Pharmacodynamic modeling of the evolution of levofloxacin resistance in Staphylococcus aureus

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Comparison of Staphylococcus aureus tolerance between antimicrobial blue light, levofloxacin, and rifampin

Background

Bacterial biofilms readily develop on all medical implants, including percutaneous osseointegrated (OI) implants. With the growing rate of antibiotic resistance, exploring alternative options for managing biofilm-related infections is necessary. Antimicrobial blue light (aBL) is a unique therapy that can potentially manage biofilm-related infections at the skin-implant interface of OI implants. Antibiotics are known to have antimicrobial efficacy disparities between the planktonic and biofilm bacterial phenotypes, but it is unknown if this characteristic also pertains to aBL. In response, we developed experiments to explore this aspect of aBL therapy.

Methods

We determined minimum bactericidal concentrations (MBCs) and antibiofilm efficacies for aBL, levofloxacin, and rifampin against Staphylococcus aureus ATCC 6538 planktonic and biofilm bacteria. Using student t-tests (p < 0.05), we compared the efficacy profiles between the planktonic and biofilm states for the three independent treatments and a levofloxacin + rifampin combination. Additionally, we compared antimicrobial efficacy patterns for levofloxacin and aBL against biofilms as dosages increased.

Results

aBL had the most significant efficacy disparity between the planktonic and biofilm phenotypes (a 2.5 log₁₀ unit difference). However, further testing against biofilms revealed that aBL had a positive correlation between increasing efficacy and exposure time, while levofloxacin encountered a plateau. While aBL efficacy was affected the most by the biofilm phenotype, its antimicrobial efficacy did not reach a maximum.

Discussion/conclusion

We determined that phenotype is an important characteristic to consider when determining aBL parameters for treating OI implant infections. Future research would benefit from expanding these findings against clinical S. aureus isolates and other bacterial strains, as well as the safety of long aBL exposures on human cells.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Translational PK/PD of anti-infective therapeutics

Translational PK/PD modeling has emerged as a critical technique for quantitative analysis of the relationship between dose, exposure and response of antibiotics. By combining model components for pharmacokinetics, bacterial growth kinetics and concentration-dependent drug effects, these models are able to quantitatively capture and simulate the complex interplay between antibiotic, bacterium and host organism. Fine-tuning of these basic model structures allows to further account for complicating factors such as resistance development, combination therapy, or host responses. With this tool set at hand, mechanism-based PK/PD modeling and simulation allows to develop optimal dosing regimens for novel and established antibiotics for maximum efficacy and minimal resistance development.

Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

Quantifying subpopulation synergy for antibiotic combinations via mechanism-based modeling and a sequential dosing design

Quantitative modeling of combination therapy can describe the effects of each antibiotic against multiple bacterial populations. Our aim was to develop an efficient experimental and modeling strategy that evaluates different synergy mechanisms using a rapidly killing peptide antibiotic (nisin) combined with amikacin or linezolid as probe drugs. Serial viable counts over 48 h were obtained in time-kill experiments with all three antibiotics in monotherapy against a methicillin-resistant Staphylococcus aureus USA300 strain (inoculum, 10(8) CFU/ml). A sequential design (initial dosing of 8 or 32 mg/liter nisin, switched to amikacin or linezolid at 1.5 h) assessed the rate of killing by amikacin and linezolid against nisin-intermediate and nisin-resistant populations. Simultaneous combinations were additionally studied and all viable count profiles comodeled in S-ADAPT and NONMEM. A mechanism-based model with six populations (three for nisin times two for amikacin) yielded unbiased and precise (r = 0.99, slope = 1.00; S-ADAPT) individual fits. The second-order killing rate constants for nisin against the three populations were 5.67, 0.0664, and 0.00691 liter/(mg · h). For amikacin, the maximum killing rate constants were 10.1 h(-1) against its susceptible and 0.771 h(-1) against its less-susceptible populations, with 14.7 mg/liter amikacin causing half-maximal killing. After incorporating the effects of nisin and amikacin against each population, no additional synergy function was needed. Linezolid inhibited successful bacterial replication but did not efficiently kill populations less susceptible to nisin. Nisin plus amikacin achieved subpopulation synergy. The proposed sequential and simultaneous dosing design offers an efficient approach to quantitatively characterize antibiotic synergy over time and prospectively evaluate antibiotic combination dosing strategies.

