Which sampling approach describes dense sampling in a few individuals to characterize PK?

• A. Rich sampling for individuals.
• B. Sparse sampling across population.
• C. Random sampling.
• D. No sampling.

Answer: (A)

Dense sampling of a few individuals provides a detailed concentration–time profile for each person, allowing precise estimation of PK parameters and a clear view of the entire curve (absorption, distribution, and elimination phases). This approach reduces uncertainty in parameter fits because many timepoints per subject mean the model can capture the true shape of the profile, detect any nonlinearities, and characterize individual variability with high fidelity. It’s the best way to “characterize” PK for those individuals because the data are rich enough to define their pharmacokinetics from the curve itself.

In contrast, sparse sampling across a population collects few samples per subject but from many individuals, which is ideal for estimating population-level parameters with mixed-effects models, not for detailed per-subject curves. Random sampling doesn’t implement a specific PK design, and no sampling obviously cannot describe PK.