4.6 Article

Predicting Antimicrobial Activity at the Target Site: Pharmacokinetic/Pharmacodynamic Indices versus Time-Kill Approaches

Journal

ANTIBIOTICS-BASEL
Volume 10, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/antibiotics10121485

Keywords

pharmacokinetics; pharmacodynamics; target site; probability of target attainment; time-kill curves

Funding

  1. European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [861323]
  2. Marie Curie Actions (MSCA) [861323] Funding Source: Marie Curie Actions (MSCA)

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Antibiotic dosing strategies should be based on drug concentrations at the infection site rather than systemic concentrations. Time-kill curves reflect bacterial response over time, while computational PK/PD models can bridge translational gaps between PK and PD.
Antibiotic dosing strategies are generally based on systemic drug concentrations. However, drug concentrations at the infection site drive antimicrobial effect, and efficacy predictions and dosing strategies should be based on these concentrations. We set out to review different translational pharmacokinetic-pharmacodynamic (PK/PD) approaches from a target site perspective. The most common approach involves calculating the probability of attaining animal-derived PK/PD index targets, which link PK parameters to antimicrobial susceptibility measures. This approach is time efficient but ignores some aspects of the shape of the PK profile and inter-species differences in drug clearance and distribution, and provides no information on the PD time-course. Time-kill curves, in contrast, depict bacterial response over time. In vitro dynamic time-kill setups allow for the evaluation of bacterial response to clinical PK profiles, but are not representative of the infection site environment. The translational value of in vivo time-kill experiments, conversely, is limited from a PK perspective. Computational PK/PD models, especially when developed using both in vitro and in vivo data and coupled to target site PK models, can bridge translational gaps in both PK and PD. Ultimately, clinical PK and experimental and computational tools should be combined to tailor antibiotic treatment strategies to the site of infection.

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