In pursuit of the triple crown: mechanism-based pharmacodynamic modelling for the optimization of three-drug combinations against KPC-producing Klebsiella pneumoniae.

OBJECTIVES: Optimal combination therapy for Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae (KPC-Kp) is unknown. The present study sought to characterize the pharmacodynamics (PD) of polymyxin B (PMB), meropenem (MEM) and rifampin (RIF) alone and in combination using a hollow fibre infection model (HFIM) coupled with mechanism-based modelling (MBM). METHODS: A 10-day HFIM was utilized to simulate human pharmacokinetics (PK) of various PMB, MEM and RIF dosing regimens against a clinical KPC-Kp isolate, with total and resistant subpopulations quantified to capture PD response. A MBM was developed to characterize bacterial subpopulations and synergy between agents. Simulations using the MBM and published population PK models were employed to forecast the bacterial time course and the extent of its variability in infected patients for three-drug regimens. RESULTS: In the HFIM, a PMB single-dose ('burst') regimen of 5.53 mg/kg combined with MEM 8 g using a 3-hr prolonged infusion every 8 hr and RIF 600 mg every 24 hr resulted in bacterial counts below the quantitative limit within 24 hr and remained undetectable throughout the 10-day experiment. The final MBM consisted of two bacterial subpopulations of differing PMB and MEM joint susceptibility and the ability to form a non-replicating, tolerant subpopulation. Synergistic interactions between PMB, MEM and RIF were well quantified, with the MBM providing adequate capture of the observed data. DISCUSSION: An in vitro-in silico approach answers questions related to PD optimization as well as overall feasibility of combination therapy against KPC-Kp, offering crucial insights in the absence of clinical trials.

Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii.

OBJECTIVES: Increased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this study were: (a) to evaluate the in vitro pharmacodynamics of meropenem and polymyxin B (PMB) combinations against A. baumannii; (b) to utilize a mechanism-based mathematical model to quantify bacterial killing; and (c) to develop a genetic algorithm (GA) to define optimal dosing strategies for meropenem and PMB. METHODS: A. baumannii (N16870; MICmeropenem = 16 mg/L, MICPMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 108 cfu/mL) over 14 days against meropenem and PMB combinations. A mechanism-based model of the data and population pharmacokinetics of each drug were used to develop a GA to define the optimal regimen parameters. RESULTS: Monotherapies resulted in regrowth to ~1010 cfu/mL by 24 h, while combination regimens employing high-intensity PMB exposure achieved complete bacterial eradication (0 cfu/mL) by 336 h. The mechanism-based model demonstrated an SC50 (PMB concentration for 50% of maximum synergy on meropenem killing) of 0.0927 mg/L for PMB-susceptible subpopulations versus 3.40 mg/L for PMB-resistant subpopulations. The GA had a preference for meropenem regimens that improved the %T > MIC via longer infusion times and shorter dosing intervals. The GA predicted that treating 90% of simulated subjects harbouring a 108 cfu/mL starting inoculum to a point of 100 cfu/mL would require a regimen of meropenem 19.6 g/day 2 h prolonged infusion (2 hPI) q5h + PMB 5.17 mg/kg/day 2 hPI q6h (where the 0 h meropenem and PMB doses should be 'loaded' with 80.5% and 42.2% of the daily dose, respectively). CONCLUSION: This study provides a methodology leveraging in vitro experimental data, a mathematical pharmacodynamic model, and population pharmacokinetics provide a possible avenue to optimize treatment regimens beyond the use of the 'traditional' indices of antibiotic action.

The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections.

Critically ill patients with severe infections are at high risk of suboptimal antimicrobial dosing. The pharmacokinetics (PK) and pharmacodynamics (PD) of antimicrobials in these patients differ significantly from the patient groups from whose data the conventional dosing regimens were developed. Use of such regimens often results in inadequate antimicrobial concentrations at the site of infection and is associated with poor patient outcomes. In this article, we describe the potential of in vitro and in vivo infection models, clinical pharmacokinetic data and pharmacokinetic/pharmacodynamic models to guide the design of more effective antimicrobial dosing regimens. Individualised dosing, based on population PK models and patient factors (e.g. renal function and weight) known to influence antimicrobial PK, increases the probability of achieving therapeutic drug exposures while at the same time avoiding toxic concentrations. When therapeutic drug monitoring (TDM) is applied, early dose adaptation to the needs of the individual patient is possible. TDM is likely to be of particular importance for infected critically ill patients, where profound PK changes are present and prompt appropriate antibiotic therapy is crucial. In the light of the continued high mortality rates in critically ill patients with severe infections, a paradigm shift to refined dosing strategies for antimicrobials is warranted to enhance the probability of achieving drug concentrations that increase the likelihood of clinical success.