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Background

The emergence of β-lactamase-producing bacteria limits the effectiveness of β-lactam (BL) antibiotics, and the combination with a β-lactamase inhibitor (BLI) aims to counteract this resistance. However, existing guidelines primarily focus on optimizing the dosing of BLs and do not adequately address the interaction between BLs and BLIs, leading to uncertain pharmacokinetic/pharmacodynamic (PK/PD) targets and potentially suboptimal dosing strategies.

Objectives

To investigate optimal PK/PD targets and dosing strategies for avibactam (BLI) combined with ceftazidime (BL) using mechanism-based PKPD models.

Methods

PK models for ceftazidime and avibactam were integrated with mechanism-based PKPD models for Gram-negative bacteria. Simulations explored dose regimens in mice and humans, evaluating PK/PD indices and computing the PTA for diverse dosing strategies and infusion modes.

Results

fAUC/MIC_CAZ/AVI was the most predictive index for avibactam against Enterobacteriaceae in both mice and humans, regardless of infusion mode. Against Pseudomonas aeruginosa, fT > C_T predicted efficacy in mice, while fAUC/MIC_CAZ/AVI and fCmax/MIC_CAZ/AVI were more predictive in humans, particularly for continuous infusion regimens. Higher PTAs were achieved with increased avibactam doses relative to ceftazidime, particularly with 1:1 and 2:1 ceftazidime:avibactam ratios. Continuous infusion improved PTA against P. aeruginosa but had limited impact on Enterobacteriaceae.

Conclusion

The PK/PD indices predictive of avibactam efficacy varied by species (mice and humans), bacterial strains, and mode of infusion. Dosing simulations suggest that increasing avibactam relative to ceftazidime and using continuous infusion regimens may enhance bacterial killing. These findings highlight the importance of refining dosing strategies for both components of the combination therapy.

Objectives:

To expand a translational pharmacokinetic-pharmacodynamic (PKPD) modelling approach for assessing the combined effect of polymyxin B and minocycline against Klebsiella pneumoniae.

Methods:

A PKPD model developed based on in vitro static time-kill experiments of one strain (ARU613) was first translated to characterize that of a more susceptible strain (ARU705), and thereafter to dynamic time-kill experiments (both strains) and to a murine thigh infection model (ARU705 only). The PKPD model was updated stepwise using accumulated data. Predictions of bacterial killing in humans were performed.

Results:

The same model structure could be used in each translational step, with parameters being re- estimated. Dynamic data were well predicted by static-data-based models. The in vitro/in vivo differences were primarily quantified as a change in polymyxin B effect: a lower killing rate constant in vivo compared with in vitro (concentration of 3 mg/L corresponds to 0.05/h and 57/h, respectively), and a slower adaptive resistance rate (the constant in vivo was 2.5% of that in vitro ). There was no significant difference in polymyxin B-minocycline interaction functions. Predictions based on both in vitro and in vivo parameters indicated that the combination has a greater-than-monotherapy antibacterial effect in humans, forecasting a reduction of approximately 5 and 2 log10 colony-forming units/mL at 24 h, respectively, under combined therapy, while the maximum bacterial load was reached in monotherapy.

Conclusions:

This study demonstrated the utility of the PKPD modelling approach to understand translation of antibiotic effects across experimental systems, and showed a promising antibacterial effect of polymyxin B and minocycline in combination against K. pneumoniae.

Purpose: To investigate the pharmacokinetics (PK) of ceftazidime-avibactam (CAZ-AVI) in critically ill patients undergoing continuous venovenous hemodiafiltration (CVVHDF), and compare with a general phase III trial population.

Methods: A prospective PK study was conducted in critically ill patients who received CVVHDF for acute kidney injury, treated with CAZ-AVI (1000/250 mg or 2000/500 mg q8h). Plasma and CVVHDF-circuit samples were collected to determine CAZ-AVI concentrations. Individual PK parameters at steady-state were estimated using non-compartmental analysis. For visual comparison, plasma concentrations from CVVHDF patients were overlaid with simulated data from patients not receiving CVVHDF based on previously developed population PK models.

