Development of a priori dosing nomograms for daptomycin in patients at Swiss university hospitals
Charlotte Kern (1, 2), Claudia Suenderhauf (3), Stephan Krähenbühl (3), Pascal André (4), Thierry Buclin (4), and Felix Hammann (1, 3)
(1) Division of Clinical Pharmacology & Toxicology, Department of Internal Medicine, University Hospital Bern, Switzerland; (2) Graduate School for Health Sciences, University of Bern, Switzerland; (3) Division of Clinical Pharmacology & Toxicology, Department of Biomedicine and Clinical Research, University and University Hospital Basel, Switzerland; (4) Service of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Switzerland.
Objectives:
Daptomycin is a cyclic lipopeptide with activity against gram-positive bacteria (e.g. Staphylococcus spp., Enterococcus spp.) [1]. This antibiotic has dose-linear pharmacokinetics and is commonly used in the treatment of complicated skin and soft tissue infections. Treatment efficacy correlates with the ratio of area under the drug concentration-time curve (AUC) over minimum inhibitory concentration (MIC) [2], with either bactericidal effect (AUC/MIC > 800) or only bacteriostatic effect (400 < AUC/MIC ≤ 800). The trough level (C24h) should be kept < 24 mg/L to minimize the risk of rhabdomyolysis [3]. A common way to optimize efficacy and prevent toxicity is a posteriori dose adjustment with therapeutic drug monitoring (TDM). In addition to expert knowledge and guidelines, pharmacometric models can be used for initial (a priori) dosage optimization but involve specialist knowledge often not readily available at point-of-care. Dosing nomograms developed from pharmacometric models and TDM data could offer a solution to shorten time to target attainment.
Methods: We performed a retrospective study with inpatients receiving routine TDM of their daptomycin treatment at the University Hospital Basel (Universitätsspital Basel, UHBS) and Lausanne University Hospital (Centre Hospitalier Universitaire Vaudois, CHUV). Available covariates were demographic data, chemistry and hematology results, infection specific data (type, site, organism, MIC when available) and clinical outcome at time of discharge from hospital. We used patient data to build a population-based pharmacokinetic model with NONMEM (Icon Development Solutions, Ellicott City, MD, USA) and to generate dosing nomograms.
Results: The study included 58 patients (n=31 at UHBS, n=27 at CHUV), providing a total of 174 samples. The final model was a one-compartment model with linear elimination (volume of distribution (Vd) 15.90 L (inter-individual variability (IIV): 40%) and clearance (CL) 0.79 L/h (IIV: 33%)). Influential covariates on clearance were serum albumin concentration and renal function (as estimated by the Cockcroft-Gault equation). We generated dosing nomograms by simulating concentration profiles at steady state for a broad range of doses (2-14 mg/kg) and computing AUC 0-24h for typical patients at serum albumin levels of 20 g/L or 35 g/L.
Conclusions: Dosage individualization of antimicrobials in critically ill patients (i.e. with sepsis, osteomyelitis, bacteremia or endocarditis) is a challenge. Due to the alterations in physiology typically seen in these patients, it is crucial to prescribe dosing regimens that ensure concentration exposure associated with optimal outcomes. Dosing nomograms can help clinicians to inform their dosing strategies in a timely manner. They are generally easy to integrate into clinical practice and do not require extensive resources while conveying complex and multidimensional information. Model-based a priori dosing decisions can be complemented a posteriori by TDM and dosage readjustment in selected cases.
References:
[1] Bidell, M.R. and T.P. Lodise, Suboptimal Clinical Response Rates with Newer Antibiotics Among Patients with Moderate Renal Impairment: Review of the Literature and Potential Pharmacokinetic and Pharmacodynamic Considerations for Observed Findings. Pharmacotherapy, 2018. 38(12): p. 1205-1215.
[2] European Committee on Antimicrobial Susceptibility Testing Steering, C., EUCAST Technical Note on daptomycin. Clin Microbiol Infect, 2006. 12(6): p. 599-601.
[3] Bhavnani, S.M., et al., Daptomycin exposure and the probability of elevations in the creatine phosphokinase level: data from a randomized trial of patients with bacteremia and endocarditis. Clin Infect Dis, 2010. 50(12): p. 1568-74.
