Colistin Pharmacokinetics and Pharmacodynamics

Vision The vision of the project is to increase feasibility of antibiotic drug development, and was initially inspired by Rex et al. (2013). The project will provide a strategy to a reduce the number of subject needed to support clinical efficacy of an antibiotic against resistant bacteria. The strategy will consist of a model-based framework for the analysis and design of clinical trials.

Aims

• Establish methodology for model-based analysis of clinical trial data that will lead to increased knowledge and quantitative information on treatments in specific patient populations.
• Demonstrate the value of model-based vs. standard statistical methods in reducing the number of patients needed to be studied.
• Develop methodology for formal inclusion of prior PKPD information and extrapolation to populations with resistant bacteria.
• Explore model-based adaptive optimal design approaches that maximize efficiency of clinical trials for antibiotic treatments.

Project Plan

Project 1: Population pharmacokinetics of colistin and the relation to survival in critically ill patients infected with colistin susceptible and carbapenem-resistant bacteria

Paul et al. (2018) have conducted one of the few successful randomized controlled trials (RCTs) on MDR-infections. This gave us a unique opportunity to apply the aforementioned models to analyze the resulting data. The aim was to analyze the population pharmacokinetics of colistin and to explore the relationship between colistin exposure and time to death. This project has since been published (Kristoffersson et al. 2020). Another paper we coauthored looked at the emergence of colistin resistance in the trail population (Dickstein et al. 2019).

Project 2: Model-based analysis of colistin’s impact on creatinine in critically ill patients

Here we aim to develop a model relating colistin exposure to changes in creatinine. Colistin’s impact on kidney function is documented in the literature (Forrest et al. 2017). We also aim to determine the structural and statistical model that best explain the variability in serum creatinine (SCr) in the studied patient population.

Project 3: Usage of prior data to support clinical development

The aim in this part, is to develop an approach on including preclinical PKPD data to the analysis of clinical trials of antibiotics. Prior PKPD data will be incorporated into the model from Project 1.

Project 4: Model-Based Adaptive Study Design

The aim is to explore adaptive model-based designs for mono- and combination therapy in order to reduce trial sizes. Two novel drug combinations, of clinically available drugs, are to be suggested for clinical testing based on a generated PKPD database.