Results: A total of 35 plasma samples and 16 CVVHDF-circuit samples were obtained from four patients, with two patients sampled on two separate occasions. Median total clearance and volume of distribution were 4.54 L/h and 73.2 L for CAZ and 10.5 L/h and 102 L for AVI, respectively. Median contribution of CVVHDF to total clearance was 19.8% for CAZ and 5.3% for AVI. Observed CAZ-AVI PK profiles were generally within the 90% confidence interval of model predictions, but the observed concentrations were notably lower early (0-2 h) and higher later (4-8 h) in the dosing interval, suggesting a higher volume of distribution.

Conclusions: These results suggest that the CAZ-AVI dose regimens used in this study can be applicable in critically ill patients undergoing CVVHDF, despite the different shape of the PK profiles observed in this population. Further research with a larger patient cohort is warranted to validate and refine these findings.

BACKGROUND: Current guidelines recommend dosing vancomycin based on the area under the concentration time curve (AUC) to maximise efficacy and minimise the risk of nephrotoxicity. The preferred approach to AUC-guided therapy is to apply model-informed precision dosing (MIPD). However, the adoption in clinical practice has been slow.

AIM: We aimed to develop an intervention, including a standardised MIPD workflow and an implementation plan for vancomycin AUC-guided dosing, in a Swedish tertiary hospital.

METHOD: The intervention was developed in a framework-guided process. The design phase included stakeholder feedback (nurses, pharmacists, physicians), local data collection and feasibility testing of intervention components with parallel consideration of implementation aspects. The hypothesised relationships between the different components, implementation strategies and the mechanism of action resulting in expected outcomes were represented by a logic model.

RESULTS: The final intervention consisted of a workflow for MIPD, with defined roles and responsibilities, as well as processes for data and information transfer. Details were provided in supportive documents; an instruction on therapeutic drug monitoring (TDM) sampling and documentation for nurses, and a detailed dosing software instruction for MIPD consultants and clinical pharmacists. Activities to facilitate implementation included the development of a local clinical routine for vancomycin dosing, staff training and recurring MIPD rounds.

CONCLUSION: An intervention for MIPD, with an implementation plan for AUC-guided dosing of vancomycin, was developed for a tertiary hospital setting. The process can be used as guidance for other institutions with similar context wishing to initiate MIPD.

In vitro time-kill curve (TKC) experiments are an important part of the pharmacokinetic- pharmacodynamic (PKPD) characterisation of antibiotics. Traditional TKCs use Mueller-Hinton broth (MHB), which lacks specific plasma components that could potentially influence the bacterial growth and killing dynamics, and affect translation to in vivo. This study aimed to evaluate the impact of plasma on the PKPD characterisation of two antibiotics; cefazolin and clindamycin. TKC experiments were conducted in pure MHB, and MHB spiked with 20% and 70% human plasma. Plasma protein binding (PPB) data were available, and a linear model described cefazolin's PPB, while clindamycin's PPB was best described by a second-order polynomial model. PKPD models were developed based on pure MHB and described drug effects using an E_max model, with consideration of adaptive resistance for cefazolin. The observed bacterial growth and killing in the plasma-spiked MHB TKC data was insufficiently described when applying the developed PPB and PKPD models. In plasma spiked MHB, a growth delay was observed, estimated to 0.25 h (20% plasma), or 2.90 h (70% plasma) for cefazolin, and 0.64 h (20% plasma), or 1.40 h (70% plasma) for clindamycin. Furthermore, the drug effect was higher than expected in plasma-spiked MHB, with bacterial stasis and/or killing at unbound concentrations below MIC, necessitating drug effect parameter scaling (C₅₀ for cefazolin, Hill coefficient for clindamycin). The findings highlight significant differences in bacterial growth and killing dynamics between pure MHB and plasma-spiked MHB and exemplify how PKPD modelling may be used to improve the translation of in vitro results.

