Historically, pharmacokinetic (PK) studies and therapeutic drug monitoring (TDM) have relied on plasma as a sampling matrix. Noninvasive sampling matrices, such as saliva, can reduce the burden on pediatric patients. The variable plasma–saliva relationship can be quantified using population PK models (nonlinear mixed-effect models). However, criteria regarding acceptable levels of variability in such models remain unclear. In this simulation study, the authors aimed to propose a saliva TDM evaluation framework and evaluate model requirements in the context of TDM, with gentamicin and lamotrigine as model compounds.
Two population pharmacokinetic models for gentamicin in neonates and lamotrigine in pediatrics were extended with a saliva compartment including a delay constant (kSALIVA), a saliva:plasma ratio, and between-subject variability (BSV) on both parameters. Subjects were simulated using a realistic covariate distribution. Bayesian maximum a posteriori TDM was applied to assess the performance of an increasing number of TDM saliva samples and varying levels of BSV and residual variability. Saliva TDM performance was compared with plasma TDM performance. The framework was applied to a known voriconazole saliva model as a case study.
TDM performed using saliva resulted in higher target attainment than no TDM, and a residual proportional error <25% on saliva observations led to saliva TDM performance comparable with plasma TDM. BSV on kSALIVA did not affect performance, whereas increasing BSV on saliva:plasma ratios by >25% for gentamicin and >50% for lamotrigine reduced performance. The simulated target attainment for voriconazole saliva TDM was >90%.
Saliva as an alternative matrix for noninvasive TDM is possible using nonlinear mixed-effect models combined with Bayesian optimization. This article provides a workflow to explore TDM performance for compounds measured in saliva and can be used for evaluation during model building.