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Antifungal Pharmacokinetics and Pharmacodynamics

  • David R. Andes
  • Alex J. Lepak
Chapter

Abstract

The critical role of pharmacokinetics and pharmacodynamics in the selection and dosing of antimicrobial therapeutics, including antifungal agents, has gained increasing recognition [1–4]. The study of pharmacokinetics involves understanding the interaction of a drug with the host, including measurements of absorption, distribution, metabolism, and elimination. The study of antimicrobial pharmacodynamics provides insight into the link between drug pharmacokinetics, in vitro susceptibility, and treatment outcome. Knowledge of the pharmacokinetic/pharmacodynamic index and magnitude associated with efficacy can be helpful for clinicians to predict therapeutic success/failure, guide optimal dosing levels and intervals, aid in susceptibility breakpoint development, guide therapeutic drug monitoring, and limit potential adverse outcomes, including toxicity and the development of resistance [5–8]. Numerous in vitro, animal, and clinical studies have been instrumental in characterizing the pharmacodynamic activity of the clinically available antifungal drug classes, including triazoles, polyenes, flucytosine, and echinocandins [6–18]. The analyses of data with these antifungal drug classes have identified distinct pharmacodynamic characteristics that result in different optimal dosing strategies. Accumulating clinical data have also become available with several antifungals that allow pharmacodynamic data analyses [19–25]. Most often the results of these investigations have corroborated information from experimental models. The following chapter outlines the pharmacodynamic characteristics of antifungals and presents evidence of the clinical relevance of these concepts.

Keywords

Minimum Inhibitory Concentration Candida Species Invasive Aspergillosis Epithelial Line Fluid Free Drug Concentration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Medicine, and Medical Microbiology and ImmunologySection of Infectious DiseasesMadisonUSA

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