title: Simplified equations using two concentrations to calculate area under the curve for antimicrobials with concentration-dependent pharmacodynamics: daptomycin as a motivating example.
abstract: The effects of several antimicrobial agents are predicted by the ratio of the area under the concentration-time curve (AUC) to the MIC (AUC/MIC). Peak (Cp) and trough (Ct) concentrations are often measured clinically as surrogates of AUC because actual computation of AUC from 1 or 2 samples requires sophisticated mathematical methods. Given that the effects of daptomycin are predicted by AUC/MIC, our objective was to compare simple equation calculated AUC based on Cp and Ct to model integrated AUC. A standard population pharmacokinetic model was used to simulate 5,000 daptomycin concentration-time profiles after 5 doses of 6 mg/kg of body weight/day (0.5-h infusions). The AUC for the 24-h period was computed by integration and by equations with 110 Cp-Ct combination pairs. The Cp time points were in 15-min increments between 0.5 h and 3 h and Ct in 15-min increments within an hour of the end of the dosing interval for each dose. The precision and bias of the calculated AUC relative to the integrated AUC were determined to identify Cp-Ct pairs associated with the lowest bias and highest precision. The equations were further validated using two daptomycin concentration-time data sets from healthy volunteers and critically ill patients. The precision and bias of calculated AUC were based primarily on Cp, and use of a daptomycin Cp 1.5 h to 3 h from the start of infusion was associated with a bias of <10% and an R(2) of >0.95. Data from the healthy volunteers and critically ill patients also demonstrated declining bias with use of Cp ≥ 1.5 h from the start of infusion with relatively good precision. Simplified equations using a daptomycin Cp approximately 2 h from the start of infusion and a Ct within an hour of the end of the dosing interval should yield precise and unbiased estimates of daptomycin AUC.
authors.affiliation: Albany College of Pharmacy and Health Sciences, Albany, New York, USA.
authors.forename: Manjunath P
authors.initials: MP
authors.lastname: Pai
authors.affiliation: Department of Public Health and Infectious Diseases, "Sapienza" University of Rome, Policlinico Umberto I, Rome, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Russo
authors.affiliation: Clinical Pharmacology and Oncology Section, University of Florence, Florence, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Novelli
authors.affiliation: Department of Public Health and Infectious Diseases, "Sapienza" University of Rome, Policlinico Umberto I, Rome, Italy.
authors.forename: Mario
authors.initials: M
authors.lastname: Venditti
authors.affiliation: Department of Public Health and Infectious Diseases, "Sapienza" University of Rome, Policlinico Umberto I, Rome, Italy marcofalc@libero.it.
authors.forename: Marco
authors.initials: M
authors.lastname: Falcone
mesh_terms: D000328:Adult; D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000494:pharmacology*; D019540:Area Under Curve; D003198:Computer Simulation; D016638:Critical Illness; D017576:Daptomycin / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000494:pharmacology*; D005260:Female; D006801:Humans; D007262:Infusions, Intravenous; D008297:Male; D009767:Obesity, Morbid / Q000378:metabolism; D011153:Population; D015203:Reproducibility of Results; D013211:Staphylococcus aureus / Q000187:drug effects
chemical_list: D000900:Anti-Bacterial Agents; D017576:Daptomycin