Adaptation beyond the stress response: cell structure dynamics and population heterogeneity in Staphylococcus aureus

Staphylococcus aureus, a major opportunistic pathogen responsible for a broad spectrum of infections, naturally inhabits the human nasal cavity in about 30% of the population. The unique adaptive potential displayed by S. aureus has made it one of the major causes of nosocomial infections today, emphasized by the rapid emergence of multiple antibiotic-resistant strains over the past few decades. The uncanny ability to adapt to harsh environments is essential for staphylococcal persistence in infections or as a commensal, and a growing body of evidence has revealed critical roles in this process for cellular structural dynamics, and population heterogeneity. These two exciting areas of research are now being explored to identify new molecular mechanisms governing these adaptational strategies.

Contribution of mathematical modeling to the fight against bacterial antibiotic resistance

PURPOSE OF REVIEW

Modeling of antibiotic resistance in pathogenic bacteria responsible for human disease has developed considerably over the last decade. Herein, we summarize the main published studies to illustrate the contribution of models for understanding both within-host and population-based phenomena. We then suggest possible topics for future studies.

RECENT FINDINGS

Model building of bacterial resistance has involved epidemiologists, biologists and modelers with two different objectives. First, modeling has helped largely in identifying and understanding the factors and biological phenomena responsible for the emergence and spread of resistant strains. Second, these models have become important decision support tools for medicine and public health.

SUMMARY

Major improvements of models in the coming years should take into account specific pathogen characteristics (resistance mechanisms, multiple colonization phenomena, cooperation and competition among species) and better description of the contacts associated with transmission risk within populations.

In vitro pharmacodynamic models to determine the effect of antibacterial drugs

In vitro pharmacodynamic (PD) models are used to obtain useful quantitative information on the effect of either single drugs or drug combinations against bacteria. This review provides an overview of in vitro PD models and their experimental implementation. Models are categorized on the basis of whether the drug concentration remains constant or changes and whether there is a loss of bacteria from the system. Further subdifferentiation is based on whether bacterial loss involves dilution of the medium or is associated with dialysis or diffusion. For comprehension of the underlying principles, experimental settings are simplified and schematically illustrated, including the simulations of various in vivo routes of administration. The different model types are categorized and their (dis)advantages discussed. The application of in vitro models to special organs, infections and pathogens is comprehensively presented. Finally, the relevance and perspectives of in vitro investigations in drug discovery and clinical research are elucidated and discussed.

Influence of inoculum size and marbofloxacin plasma exposure on the amplification of resistant subpopulations of Klebsiella pneumoniae in a rat lung infection model

We tested the hypothesis that the bacterial load at the infection site could impact considerably on the pharmacokinetic/pharmacodynamic (PK/PD) parameters of fluoroquinolones. Using a rat lung infection model, we measured the influence of different marbofloxacin dosage regimens on selection of resistant bacteria after infection with a low (10(5) CFU) or a high (10(9) CFU) inoculum of Klebsiella pneumoniae. For daily fractionated doses of marbofloxacin, prevention of resistance occurred for an area-under-the-concentration-time-curve (AUC)/MIC ratio of 189 h for the low inoculum, whereas for the high inoculum, resistant-subpopulation enrichment occurred for AUC/MIC ratios up to 756 h. For the high-inoculum-infected rats, the AUC/MIC ratio, C(max)/MIC ratio, and time within the mutant selection window (T(MSW)) were not found to be effective predictors of resistance prevention upon comparison of fractionated and single administrations. An index corresponding to the ratio of the time that the drug concentrations were above the mutant prevention concentration (MPC) over the time that the drug concentrations were within the MSW (T(>MPC)/T(MSW)) was the best predictor of the emergence of resistance: a T(>MPC)/T(MSW) ratio of 0.54 was associated with prevention of resistance for both fractionated and single administrations. These results suggest that the enrichment of resistant bacteria depends heavily on the inoculum size at the start of an antimicrobial treatment and that classical PK/PD parameters cannot adequately describe the impact of different dosage regimens on enrichment of resistant bacteria. We propose an original index, the T(>MPC)/T(MSW) ratio, which reflects the ratio of the time that the less susceptible bacterial subpopulation is killed over the time that it is selected, as a potentially powerful indicator of prevention of enrichment of resistant bacteria. This ratio is valid only if plasma concentrations achieve the MPC.

