Pharmacodynamic modeling of ciprofloxacin resistance in Staphylococcus aureus

Distinguishing Inducible and Non-Inducible Resistance to Colistin in Pseudomonas aeruginosa by Quantitative and Systems Pharmacology Modeling at Low and Standard Inocula

Colistin is a polymyxin and peptide antibiotic that can yield rapid bacterial killing, but also leads to resistance emergence. We aimed to develop a novel experimental and Quantitative and Systems Pharmacology approach to distinguish between inducible and non-inducible resistance. Viable count profiles for the total and less susceptible populations of Pseudomonas aeruginosa ATCC 27853 from static and dynamic in vitro infection models were simultaneously modeled. We studied low and normal initial inocula to distinguish between inducible and non-inducible resistance. A novel cutoff filter approach allowed us to describe the eradication and inter-conversion of bacterial populations. At all inocula, 4.84 mg/L of colistin (sulfate) yielded ≥4 log10 killing, followed by >4 log10 regrowth. A pre-existing, less susceptible population was present at standard but not at low inocula. Formation of a non-pre-existing, less susceptible population was most pronounced at intermediate colistin (sulfate) concentrations (0.9 to 5 mg/L). Both less susceptible populations inter-converted with the susceptible population. Simultaneously modeling of the total and less susceptible populations at low and standard inocula enabled us to identify the de novo formation of an inducible, less susceptible population. Inducible resistance at intermediate colistin concentrations highlights the importance of rapidly achieving efficacious polymyxin concentrations by front-loaded dosage regimens.

Time-Kill Analysis of Canine Skin Pathogens: A Comparison of Pradofloxacin and Marbofloxacin

Time-kill curves (TKCs) are more informative compared with the use of minimum inhibitory concentration (MIC) as they allow the capture of bacterial growth and the development of drug killing rates over time, which allows to compute key pharmacodynamic (PD) parameters. Our study aimed, using a semi-mechanistic mathematical model, to estimate the best pharmacokinetic/pharmacodynamic (PK/PD) indices (ƒAUC/MIC or %ƒT > MIC) for the prediction of clinical efficacy of veterinary FQs in Staphylococcus pseudintermedius, Staphylococcus aureus, and Escherichia coli collected from canine pyoderma cases with a focus on the comparison between marbofloxacin and pradofloxacin. Eight TCKs for each bacterial species (4 susceptible and 4 resistant) were analysed in duplicate. The best PK/PD index was ƒAUC24h/MIC in both staphylococci and E. coli. For staphylococci, values of 25-40 h were necessary to achieve a bactericidal effect, whereas the calculated values (25-35 h) for E. coli were lower than those predicting a positive clinical outcome (100-120 h) in murine models. Pradofloxacin showed a higher potency (lower EC50) in comparison with marbofloxacin. However, no difference in terms of a maximal possible pharmacological killing rate (Emax) was observed. Taking into account in vivo exposure at the recommended dosage regimen (3 and 2 mg/kg for pradofloxacin and marbofloxacin, respectively), the overall killing rates (Kdrug) computed were also similar in most instances.

Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance

Antimicrobial resistance is a major public health issue. The pharmacokinetic/pharmacodynamic (PK/PD) model is an essential tool to optimize dosage regimens and alleviate the emergence of resistance. The semi-mechanistic PK/PD model is a mathematical quantitative tool to capture the relationship between dose, exposure, and response, in terms of the mechanism. Understanding the different resistant mechanisms of bacteria to various antibacterials and presenting this as mathematical equations, the semi-mechanistic PK/PD model can capture and simulate the progress of bacterial growth and the variation in susceptibility. In this review, we outline the bacterial growth model and antibacterial effect model, including different resistant mechanisms, such as persisting resistance, adaptive resistance, and pre-existing resistance, of antibacterials against bacteria. The application of the semi-mechanistic PK/PD model, such as the determination of PK/PD breakpoints, combination therapy, and dosage optimization, are also summarized. Additionally, it is important to integrate the PD effect, such as the inoculum effect and host response, in order to develop a comprehensive mechanism model. In conclusion, with the semi-mechanistic PK/PD model, the dosage regimen can be reasonably determined, which can suppress bacterial growth and resistance development.

