In critically ill patients, despite advances of antimicrobial therapy, the outcome of severe infections remains poor with high mortality rates. ICU environment requires existing antimicrobial agents to be used more judiciously with the aim at the reduction of antimicrobial resistance.
The antimicrobial pharmacokinetics (PK) and pharmacodynamics (PD) in ICU differ significantly from the patients where conventional dosing regimens were developed, considering both the most widely used PD assessment and PK model. The first one is based on static time-kill models, from whose measure of minimum inhibitory concentration (MIC) or the lowest concentration required to inhibit visible growth of the organism in vitro are derived. While PK model, the simplest structural model, includes one compartment and two parameters, typically the volume of distribution and clearance. However, the PD data obtained during static concentrations and the complexity and heterogeneity of PK in critical patient implie the need to individualise antimicrobial therapy. In this context, the kidney is often the most important organ as many of the commonly used antimicrobials are cleared by the renal route.
PK properties are harder to estimate in animal models, as a result of much faster drug clearance, being essential for developing population PK models and collecting concentration-time data of antimicrobials in critically ill patients. These data emphasise the need of robust study design and optimal sampling, and the collection of relevant physiological parameters in critically ill patients, with careful consideration for the frequent presence of renal replacement therapy (RRT) and extracorporeal membrane oxygenation (ECMO).
The authors of the current study review the potential roles of various infections in ‘in vitro’ and ‘in vivo’ models and clinical PK/PD modelling for the development of optimised dosing regimens to be used in critically ill patients. PK/PD models can combine available knowledge of PD with clinical PK, ensuring the evolution of therapeutic drug monitoring (TDM), not just used to minimise drug toxicity, but to increase and maximise drug efficacy. The clinical PK/PD analysis is therefore limited to analyses that identify exposure thresholds best separating cure/no cure or mortality/survival (Classification and Regression Tree [CART] analysis). CART analyses can be used to suggest a drug exposure threshold which is associated with antimicrobial toxicity and describe the antimicrobial effects of monotherapy at a defined dosing regimen. However, these approaches are difficult to apply to regimens with changing doses over the course of therapy and combinations. However, as for all analyses using PD targets that ignore the time course of effects, exploration of different dosing regimens (e.g. bolus doses and extended or continuous infusions) should be made with caution. In this context, mechanism based on dynamic models might be useful to provide a more thorough understanding and to predict the time course of antimicrobial toxicity and safety. In particular, by coupling a PK/PD model, developed on the basis of in vitro data to clinical concentration-time profiles, it can be shown that prolonged infusions are preferable for patients with augmented clearances which has also been suggested for critically ill patients.
Ideally, future clinical studies should assess drug concentrations at the infection site, susceptibility of the causative pathogen and clinical outcome to determine relevant PK/PD targets, individual populations and different types of infection. If high value of TDM is shown, then an important future step would be to assess the cost-effectiveness of PK/PD-optimised therapy in critically ill patient subgroups and infections through comprehensive and generalisable prospective clinical studies.
Article review was prepared by EJRC members Drs. Temistocle Taccheri and Gennaro De (Pascale Department and Anaesthesiology and Intensive Care. Fondazione Policlinico Agostino Gemelli, Rome, Italy).
Tängdén T et al. The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections. Intensive Care Med (2017) 43:1021–1032DOI 10.1007/s00134-017-4780-6