Bioequivalence and Bioavailability Forum

Hi sciguy,

❝ […] I am assessing relative bioavailability (based on the usual AUC and Cmax metrics) of two formulations in a parallel design, phase III study. This is a patient population so I am trying to decide what covariates should be looked at in the PK analysis. I am thinking age, gender, race, weight (vs. bmi?), renal function, smoking. I think these are 'popular' covariates to look at but I wonder for the purpose of submissions if there is anything else regulators would be looking for? Does anyone have any suggestions or more experience with this?

Two-stage analysis (either classical modeling or NCA; both followed by regression) is somewhat outdated. Nowadays most people use it only in screening for covariates which might be worthwhile including in a PopPK model. In my experience BMI rarely gives much information. Additionally to body weight you can try body surface area. If your drug is renally excreted to a substantial degree, creatinine (from lab. exams) is quite often a very good covariate. IMHO classical two-stage makes only sense if you run regression on the covariates in different models. Multivariate regression (likely with different weighting schemes) is a plain nightmare.

PopPK has the advantage that you can model the error of the covariate in many different ways (additive, proportional, mixed,…).