Evaluating a priori and a posteriori dose adjustments in silico: the case of pharmacogenetics and TDM

Abstract

INTRODUCTION: High variability in drug exposure and subsequent drug response can hamper effectiveness and safety. Dose adjustments can be made a priori (e.g. pharmacogenetic testing) or a posteriori (e.g. therapeutic drug monitoring (TDM)). Although combining both would be ideal, this is not always possible due to scarcity of resources. High unexplained inter-individual variability (IIV) can be captured by TDM, while pharmacogenetics may be a better option in case of high inter-occasion variability (IOV) and residual unexplained variability (RUV). This study explores the effect of these variabilities and evaluates the cut-off points between a priori (in form of pharmacogenetics) and a posteriori (in form of TDM) based on those variabilities to inform decision making on those adjustment strategy. METHODS: Pharmacokinetic models with pharmacogenetic covariates of drugs of which dosages are adjusted by both pharmacogenetic and TDM were retrieved from Pubmed. Simulations with those models were performed under different magnitudes of variability (IIV 0-1, IOV 0-1 and RUV 0- 0.5,proportional). A priori simulation based on a covariate subpopulation average and a posterori simulation including all types of variability and the covariate value were compared with a theoretical true value including only the covariate average and IIV. RESULTS: Five cases were included: tacrolimus, tamoxifen, efavirenz, risperidone and vincristine. The general pattern was similar for all the included models, where increase in IOV and RUV gave preference to a priori and increase in unexplained IIV gave preference to a posteriori. CONCLUSION: The trade-off between the degree of explainable IIV for pharmacogenetics, versus IOV with RUV for TDM on the other hand has been visualized. TDM seems the optimal strategy for all cases, except vincristine. This is possibly influenced by the outcome measure being AUC. Our results can be used as a theoretical framework to inform biomarker selection for dosing adjustments on the included drugs.