Biomarker‐Guided Individualization of Antibiotic Therapy

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.

Design principles of collateral sensitivity-based dosing strategies

“…We expect that such factors will not affect specific subpopulations studied in different ways and therefore not have a great impact on the general findings we do in this analysis. The developed modelling framework is applicable for design of clinical treatment designs for specific antibiotic agents and pathogens, where the model can be further expanded with additional pathogen-, drug-, and patient-specific characteristics 36 , derived from separate experimental studies and by utilizing published clinical population PK models for specific antibiotics 37,38 , which include inter-individual variability or target site concentrations at the site of infection. This would thus allow to derive tailored CS-based dosing regimens for specific antibiotics and pathogens.…”

Translation of Pharmacodynamic Biomarkers of Antibiotic Efficacy in Specific Populations to Optimize Doses

“…The complexity of modeling endogenous substrates, however, requires a good understanding of the kinetics of production and degradation. A recent analysis based on daily measurements of procalcitonin in patients with sepsis has allowed for the construction of such a model that includes initial conditions at the start of therapy, a lag time for response to therapy, first-order production and degradation rate constants for procalcitonin, and random effect terms to account for treatment variation and immune response [ 48 ]. These non-linear mixed effects models extend the simple single-point interpretations of biomarkers such as procalcitonin by integrating repeated measures and forecasting the response to therapy.…”

“…Drug effect was parameterized using a classical inhibitory function with the set to the theoretical upper limit of 1 (100% inhibition of procalcitonin production) and the vancomycin concentration ( ) producing 50% of the maximal response ( ) set to 10 mg/L, which approximates a clinically relevant vancomycin trough concentration. The first-order elimination rate constant of procalcitonin ( k out ) was set to 0.0289 h −1 ( t 1/2 24 h) [ 48 ]. The population baseline procalcitonin level was set to 3.6 ng/mL to be representative of a S. aureus bacteremia population [ 53 ].…”

Evaluation of the Diagnostic and Prognostic Value of CSF Presepsin Levels in Patients with Postneurosurgical Ventriculitis/Meningitis

“…As a new type of marker, sCD14 (presepsin) has received extensive clinical and laboratory attention. 10 , 11 It is a truncated subtype of soluble CD14 composed of 64 amino acids and induces the differentiation of activated macrophages in response to membrane-bound protein soluble fragment of 14 (CD14) expressed by bacterial lipopolysaccharides. 12 CD14 is a coreceptor of Toll-like receptor 4 (TLR4) that binds the lipopolysaccharide (LPS)-lipopolysaccharide binding protein (LBP) complex, thereby promoting LPS-induced TLR4 activation.…”