicated.

Also, what has become another option is, instead of a regular two-way
crossover, to give each subject the same product more than once. That\'s
what we call a replicate design. It has some nice properties, although
it means that each subject has to be in a four-arm study rather than a
two-arm study. The overall number of subjects is less, but the number of
times each subject has to come in is more. The advantage of that is it
gives us an idea of within-subject variability whereas the two-way
crossover simply gives you an idea of the between-subject variability as
far as the comparison of these two products.

Also in certain cases we have to do multiple-dose two-way crossovers.
This is often when we have to use patients in our trials if the drug is
too toxic to be given to normal volunteers. Often the patients can\'t
simply be studied in a single dose. They need the medication so we have
to fit our study design into their normal treatment schedule.

Finally, clinical endpoint studies are often done with topicals or
locally acting products. As I discussed before, we have no choice but to
look at the normal clinical endpoints and do studies in patients because
blood concentrations are not valid for those types of products.

[SLIDE 22]{.underline}

The Regulations allow us to not have to study every single strength of a
product. For example, there are cases where we do a bioequivalence
determination with full studies on a higher strength. The lower
strengths of the test and reference products are proportional to their
matching higher strengths and we do some in vitro testing to assure us
that this is true. There is no reason to repeat the bioequivalence in
vivo studies on every single strength. I think that\'s fairly well
accepted in the scientific community as an efficient way to perform
these studies. Often we grant waivers of those bioequivalence studies
for lower strengths and the criteria are listed in our regulations at 21
CFR 320.22.

For other types of products, such as topical solutions for the skin that
don\'t have the transition from a solid state to a liquid state and are
already in solution, we consider the bioequivalence to be self-evident
since they really don\'t have anything in their formulation that could
alter the bioavailability.

[SLIDE 23]{.underline}

The two parameters that we look at to determine bioequivalence that are
derived from plasma concentrations are area under the plasma
concentration time curve, or AUC, and maximum plasma concentration
that\'s achieved after a dose, or Cmax. These are very simple
pharmacokinetic parameters. You don\'t have to be an expert in
pharmacokinetics to be able to figure them out. They are quite simple.
They are not really overly dependent on any model or assumption. The AUC
relates to the extent or how much drug is absorbed from a dosage form.
Cmax is related to the rate. With a difference in rate, the maximum
concentration or peak goes up or down so that it\'s sensitive to the
rate of drug input from the formulation. You could conceivably have two
formulations that would deliver the exact same amount of drug to the
body but do it at very different rates. One could be fairly rapid with a
high peak and one could take quite a bit longer with a lower peak. Those
would not be considered equivalent if they were considerably different
from each other.

[SLIDE 24]{.underline}

We assess these parameters by using 90% confidence intervals and these
must fit between 80% and 125%. It\'s a misconception by most people,
consumers and physicians alike, that we allow the mean of the data that
we get from our studies to be as much as 80% below or 125% above.
That\'s really not true. Because we\'re using these confidence
intervals, the mean of the data never really gets close to these bounds.
That\'s a very common misconception.

When we talk about the 80% and 125%, we\'re talking about confidence
intervals and for those of us who are not statisticians, which is
probably most of this audience, it is very difficult to figure out what
we mean by confidence intervals. In effect, it\'s an expression of
variability about the mean from a study so you might calculate a
standard deviation or a coefficient of variation. If you know the number
of patients or subjects in your study and you know that variability, the
confidence interval calculations, whether they are 90% or 95%, are
derived directly from those. It\'s yet another expression of variability
of my data and how confident I am that I can extrapolate that small
sample to determine what the true mean is in my entire population, which
would be in all patients in this respect.

The confidence interval calculation on these relatively small studies
yields a set of numbers with a width of the confidence interval. You
have an upper bound and a lower bound for the confidence intervals and
the mean sits in the center of those bounds. Therefore, the edge or
either side of those confidence intervals is the area that can\'t exceed
the 80% or 125%. This means that when the edge of the confidence
interval reaches there and gets to one of these bounds, the mean of the
data is still well inside the bounds.

You may have read in the medical literature that there can be as much as
a

46% difference in generic products and the question is, can there be a
46% difference? When you really know how this works, it\'s actually kind
of a ridiculous contention because the mean of the data never really
gets close to that. When we have more or less failing studies, the mean
of the data from our studies still isn\'t anywhere close to that. So 46%
difference in the mean is just impossible.

