Evaluation Strategies for Triple-Drug Combinations against Carbapenemase-Producing Klebsiella Pneumoniae in an In Vitro Hollow-Fiber Infection Model

Model-based learn and confirm: designing effective treatment regimens against multidrug resistant Gram-negative pathogens

Over the last decade, there has been a growing appreciation for the use of in vitro and in vivo infection models to generate robust and informative nonclinical PK/PD data to accelerate the clinical translation of treatment regimens. The objective of this study was to develop a model-based "learn and confirm" approach to help with the design of combination regimens using in vitro infection models to optimise the clinical utility of existing antibiotics. Static concentration time-kill studies were used to evaluate the PD activity of polymyxin B (PMB) and meropenem against two carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates; BAA2146 (PMB-susceptible) and BRKP67 (PMB-resistant). A mechanism-based model (MBM) was developed to quantify the joint activity of PMB and meropenem. In silico simulations were used to predict the time-course of bacterial killing using clinically-relevant PK exposure profiles. The predictive accuracy of the model was further evaluated by validating the model predictions using a one-compartment PK/PD in vitro dynamic infection model (IVDIM). The MBM captured the reduction in bacterial burden and regrowth well in both the BAA2146 and BRKP67 isolate (R2 = 0.900 and 0.940, respectively). The bacterial killing and regrowth predicted by the MBM were consistent with observations in the IVDIM: sustained activity against BAA2146 and complete regrowth of the BRKP67 isolate. Differences observed in PD activity suggest that additional dose optimisation might be beneficial in PMB-resistant isolates. The model-based approach presented here demonstrates the utility of the MBM as a translational tool from static to dynamic in vitro systems to effectively perform model-informed drug optimisation.

A population pharmacokinetic model of polymyxin B based on prospective clinical data to inform dosing in hospitalized patients

OBJECTIVES

To develop a population pharmacokinetic (PK) model with data from the largest polymyxin B-treated patient population studied to date to optimize its dosing in hospitalized patients.

METHODS

Hospitalized patients receiving intravenous polymyxin B for ≥48 hours were enrolled. Blood samples were collected at steady state and drug concentrations were analysed by liquid chromotography tandem mass spectrometry (LC-MS/MS). Population PK analysis and Monte Carlo simulations were performed to determine the probability of target attainment (PTA).

RESULTS

One hundred and forty-two patients received intravenous polymyxin B (1.33-6 mg/kg/day), providing 681 plasma samples. Twenty-four patients were on renal replacement therapy, including 13 on continuous veno-venous hemodiafiltration (CVVHDF). A 2-compartment model adequately described the PK with body weight as a covariate on the volume of distribution that affected Cmax, but it did not impact clearance or exposure. Creatinine clearance was a statistically significant covariate on clearance, although clinically relevant variations of dose-normalized drug exposure were not observed across a wide creatinine clearance range. The model described higher clearance in CVVHDF patients than in non-CVVHDF patients. Maintenance doses of ≥2.5 mg/kg/day or ≥150 mg/day had a PTA ≥90% (for non-pulmonary infections target) at a steady state for minimum inhibitory concentrations ≤2 mg/L. The PTA at a steady state for CVVHDF patients was lower.

DISCUSSION

Fixed loading and maintenance doses of polymyxin B seemed to be more appropriate than weight-based dosing regimens in patients weighing 45-90 kg. Higher doses may be needed in patients on CVVHDF. Substantial variability in polymyxin B clearance and volume of distribution was found, suggesting that therapeutic drug monitoring may be indicated.

Proof-of-concept for incorporating mechanistic insights from multi-omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae

Carbapenemase-resistant Klebsiella pneumoniae (KP) resistant to multiple antibiotic classes necessitates optimized combination therapy. Our objective is to build a workflow leveraging omics and bacterial count data to identify antibiotic mechanisms that can be used to design and optimize combination regimens. For pharmacodynamic (PD) analysis, previously published static time-kill studies (J Antimicrob Chemother 70, 2015, 2589) were used with polymyxin B (PMB) and chloramphenicol (CHL) mono and combination therapy against three KP clinical isolates over 24 h. A mechanism-based model (MBM) was developed using time-kill data in S-ADAPT describing PMB-CHL PD activity against each isolate. Previously published results of PMB (1 mg/L continuous infusion) and CHL (C_max : 8 mg/L; bolus q6h) mono and combination regimens were evaluated using an in vitro one-compartment dynamic infection model against a KP clinical isolate (10⁸ CFU/ml inoculum) over 24 h to obtain bacterial samples for multi-omics analyses. The differentially expressed genes and metabolites in these bacterial samples served as input to develop a partial least squares regression (PLSR) in R that links PD responses with the multi-omics responses via a multi-omics pathway analysis. PMB efficacy was increased when combined with CHL, and the MBM described the observed PD well for all strains. The PLSR consisted of 29 omics inputs and predicted MBM PD response (R²  = 0.946). Our analysis found that CHL downregulated metabolites and genes pertinent to lipid A, hence limiting the emergence of PMB resistance. Our workflow linked insights from analysis of multi-omics data with MBM to identify biological mechanisms explaining observed PD activity in combination therapy.

