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The area under the plasma drug concentration-time curve (AUC), representing the total drug exposure over time, is a common pharmacokinetic (PK) surrogate to inform the issue of therapy. Reliability of its estimation highly depends on the frequency of blood sampling. To reduce the cost and inconvenience of blood withdrawal, limited sampling strategies (LSS) have been proposed, with two main approaches for their development and implementation, whether the multiple linear regression-based LSS (R-LSS) or the Bayesian-based LSS (B-LSS). Regardless of the method used, evaluation of the predictive capacity of LSS is critical. Transferring an LSS between different clinical settings is an overlooked aspect, threatening thus the extension of its informed use. In the current paper, we study the reliability of a chosen LSS by proposing a hybrid approach that takes advantage of both R-LSS and B-LSS to analyze its robustness and success rate. The impact of variability on the LSS reliability is also investigated. As
a result, we were able to show that our method enhances the selection of the best LSS and informs the associated risk to their transferability. This simulation-based methodology should be added to routine procedures of LSS development to complement traditional validations.
Limited sampling strategies, Area under the curve, Multiple linear regression, Bayesian, Robustness, Transferability