Pharmacokinetics and pharmacodynamics of antimicrobial drugs

BACKGROUND

Antimicrobial drugs exhibit different characteristics in their correlation between antimicrobial drug concentrations and effects on microorganisms. These correlations have been studied using different approaches including in vitro analyses with constant and fluctuating concentrations and in vivo analyses involving animals and humans. Mathematical analysis includes correlation of pharmacokinetic-pharmacodynamic (PK-PD) indices to an outcome parameter. Further insight can be gained by mechanism-based modelling of antimicrobial drug effects.

METHODS AND RESULTS

This review aims to provide an overview on the various approaches used to analyse antimicrobial pharmacodynamics, to discuss the limitations of these approaches, to indicate recent developments and to summarise the current knowledge on PK-PD target values as derived from human studies.

CONCLUSION

It is expected that PK-PD analysis of antimicrobial drug effects will lead to a more efficient and possibly also less toxic antimicrobial drug therapy.

Pharmacokinetic/pharmacodynamic analysis of the influence of inoculum size on the selection of resistance in Escherichia coli by a quinolone in a mouse thigh bacterial infection model

Maintaining quinolone concentrations outside the mutant selection window (MSW) between the MIC and mutant prevention concentration (MPC) was suggested by in vitro and in vivo studies to prevent the selection of resistant mutants. However, selection also may depend on the presence of resistant bacterial mutants at the start of treatment, which is highly dependent on the initial inoculum size. In this study, a mouse thigh bacterial infection model was used to test the influence of different exposures to marbofloxacin on the selection of resistant bacteria after infection with a low (10(5) CFU) or high (10(8) CFU) initial inoculum of Escherichia coli. The inoculum size was shown to influence the exposure to marbofloxacin and the values of pharmacokinetic/pharmacodynamic indices. When the abilities of the indices time within the MSW (T(MSW)), area under the concentration-time curve of 0 to 24 h divided by the MIC, and the maximum concentration of drug in plasma divided by the MIC to predict the selection of resistant bacteria were compared, only T(MSW) appeared to be a good predictor of the prevention of resistance for values less than 30%. When the T(MSW) was higher than 34%, the selection of resistant bacteria occurred less often in thighs initially infected with the low inoculum (11/24; 46%) than in those infected with the high inoculum (30/36; 80%), suggesting that the selection of resistant mutants depends on both the T(MSW) and inoculum size. The relevance of these results merits further investigation to test different strategies of antibiotic therapy depending on the expected bacterial burden at the infectious site.

Quinolones: action and resistance updated

The quinolones trap DNA gyrase and DNA topoisomerase IV on DNA as complexes in which the DNA is broken but constrained by protein. Early studies suggested that drug binding occurs largely along helix-4 of the GyrA (gyrase) and ParC (topoisomerase IV) proteins. However, recent X-ray crystallography shows drug intercalating between the -1 and +1 nucleotides of cut DNA, with only one end of the drug extending to helix-4. These two models may reflect distinct structural steps in complex formation. A consequence of drug-enzyme-DNA complex formation is reversible inhibition of DNA replication; cell death arises from subsequent events in which bacterial chromosomes are fragmented through two poorly understood pathways. In one pathway, chromosome fragmentation stimulates excessive accumulation of highly toxic reactive oxygen species that are responsible for cell death. Quinolone resistance arises stepwise through selective amplification of mutants when drug concentrations are above the MIC and below the MPC, as observed with static agar plate assays, dynamic in vitro systems, and experimental infection of rabbits. The gap between MIC and MPC can be narrowed by compound design that should restrict the emergence of resistance. Resistance is likely to become increasingly important, since three types of plasmid-borne resistance have been reported.

