- On 13 Mar 2014 at 10:47:07, Wie Vraa (wievraa.at.gmail.com) sent the message

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Question on bioavailibility calculation

In a cross-over design bioavailibility can be calculated as AUCpo /

AUCiv (corrected for dose) for each subject. Then, take the (geo.)mean

of the individual bioavailibilities (method1).

Alternatively, one could take (geo.)mean-AUCpo / (geo.)mean-AUCiv

(method2). Then, there is no cross-over design needed.

In guidances (FDA: DDI, food-effect and bioequivalence) it reads that

one should report the ratio of the geometric mean AUC when comparing

dose routes, food-effect or the influence of an interacting drug (i.e.

method2).

A mathematical characteristic of geometric mean is that the geometric

mean of the ratio is the same as the ratio of two geometric means.

With other words, when using geometric means, method1 gives exactly

the same geometric mean bioavailibility as method2.

Question: Does this imply that, using geometric means, there is no

advantage of a cross-over design - because there is no difference

-when compared to a parallel design?

thanks for any comments on beforehand,

Wie - On 14 Mar 2014 at 10:48:25, Atish Salunke (atish_azad.-at-.yahoo.com) sent the message

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Dear Wie,

Since PK parameter mainly Cmax and AUC follows log normal distribution, before statistical analysis

these PK parameter are log transformed. These are then back transformed to original scale.

Exponential of the estimate of log tranformed data gives you geometric mean.

No it does not imply that, using geometric means, there is no

advantage of a cross-over design - because there is no difference

-when compared to a parallel design?

Using cross-over design is always benificial provided your study/design should allow use of

crossover.

In cross-over design subject act as his on control, sample size required is small compared to

parallel study.

Parallel study is prefered when the drug has a large half life and it is difficult to conduct a

cross-over study because duration of wash out period will very high and you may not be able to

afford the same.

Hope this helps.

Regards,

Atish - On 14 Mar 2014 at 14:28:20, Xiao Quan Zhang (XiaoZ.aaa.amphastar.com) sent the message

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FDA has a definition of long half > 24hrs. You have to use parallel study. It needs to be approved

by FDA at pre-IND meeting.

Kam - On 14 Mar 2014 at 14:30:22, Garner Consulting (garner.consulting.-at-.btconnect.com) sent the message

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Absolute bioavailability is best measured when an IV microdose is given alongside an extravascular

therapeutic dose. The microdose can be measured in plasma using various analytical technique but

14C-labelled drug and accelerator mass spectrometry bioanalysis is recommended alongside LC/MS to

measure cold drug (see Lappin G, Rowland M and Garner R C (2006) The use of isotopes in the

determination of absolute bioavailability of drugs in humans. Expert Opin Drug Metab Toxicol, 2,

419-427). This study design eliminates the variability associated with IV / oral cross-over studies.

Professor Colin Garner BPharm PhD DSc FRCPath

Exploratory Clinical Development Consultant

Garner Consulting Services

Honorary Clinical Professor

Hull York Medical School, University of York, UK - On 18 Mar 2014 at 07:12:17, Fabrice Nollevaux (fabrice.nollevaux.-a-.arlenda.com) sent the message

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Dear Wie,

The absolute bioavailability *can* indeed be estimated from a parallel design as well as from

crossover study.

<< Question: Does this imply that, using geometric means, there is no advantage of a cross-over

design - because there is no difference -when compared to a parallel design? >>

However, the absolute bioavailability will be more accurately estimated from a crossover design than

from a parallel design, since the crossover design allows to separate the inter-subject variability

from the residual (sometimes called intra-subject) variability. This is IMHO a non-negligible

advantage!

Kind regards,

Fabrice

--

Fabrice Nollevaux

Senior Pharmacometrician – Senior Statistician

Website : www.arlenda.com - On 18 Mar 2014 at 14:53:34, Angusmdmclean.-a-.aol.com sent the message

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Fabrice: if you use a cross-over Latin Square design with 12 subjects say A vs B, where B is an IV

treatment and A is an oral treatment of the same drug.

Then the mean of the ratio of the individual test Vs reference (A/B) AUC values for each of the 12

subjects is the absolute bioavailability.

Or is it the geometric mean of the ratio of individual ratios.( I think it is this).

In statistical terms I think you are saying that using the cross-over design in the same

subject,where each subject acts as his or her control , then you can estimate the inter subject

variance and the intra subject between treatment variance from the design.

It is scientifically intuitive that using the same subjects in a cross-over design will provide a

better result for the comparison. I have difficulty putting it in statistical terms. Please can

you expand a little on the statistical rationale and put it in simple language.

Angus McLean - On 20 Mar 2014 at 16:14:47, Fabrice Nollevaux (fabrice.nollevaux.aaa.arlenda.com) sent the message

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Dear Angus,

<< Then the mean of the ratio of the individual test Vs reference (A/B) AUC values for each of the

12 subjects is the absolute bioavailability. Or is it the geometric mean of the ratio of

individual ratios.( I think it is this). >>

It is the geometric mean, indeed.

<< Please can you expand a little on the statistical rationale and put it in simple language. >>

The precision on the estimates depends on the residual (i.e. unexplained) variability resulting from

your statistical model.

In a parallel design, both the intra-subject and the inter-subject components are counfounded within

the residual variability .

In a crossover design, you can identify the inter-subject component separately from the residual

variability. As the residual variability does only include the intra-subject component, it is lower

than the residual variability of a parallel design. The actual gain depends on the relative

importance of the intra- and inter-subject variability components.

Fabrice

--

Fabrice Nollevaux

Senior Pharmacometrician – Senior Statistician

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