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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.

Model-based approaches, including population pharmacokinetic-pharmacodynamic modeling, have become an essential component in the clinical phases of oncology drug development. Over the past two decades, models have evolved to describe the temporal dynamics of biomarkers and tumor size, treatment-related adverse events, and their links to survival. Integrated models, defined here as models that incorporate at least two pharmacodynamic/ outcome variables, are applied to answer drug development questions through simulations, e.g., to support the exploration of alternative dosing strategies and study designs in subgroups of patients or other tumor indications. It is expected that these pharmacometric approaches will be expanded as regulatory authorities place further emphasis on early and individualized dosage optimization and inclusive patient-focused development strategies. This review provides an overview of integrated models in the literature, examples of the considerations that need to be made when applying these advanced pharmacometric approaches, and an outlook on the expected further expansion of model-informed drug development of anticancer drugs.Keywords: Anticancer drugs; Dose individualization; Dose optimization; Joint models; Model-informed drug development; Pharmacodynamic; Pharmacokinetic; Pharmacometrics; Tumor growth inhibition model.

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.

Objectives

To evaluate the impact of antibiotic dose fractionation study design on pharmacokinetic/pharmacodynamic (PK/PD) indices and PKPD model estimation.

Methods

PKPD models for meropenem and polymyxin B (PMB) were applied to (i) simulate various dose fractionation studies in mice to derive PK/PD indices and efficacy targets and (ii) perform stochastic simulations and estimations evaluating which efficacy assessment times, in addition to 24 h, would improve the estimation of drug effect parameters.

Results

The R² values of PK/PD indices were primarily influenced by reductions of the dosing intervals for meropenem and by decreases of the lowest total daily dose for PMB. For certain study designs (e.g. frequent administration of higher meropenem doses), R² values for fT > MIC and fAUC/MIC were similar. Efficacy target magnitudes were also sensitive to the selected doses. Additional efficacy assessment times improved parameter accuracy (e.g. 40% reduction in relative root mean squared error of PMB effect slope). The model parameter accuracy was more affected by the selection of time points for meropenem, which included resistance, than for PMB. Efficacy measurements in the first hours after treatment start (e.g. 2 and 6 h), in addition to 24 h, were essential for resistance characterization.

Conclusions

The choice of doses and fractionations impacted PK/PD index selection and efficacy target magnitude. Depending on the antibiotic, the dose or fractionation selection appeared to be the most critical. Early treatment efficacy measurements were beneficial to PKPD model-based analyses, particularly to describe resistance processes.

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed‐effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

The majority of bispecific costimulatory antibodies in cancer immunotherapy are capable of exerting tumor-specific T-cell activation by simultaneously engaging both tumor-associated targets and costimulatory receptors expressed by T cells. The amount of trimeric complex formed when the bispecific antibody is bound simultaneously to the T cell receptor and the tumor-associated target follows a bell-shaped curve with increasing bispecific antibody exposure/dose. The shape of the curve is determined by the binding affinities of the bispecific antibody to its two targets and target expression. Here, using the case example of FAP-4-1BBL, a fibroblast activation protein alpha (FAP)-directed 4-1BB (CD137) costimulator, the impact of FAP-binding affinity on trimeric complex formation and pharmacology was explored using mathematical modeling and simulation. We quantified (1) the minimum number of target receptors per cell required to achieve pharmacological effect, (2) the expected coverage of the patient population for 19 different solid tumor indications, and (3) the range of pharmacologically active exposures as a function of FAP-binding affinity. A 10-fold increase in FAP-binding affinity (from a dissociation constant [K_D] of 0.7 nM–0.07 nM) was predicted to reduce the number of FAP receptors needed to achieve 90% of the maximum pharmacological effect from 13,400 to 4,000. Also, the number of patients with colon cancer that would achieve 90% of the maximum effect would increase from 6% to 39%. In this work, a workflow to select binding affinities for bispecific antibodies that integrates preclinical in vitro data, mathematical modeling and simulation, and knowledge on target expression in the patient population, is provided. The early implementation of this approach can increase the probability of success with cancer immunotherapy in clinical development.

Background: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).

Methods: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.

Results: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).

Conclusions: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Background

Continuous infusion of meropenem has been proposed to increase target attainment in critically ill patients, although stability might limit its practical use. This study investigated the impact of meropenem degradation and infusion bag changes on the concentration-time profiles and bacterial growth and killing of P. aeruginosa given different continuous-infusion solutions.

Methods

A semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) model quantifying meropenem concentrations (C_MEM) and bacterial counts of a resistant P. aeruginosa strain (ARU552, MIC = 16 mg/L) over 24 h was used to translate in vitro antibiotic effects to patients with severe infections. Concentration-dependent drug degradation of saline infusion solutions was considered using an additional compartment in the population PK model. C_MEM, fT_>MIC (time that concentrations exceed the MIC) and total bacterial load (B_TOT) after 24 h were simulated for different scenarios (n = 144), considering low- and high-dose regimens (3000/6000 mg/day±loading dose), clinically relevant infusion solutions (20/40/50 mg/mL), different intervals of infusion bag changes (every 8/24 h, q8/24 h), and varied renal function (creatinine clearance 40/80/120 mL/min) and MIC values (8/16 mg/L).

Results

Highest deviations between changing infusion bags q8h and q24h were observed for 50 mg/mL solutions and scenarios with C_{MEM_24h} close to the MIC, with differences (Δ) in C_{MEM_24h} up to 4.9 mg/L, ΔfT_>MIC≤65.7%, and ΔB_{TOT_24h}≤1.1 log10 CFU/mL, thus affecting conclusions on whether bacteriostasis was reached.

Conclusions

In summary, this study indicated that for continuous infusion of meropenem, eight-hourly infusion bag changes improved PK/PD target attainment and might be beneficial particularly for high meropenem concentrations of saline infusion solutions and for plasma concentrations in close proximity to the MIC.