Applications of the hollow-fibre infection model (HFIM) in viral infection studies

Conventional cell culture systems involve growing cells in stationary cultures in the presence of growth medium containing various types of supplements. At confluency, the cells are divided and further expanded in new culture dishes. This passage from confluent monolayer to sparse cultures does not reflect normal physiological conditions and represents quite a drastic physiological change that may affect the natural cell physiobiology. Hollow-fibre bioreactors were in part developed to overcome these limitations and since their inception, they have widely been used in production of monoclonal antibodies and recombinant proteins. These bioreactors are increasingly used to study antibacterial drug effects via simulation of in vivo pharmacokinetic profiles. The use of the hollow-fibre infection model (HFIM) in viral infection studies is less well developed and in this review we have analysed and summarized the current available literature on the use of these bioreactors, with an emphasis on viruses. Our work has demonstrated that this system can be applied for viral expansion, studies of drug resistance mechanisms, and studies of pharmacokinetic/pharmacodynamic (PK/PD) of antiviral compounds. These platforms could therefore have great applications in large-scale vaccine development, and in studies of mechanisms driving antiviral resistance, since the HFIM could recapitulate the same resistance mechanisms and mutations observed in vivo in clinic. Furthermore, some dosage and spacing regimens evaluated in the HFIM system, as allowing maximal viral suppression, are in line with clinical practice and highlight this 'in vivo-like' system as a powerful tool for experimental validation of in vitro-predicted antiviral activities.

Losing the Battle but Winning the War: Can Defeated Antibacterials Form Alliances to Combat Drug-Resistant Pathogens?

Despite the recent development of antibacterials that are active against multidrug-resistant pathogens, drug combinations are often necessary to optimize the killing of difficult-to-treat organisms. Antimicrobial combinations typically are composed of multiple agents that are active against the target organism; however, many studies have investigated the potential utility of combinations that consist of one or more antibacterials that individually are incapable of killing the relevant pathogen. The current review summarizes in vitro, in vivo, and clinical studies that evaluate combinations that include at least one drug that is not active individually against Pseudomonas aeruginosa, Klebsiella pneumoniae, Acinetobacter baumannii, or Staphylococcus aureus. Polymyxins were often included in combinations against all three of the Gram-negative pathogens, and carbapenems were commonly incorporated into combinations against K. pneumoniae and A. baumannii. Minocycline, sulbactam, and rifampin were also frequently investigated in combinations against A. baumannii, whereas the addition of ceftaroline or another β-lactam to vancomycin or daptomycin showed promise against S. aureus with reduced susceptibility to vancomycin or daptomycin. Although additional clinical studies are needed to define the optimal combination against specific drug-resistant pathogens, the large amount of in vitro and in vivo studies available in the literature may provide some guidance on the rational design of antibacterial combinations.

Antibiotic pharmacokinetic/pharmacodynamic modelling: MIC, pharmacodynamic indices and beyond

The dramatic increase in antimicrobial resistance and the limited pharmacological treatment options highlight the urgent need to optimize therapeutic regimens of new and available anti-infectives. Several in-vitro and in-vivo infection models are employed to understand the relationship between drug exposure profiles in plasma or at the site of infection (pharmacokinetics) and the time course of therapeutic response (pharmacodynamics) to select and optimize dosage regimens for new and approved drugs. Well-designed preclinical studies, combined with mathematical-model-based pharmacokinetic/pharmacodynamic analysis and in-silico simulations, are critical for the effective translation of preclinical data and design of appropriate and successful clinical trials. Integration with population pharmacokinetic modelling and simulations allows for the incorporation of interindividual variability that occurs in both pharmacokinetics and pharmacodynamics, and helps to predict the probability of target attainment and treatment outcome in patients. This article reviews the role of pharmacokinetic/pharmacodynamic approaches in the optimization of dosage regimens to maximize antibacterial efficacy while minimizing toxicity and emergence of resistance, and to achieve a high likelihood of therapeutic success. Polymyxin B, an approved drug with a narrow therapeutic window, serves as an illustrative example to highlight the importance of pharmacokinetic/pharmacodynamic modelling in conjunction with experimentation, employing static time-kill studies followed by dynamic in-vitro or in-vivo models, or both, to learn and confirm mechanistic insights necessary for translation to the bedside.