Bioequivalence and Bioavailability Forum

Hello Helmut!

❝ It is a pity that some statisticians have limited knowledge of PK.

I really understand your opinion. But I think that comments like that are unnecessary since they only spook from this discussion the good statisticians that also have PK knowledge. After all, they also could claim that “some pharmacokineticists also have limited knowledge of statistics” and I believe that this is not our intention with this ever excellent forum. What I think we need to do is to focus on Lucas’ question without any kind of judgement on which professional will be conducting the analysis. The question is only causing controversy because it goes against our personal beliefs as people who know some PK. Around here we do not make much differentiation between statisticians and pharmacokineticists because what really matters is the final result of the study and not a tiny part that would be exclusively relative to the behavior that we expect for each time point. We must not forget that each field of knowledge is oriented by a set of principles that when ignored can put into question the credibility of the whole (in this case the BE study). I think that the only reason that we envolve statistics in confirmatory assays is because we want (and need) a certain level of veracity in the results. Even though we might be well intentioned, acting by your own thinking without questioning it has never been the best way to deal with the unknown. If we only disregard one or more points of the individual profiles thinking that they’re impossible or unlikely, without the real proof of it, it might be “backfiring” since you don’t have also an assurance that all the other points are free of bias. How would we be sure of it? Critical sense? Experience? The nice shape of the profile? How would we be sure that in the same way that these points are unlikely and an discrepant Cmax would not be? We do know that exclusion of Cmax points are not well seen by regulators and that Cmax is a point like any other in the curve, also susceptible to swap of samples or mistakes in the quantification. In neither of those cases we are in favour of excluding time points because a good intention of interpolation of data may result in more bias than before.

❝ Statistics is just a tool.

I partially agree! But in the hands of those who doesn’t know how to use it ceases to be a tool to be a highly destructive weapon.

❝ Many issues could be avoided if pharmacokineticists, bioanalysts, and statisticians talk more to each other already in designing studies.

Agreed! IMHO Lucas only has raised this question because probably those three professionals are talking with each other in his company and think similarly, but ANVISA thinks different. Crystal clear rules like: “no data must be disregarded in the inference, unless there is a strong well documented reason for that” always rise the credibility of the final result. When you disconsider one or more points of the dataset, you lose information and end up changing the outcome (in this case more the AUC outcome) and consequently the whole inference. Think like this: if we have mistakes in clinical and/or analytical stages and that led to many triangulation points then it would be fair that you be penalized in terms of variability due to your lack of GCP and GLP. On the other hand, if you have triangulation points due to the kinetic of the drug that is highly variable over time, then you are not being penalized since that is the nature of the variable and it must be considered when planning the study (and the statistical analysis, sample size calculation, etc.). So, as I see, smoothing of the concentration curves by interpolation may not be the safest way to correct a value that should be already right.

Best Regards!