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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.

Background We evaluated the association between mortality and colistin resistance in Acinetobacter baumannii infections and the interaction with antibiotic therapy. Methods This is a secondary analysis of a randomized controlled trial of patients with carbapenem-resistant gram-negative bacterial infections treated with colistin or colistin-meropenem combination. We evaluated patients with infection caused by carbapenem-resistant A. baumannii (CRAB) identified as colistin susceptible (CoS) at the time of treatment and compared patients in which the isolate was confirmed as CoS with those whose isolates were retrospectively identified as colistin resistant (CoR) when tested by broth microdilution (BMD). The primary outcome was 28-day mortality. Results Data were available for 266 patients (214 CoS and 52 CoR isolates). Patients with CoR isolates had higher baseline functional capacity and lower rates of mechanical ventilation than patients with CoS isolates. All-cause 28-day mortality was 42.3% (22/52) among patients with CoR strains and 52.8% (113/214) among patients with CoS isolates (P = .174). After adjusting for variables associated with mortality, the mortality rate was lower among patients with CoR isolates (odds ratio [OR], 0.285 [95% confidence interval {CI}, .118–.686]). This difference was associated with treatment arm: Mortality rates among patients with CoR isolates were higher in those randomized to colistin-meropenem combination therapy compared to colistin monotherapy (OR, 3.065 [95% CI, 1.021–9.202]). Conclusions Colistin resistance determined by BMD was associated with lower mortality among patients with severe CRAB infections. Among patients with CoR isolates, colistin monotherapy was associated with a better outcome compared to colistin-meropenem combination therapy. Clinical Trials Registration NCT01732250

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.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.PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations.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.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.

Background Diazabicyclooctanes (DBOs) are an increasingly important group of non β-lactam β-lactamase inhibitors, employed clinically in combinations such as ceftazidime/avibactam. The dose finding of such combinations is complicated using the traditional pharmacokinetic/pharmacodynamic (PK/PD) index approach, especially if the β-lactamase inhibitor has an antibiotic effect of its own. Objectives To develop a novel mechanism-based pharmacokinetic–pharmacodynamic (PKPD) model for ceftazidime/avibactam against Gram-negative pathogens, with the potential for combination dosage simulation. Methods Four β-lactamase-producing Enterobacteriaceae, covering Ambler classes A, B and D, were exposed to ceftazidime and avibactam, alone and in combination, in static time–kill experiments. A PKPD model was developed and evaluated using internal and external evaluation, and combined with a population PK model and applied in dosage simulations. Results The developed PKPD model included the effects of ceftazidime alone, avibactam alone and an ‘enhancer’ effect of avibactam on ceftazidime in addition to the β-lactamase inhibitory effect of avibactam. The model could describe an extensive external Pseudomonas aeruginosa data set with minor modifications to the enhancer effect, and the utility of the model for clinical dosage simulation was demonstrated by investigating the influence of the addition of avibactam. Conclusions A novel mechanism-based PKPD model for the DBO/β-lactam combination ceftazidime/avibactam was developed that enables future comparison of the effect of avibactam with other DBO/β-lactam inhibitors in simulations, and may be an aid in translating PKPD results from in vitro to animals and humans.