[SLIDE 25]{.underline}

When we refer to a point estimate, we\'re really referring to the mean
of the data from our study. The data is expressed as a relationship
between the two products. When we talk about confidence intervals,
we\'re talking about confidence intervals or means of the data expressed
as T/R ratio, T being the data from the test and R being the data from
the reference. The reference is usually the reference listed drug or
brand name product. The test is usually our potential generic product.

For example, a perfect relationship would be 1 or 100%. T/R would be
exactly the same for whatever parameters we\'re looking at, AUC or Cmax.
As T has greater bioavailability than R, we get up into the 100s and if
the potential generic test product is less than the reference in its
bioavailability parameter, it would be below 100%. Unfortunately, we
have to use a little bit of statistics to explain this.

Using the bioequivalence criteria that I mentioned, there should be a
few questions that come to everyone\'s mind when you first look at this.
The first is an understanding that the mean isn\'t allowed to be as low
as 80% or as high as 125%, but how do we come up with the 80% to 125%?

It seems a bit lopsided in that when you first look at it, we allow the
90% confidence interval of the test versus reference to be 25% above, in
other words, the test is greater than the reference but on the other
side we don\'t allow the 90% confidence interval to be less than 20%
below (test is less than reference). What is the logic in that? It is
based on actual statistical calculations that were done to this
equivalence comparison. Most of the statistics that we\'ve learned in
courses and perhaps apply even to clinical studies are really attempting
to show the difference between two or more things.

When one does a classical statistical test like a T test, you really
come up with a conclusion that the two things are different or
conversely, if you\'re unable to show that they\'re different, that
you\'re unable to show that they\'re different. When you\'re unable to
show that they\'re different, it doesn\'t mean that they\'re the same.
It isn\'t a proof of sameness. It\'s simply that you failed to show
they\'re different.

What we want, however, for bioequivalence is a statistically valid
conclusion that the two things are the same within an acceptable range.
The standard statistical approaches that we all know are not appropriate
to get that conclusion. So some clever statisticians at the FDA came up
with an adaption of the usual statistics to allow us to draw a
conclusion of equivalence, not just an absence of difference.

The way they did this was called the one-sided test procedure. The
approach simply is that two one-sided tests, very similar to T tests,
are performed on this data. The first of the tests says that the test or
T is not significantly less than the reference. The second test, that
the reference is not significantly less than the test. So clinically, if
I were to have a patient who goes into the pharmacy and is already on
the brand name and the generic is substituted, I don\'t want the generic
to be significantly less than what the patient is already on.
Conversely, if the patient is on the generic and goes into the pharmacy
and the reference is substituted for that product, I don\'t want that
reference that the patient gets to be any less than what the patient is
already on.

Those are the two tests that are done. Based on a lot of clinical
experience and clinical input, the significant difference for
statistical purposes was stated at 20%. That does not mean that the mean
is allowed to be 20%. That\'s just the clinically significant difference
that one sets for statistics. That\'s done statistically at an alpha
level of 0.05 significance level for each of these tests. That\'s
important, as I\'ll explain.

To express this mathematically, the T/R ratio of the first test, the
maximum being 80/100, would be 80%. The second one, the R/T, also would
be 80%. However, for a matter of convention, we express both of those in
a comparable manner as T/R. We always speak of the test over reference.
So R/T has to be converted to T/R by inverting it and it ends up having
100/80 or 125%.

It\'s merely that you\'d have to take the reciprocal of the second one
to end up with this somewhat odd-looking number, 125%. What this really
translates into is that the test can\'t be 20% less than the reference
statistically and the reference can\'t be 20% less than the test. Those
are our two tests. That\'s why we often refer to it as 20% difference in
either direction yet the confidence interval bounds don\'t seem to look
like that.

[SLIDE 26]{.underline}

Here you see a picture of possible bioequivalence results. This is just
an illustration of the types of results and it\'s meant to illustrate a
couple of things. I have displayed these with bars and you\'ll see a bar
representing the width of the 90% confidence interval that we calculate.
You have to remember that it\'s not an evenly distributed set of data.
It\'s actually a bell-shaped curve where most of the data is in the
middle around the mean and the incidence of data at the end tapers off.
I\'ve displayed it by bars because it\'s easier to look at.

The first one is hopefully a typical test versus reference comparison
that we do on a generic product. As you\'ll see, the dark line in the
center is the mean or point estimate of the data. The bars on either
side are the width of the 90% confidence intervals. The reason we use
90% confidence intervals is because each of those tests is tested at the
alpha = 0.05 level. So you have 0.05 or 5% on one side and 0.05 and 5%
on the other side which represents the other side of the test.
Therefore, what you\'re left with in the middle is 90%.

