Project title:

Optimisation of antibiotic therapy in critically ill patients using pharmacometric modelling and simulation approaches

Project background and aims:

Effective antimicrobial therapy is dependent on the pathogen causing the infection and the infected patient. Both the most appropriate antibiotic and the necessary antibiotic exposure for a successful treatment are determined by the pathogen’s susceptibility. Yet, the adequate antibiotic dosing regimen needed to reach the necessary antibiotic exposure at the infection site can vary substantially between patients. Especially in critically ill patients a large variability of drug exposure after the same dosing regimen is observed: Physiological changes associated with critical illness often affect the pharmacokinetics (PK) of a drug and therefore might alter the drug exposure between patients and even between dosing occasions within one patient. As a consequence, using a uniform dosing regimen in a critical patient population is related to a high risk of subtherapeutic or toxic or even resistance-evoking drug exposures in this already vulnerable population.

A powerful approach to improve dosing in special populations like critically ill patients are pharmacometric approaches such as population pharmacokinetic/pharmacodynamic (PK/PD) analysis with nonlinear mixed-effects modelling. By characterising the relationship between drug concentration measurements and patient- and pathogen-specific characteristics, the typical concentration-time profile for the investigated antibiotics and different levels of variability (between and within patients) can be quantified. The final pharmacokinetic model can then be used on a population level to examine currently used dosing regimens and to propose more effective dosing regimens for instance throughout model-informed dosing tables. Furthermore, on an individual patient level, user-friendly dosing software tools employing pharmacometric models can support dose optimisation at the bedside. Based on clinical trial data and the integrated PK model, model-informed dosing software tools can select the dosing regimen with the highest probability to attain the targeted exposure for each individual patient. In this context of model-informed precision dosing (MIPD), individual PK parameter estimates, also referred to as maximum a posteriori probability (MAP) parameter estimates, aid as a crucial component in deriving the most suitable dose/dosing regimen for an individual patient. Based on these considerations, my doctoral project will focus on elucidating the relationships between dosing regimens, patients and pathogens to improve antibiotic therapy in critically ill patients.

Concretely, in the context of the ongoing TIAS study in collaboration with the Charité – Universitätsmedizin Berlin, I will perform scientific study support and collect clinical trial data (i.e. patient characteristics of the critically ill, bioanalytical data of the antibiotics) needed for PK/PD data analysis, which will be ongoing throughout the entire doctoral project. Furthermore, I will conduct a pharmacometric simulation study investigating the impact of sample time uncertainty on the MAP parameter estimates due to commonly observed and so far, not adequately considered inaccuracies in routinely generated clinical data. To integrate model-informed dosing regimens into the clinical routine (i) model-informed dosing tables and (ii) model-informed dosing software tools will be developed and refined in the light of newly gained information during the study. In a subsequent part, it will be analysed to which extent these two dosing strategies lead to a clinical benefit in terms of target attainment. To further increase user-friendliness, a mobile device version (e.g. iOS, Android) of the model-informed dosing software tool shall be developed.