“De-Shrinking” EBEs: The Solution for Bayesian Therapeutic Drug Monitoring

Abstract

Background Therapeutic drug monitoring (TDM) aims at individualising a dosage regimen and is increasingly being performed by estimating individual pharmacokinetic parameters via empirical Bayes estimates (EBEs). However, EBEs suffer from shrinkage that makes them biased. This bias is a weakness for TDM and probably a barrier to the acceptance of drug dosage adjustments by prescribers. Objective The aim of this article is to propose a methodology that allows a correction of EBE shrinkage and an improvement in their precision. Methods As EBEs are defined, they can be seen as a special case of ridge estimators depending on a parameter usually denoted λ. After a bias correction depending on λ, we chose λ so that the individual pharmacokinetic estimations have minimal imprecision. Our estimate is by construction always better than EBE with respect to bias (i.e. shrinkage) and precision. ResultsWe illustrate the performance of this approach with two different drugs: iohexol and isavuconazole. Depending on the patient's actual pharmacokinetic parameter values, the improvement given by our approach ranged from 0 to 100%. Conclusion This innovative methodology is promising since, to the best of our knowledge, no other individual shrinkage correction has been proposed.

Multivariate Exact Discrepancy: A New Tool for PK/PD Model Evaluation

“…However, due to shrinkage [ 1 , 2 ], the prediction of individual PK parameters can sometimes be poor, especially when there are only a small number of concentrations per individual. While there are some corrections of shrinkage that can be considered [ 3 ], the average distance (actually the rate of convergence) to the actual value of these parameters cannot be smaller than a value inversely proportional to the number of subjects.…”