Pharmacometric Antibiotics Research

Lena Friberg, Mats Karlsson, Elisabet Nielsen
 

Antibiotics are considered one of the greatest discoveries of modern therapeutic medicine and have turned previously fatal diseases into treatable minor illnesses. Today, treatment failures due to multidrug-resistant bacteria are becoming more frequently observed. The evolution of resistance is a natural phenomenon; however, the use and misuse of antibiotics will accelerate this phenomenon. We aim to advance the understanding of pharmacokinetic-pharmacodynamic relationships for antibiotics of value for a more streamlined drug development process and an improved therapeutic use of clinically available antibiotics that maximize the efficacy and minimize resistance development.

Today, dosing regimens are typically selected based on PK/PD indices that discard information on dynamic changes in the drug-bacteria interaction. Mechanism-based models describing time-kill curves from in vitro experiments form the basis for the modelling. The models have shown to be applicable across drugs and bacteria strains (including clinical isolates), for both static and dynamic concentration experiments, for different sizes of start inocula, for mixtures of wild-type and resistant bacteria, for drug combinations, and for predicting competition experiments of wild-type and mutants. Based on developed models, optimal experimental design techniques are applied to find experimental protocols that increase the efficiency of both pre-clinical and clinical studies. Typically, the PKPD characterisation of antibiotics is done based on pre-clinical data and high performing translational methods are thus central in the assessment of appropriate drug use. While the methodology based on PK/PD indices has been shown to be sensitive to experimental design, misspecification of the MIC, and differences in PK characteristics, the use of a mechanism-based PKPD modelling approach in dose selection has been suggested for increased robustness and extrapolation potential, especially for special patient populations. To further increase the translatability of pre-clinical results, our current research aim to incorporate the effect of the innate immune response in the model predictions. Pharmacometric models are developed describing the activation and effect of the innate immune system following endotoxin and/or bacterial exposures. Such models could be combined with the PKPD models and help to improve the understanding of the development of manifest inflammations/infections and how dosing regimens should be designed to optimize the efficacy and at the same time minimize the emergence of resistance.

Polymyxins have regained interest in recent years to overcome antibiotic drug resistance. We have assayed clinical data on colistin and CMS using an in-house developed LC-MS-MS method to understand the PK in different patient populations and the need for a loading dose. Whole-body Physiology-based Pharmacokinetic (WBPBK) models for CMS, colistin and ciprofloxacin have been developed based on data from various sources, including patients, healthy volunteers and several animal species. Such models can be used to understand the time-courses of the antibiotics, and thereby the bacterial killing, in different tissues.

One strategy to overcome and prevent emergence of resistance is to use antibiotic combination therapy. However, the search for effective combinations is challenging given the number of permutations of doses that could be tested. The use of alternative methods, such as digital time-lapse microscopy, to economically screen for potentially effective combinations are currently investigated. Further, for combinations, the drug-drug concentration ratio will vary over time depending on the PK profiles of the drugs. In this situation the use of mechanism-based modelling, that describes the combined effect on the bacterial killing, taking the time-aspect of PK as well as PD into account, is highly advantageous. The developed models can hence facilitate the translation of in vitro information to in vivo.