Pharmacodynamics models to bridge the gap between exposure and response to antimicrobial treatments

Dosage regimens are based on randomised controlled trials that usually do not represent the heterogeneity of the final intended real-world treatment population well. Real-world variability in drug exposure is common and can result in suboptimal treatment. To hit a predefined exposure target in each patient, pharmacokinetic (PK) monitoring can be implemented. PK monitoring facilitates the attainment of a desired exposure target by guiding individualised dosing based on drug concentration measurements. However, the successful attainment of an exposure target is not necessarily reflected in a favourable clinical outcome. Despite the growing interest in PK monitoring during the last decade, the quality of evidence remains low. We believe that dose optimisation practices will only reach their optimal success when combining PK monitoring with pharmacodynamic (PD) monitoring of biomarkers and clinical markers of surrogate response such as disease activity scores. Therefore, we will develop state-of-the-art pharmacometric models to quantitatively describe, understand, and predict the relationship between drug dose, drug exposure, biomarker/surrogate responses, and clinically relevant endpoints. We propose a disease-oriented modelling and simulation approach based on real-world data of critically ill patients on antimicrobial drugs. We hypothesise that our models will facilitate more efficient drug dosing.

Keywords:Pharmacometrics, Mixed effects modelling and simulation, Dose individualisation, Real-world data

Disciplines:Biomarker evaluation, Computational biomodelling and machine learning, Pharmacodynamics, Pharmacokinetics, Pharmacotherapy