Nerea Jauregizar (1), Sandra Gil-Alonso (1), Ignacio Ortega (2), Elena Eraso (3) and Guillermo Quindós (3)
(1) Department of Pharmacology, Faculty of Medicine, University of the Basque Country (UPV/EHU), Spain. UFI11/25 “Microbios y Salud”. (2) Research Development and Innovation Department, Faes Farma S.A. Leioa. Spain. (3) Department of Immunology, Microbiology and Parasitology, Faculty of Medicine, University of the Basque Country (UPV/EHU), Spain. UFI11/25 “Microbios y Salud”
Objectives: In vitro time-kill curves are attractive tools for studying the pharmacodynamics of antimicrobial agents as they provide detailed information of antimicrobial efficacy as a function of both time and concentration [1]. The aim of this study was to apply a mathematical model that is appropriate for characterizing pharmacodynamics of anidulafungin, from dynamic time-kill experiments with changing anidulafungin concentrations, against Candida.
Methods: A one-compartment in vitro infection model was developed to simulate exponentially changing drug concentrations of anidulafungin in the presence of viable Candida cells, at Cmax=5.47 µg/ml. The culture broth from the central compartment was continuously supplied and removed at a flow rate adjusted to simulate a half-life equal to 25.6 h. Samples for viable counts were taken at time points 0, 2, 4, 6, 24 and 48 h after start of experiments. Data was modeled using NONMEM V7.2.0 [2] with first order conditional estimation method. Diagnostic plots and precision of parameter estimates were evaluated to assess model performance. Additionally, human PK data for anidulafungin were used to simulate expected time-kill curves for anidulafungin under typical dosing regimens.
Results: Time-kill data were best fit by using an adapted sigmoidal Emax model that corrected for delay in the growth of Candida and the onset of the anidulafungin activity, steepness of the concentration-response curve, and saturation of the cell number of Candida. Dynamic time-kill curves of assayed strains were well predicted by the model. Moreover, the mathematical model can be used to simulate expected kill curves for anidulafungin dosing regimens.
Conclusions: We have shown that it is feasible to fit dynamic time-kill data of anidulafungin against Candida by using an adapted Emax mathematical model. Our approach of combining in vitro time-kill data with existing in vivo PK data might serve as a model for future studies to define optimal antifungal regimens.
References:
[1] Liu P, Rand KH, Obermann B, Derendorf H. Pharmacokinetic-pharmacodynamic modelling of antibacterial activity of cefpodoxime and cefixime in in vitro kinetic models. Int J Antimicrob Agents (2005) 25(2): 120-29.
[2] Beal SL, Sheiner LB, Boeckmann AJ & Bauer RJ (Eds.) NONMEM Users Guides. 1989-2011. Icon Development Solutions, Ellicott City, Maryland, USA.
Reference: PAGE 24 () Abstr 3356 [www.page-meeting.org/?abstract=3356]
Poster: Drug/Disease modeling - Infection