<bold>Background: </bold>Only about 50% of intensive care unit (ICU) patients reach a free trough concentration above MIC (100% fT > MIC) of beta-lactam antibiotics. Although dose adjustments based on therapeutic drug monitoring (TDM) could be beneficial, TDM is not widely available. We investigated serum creatinine-based estimated GFR (eGFR) as a rapid screening tool to identify ICU patients at risk of insufficient exposure. <bold>Method: </bold>Ninety-three adult patients admitted to four ICUs in southeast Sweden treated with piperacillin/tazobactam, meropenem, or cefotaxime were included. Beta-lactam trough concentrations were measured. The concentration target was set to 100% fT > MICECOFF (2, 4, and 16 mg/L based on calculated free levels for meropenem, cefotaxime, and piperacillin, respectively). eGFR was primarily determined via Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) and compared to three other eGFR equations. Data was analysed using logistic regression and receiver operative characteristic (ROC) curves. <bold>Results: </bold>With intermittent standard dosing, insufficient exposure was common in patients with a relative eGFR >= 48mL/min/1.73m(2) [85%, (45/53)], particularly when treated with cefotaxime [96%, (24/25)]. This eGFR cut-off had a sensitivity of 92% and specificity of 82% (AUC 0.871, p < 0.001) in identifying insufficient exposure. In contrast, patients with eGFR <48mL/min/1.73m(2) had high target attainment [90%, (36/40)] with a wide variability in drug exposure. There was no difference between the four eGFR equations (AUC 0.866-0.872, cut-offs 44-51 ml/min/1.73m(2)). <bold>Conclusion: </bold>Serum creatinine-based eGFR is a simple and widely available surrogate marker with potential for early identification of ICU patients at risk of insufficient exposure to piperacillin, meropenem, and cefotaxime.

Background: Patient involvement in medical-decision making is linked to improved patient outcomes and increased patient satisfaction.

Objectives: The aim was to explore how hospitalised older patients are and wish to be involved in medication decisions affecting their medication therapy after hospital discharge.

Methods: Naturalistic observations of consultations between healthcare professionals and hospitalised older patients who were about to be discharged were performed at in total three medical wards at two hospitals in Sweden. Subsequent semi-structured interviews with the patients were conducted within one week after discharge. The data were thematically analysed, guided by systematic text condensation.

Results: Twenty patients were included (mean age: 81 (SD 8) years, 45 % female). Three themes were identified: 1) Predetermined authoritarian structures; describes that neither patients nor healthcare professionals expected patients to be involved in medication decisions. The medication decisions were frequently already taken by the healthcare professionals prior to the consultations, 2) Difficulties in finding the right time and setting; displays inhibitory factors in patient involvement in medication decisions when the consultations occur in hospital, and 3) Communication focusing on benefits over side-effects; demonstrates that newly prescribed medications were rarely accompanied with information about side-effects. Patients felt they lacked sufficient knowledge to take informed decisions about medications.

Conclusions: There are structures limiting involvement of older patients in medication decisions prior to hospital discharge. A change in approach to consultations from both the patients and healthcare professionals is needed to provide patients with the knowledge they feel is needed to be sufficiently involved.

Background: Translation of experimental data on antibiotic activity typically relies on pharmacokinetic/pharmacodynamic (PK/PD) indices. Model-based approaches, considering the full antibiotic killing time course, could be an alternative.

Objectives: To develop a mechanism-based modelling framework to assess the in vitro and in vivo activity of the FabI inhibitor antibiotic afabicin, and explore the ability of a model built on in vitro data to predict in vivo outcome.

