Daptomycin, an anti-methicillin-resistant Staphylococcus aureus drug, causes exposure-dependent muscle toxicity and eosinophilic pneumonia. Although the area under the concentration-time curve (AUC)-guided dosing is crucial, an optimal blood sampling strategy is lacking. This study aimed to identify an optimal limited sampling strategy using Bayesian forecasting to rapidly achieve the target AUC. Two validated population pharmacokinetic models generated a virtual population of 1000 individuals (models 1 and 2 represent diverse patients and kidney transplant recipients, respectively). The AUC for each blood sample was assessed using the probability of achieving the estimated/reference AUC ratio on the second day (AUC24-48) and at the steady state (AUCss). In Model 1, Bayesian posterior probabilities for AUC24-48 increased from 50.7% (a priori) to 59.4% and for AUCss from 48.9% (a priori) to 61.9%, with one-point Ctrough sampling at 24 h. With two-point sampling at 7 and 24 h, the probabilities increased to 73.8% for AUC24-48 and 69.7% for AUCss. In Model 2, the probabilities for both AUC24-48 and AUCss with one-point Ctrough or two-point sampling incorporating Ctrough sampling increased compared to a priori probabilities. These results suggest that two-point sampling incorporating Ctrough during initial dosing enhanced achieving the target AUC24-48 and AUCss rapidly.
Keywords: area under the concentration‐time curve; daptomycin; limited sampling; model‐informed precision dosing; therapeutic drug monitoring.
© 2024 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society). Published by John Wiley & Sons Ltd.