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

OBJECTIVES: The aim was to analyse the population pharmacokinetics of colistin and to explore the relationship between colistin exposure and time to death. METHODS: Patients included in the AIDA randomized controlled trial were treated with colistin for severe infections caused by carbapenem-resistant Gram-negative bacteria. All subjects received a 9 million units (MU) loading dose, followed by a 4.5 MU twice daily maintenance dose, with dose reduction if creatinine clearance (CrCL) < 50 mL/min. Individual colistin exposures were estimated from the developed population pharmacokinetic model and an optimized two-sample per patient sampling design. Time to death was evaluated in a parametric survival analysis. RESULTS: Out of 406 randomized patients, 349 contributed pharmacokinetic data. The median (90% range) colistin plasma concentration was 0.44 (0.14-1.59) mg/L at 15 minutes after the end of first infusion. In samples drawn 10 hr after a maintenance dose, concentrations were >2 mg/L in 94% (195/208) and 44% (38/87) of patients with CrCL ≤120 mL/min, and >120 mL/min, respectively. Colistin methanesulfonate sodium (CMS) and colistin clearances were strongly dependent on CrCL. High colistin exposure to MIC ratio was associated with increased hazard of death in the multivariate analysis (adjusted hazard ratio (95% CI): 1.07 (1.03-1.12)). Other significant predictors included SOFA score at baseline (HR 1.24 (1.19-1.30) per score increase), age and Acinetobacter or Pseudomonas as index pathogen. DISCUSSION: The population pharmacokinetic model predicted that >90% of the patients had colistin concentrations >2 mg/L at steady state, but only 66% at 4 hr after start of treatment. High colistin exposure was associated with poor kidney function, and was not related to a prolonged survival.

Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations.

BACKGROUND: Deriving suitable dosing regimens for antibiotic combination therapy poses several challenges as the drug interaction can be highly complex, the traditional pharmacokinetic-pharmacodynamic (PKPD) index methodology cannot be applied straightforwardly, and exploring all possible dose combinations is unfeasible. Therefore, semi-mechanistic PKPD models developed based on in vitro single and combination experiments can be valuable to suggest suitable combination dosing regimens. AIMS: To outline how the interaction between two antibiotics has been characterized in semi-mechanistic PKPD models. We also explain how such models can be applied to support dosing regimens and design future studies. SOURCES: PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations. CONTENT: Thirteen publications were identified where ten had applied subpopulation synergy to characterize the combined effect, i.e. independent killing rates for each drug and bacterial subpopulation. We report the various types of interaction functions that have been used to describe the combined drug effects and that characterized potential deviations from additivity under the PKPD model. Simulations from the models had commonly been performed to compare single versus combined dosing regimens and/or to propose improved dosing regimens. IMPLICATIONS: Semi-mechanistic PKPD models allow for integration of knowledge on the interaction between antibiotics for various PK and PD profiles, and can account for associated variability within the population as well as parameter uncertainty. Decisions on suitable combination regimens can thereby be facilitated. We find the application of semi-mechanistic PKPD models to be essential for efficient development of antibiotic combination regimens that optimize bacterial killing and/or suppress resistance development.