Mechanism-based pharmacokinetic–pharmacodynamic modeling of antimicrobial drug effects

Mathematical modeling of drug effects maximizes the information gained from an experiment, provides further insight into the mechanisms of drug effects, and allows for simulations in order to design studies or even to derive clinical treatment strategies. We reviewed modeling of antimicrobial drug effects and show that most of the published mathematical models can be derived from one common mechanism-based PK-PD model premised on cell growth and cell killing processes. The general sigmoid Emax model applies to cell killing and the various parameters can be related to common pharmacodynamics, which enabled us to synthesize and compare the different parameter estimates for a total of 24 antimicrobial drugs from published literature. Furthermore, the common model allows the parameters of these models to be related to the MIC and to a common set of PK-PD indices. Theoretically, a high Hill coefficient and a low maximum kill rate indicate so-called time-dependent antimicrobial effects, whereas a low Hill coefficient and a high maximum kill rate indicate so-called concentration-dependent effects, as illustrated in the garenoxacin and meropenem examples. Finally, a new equation predicting the time to microorganism eradication after repeated drug doses was derived that is based on the area under the kill-rate curve.

Mutant selection window hypothesis updated

The mutant selection window hypothesis postulates that, for each antimicrobial-pathogen combination, an antimicrobial concentration range exists in which selective amplification of single-step, drug-resistant mutants occurs. This hypothesis suggests an antimutant dosing strategy that is keyed to the upper boundary of the selection window: the mutant prevention concentration. Correlations are described between the mutant prevention concentration--a static parameter that is measured with agar plates--and fluctuating drug concentrations that restrict mutant amplification in vitro and in animals. When drug resistance is acquired stepwise, the mutant selection window increases, making the suppression of each successive mutant increasingly more difficult. For agents that kill drug-resistant mutants in a drug concentration-dependent manner, the use of the area under the 24-h time-drug concentration curve value divided by the value of the mutant prevention concentration is suggested as an index for designing antimutant dosing regimens. The need for such regimens is emphasized by a clinical example in which acquisition of drug resistance occurs concurrently with eradication of susceptible bacterial cells. These data support using the mutant selection window to optimize antimicrobial dosing regimens.

Mechanism-based pharmacodynamic models of fluoroquinolone resistance in Staphylococcus aureus

Pharmacodynamic modeling from earlier experiments in which two ciprofloxacin-susceptible Staphylococcus aureus strains and their corresponding resistant grlA mutants were exposed to a series of ciprofloxacin (J. J. Campion, P. J. McNamara, and M. E. Evans, Antimicrob. Agents Chemother. 49:209-219, 2005) and levofloxacin (J. J. Campion et al., Antimicrob. Agents Chemother. 49:2189-2199, 2005) pharmacokinetic profiles in an in vitro system indicated that the subpopulation-specific estimated maximal killing rate constants were similar for both agents, suggesting a common mechanism of action. We propose two novel pharmacodynamic models that assign mechanisms of action to fluoroquinolones (growth inhibition or death stimulation) and compare the abilities of these models and two other maximum effect models (net effect and MIC based) to describe and predict the changes in the population dynamics observed during our previous in vitro system experiments with ciprofloxacin. A high correlation between predicted and observed viable counts was observed for all models, but the best fits, as assessed by diagnostic tests, and the most precise parameter estimates were obtained with the growth inhibition and net effect models. All models, except the death stimulation model, correctly predicted that resistant subpopulations would not emerge when a high-density culture was exposed to a high initial concentration designed to rapidly eradicate low-level-resistant grlA mutants. Additional experiments are necessary to elucidate which of the proposed mechanistic models best characterizes the antibacterial effects of fluoroquinolone antimicrobial agents.

The Crisis of Resistance: Identifying Drug Exposures to Suppress Amplification of Resistant Mutant Subpopulations

Antibiotic resistance is seen in both the hospital and community settings. Approaches are required to minimize the increase in resistant strains, such as good antibiotic stewardship and the limiting of antibiotic use to appropriate circumstances. There are instances when drug dose and/or schedule can be used to minimize the probability that mutants will take over the bacterial population. Over the past several years, significant advances have been made in understanding the relationship between drug concentrations and amplification of resistant mutant subpopulations. In this review, we examine the use of preclinical models for facilitating this understanding. We also use mathematical techniques, including Monte Carlo simulation, to bridge between the identification of exposures to minimize resistance and the examination of candidate drug doses to achieve this end. Examples are provided for Pseudomonas aeruginosa, Streptococcus pneumoniae, Staphylococcus aureus, and Mycobacterium tuberculosis. In each instance, quinolone antimicrobials were examined. More investigations with other pathogens and drug classes are required.