2018 - Montreux - Switzerland

PAGE 2018: Drug/Disease modelling - Infection
Iasonas Kapralos

Population Pharmacokinetics of Anidulafungin in ICU patients

Iasonas Kapralos, Maria Siopi, Efstratios Mainas, Olympia Apostolopoulou, Efthymios Neroutsos, Styliani Apostolidi, George Dimopoulos, Helen Sambatakou, Joseph Meletiadis, Georgia Valsami, Aris Dokoumetzidis

Department of Pharmacy, National and Kapodistrian University of Athens ; Clinical Microbiology Laboratory, Attikon Hospital ; Erasmus Medical Center ; Medical School, National and Kapodistrian University of Athens

Objectives: Anidulafungin (ANF), an echinocandin-class antifungal, is considered to be initial therapy for invasive candidiasis. Although echinocandins have shown an ideal pharmacologic profile, with limited adverse reactions and drug-drug interactions, as well as low exposure variability among patient populations, dose optimization may be considered, in the context of the increasing antifungal resistance and the unstable nature of ICU patients. Thus, aim of our study is to develop a pharmacokinetic model, which describes Anidulafungin pharmacokinetics in critically ill patients, and identify covariates.

Methods: Pharmacokinetic data were obtained by two clinical studies, conducted at the Intensive Care Units of Attikon and Ippokrateion University Hospitals of Athens. A total of 192 plasma samples were collected from 13 patients, receiving ANF upon proven invasive candidiasis, as an empiric treatment or prophylaxis. ANF was administered as a short-term intravenous infusion in a dose of 100mg once a day, while 9 patients received a loading dose of 200mg on the first day. A dense sample strategy, which included a pre-dose sample and 5 to 7 samples in a 24 hour time interval after the start of infusion, was followed. Plasma Anidulafungin concentrations were measured with a validated HPLC-fluorescence plasma assay method. We performed the population PK analysis, using non-linear mixed effects modelling in NONMEM® (version 7.3) and the FOCEI method. The development of the base model included the implementation of 1-compartment, 2-compartment and 3-compartment structural PK forms, as well as the use of additive, proportional and combined models to describe the residual variability. Inter-individual variability (IIV) was modelled using an exponential function, and then subsequently an inter-occasion variability (IOV) component was taken into consideration. Covariates including body weight, height. BMI, BSA, Creatinine Clearance and age, in addition to ICU specific covariates as the SOFA Score and APACHE II score were examined. Models have been evaluated based on the criteria of successful minimization, assessment of diagnostic plots and visual predictive checks, and bootstrap as a measure of the estimation precision. The selection of the covariates on the PK parameters was based on the Likelihood Ratio Test with a significance level of 0.01.

Results: A two-compartment model, with first-order elimination and proportional residual error, was found to best describe the time course of plasma Anidulafungin concentrations in the specific population. The estimates of the PK parameters (inter-individual variability calculated as CV %) were: Clearance (CL) = 0.816 L/h (37.4%), central volume of distribution (V1)= 9.96 L (30.1%) , peripheral volume of distribution (V2)= 22.5 L/h (39.2%) , and inter-compartmental clearance (Q)= 5.8 L/h (40.1%). A significant inter-occasion variability was estimated for Clearance and Central Volume to be 28.3 (%CV) and 38.5 (%CV) respectively. SOFA score was found to be statistically significant covariate for CL and V1. An increase in SOFA score from 7 to 17 is found to result in a 52.3 % reduction on the Clearance estimation.

Conclusions: A model was developed for Anidulafungin PK in ICU patients, based on dense data. The pronounced inter-occasion variability in the exposure of ICU patients to ANF which was observed, challenges the ability to individualize the dose of Anidulafungin in critically ill patients. Further analysis, could examine the influence of time-varying covariates on PK parameters and explain the observed inter-occasion variability.

Reference: PAGE 27 (2018) Abstr 8604 [www.page-meeting.org/?abstract=8604]
Poster: Drug/Disease modelling - Infection