## Don’t worry, be happy! [Study As­sess­ment]

Hi John,

❝ ❝ See this case (slides 16–21). A very old and ‘popular’ drug.

❝ Thanks! The scenario on page 20 is exactly what I am facing but the difference in t1/2 is much larger than yours (i.e. 2.5 hrs vs 4.5 hrs).

So? My drug had a half life of 2–3 hours acc. to the literature.
With the proper estimation I got 4.63 ± 1.07 h (test) and 5.59 ± 1.19 h (reference).
Which half lives were given in your case and which ones did you estimate?

❝ It's an IR product and the drug is very old.

Paracetamol?

❝ ❝ ❝ FYI, i just let Phoenix to pick the timepoints itself to calculate the half-life. Of course I can override and select the timepoint as well but then the R2 might not be optimal.

❝ ❝ Define “optimal”.

❝ Maybe R2 from PHX was like .99xxx and my R2 was like .7? PHX only picked timepoints along the "2nd phase" of the curve …

Makes sense.

❝ … while I picked data from more timepoints from both the 1nd phase and the 2nd phase of the elimination phase.

Don’t. Then you are doing sumfink similar to the example in my slide 17. Even if not that extreme, you must not “mix” phases. It might well be that the second phase is not relevant. That’s why Harold Boxenbaum developed the concept of effective half life.
In a nutshell: Fit a multi-compartment model with hybrid constants (not volumes of distribution, and rate constants or clearances). $$C(t)=A_1\exp(\alpha_1)+A_2\exp(\alpha_2)+\ldots+A_n\exp(\alpha_n).$$ Then $$AUC_{0-\infty}=\frac{A_1}{\alpha_1}+\frac{A_2}{\alpha_2}+\ldots+\frac{A_n}{\alpha_n}.$$ Assess the summands as fractions of $$\small{AUC_{0-\infty}}$$ and rank them in descending order. This gives you an idea, which one will be relevant in clinical practice, i.e., when you go to steady state. In many cases the slow phase(s) are not relevant at all.
In my example, the first phase accounted for 98.3% of the $$\small{AUC_{0-\infty}}$$ and the second only 1.7%.

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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