Limited sampling approach for model-informed precision dosing of daptomycin to rapidly achieving the target area under the concentration-time curve: A simulation study.

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 (AUC) and at the steady state (AUC). In Model 1, Bayesian posterior probabilities for AUC increased from 50.7% (a priori) to 59.4% and for AUC from 48.9% (a priori) to 61.9%, with one-point C sampling at 24 h. With two-point sampling at 7 and 24 h, the probabilities increased to 73.8% for AUC and 69.7% for AUC. In Model 2, the probabilities for both AUC and AUC with one-point C or two-point sampling incorporating C sampling increased compared to a priori probabilities. These results suggest that two-point sampling incorporating C during initial dosing enhanced achieving the target AUC and AUC rapidly.