Effects of Growth Medium and Inoculum Size on Pharmacodynamics Activity of Marbofloxacin against Staphylococcus aureus Isolated from Caprine Clinical Mastitis

Staphylococcus aureus (S. aureus) is an important pathogen that causes clinical mastitis in goats and produces infections difficult to cure. Different antimicrobials as fluoroquinolones have been used against S. aureus. However, the studies developed to evaluate the bacterial drug interaction only have used the MIC as a single reference point with artificial growth media. The aims of this study were to describe the effect of marbofloxacin on S. aureus isolated from mastitis goats' milk by different approaches as the minimum inhibitory and bactericidal concentrations (MIC and MBC) in cation adjusted Mueller-Hinton broth (CAMHB), serum and milk of goats at two inoculum sizes of 10⁵ and 10⁸ CFU/mL, the determination and analysis of the time kill curves (TKC) by non-linear mixed effect models in each growth medium and inoculum size, as well as the estimation of their pharmacokinetics/pharmacodynamics (PK/PD) cutoff values. The results obtained indicate that MIC values were higher and increases 2,4-fold in serum and 3,6-fold in milk at high inoculum, as well as the EC₅₀ values determined by each pharmacodynamics model. Finally, the PK/PD cutoff values defined as fAUC₂₄/MIC ratios to achieve clinical efficacy were highly dependent on inoculum and growth medium, with median values of 60-180, especially at high inoculum in milk, suggesting that further studies are necessary to evaluate and optimize the best therapeutic strategies for treating S. aureus in lactating goats.

The pharmacokinetic/pharmacodynamic paradigm for antimicrobial drugs in veterinary medicine: Recent advances and critical appraisal

Pharmacokinetic/pharmacodynamic (PK/PD) modelling is the initial step in the semi-mechanistic approach for optimizing dosage regimens for systemically acting antimicrobial drugs (AMDs). Numerical values of PK/PD indices are used to predict dose and dosing interval on a rational basis followed by confirmation in clinical trials. The value of PK/PD indices lies in their universal applicability amongst animal species. Two PK/PD indices are routinely used in veterinary medicine, the ratio of the area under the curve of the free drug plasma concentration to the minimum inhibitory concentration (MIC) (fAUC/MIC) and the time that free plasma concentration exceeds the MIC over the dosing interval (fT > MIC). The basic concepts of PK/PD modelling of AMDs were established some 20 years ago. Earlier studies have been reviewed previously and are not reconsidered in this review. This review describes and provides a critical appraisal of more recent, advanced PK/PD approaches, with particular reference to their application in veterinary medicine. Also discussed are some hypotheses and new areas for future developments.First, a brief overview of PK/PD principles is presented as the basis for then reviewing more advanced mechanistic considerations on the precise nature of selected indices. Then, several new approaches to selecting PK/PD indices and establishing their numerical values are reviewed, including (a) the modelling of time-kill curves and (b) the use of population PK investigations. PK/PD indices can be used for dose determination, and they are required to establish clinical breakpoints for antimicrobial susceptibility testing. A particular consideration is given to the precise nature of MIC, because it is pivotal in establishing PK/PD indices, explaining that it is not a "pharmacodynamic parameter" in the usual sense of this term.

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

Experiments were conducted with polymyxin B and two Klebsiella pneumonia isogenic strains (the wild type, KP_WT, and its transconjugant carrying the mobile colistin resistance gene, KP_MCR-1) to demonstrate that conducting two consecutive time-kill experiments (sequential TK) represents a simple approach to discriminate between pharmacokinetics/pharmacodynamics models with two heterogeneous subpopulations or adaptive resistance.

A survey of within-host and between-hosts modelling for antibiotic resistance

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

A dose response model for quantifying the infection risk of antibiotic-resistant bacteria

Quantifying the human health risk of microbial infection helps inform regulatory policies concerning pathogens, and the associated public health measures. Estimating the infection risk requires knowledge of the probability of a person being infected by a given quantity of pathogens, and this relationship is modeled using pathogen specific dose response models (DRMs). However, risk quantification for antibiotic-resistant bacteria (ARB) has been hindered by the absence of suitable DRMs for ARB. A new approach to DRMs is introduced to capture ARB and antibiotic-susceptible bacteria (ASB) dynamics as a stochastic simple death (SD) process. By bridging SD with data from bench experiments, we demonstrate methods to (1) account for the effect of antibiotic concentrations and horizontal gene transfer on risk; (2) compute total risk for samples containing multiple bacterial types (e.g., ASB, ARB); and (3) predict if illness is treatable with antibiotics. We present a case study of exposure to a mixed population of Gentamicin-susceptible and resistant Escherichia coli and predict the health outcomes for varying Gentamicin concentrations. Thus, this research establishes a new framework to quantify the risk posed by ARB and antibiotics.

