A Method for Evaluating Robustness of Limited Sampling Strategies-Exemplified by Serum Iohexol Clearance for Determination of Measured Glomerular Filtration Rate

In combination with Bayesian estimates based on a population pharmacokinetic model, limited sampling strategies (LSS) may reduce the number of samples required for individual pharmacokinetic parameter estimations. Such strategies reduce the burden when assessing the area under the concentration versus time curves (AUC) in therapeutic drug monitoring. However, it is not uncommon for the actual sample time to deviate from the optimal one. In this work, we evaluate the robustness of parameter estimations to such deviations in an LSS. A previously developed 4-point LSS for estimation of serum iohexol clearance (i.e., dose/AUC) was used to exemplify the effect of sample time deviations. Two parallel strategies were used: (a) shifting the exact sampling time by an empirical amount of time for each of the four individual sample points, and (b) introducing a random error across all sample points. The investigated iohexol LSS appeared robust to deviations from optimal sample times, both across individual and multiple sample points. The proportion of individuals with a relative error greater than 15% (P15) was 5.3% in the reference run with optimally timed sampling, which increased to a maximum of 8.3% following the introduction of random error in sample time across all four time points. We propose to apply the present method for the validation of LSS developed for clinical use.

Automated summary

Limited sampling strategies (LSS) combined with Bayesian estimates and a population pharmacokinetic model can reduce the number of samples required for individual pharmacokinetic parameter estimations and lighten the burden of assessing AUC in therapeutic drug monitoring. This study evaluated the robustness of parameter estimations in an LSS to deviations from the optimal sample time. The investigated iohexol LSS showed good resilience to deviations in sample time.