This is very nice. It\'s normal variability of the type of test that we
see. The mean is depicted to be pretty much perfect. It\'s right on the
ratio of 1 and the confidence intervals fit well within the 80 to 125%
bound so this would be an acceptable product as far as a generic drug
product. It passes and the mean or the center of the data is centered
exactly where it should be.

However, if one has a slightly less variability, in other words, we have
more certainty of the response or relationship, the confidence interval
limit bounds allow it to be slightly off-center. So the second one again
shows a passing study; however, the point estimate or mean of the data
from the study is, in this case, less than 1. So it may be 90% or 92%,
something like that. Yet, because of the low variability, the confidence
interval bounds still fit within our acceptance criteria. The lower the
variability, the more the system allows the mean of the data to be
off-center, although this is not an effect that will allow anything to
pass as you\'ll see.

The third one is kind of an extreme example. We would never see it
because no one would submit it to us. This is a study where, even though
the mean or the point estimate or center of the data is perfect, right
on a ratio of 1, the variability and confidence intervals of this
product, and it could be variability of the product or inherent
variability of the drug itself in the pharmacokinetics, are so wide that
it fails on both sides. It\'s simply unacceptable even though the center
of the data is exactly where it should be. One can see if you have high
variability or a lot of uncertainty in this relationship, it would fail
as well and would be an unacceptable product.

The next one shows a product where it\'s kind of a close call, but it
does fail. This study would not support the approval of a generic drug
product. Even though the mean or point estimate is well within bounds,
the upper bound of the 90% confidence interval goes over our goal post
or range and is probably 126% or so. Therefore, this is a failure of a
study. If this were the only study we had to look at, we would not
approve this as a generic product.

One of the things that affects this confidence interval is the number of
subjects you use in your study. If a firm had this type of result and
this was a true depiction of how their drug product performed, they
could probably go back and do a lot larger study and maybe this might
pass the next time around. It\'s not a product that\'s so different from
the reference that it would be guaranteed to fail every time. Sometimes
doing a better or more appropriately powered study might have a chance
of passing. Then again, it might not.

The next one is even worse in that even the point estimate or mean is
over the edge. It is possible but very unlikely that you could do
anything to save this product. Doing another study wouldn\'t be helpful
at all. If the mean is over, chances are that just increasing the power
probably wouldn\'t help you.

The last one depicts a product that is totally outside of the confidence
interval bounds. It\'s very, very different. In no respect would this be
ever considered equivalent. In fact, there are some people out there who
informally call a result like this inequivalent, proof that these
products are definitely not equivalent and never will be. A company that
gets this should just go back and totally redo the product because there
is absolutely no way that this product would ever be considered
equivalent or same or therapeutically equivalent.

These tests are actually quite strict and control most drug products
quite well. There\'s a lot of belief out there that\'s not correct, that
this is some kind of liberal system that allows any types of products to
get through and that nothing ever fails. This is something that is not
true. This is actually quite a stringent test of products. Perhaps it
could be argued that it might be too strict for a lot of products.

[SLIDE 27]{.underline}

One of the things that people continually have great concern over is
what\'s termed now as Narrow Therapeutic Index Drugs (NTIs). These are
fortunately a relatively small number of drugs where the dose or
therapeutic concentration of drug that must be given to obtain the
therapeutic effect is very, very close to the dose that gives serious
toxic effects. There\'s not really much room for error on these. That\'s
why they\'re carefully monitored using both clinical and plasma
concentration monitoring.

There is a lot of concern with generics of these products. The concern
is that, if the generic product gives a clinically significant higher
dose than the reference product, we will see toxicity that we wouldn\'t
see with the brand name product, even at the exact same dose. Some
examples of these products are digoxin, lithium, phenytoin, and warfarin
of which all have generics.

There was considerable controversy surrounding these products. A lot of
it was from the innovator companies but there were also many concerned
clinicians who were honestly concerned about the welfare of their
patients and whether we were doing the right thing in assessing NTI
drugs correctly. We haven\'t found it necessary to alter the
bioequivalence limits simply because they\'re quite strict to begin
with. We are constantly assessing the adequacy of these limits for these
products and other products and so far, at least for the NTI products
we\'ve looked at, we don\'t believe based on our scientific assessment
that there\'s any need to change or tighten up these limits. However, we
are constantly looking at those things and assessing data as it comes in
and we will act accordingly should the overall data actually support the
need for decreasing those confidence interval goalposts for a given NTI
product.

[SLIDE 28\
]{.underline}

This concludes our overview of the generics drug approval process. Thank
you.