Generating Robust and Informative Nonclinical In Vitro and In Vivo Bacterial Infection Model Efficacy Data To Support Translation to Humans

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration.

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration. This and the accompanying review on clinical PK/PD summarize the workshop discussions and recommendations. Nonclinical PK/PD models play a critical role in designing human dosage regimens and are essential tools for drug development. These include in vitro and in vivo efficacy models that provide valuable and complementary information for dose selection and translation from the laboratory to human. It is crucial that studies be designed, conducted, and interpreted appropriately. For antibacterial PK/PD, extensive published data and expertise are available. These have been leveraged to develop recommendations, identify common pitfalls, and describe the applications, strengths, and limitations of various nonclinical infection models and translational approaches. Despite these robust tools and published guidance, characterizing nonclinical PK/PD relationships may not be straightforward, especially for a new drug or new class. Antimicrobial PK/PD is an evolving discipline that needs to adapt to future research and development needs. Open communication between academia, pharmaceutical industry, government, and regulatory bodies is essential to share perspectives and collectively solve future challenges.

Morphological and Dose-Dependent Study on the Effect of Methyl, Hexyl, and Dodecyl Rosmarinate on Staphylococcus carnosus LTH1502: Use of the Weibull Model

The mechanisms of three antimicrobial rosmarinates (methyl-RE1, hexyl-RE6, and dodecyl-RE12) were investigated against Staphylococcus carnosus LTH1502. Scanning electron microscopy was used to determine the morphology of treated cells to gain information on potential changes in the site of action of compounds. The survival data obtained from antimicrobial activity assays were fitted to a nonlinear Weibull model to assess changes in inactivation behavior. Generally, esters became more effective with increasing length of the alkyl chain, resulting in a lower concentration for inhibition and inactivation. Weibull distribution parameters showed a downward concave inactivation pattern for RE1 above a critical concentration, indicative of a delayed log phase of the antimicrobial activity, with few cells being inactivated immediately after treatment and more cells being affected at later times. In contrast, esters having longer alkyl chains (RE6 and RE12) had an upward concave inactivation behavior, with more cells being inactivated immediately after addition of compounds. Cellular morphologies suggest that the antimicrobial mode of action of esters transitions from one that acts intracellularly (RE1) to one that predominately affects bacterial membrane (RE6 and RE12) due to changes in physicochemical properties of esters. Assessment that is based on the parameters of the Weibull model could, thus, be used to evaluate antimicrobial efficiency, in addition to MIC.

Mathematical modelling of the antibiotic-induced morphological transition of Pseudomonas aeruginosa

Here we formulate a mechanistic mathematical model to describe the growth dynamics of P. aeruginosa in the presence of the β-lactam antibiotic meropenem. The model is mechanistic in the sense that carrying capacity is taken into account through the dynamics of nutrient availability rather than via logistic growth. In accordance with our experimental results we incorporate a sub-population of cells, differing in morphology from the normal bacillary shape of P. aeruginosa bacteria, which we assume have immunity from direct antibiotic action. By fitting this model to experimental data we obtain parameter values that give insight into the growth of a bacterial population that includes different cell morphologies. The analysis of two parameters sets, that produce different long term behaviour, allows us to manipulate the system theoretically in order to explore the advantages of a shape transition that may potentially be a mechanism that allows P. aeruginosa to withstand antibiotic effects. Our results suggest that inhibition of this shape transition may be detrimental to bacterial growth and thus suggest that the transition may be a defensive mechanism implemented by bacterial machinery. In addition to this we provide strong theoretical evidence for the potential therapeutic strategy of using antimicrobial peptides (AMPs) in combination with meropenem. This proposed combination therapy exploits the shape transition as AMPs induce cell lysis by forming pores in the cytoplasmic membrane, which becomes exposed in the spherical cells.

Modeling the growth dynamics of multiple Escherichia coli strains in the pig intestine following intramuscular ampicillin treatment

Background

This study evaluated how dosing regimen for intramuscularly-administered ampicillin, composition of Escherichia coli strains with regard to ampicillin susceptibility, and excretion of bacteria from the intestine affected the level of resistance among Escherichia coli strains in the intestine of nursery pigs. It also examined the dynamics of the composition of bacterial strains during and after the treatment. The growth responses of strains to ampicillin concentrations were determined using in vitro growth curves. Using these results as input data, growth predictions were generated using a mathematical model to simulate the competitive growth of E. coli strains in a pig intestine under specified plasma concentration profiles of ampicillin.

