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Stocker, Sophie L.

https://orcid.org/0000-0002-2114-587X

Mayo Clinic

The University of Sydney

Citations 1

Dose individualisation of antibiotics in critically ill patients with inflammation: A narrative review
Dräger, Sarah , Minichmayr, Iris K. , Alipanah, Narges , Frazee, Erin N. , Bos, Lieuwe D. J. , Fleuren, Lucas M. , Hunfeld, Nicole , Mathew, Sumith K. , Stocker, Sophie L. , Torres, Antoni , Alipanah-Lechner, Narges , Frazee, Erin N , Hunfeld, Nicole G M , Telles, João Paulo , Torres, Antoni , Koch, Birgit C P , Endeman, Henrik

British Journal of Clinical Pharmacology | August 15, 2025 | Volume: 91 | Issue: 11 | Pages: 3042-3053

10.1002/bcp.70185 Get It

Due to extensive pathophysiological changes in critically ill patients, standard dosing of antibiotics may lead to inadequate drug exposure. This is concerning, as insufficient plasma drug concentrations may lead to treatment failure, whereas excessive drug exposure may increase the risk of toxic adverse events. The role of inflammation as a factor influencing the pharmacokinetics (PK) and pharmacodynamics (PD) of antibiotics remains largely unknown. PK/PD target attainment of antibiotics can be improved through therapeutic drug monitoring, i.e., measurement of drug concentrations in the blood with subsequent dosage adjustment to reach a certain target. Besides, population PK models may be used to predict drug exposure and tailor dosing in an individual patient (model‐informed precision dosing). Inflammatory biomarkers have been proposed to measure inflammation levels and guide antibiotic treatment. However, their potential to guide antibiotic dosing is unclear. This narrative review describes associations between inflammation and PK/PD of antibiotics in critically ill patients, and the role of biomarkers, therapeutic drug monitoring and model‐informed precision dosing in improving antibiotic dosing. A focus of future research should be on the interplay between inflammation and PK/PD of antibiotics by including inflammatory biomarkers in PK/PD models and using big data to predict antibiotic exposure in critically ill patients.