Sequential Time-Kill, a Simple Experimental Trick To Discriminate between Pharmacokinetics/Pharmacodynamics Models with Distinct Heterogeneous Subpopulations versus Homogenous Population with Adaptive Resistance

Characterization of Pseudomonas aeruginosa resistance to ceftolozane-tazobactam due to ampC and/or ampD mutations observed during treatment using semi-mechanistic PKPD modeling

A double ampC (AmpCG183D) and ampD (AmpDH157Y) genes mutations have been identified by whole genome sequencing in a Pseudomonas aeruginosa (PaS) that became resistant (PaR) in a patient treated by ceftolozane/tazobactam (C/T). To precisely characterize the respective contributions of these mutations on the decreased susceptibility to C/T and on the parallel increased susceptibility to imipenem (IMI), mutants were generated by homologous recombination in PAO1 reference strain (PAO1- AmpCG183D, PAO1-AmpDH157Y, PAO1-AmpCG183D/AmpDH157Y) and in PaR (PaR-AmpCPaS/AmpDPaS). Sequential time-kill curve experiments were conducted on all strains and analyzed by semi-mechanistic PKPD modeling. A PKPD model with adaptation successfully described the data, allowing discrimination between initial and time-related (adaptive resistance) effects of mutations. With PAO1 and mutant-derived strains, initial EC50 values increased by 1.4, 4.1, and 29-fold after AmpCG183D , AmpDH157Y and AmpCG183D/AmpDH157Y mutations, respectively. EC50 values were increased by 320, 12.4, and 55-fold at the end of the 2 nd experiment. EC50 of PAO1-AmpCG183D/AmpDH157Y was higher than that of single mutants at any time of the experiments. Within the PaR clinical background, reversal of AmpCG183D, and AmpDH157Y mutations led to an important decrease of EC50 value, from 80.5 mg/L to 6.77 mg/L for PaR and PaR-AmpCPaS/AmpDPaS, respectively. The effect of mutations on IMI susceptibility mainly showed that the AmpCG183D mutation prevented the emergence of adaptive resistance. The model successfully described the separate and combined effect of AmpCG183D and AmpDH157Y mutations against C/T and IMI, allowing discrimination and quantification of the initial and time-related effects of mutations. This method could be reproduced in clinical strains to decipher complex resistance mechanisms.

Quantitative Pharmacodynamic Characterization of Resistance versus Heteroresistance of Colistin in E. coli Using a Semimechanistic Modeling of Killing Curves

Heteroresistance corresponds to the presence, in a bacterial isolate, of an initial small subpopulation of bacteria characterized by a significant reduction in their sensitivity to a given antibiotic. Mechanisms of heteroresistance versus resistance are poorly understood. The aim of this study was to explore heteroresistance in mcr-positive and mcr-negative Escherichia coli strains exposed to colistin by use of modeling killing curves with a semimechanistic model. We quantify, for a range of phenotypically (susceptibility based on MIC) and genotypically (carriage of mcr-1 or mcr-3 or mcr-negative) different bacteria, a maximum killing rate (E_max) of colistin and the corresponding potency (EC₅₀), i.e., the colistin concentrations corresponding to E_max/2. Heteroresistant subpopulations were identified in both mcr-negative and mcr-positive E. coli as around 0.06% of the starting population. Minority heteroresistant bacteria, both for mcr-negative and mcr-positive strains, differed from the corresponding dominant populations only by the maximum killing rate of colistin (differences for E_max by a factor of 12.66 and 3.76 for mcr-negative and mcr-positive strains, respectively) and without alteration of their EC₅₀s. On the other hand, the resistant mcr-positive strains are distinguished from the mcr-negative strains by differences in their EC₅₀, which can reach a factor of 44 for their dominant population and 22 for their heteroresistant subpopulations. It is suggested that the underlying physiological mechanisms differ between resistance and heteroresistance, with resistance being linked to a decrease in the affinity of colistin for its site of action, whereas heteroresistance would, rather, be linked to an alteration of the target, which will be more difficult to be further changed or destroyed.

Pharmacodynamic Parameters of Pharmacokinetic/Pharmacodynamic (PK/PD) Integration Models

Pharmacokinetic/pharmacodynamic (PK/PD) integration models are used to investigate the antimicrobial activity characteristics of drugs targeting pathogenic bacteria through comprehensive analysis of the interactions between PK and PD parameters. PK/PD models have been widely applied in the development of new drugs, optimization of the dosage regimen, and prevention and treatment of drug-resistant bacteria. In PK/PD analysis, minimal inhibitory concentration (MIC) is the most commonly applied PD parameter. However, accurately determining MIC is challenging and this can influence the therapeutic effect. Therefore, it is necessary to optimize PD indices to generate more rational results. Researchers have attempted to optimize PD parameters using mutant prevention concentration (MPC)-based PK/PD models, multiple PD parameter-based PK/PD models, kill rate-based PK/PD models, and others. In this review, we discuss progress on PD parameters for PK/PD models to provide a valuable reference for drug development, determining the dosage regimen, and preventing drug-resistant mutations.

Lipid Nanoparticles Loaded with Farnesol or Geraniol to Enhance the Susceptibility of E. coli MCR-1 to Colistin

Resistance to colistin, one of the antibiotics of last resort against multidrug-resistant Gram-negative bacteria, is increasingly reported. Notably, MCR plasmids discovered in 2015 have now been reported worldwide in humans. To keep this antibiotic of last resort efficient, a way to tackle this mechanism seems essential. Terpene alcohols such as farnesol have been shown to improve the efficacy of some antibiotics. However, their high lipophilicity makes them difficult to use. This problem can be solved by encapsulating them in water-dispersible lipid nanoparticles (LNPs). The aim of this study was to discover, using checkerboard tests and time-kill curve experiments, an association between colistin and farnesol or geraniol loaded in LNPs, which would improve the efficacy of colistin against E. coli and, in particular, MCR-1 transconjugants. Then, the effect of the combination on E. coli inner membrane permeabilisation was evaluated using propidium iodide (PI) uptake and compared to human red blood cells plasma membrane permeabilisation. Both terpene alcohols were able to restore the susceptibility of E. coli J53 MCR-1 to colistin with the same efficacy (E_max = 16, i.e., colistin MIC was decreased from 8 to 0.5 mg/L). However, with an EC₅₀ of 2.69 mg/L, farnesol was more potent than geraniol (EC₅₀ = 39.49 mg/L). Time-kill studies showed a bactericidal effect on MCR-1 transconjugant 6 h after incubation, with no regrowth up to 30 h in the presence of 1 mg/L colistin (1/8 MIC) and 60 mg/L or 200 mg/L farnesol or geraniol, respectively. Colistin alone was more potent in increasing PI uptake rate in the susceptible strain (EC₅₀ = 0.86 ± 0.08 mg/L) than in the MCR-1 one (EC₅₀ = 7.38 ± 0.85 mg/L). Against the MCR-1 strain, farnesol-loaded LNP at 60 mg/L enhanced the colistin-induced inner membrane permeabilization effect up to 5-fold and also increased its potency as shown by the decrease in its EC₅₀ from 7.38 ± 0.85 mg/L to 2.69 ± 0.25 mg/L. Importantly, no hemolysis was observed for LNPs loaded with farnesol or geraniol, alone or in combination with colistin, at the concentrations showing the maximum decrease in colistin MICs. The results presented here indicate that farnesol-loaded LNPs should be studied as combination therapy with colistin to prevent the development of resistance to this antibiotic of last resort.

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.