Results

In vitro growth results demonstrated that the resistant strains did not carry a fitness cost for their resistance, and that the most susceptible strains were more affected by increasing concentrations of antibiotics that the rest of the strains. The modeling revealed that short treatment duration resulted in lower levels of resistance and that dosing frequency did not substantially influence the growth of resistant strains. Resistance levels were found to be sensitive to the number of competing strains, and this effect was enhanced by longer duration of treatment. High excretion of bacteria from the intestine favored resistant strains over sensitive strains, but at the same time it resulted in a faster return to pre-treatment levels after the treatment ended. When the duration of high excretion was set to be limited to the treatment time (i.e. the treatment was assumed to result in a cure of diarrhea) resistant strains required longer time to reach the previous level.

Conclusion

No fitness cost was found to be associated with ampicillin resistance in E. coli. Besides dosing factors, epidemiological factors (such as number of competing strains and bacterial excretion) influenced resistance development and need to be considered further in relation to optimal treatment strategies. The modeling approach used in the study is generic, and could be used for prediction of the effect of treatment with other drugs and other administration routes for effect on resistance development in the intestine of pigs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-016-0823-3) contains supplementary material, which is available to authorized users.

Multistrain models predict sequential multidrug treatment strategies to result in less antimicrobial resistance than combination treatment

Background

Combination treatment is increasingly used to fight infections caused by bacteria resistant to two or more antimicrobials. While multiple studies have evaluated treatment strategies to minimize the emergence of resistant strains for single antimicrobial treatment, fewer studies have considered combination treatments. The current study modeled bacterial growth in the intestine of pigs after intramuscular combination treatment (i.e. using two antibiotics simultaneously) and sequential treatments (i.e. alternating between two antibiotics) in order to identify the factors that favor the sensitive fraction of the commensal flora.

Growth parameters for competing bacterial strains were estimated from the combined in vitro pharmacodynamic effect of two antimicrobials using the relationship between concentration and net bacterial growth rate. Predictions of in vivo bacterial growth were generated by a mathematical model of the competitive growth of multiple strains of Escherichia coli.

Results

Simulation studies showed that sequential use of tetracycline and ampicillin reduced the level of double resistance, when compared to the combination treatment. The effect of the cycling frequency (how frequently antibiotics are alternated in a sequential treatment) of the two drugs was dependent upon the order in which the two drugs were used.

Conclusion

Sequential treatment was more effective in preventing the growth of resistant strains when compared to the combination treatment. The cycling frequency did not play a role in suppressing the growth of resistant strains, but the specific order of the two antimicrobials did. Predictions made from the study could be used to redesign multidrug treatment strategies not only for intramuscular treatment in pigs, but also for other dosing routes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-016-0724-5) contains supplementary material, which is available to authorized users.

A pharmacokinetic-pharmacodynamic model characterizing the emergence of resistant Escherichia coli subpopulations during ertapenem exposure

OBJECTIVES

Resistant subpopulations with reduced expression of outer membrane porins have been observed in ESBL-producing Escherichia coli during exposure to ertapenem. The aim of this work was to develop a pharmacokinetic-pharmacodynamic (PKPD) model to characterize the emergence of resistant E. coli during exposure to ertapenem and to predict bacterial killing following different dosing regimens of ertapenem.

METHODS

Data from in vitro time-kill experiments were used to develop a mechanism-based PKPD model for three E. coli strains: a native strain, an ESBL-producing strain, and an ESBL-producing strain with reduced expression of porins OmpF and OmpC. Each strain was exposed to static ertapenem concentrations (1-512 × MIC) for 24 h using starting inocula of ∼10(6) and 10(8) cfu/mL.

RESULTS

The developed PKPD model consisted of three bacterial states: susceptible growing, less susceptible non-growing, and non-susceptible non-growing bacteria. A pre-existing bacterial subpopulation was used to describe the emergence of resistance. The PKPD model adequately characterized the data of the three E. coli strains investigated. Results from predictions suggest that the conventional dosage (1 g intravenously once daily) might result in regrowth of resistant subpopulations when used to treat infection caused by ESBL-producing strains.

CONCLUSIONS

Resistant subpopulations frequently emerged in E. coli when exposed to ertapenem, supporting that the time course of emergence of resistance should be taken into consideration when selecting dosing regimens.