Background The β-lactam antibiotic piperacillin (in combination with tazobactam) is commonly chosen for empirical treatment of suspected bacterial infections. However, pharmacokinetic variability among patient populations and across ages leads to uncertainty when selecting a dosing regimen to achieve an appropriate pharmacodynamic target. Objectives To guide dosing by establishing a population pharmacokinetic model for unbound piperacillin in febrile children receiving cancer chemotherapy, and to assess pharmacokinetic/pharmacodynamic target attainment (100% fT > 1×MIC and 50% fT > 4×MIC) and resultant exposure, across body weights. Methods Forty-three children admitted for 89 febrile episodes contributed 482 samples to the pharmacokinetic analysis. The typical doses required for target attainment were compared for various dosing regimens, in particular prolonged infusions, across MICs and body weights. Results A two-compartment model with inter-fever-episode variability in CL, and body weight included through allometry, described the data. A high CL of 15.4 L/h (70 kg) combined with high glomerular filtration rate (GFR) values indicated rapid elimination and hyperfiltration. The target of 50% fT > 4×MIC was achieved for an MIC of 4.0 mg/L in a typical patient with extended infusions of 2–3 (q6h) or 3–4 (q8h) h, at or below the standard adult dose (75 and 100 mg/kg/dose for q6h and q8h, respectively). Higher doses or continuous infusion were needed to achieve 100% fT > 1×MIC due to the rapid piperacillin elimination. Conclusions The licensed dose for children with febrile neutropenia (80 mg/kg q6h as a 30 min infusion) performs poorly for attainment of fT>MIC pharmacokinetic/pharmacodynamic targets. Given the population pharmacokinetic profile, feasible dosing regimens with reasonable exposure are continuous infusion (100% fT > 1×MIC) or prolonged infusions (50% fT > 4×MIC).

BACKGROUND: Data on piperacillin-tazobactam pharmacokinetics and optimal dosing in children with cancer and fever are limited. Our objective was to investigate piperacillin pharmacokinetics and the probability of target attainment (PTA) with standard intermittent administration (IA), and to simulate PTA in other dosing regimens.PROCEDURE: This prospective pharmacokinetic study was conducted from April 2016 to January 2018. Children with cancer receiving empiric piperacillin-tazobactam to treat infections were included. Piperacillin-tazobactam 100 mg/kg was infused over 5 min every 8 hours (IA). An optimized sample schedule provided six blood samples per subject for piperacillin concentration determination. The evaluated targets included: (1) 100% time of free piperacillin concentration above the minimum inhibitory concentration (fT > MIC) and (2) 50% fT > 4× MIC. MIC50 and MIC90 were defined based on an intrainstitutional MIC range.RESULTS: A total of 482 piperacillin concentrations were obtained from 43 children (aged 1-18 years) during 89 fever episodes. Standard IA resulted in insufficient target attainment, with significant differences in piperacillin pharmacokinetics for different body weights. Median fT > MIC was 61.2%, 53.5%, and 36.3% for MIC50 (2.0 mg/L), MIC90 (4.0 mg/L), and breakpoint for Pseudomonas aeruginosa (16.0 mg/L), respectively. Correspondingly, the median fT > 4× MIC was 43%, 36.3%, and 20.1%. Simulations showed that only continuous infusion reached a PTA of 95% for MIC = 16.0 mg/L, while extended infusion lasting half of the dosing interval reached a PTA of 95% for MIC ≤ 8 mg/L.CONCLUSIONS: Our data revealed insufficient PTA with standard IA of piperacillin-tazobactam in children with cancer and fever. Alternative dosing strategies, preferably continuous infusion, are required to ensure adequate PTA.

Predicting competition between antibiotic-susceptible wild-type (WT) and less susceptible mutant (MT) bacteria is valuable for understanding how drug concentrations influence the emergence of resistance. Pharmacokinetic/pharmacodynamic (PK/PD) models predicting the rate and extent of takeover of resistant bacteria during different antibiotic pressures can thus be a valuable tool in improving treatment regimens. The aim of this study was to evaluate a previously developed mechanism-based PK/PD model for its ability to predict in vitro mixed-population experiments with competition between Escherichia coli (E. coli) WT and three well-defined E. coli resistant MTs when exposed to ciprofloxacin. Model predictions for each bacterial strain and ciprofloxacin concentration were made for in vitro static and dynamic time–kill experiments measuring CFU (colony forming units)/mL up to 24 h with concentrations close to or below the minimum inhibitory concentration (MIC), as well as for serial passage experiments with concentrations well below the MIC measuring ratios between the two strains with flow cytometry. The model was found to reasonably well predict the initial bacterial growth and killing of most static and dynamic time–kill competition experiments without need for parameter re-estimation. With parameter re-estimation of growth rates, an adequate fit was also obtained for the 6-day serial passage competition experiments. No bacterial interaction in growth was observed. This study demonstrates the predictive capacity of a PK/PD model and further supports the application of PK/PD modelling for prediction of bacterial kill in different settings, including resistance selection.

