Daptomycin Exposure Prediction With a Limited Sampling Strategy

Abstract

Background: Daptomycin is a cyclic lipopeptide antibiotic used to treat serious infectious endocarditis caused by Staphylococcus aureus. The pharmacodynamic parameter correlating best with efficacy is the ratio of the estimated area under the concentration (AUC0-24)–time curve to the minimum inhibitory concentration. The aim of the study is to develop a limited sampling strategy to estimate AUC0-24 using a reduced number of samples. Methods: Sixty-eight daptomycin AUC0-24 values were calculated for 50 White patients who underwent treatment for at least 5 consecutive days. Plasma concentrations were detected using a validated high-performance liquid chromatography–tandem mass spectrometry analytical method, with daptomycin-d5 as an internal standard. Multiple regression was used to evaluate the ability of 2 concentration–time points to predict the AUC0-24 calculated from the entire pharmacokinetic profile. Prediction bias was calculated as the mean prediction error, whereas prediction precision was estimated as the mean absolute prediction error. The development and validation datasets comprised 40 and 10 randomly selected patients, respectively. Results: The AUC0-24 (mg*h/L) was best estimated using the daptomycin trough concentration and plasma concentrations detected 2 hours after dosing. We calculated a mean prediction error of 1.6 (95% confidence interval, −10.7 to 10.9) and a mean absolute prediction error of 11.8 (95% confidence interval, 5.3–18.3), with 73% of prediction errors within ±15%. Conclusions: An equation was developed to estimate daptomycin exposure (AUC0-24), offering clinical applicability and utility in generating personalized dosing regimens, especially for individuals at high risk of treatment failure or delayed response.