Distinguishing Antimicrobial Models with Different Resistance Mechanisms via Population Pharmacodynamic Modeling

Semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling is increasingly used for antimicrobial drug development and optimization of dosage regimens, but systematic simulation-estimation studies to distinguish between competing PD models are lacking. This study compared the ability of static and dynamic in vitro infection models to distinguish between models with different resistance mechanisms and support accurate and precise parameter estimation. Monte Carlo simulations (MCS) were performed for models with one susceptible bacterial population without (M1) or with a resting stage (M2), a one population model with adaptive resistance (M5), models with pre-existing susceptible and resistant populations without (M3) or with (M4) inter-conversion, and a model with two pre-existing populations with adaptive resistance (M6). For each model, 200 datasets of the total bacterial population were simulated over 24h using static antibiotic concentrations (256-fold concentration range) or over 48h under dynamic conditions (dosing every 12h; elimination half-life: 1h). Twelve-hundred random datasets (each containing 20 curves for static or four curves for dynamic conditions) were generated by bootstrapping. Each dataset was estimated by all six models via population PD modeling to compare bias and precision. For M1 and M3, most parameter estimates were unbiased (<10%) and had good imprecision (<30%). However, parameters for adaptive resistance and inter-conversion for M2, M4, M5 and M6 had poor bias and large imprecision under static and dynamic conditions. For datasets that only contained viable counts of the total population, common statistical criteria and diagnostic plots did not support sound identification of the true resistance mechanism. Therefore, it seems advisable to quantify resistant bacteria and characterize their MICs and resistance mechanisms to support extended simulations and translate from in vitro experiments to animal infection models and ultimately patients.

Mathematical models are increasingly used for analysis and interpretation of in vitro efficacy results of antimicrobial drugs. Various models are employed in the scientific literature and it seems that they are equally able to describe the observed data. The aim of the present study was to compare different models in various experimental designs and with different resistance mechanisms of bacteria. For that purpose we have generated experimental data through Monte-Carlo simulations and then used six different mathematical models to analyze these results. We showed that statistical comparison of models did not allow determining which was the true mechanism of resistance, i.e. the one used for the simulation step. Moreover mathematical parameters for bacterial resistance were estimated with bias and with a low precision except for the simpler cases. This suggests that the choice of the mathematical model for data analysis should be guided by experimental characterization of the bacterial mechanism of resistance.

Pharmacokinetic-pharmacodynamic model to evaluate intramuscular tetracycline treatment protocols to prevent antimicrobial resistance in pigs

High instances of antimicrobial resistance are linked to both routine and excessive antimicrobial use, but excessive or inappropriate use represents an unnecessary risk. The competitive growth advantages of resistant bacteria may be amplified by the strain dynamics; in particular, the extent to which resistant strains outcompete susceptible strains under antimicrobial pressure may depend not only on the antimicrobial treatment strategies but also on the epidemiological parameters, such as the composition of the bacterial strains in a pig. This study evaluated how variation in the dosing protocol for intramuscular administration of tetracycline and the composition of bacterial strains in a pig affect the level of resistance in the intestine of a pig. Predictions were generated by a mathematical model of competitive growth of Escherichia coli strains in pigs under specified plasma concentration profiles of tetracycline. All dosing regimens result in a clear growth advantage for resistant strains. Short treatment duration was found to be preferable, since it allowed less time for resistant strains to outcompete the susceptible ones. Dosing frequency appeared to be ineffective at reducing the resistance levels. The number of competing strains had no apparent effect on the resistance level during treatment, but possession of fewer strains reduced the time to reach equilibrium after the end of treatment. To sum up, epidemiological parameters may have more profound influence on growth dynamics than dosing regimens and should be considered when designing improved treatment protocols.

Comparison of intrapulmonary and systemic pharmacokinetics of colistin methanesulfonate (CMS) and colistin after aerosol delivery and intravenous administration of CMS in critically ill patients

Colistin is an old antibiotic that has recently gained a considerable renewal of interest for the treatment of pulmonary infections due to multidrug-resistant Gram-negative bacteria. Nebulization seems to be a promising form of administration, but colistin is administered as an inactive prodrug, colistin methanesulfonate (CMS); however, differences between the intrapulmonary concentrations of the active moiety as a function of the route of administration in critically ill patients have not been precisely documented. In this study, CMS and colistin concentrations were measured on two separate occasions within the plasma and epithelial lining fluid (ELF) of critically ill patients (n = 12) who had received 2 million international units (MIU) of CMS by aerosol delivery and then intravenous administration. The pharmacokinetic analysis was conducted using a population approach and completed by pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulations. The ELF colistin concentrations varied considerably (9.53 to 1,137 mg/liter), but they were much higher than those in plasma (0.15 to 0.73 mg/liter) after aerosol delivery but not after intravenous administration of CMS. Following CMS aerosol delivery, typically, 9% of the CMS dose reached the ELF, and only 1.4% was presystemically converted into colistin. PK-PD analysis concluded that there was much higher antimicrobial efficacy after CMS aerosol delivery than after intravenous administration. These new data seem to support the use of aerosol delivery of CMS for the treatment of pulmonary infections in critical care patients.

