Biomarker-Guided Individualization of Antibiotic Therapy: From CRP and PCT to Pharmacometric Modeling
This tutorial reviews how host-response biomarkers including CRP, procalcitonin, IL-6, and presepsin can be used to individualize antibiotic treatment beyond simple start/stop decisions. It introduces pharmacometric and systems pharmacology modeling approaches that quantitatively link biomarker dynamics to drug exposure and treatment response. A case study modeling procalcitonin kinetics during sepsis antibiotic therapy illustrates how these frameworks could enable truly personalized dosing strategies.
The original study
Biomarker-Guided Individualization of Antibiotic Therapy.
- Authors
- Aulin LBS, de Lange DW, Saleh MAA, van der Graaf PH, Völler S, van Hasselt JGC
- Journal
- Clinical pharmacology and therapeutics
- Type
- Journal Article, Research Support, Non-U.S. Gov't, Review
- PMID
- 33559152
Original abstract
Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.