Pathophysiological changes are common in critically ill patients, and can alter the time course of drug concentrations following dosing. The latter is termed pharmacokinetics (PK), and describes the relationship between dose administered and drug concentrations in plasma. Thus, modifications in PK necessitate dose adjustment, to optimize drug therapy in critical care. An understanding of basic PK principles is therefore required, to improve dosage guidelines in the population treated. Here, we define the key PK parameters, with specific application to critically ill patients. We then overview the methods used for PK analysis, in both research and in the clinical setting. Traditionally, non-compartmental and standard two-stage approaches have been used in small groups of patients with similar demographics and pathophysiology. However, these methods require intensive sampling, and do not explicitly describe inter-individual variability, or errors associated with measurement or sampling. Population PK (POPPK) modelling is advantageous in this regard, and can use both sparse and rich datasets to provide accurate estimates for between-subject variability (BSV). In addition, POPPK can explore patient parameter-covariate relationships, to account for some of the BSV in PK. This information is useful with assisting individualized dosing in the clinic. While the above methods are suitable for research, they are too time-consuming in the clinical setting, and Bayesian approaches have been adopted to optimize dosing. These methods, together with POPPK and appropriate study design are recommended for improved dosing in critical care.
Keywords: Bayesian methods, clearance, critically ill, dose optimization, pharmacokinetics, population modeling, volume of distribution, patient parameter-covariate relationships, critical care, pharmacodynamic (PD), systemic circulation, metabolic rates, lipophilic drugs, Acidic drugs predominantly, Hypoalbuminaemia
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