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The aim of antibacterial chemotherapy is to achieve sufficient drug concentrations at the site of infection for an adequate length of time to ensure bacterial eradication and optimize clinical success. Whether the desired outcome is achieved or not depends on a number of pathogen-, drug- and patient-related factors. Neither microbiologic activity nor antibacterial pharmacokinetic data alone can adequately describe the complex interaction between pathogen, host and antibacterial during the disease process. A relatively new discipline - pharmacodynamics - seeks to integrate both microbiologic and pharmacokinetic data. The particular model that best predicts clinical outcome depends on the pattern of microbial killing and the persistence of antibacterial effects after plasma concentrations have fallen below the minimum inhibitory concentration (MIC) for the target pathogen (post-antibiotic effect [PAE]). The beta-lactams, for example, exhibit time-dependent bacterial killing with minimal persistent effects. Time above MIC (T(MIC)) is therefore the parameter that best correlates with clinical efficacy for these agents and that, in turn, necessitates multiple daily dosing to optimize the duration of exposure. The macrolides erythromycinA and clarithromycin exhibit a similar pharmacokinetic/pharmacodynamic relationship to that of the beta-lactams, although for clarithromycin the area under the concentration-time curve (AUC) also correlates with clinical outcome (reflecting the more prolonged PAE of this agent). Azithromycin, ketolides, such as telithromycin (HMR 3647), streptogramins and fluoroquinolones exhibit concentration-dependent killing and have prolonged persistent effects, such that the AUC:MIC or Cmax:MIC ratio correlates most closely with clinical efficacy. For these agents the aim is to maximize drug concentrations to which the target pathogen is exposed and this may require higher doses and hence enable longer dosing intervals to be used. In summary, pharmacodynamic models provide a unique approach to determining likely in vivo activity of individual antibacterial agents and prediction of clinical outcomes. |
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