Rattleback [NCA / SHAM]

posted by Astea – Russia, 2020-06-13 12:17 (1434 d 19:42 ago) – Posting: # 21535
Views: 6,555

Dear Helmut!

❝ Not only that (it’s a side-effect). What are we interested in? Extent of absoption (cleary AUC) and rate of absorption (ka and possibly tlag). ka is not easily accessible in NCA. Cmax is a composite surrogate (because influenced by AUC). Easy to show: Define any PK model and vary ƒ whilst keeping ka and tlag constant. Cmax will change… Cmax/AUC is an attempt to deal with that.

So what are the main requirements for the ideal PK metric? Not to change and have low variability? Returning to Cmax/AUC - do we need more strict conditions to prove BE on its base keeping in mind its much lower variability (narrow CI for example) - cause it's always within the limits? (In old (e.g. below 2013) russian protocols I even saw the opposite situation when the confidence limits for Cmax/AUC (as well as Cmax) were choosen to be "75,00-133,00%")

❝ Seriously, László (The Younger) asked me the same question years ago, which I could not answer. Martin helped us out. It doesn’t matter: The sum/difference of two normal distributions will be normal, the same here: It will be log-normal.

Much easily I should have read something about it somewhere on the forum. Can you please also clarify the distribution of T1/2: it is always presented as Mean±SD, is it correct? (Mean=Arithmetic Mean). It seemed to me I've read somewhere on the forum that for study planning we should use not the mean T1/2 but the confidence level for it, but I couldn't find this thread now. Is it correct to use standard approach to calculate CI for untransformed T1/2 as normally distributed data or should we use other approaches like nonparametric median confidence intervals or bootstrap?

❝ The jury is out. E.g., for biphasic methylphenidate the cut-off time (FDA: 3 h fasting, 4 h fed) is based on PD indeed (at that time ~90% of patients show the maximum effect). Makes sense.

That's correct but too much individual approach - we couldn't know beforehand all the thin nuances of the PD of the specific drug, sometimes it is even impossible to find in literature any PK data for some drugs while planning new study... So I think the overall approach should be more general.

❝ OK, some people calculated \(\small{VRT = S_2/S_0-(S_1/S_0)^2}\), the “Variance of Residence Times” or “Gravity Duration” (stop searching; out of fashion for decades). The coordinates \(\small{\{MRT\:|\:VRT\}}\) define the “Center of Gravity” of the curve.

Why not? The static moments of the plain curve 'Concentration-time' (C(t)) are:

\(M_t=\int t\cdot C(t)dt\);
\(M_C=1/2\cdot\int C(t)^2dt\)

First is connected with MRT (muliplyed by total square of the curve), and the second should reflect some ideal Concentration-unit parameter (the height of the gravity center). Why not to use it like some alternative to Cmax?

❝ Only nice to print a profile, cut it a out, push a pin through it, and make a weird whizz wheel for kids.

Oh, if we'll proceed further in the power of moments we should be able to calculate moment of inertia - then one can take that curve to the orbital station in order to research Dzhanibekov effect. ;-)

❝ There is a big problem with it. To get a reliable estimate of AUC one has to cover 95% (!) of AUC0–∞ (note that I’m not taking about BE but hard-core PK). For AUMC is should be 99%. I’m quoting Les Benet. Don’t blame me.

Could you please explain this more detaily? What do you mean in getting a reliable estimate of AUC?

"Being in minority, even a minority of one, did not make you mad"

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