Antibiotic dose predictions based on PK/PD indices rely on that the index type and magnitude is insensitive to the pharmacokinetics (PK), the dosing regimen, and bacterial susceptibility. In this work we perform simulations to challenge these assumptions for meropenem and Pseudomonas aeruginosa. A published murine dose fractionation study was replicated in silico. The sensitivity of the PK/PD index towards experimental design, drug susceptibility, uncertainty in MIC and different PK profiles was evaluated. The previous murine study data were well replicated with fT > MIC selected as the best predictor. However, for increased dosing frequencies fAUC/MIC was found to be more predictive and the magnitude of the index was sensitive to drug susceptibility. With human PK fT > MIC and fAUC/MIC had similar predictive capacities with preference for fT > MIC when short t1/2 and fAUC/MIC when long t1/2. A longitudinal PKPD model based on in vitro data successfully predicted a previous in vivo study of meropenem. The type and magnitude of the PK/PD index were sensitive to the experimental design, the MIC and the PK. Therefore, it may be preferable to perform simulations for dose selection based on an integrated PK-PKPD model rather than using a fixed PK/PD index target.

Current excessive use and abuse of antibiotics has resulted in increasing bacterial resistance to common treatment options which is threatening to deprive us of a pillar of modern medicine. In this work methods to optimize the use of existing antibiotics and to help development of new antibiotics were developed and applied.Semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) models were developed to describe the time course of the dynamic effect and interaction of combinations of antibiotics. The models were applied to illustrate that colistin combined with a high dose of meropenem may overcome meropenem-resistant P. aeruginosa infections.The results from an in vivo dose finding study of meropenem was successfully predicted by the meropenem PKPD model in combination with a murine PK model, which supports model based dosage selection. However, the traditional PK/PD index based dose selection was predicted to have poor extrapolation properties from pre-clinical to clinical settings, and across patient populations.The precision of the model parameters, and hence the model predictions, is dependent on the experimental design. A limited study design is dictated by cost and, for in vivo studies, ethical reasons. In this work optimal design (OD) was demonstrated to be able to reduce the experimental effort in time-kill curve experiments and was utilized to suggest the experimental design for identification and estimation of an interaction between antibiotics.OD methods to handle inter occasion variability (IOV) in optimization of individual PK parameter estimates were proposed. The strategy was applied in the design of a sparse sampling schedule that aim to estimate individual exposures of colistin in a multi-centre clinical study. Plasma concentration samples from the first 100 patients have been analysed and indicate that the performance of the design is close to the predicted.The methods described in this thesis holds promise to facilitate the development of new antibiotics and to improve the use of existing antibiotics.

Inter occasion variability (IOV) is of importance to consider in the development of a design where individual pharmacokinetic or pharmacodynamic parameters are of interest. IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters, yet the influence of inclusion of IOV in optimal design for estimation of individual parameters has not been investigated. In this work two methods of including IOV in the maximum a posteriori Fisher information matrix (FIMMAP) are evaluated: (i) MAP(occ)-the IOV is included as a fixed effect deviation per occasion and individual, and (ii) POPocc-the IOV is included as an occasion random effect. Sparse sampling schedules were designed for two test models and compared to a scenario where IOV is ignored, either by omitting known IOV (Omit) or by mimicking a situation where unknown IOV has inflated the IIV (Inflate). Accounting for IOV in the FIMMAP markedly affected the designs compared to ignoring IOV and, as evaluated by stochastic simulation and estimation, resulted in superior precision in the individual parameters. In addition MAP(occ) and POPocc accurately predicted precision and shrinkage. For the investigated designs, the MAP(occ) method was on average slightly superior to POPocc and was less computationally intensive.