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Dear Colleagues:
I am wondering if someone has any success in recent times in design,
conduct and FDA blessing of a parallel study design used in
establishing Bioequalence study for generic product. I am talking
about a product with lot of variability, and a long half life so I am
considering a parallel study but looking for experienced persons
guiding me about the potential for success. I am willing to take the
risk but I don't want be the first guy experimenting this design (GRIN)
Thanks a lot for your help.
Prasad
Prasad NV Tata, MS, Ph.D., FCP
Manager-Pharmacokinetics
Mallinckrodt, Inc.
675 McDonnell Blvd.
Saint Louis, MO 63134
e-mail: prasad.tata.aaa.covidien.com
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The following message was posted to: PharmPK
Hi Prasad,
You are not the first one to conduct a parallel design for the FDA
(I've seen many), but if it's a high variability drug you might want
to reconsider even if the half life is long. Parallel designs are not
good for highly variable drugs. You are dealing with both inter- and
intra-subject components of variability. I've seen sponsors rush into
parallel designs because of time constraints, and more often then not
they wasted their time and money because the study failed. If your
washout is on the order of months rather than weeks and you really
can't wait, then try aiming for higher power then you think you need,
and hold your breath. Well, not for the whole washout, that would be
bad.
Best,
-Dave
--
David Dubins B.Eng, Ph.D.
Assistant Professor
Leslie Dan Faculty of Pharmacy, University of Toronto
Global Bioequivalence Consulting
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Dave,
The increased power of a cross-over design comes from the relative
size of the within subject variability (WSV) compared to the between
subject variability (BSV). If BSV is small then the crossover design
will be superior. But for 'highly variable' drugs the variability is
likely to be due to relatively large WSV e.g. day to day variability
in rate or extent of absorption. Therefore in exactly the situation
where one wants the crossover design to be helpful it is most likely
not to deliver the goods. This discussion would be informed by
realistic, observation based estimates of BSV and WSV. Anecdotes about
'failed' or 'successful' studies contribute little to rational debate.
Nick
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New
Zealand
n.holford.-at-.auckland.ac.nz
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
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Hi Prasad
I cannot comment much since I do not know what washout period is going
to be like and whether there is more of "intra" or "inter" subject
variability...
In the past, I have been associated with a company that has conducted
a 2 period crossover study that had 4 days of in-house stay in each
period, 4-5 visits post discharge for each period and washout of at
least 3 weeks. We had problems in study because few persons lost
patience and took prohibited foodstuff/items, few lost interest, and
few had genuine problems due to family reasons. Hence it was better to
have a larger sample size/"buffers" and we still went in for crossover
design.
I'd still say a crossover design is better. However you may want to
choose higher number of subjects due to the variability factor and
most importantly, since the timeframe of total-duration of study is
higher. Budget and planning need to be strong!!!
Regards Dr. Gagandeep Singh
Sr. Consultant, Cognizant - Solutions and Consulting Group
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The following message was posted to: PharmPK
Dear Colleagues,
This thread is reminding me of two questions I would like the forum's
thoughts on.
One is to what extent could we identify reasons for the high
variability and consider their clinical relevance separately and even
correct the outcome. For example if one tablet is absorbed faster than
another then Cmax will be higher and so will AUC overall probably. If
we remove that difference what is left and can we break it down further?
At one end of the cale removing it is logical because the rate of
absorption, even within indivdual patients, could possibly differ just
as much in clinical use anyway, making the tablet difference a minor
factor. On the other hand the classic example is of course digoxin
when it was first micronised and caused Cmaxs at toxic levels, but
that was such a large and consistent effect that it was quite logical
to think of it as the main factor. That points to a range of
relevance for the factor "rate of absorption".(Presumably being
interpreted in relation to therapeutic ratio or range)
This thought leads to my second question about the relevance of the
two designs. The larger intersubject variation becomes the more
equivalence in a parallel study just means that you have similar
variance overall... so to what extent has that happened by chance? In
crossovers you remove most of the other variables, such as
intersubject absorption rates or volumes of distribution, so the
remaining differences are more likely to be due to the formulation.
Since that is the question you are actually asking I believe it is the
more specific and accurate design.
To cope with the increased chance factor shouldn't parallel designs be
done at higher levels of statistical power? If so, wouldn't the
increased group sizes defeat the object of saving money and time? It
is easier to find the first 10 volunteers than the second, ... third
and ...fourth
Andrew Sutton
Consultant in Clinical Pharmacology
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The following message was posted to: PharmPK
Nick,
Actually, upon re-reading our posts, I can see that we are both wrong.
A parallel study properly powered (to, say, 80%) should by definition
yield the same Type II error as a crossover powered to 80%, un-
anecdotally. Which weighs more, a tonne of feathers, or a tonne of
bricks? :) My industry hat was on when I answered Prasad's question,
not my academic hat.
Best,
-Dave
--
David Dubins B.Eng, Ph.D.
Assistant Professor
Leslie Dan Faculty of Pharmacy, University of Toronto
Global Bioequivalence Consulting
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The following message was posted to: PharmPK
Prasad,
I think it's important to do as much as possible to understand what's
causing the variability. It has to be due to a finite, and probably
small,
number of mechanistic factors, perhaps some combination of regionally
dependent dissolution, permeability, and first pass extraction.
I know that you know that gastrointestinal absorption is a complicated
process for some drugs, so my comments below are for the general
audience
here.
First - a matter of terminology. Some responses to this thread refer to
"absorption" and "absorption rate" in ways that I (perhaps incorrectly)
infer as implying that absorption rate is a constant for a subject and
varies across subjects, as well as that "absorption" may be being used
in
the sense of bioavailability as opposed to the amount of drug entering
the
enterocytes prior to any first pass extraction (the modern FDA
definition).
Let's be careful with this terminology - absorption simply means
getting out
of the lumen and into the enterocytes. What happens after that can
result in
bioavailability being essentially equal to absorption, or quite a bit
less.
Absorption rate is a time-dependent variable, typically much higher
immediately after dosing and dropping significantly as the remaining
drug
transits the intestinal tract for immediate release dosage forms.
In our experience, high variability in Cmax and AUC are usually due to
differences in (true) absorption, differences in first pass extraction,
differences in distribution, and/or differences in systemic PK.
I would spend a little time and money to ensure that sufficient in vitro
data have been obtained with respect to such phenomena prior to
attempting
the human BE studies. Contributing factors that can be identified and
quantified can then be used in simulation studies. Both sensitivity
analyses
and virtual trials (simulated populations) can then be run to assess the
likely results of BE studies with different numbers of subjects.
The time and cost to do these studies is less than even a small human
trial.
They're not perfect, but we believe they do provide valuable information
regarding how formulation and physiology interact to produce
variability.
We've seen failed studies that could have been avoided this way.
Best regards,
Walt Woltosz
Chairman & CEO
Simulations Plus, Inc. (NASDAQ: SLP)
42505 10th Street West
Lancaster, CA 93534-7059
U.S.A.
http://www.simulations-plus.com
E-mail: walt.aaa.simulations-plus.com
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