Methods: A PK/PD model was built to describe bacterial counts from 162 static in vitro time-kill curves evaluating the effect of afabicin desphosphono, the active moiety of the prodrug afabicin, against 21 Staphylococcus aureus strains. Combined with a mouse PK model, outcomes of afabicin doses of 0.011-190 mg/kg q6h against nine S. aureus strains in a murine thigh infection model were predicted, and thereafter refined by estimating PD parameters.

Results: A sigmoid Emax model, with EC50 scaled by the MIC described the afabicin desphosphono killing in vitro. This model predicted, without parameter re-estimation, the in vivo bacterial counts at 24 h within a ±1 log margin for most dosing groups. When parameters were allowed to be estimated, EC50 was 38%-45% lower in vivo, compared with in vitro, within the studied MIC range.

Conclusions: The developed PK/PD model described the time course of afabicin activity across experimental conditions and bacterial strains. This model showed translational capacity as parameters estimated on in vitro time-kill data could well predict the in vivo outcome for a wide variety of doses in a mouse thigh infection model.

Objectives

Applying physiologically-based pharmacokinetic (PBPK) modelling in sepsis could help to better understand how PK changes are influenced by drug- and patient-related factors. We aimed to elucidate the influence of sepsis pathophysiology on the PK of meropenem by applying PBPK modelling.

Methods

A whole-body meropenem PBPK model was developed and evaluated in healthy individuals, and renally impaired non-septic patients. Sepsis-induced physiological changes in body composition, organ blood flow, kidney function, albumin, and haematocrit were implemented according to a previously proposed PBPK sepsis model. Model performance was evaluated, and a local sensitivity analysis was conducted.

Results

The model-predicted PK metrics (AUC, C_max, CL, V_ss) were within 1.33-fold-error margin of published data for 87.5% of the simulated profiles in healthy individuals. In sepsis, the model provided good predictions for literature-digitised average plasma and tissue exposure data, where the model-predicted AUC was within 1.33-fold-error margin for 9 out 11 simulated study profiles. Furthermore, the model was applied to individual plasma concentration data from 52 septic patients, where the model-predicted AUC, C_max, and CL had a fold-error ratio range of 0.98–1.12, with alignment of the predicted and observed variability. For V_ss, the fold-error ratio was 0.81, and the model underpredicted the population variability. CL was sensitive to renal plasma clearance, and kidney volume, whereas V_ss was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume.

Conclusions

These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure.

Background: Carbapenem-resistant bacteria pose a threat to public health. Characterising thepharmacokinetics-pharmacodynamics (PKPD) of meropenem longitudinally in vivo against resistant bacteria could provide valuable information for development and translation of carbapenem-based therapies.

Objectives: To assess the time course of meropenem effects in vivo against strains with high MIC topredict PK/PD indices and expected efficacy in patients using a modelling approach.

Methods: A PKPD model was built on longitudinal bacterial count data to describe meropenem effectsagainst six Escherichia coli and Klebsiella pneumoniae strains (MIC values 32–128 mg/L) in a 24 h mousethigh infection model. The model was used to derive PK/PD indices from simulated studies in mice andto predict the efficacy of different infusion durations with high-dose meropenem (2 g q8 h/q12 h fornormal/reduced kidney function) in patients.

Results: Data from 592 mice were available for model development. The estimated meropenemconcentration-dependent killing rate was not associated with differences in MIC. The fraction of timethat unbound concentrations exceeded EC50 (fT>EC50, EC50 = 1.01 mg/L) showed higher correlations thanfT>MIC. For all investigated strains, bacteriostasis at 24 h was predicted for prolonged infusions of highdose meropenem monotherapy in >90% of patients.

Conclusions: The developed PKPD model successfully described bacterial growth and meropenem killingover time in the thigh infection model. For the investigated strains, the MIC, determined in vitro, orMIC-based PK/PD indices, did not predict in vivo response. Simulations suggested prolonged infusions ofhigh-dose meropenem to be efficacious in patients infected by the studied strains