no lambda(z), no NCA? [NCA / SHAM]

posted by yjlee168 Homepage – Kaohsiung, Taiwan, 2009-07-16 13:53 (5445 d 22:09 ago) – Posting: # 3957
Views: 13,968

Dear Helmut,

Thank you for your messages.

❝ Most people would do so – although I’ve seen others who took samples after τ.


For what purpose to take blood sample after tau? Is it worth to do so?

❝ We should be cautious in using ‘terminal phase’ – especially in NCA. Personally I prefer ‘apparent elimination’ [...]



Absolutely agree.

❝ In steady state within τ you may get a value similar to SD, but with noisy data – and if more than one compartment is needed to describe [...]



So, it looks like that no lambdaz, no NCA... :-(

❝ Tricky. I would not try to play around with elimination from steady state within τ.


It is inevitable for us in Taiwan or in China to conduct a multiple-dose BE/BA study when the target product is modified release dosage form.

❝ Accumulation index is easy – and actually the most interesting metric if we compare SD with MD. I only use

\(R=\frac{AUC_{0-\tau}}{AUC_{0-\infty}}\; \begin{matrix}
\text{(steady state)}\\
\text{(single dose)}
\end{matrix}\)

The formula is only for when conducting both a SD and a MD BE/BA studies simultaneously. If we just have a MD BE study, we still need a lambdaz.

❝ Vss is a strange metric. It sounds so easy to divide [...]



Indeed. We calculate Vss just because WinNonlin does so. Not a necessary procedure in a BE study.

❝ That’s rubbish. WinNonlin uses

\(\small{R=}\frac{1}{\left| 1-\text{e}^{-\lambda_z\cdot \tau} \right|}\)

❝ which needs λz, a constant τ (what about a dose regimen of 6/6/12 or actual sampling times?), and is only valid for a one-compartmental model (the most serious drawback). [...]



Good point!
Since we're about to release next version (v2.3.3) of bear, we added data analysis for MD BE/BA since this release. I used to estimate lambdaz of MD study with the same method that we did in SD study. However, I was not very comfortable to do so for a long time.

All the best,
-- Yung-jin Lee
bear v2.9.1:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here

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