Bioequivalence and Bioavailability Forum

Hi Samir & ElMaestro!

❝ ❝ this data is mean of 16 volunteers,

Hopefully not the arithmetic means? Geometric means would make more sense.

❝ ❝ Which method (ARS, TTT, any other) should be used? And which time points?

As ElMaestro pointed out there is subjectivity in it. There’s no ‘one-size-fits-all’ (aka automatic) method which will work in all situations. For a one-compartment model both ARS and TTT generally work reasonably well (though I would prefer the latter). TTT is not suitable for 2+ compartments (see the original paper; authors recommend as a starting point the inflection of the curve). ARS might fail as well. No automatic method can handle multiple peaks.

❝ ❝ do I need to check the plot for each volunteer or the plot of mean concentration.

The former.

❝ ❝ In the earlier posts it has been mentioned that looking at individual plots is not correct. What would be your opinion?

Which post? Mean plots are nice but unsuitable to derive any PK conclusions. Imagine an extreme situation: You have two subjects with exactly the same PK, except a large difference in lag-times. The mean plot is nonsense.

❝ I am sorry, I might have misunderstood you then. In BE you generally to go through all individual plots and derive an elimination constant. But if you are working with the overall mean plot here then you might not be doing BE but rather characterising a new chemical entity?

❝ If so, then I guess you can consider the grand mean, pop PK as well as individual plots.

❝

❝ Perhaps you can briefly explain the overall purpose of the trial?

Yes, what are you trying to achieve? In NCA you estimate λ_z individually. If you want to present mean values, hard-core pharmacokineticists would present the harmonic mean and a jacknife standard deviation. Next comes the geometric mean. Arithmetic means are the worst, IMHO.

Concerning what’s the best: If I ignore the fact that this are means I can fit a PK model. Best was a two-compartment with lag-time, weights C_pred^-2. I got an elimination of 16.80456 h.



In NCA you must not used weights anyhow. I got (almost) the same values, both if the last 3 or 4 values are fitted. 3 values are slightly better, if I assume the model’s as the ‘true’ value. Bias -0.38% (3) and -0.49% (4).

If you want to do PK modeling you essentially have two options:

• Classical 2-step: Fit subjects and calculate mean values of PK parameters. There’s a lot of literature which mean to use (harmonic means for rate constants/half lives, geometric means for volumes and clearances). This method works only if all subjects follow the same PK-model!
  
• Population PK: Preferred nowadays. You may include covariates (e.g., sex, body weight, creatinine clearance,…) in the model in order to ‘explain’ part of the variance. Needs experience + patience.