Mathematical modeling of bacterial kinetics to predict the impact of antibiotic colonic exposure and treatment duration on the amount of resistant enterobacteria excreted

Fecal excretion of antibiotics and resistant bacteria in the environment are major public health threats associated with extensive farming and modern medical care. Innovative strategies that can reduce the intestinal antibiotic concentrations during treatments are in development. However, the effect of lower exposure on the amount of resistant enterobacteria excreted has not been quantified, making it difficult to anticipate the impact of these strategies. Here, we introduce a bacterial kinetic model to capture the complex relationships between drug exposure, loss of susceptible enterobacteria and growth of resistant strains in the feces of piglets receiving placebo, 1.5 or 15 mg/kg/day ciprofloxacin, a fluoroquinolone, for 5 days. The model could well describe the kinetics of drug susceptible and resistant enterobacteria observed during treatment, and up to 22 days after treatment cessation. Next, the model was used to predict the expected amount of resistant enterobacteria excreted over an average piglet's lifetime (150 days) when varying drug exposure and treatment duration. For the clinically relevant dose of 15 mg/kg/day for 5 days, the total amount of resistant enterobacteria excreted was predicted to be reduced by 75% and 98% when reducing treatment duration to 3 and 1 day treatment, respectively. Alternatively, for a fixed 5-days treatment, the level of resistance excreted could be reduced by 18%, 33%, 57.5% and 97% if 3, 5, 10 and 30 times lower levels of colonic drug concentrations were achieved, respectively. This characterization on in vivo data of the dynamics of resistance to antibiotics in the colonic flora could provide new insights into the mechanism of dissemination of resistance and can be used to design strategies aiming to reduce it.

Fecal excretion of antibiotics and resistant bacteria in the environment are major public health threats associated with extensive farming. Innovative strategies that reduce the intestinal antibiotic concentrations during treatment are in development and could help prevent the dissemination of resistance. In order to anticipate the impact of these strategies, the effect of lower exposure on the amount of resistant enterobacteria excreted needs to be quantified precisely. Here, we introduce a bacterial kinetic model to capture the complex relationships between dosage regimen, antibiotic fecal concentrations, loss of susceptible enterobacteria and growth of resistant strains in the feces of piglets receiving different doses of ciprofloxacin for 5 days. We use this model to evaluate by simulation how much it would be necessary to reduce the antibiotic colonic concentration in order to prevent the expansion of antibiotic resistance. This approach provides new insights into the mechanism of dissemination of resistance during treatments and can be used to design strategies to reduce it.

A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models

The use of pharmacokinetic (PK) and pharmacodynamic (PD) models is a common and widespread practice in the preliminary stages of drug development. However, PK-PD models may be affected by structural identifiability issues intrinsically related to their mathematical formulation. A preliminary structural identifiability analysis is usually carried out to check if the set of model parameters can be uniquely determined from experimental observations under the ideal assumptions of noise-free data and no model uncertainty. However, even for structurally identifiable models, real-life experimental conditions and model uncertainty may strongly affect the practical possibility to estimate the model parameters in a statistically sound way. A systematic procedure coupling the numerical assessment of structural identifiability with advanced model-based design of experiments formulations is presented in this paper. The objective is to propose a general approach to design experiments in an optimal way, detecting a proper set of experimental settings that ensure the practical identifiability of PK-PD models. Two simulated case studies based on in vitro bacterial growth and killing models are presented to demonstrate the applicability and generality of the methodology to tackle model identifiability issues effectively, through the design of feasible and highly informative experiments.

Evaluation of pharmacokinetic/pharmacodynamic relationships of PD-0162819, a biotin carboxylase inhibitor representing a new class of antibacterial compounds, using in vitro infection models

The present study investigated the pharmacokinetic/pharmacodynamic (PK/PD) relationships of a prototype biotin carboxylase (BC) inhibitor, PD-0162819, against Haemophilus influenzae 3113 in static concentration time-kill (SCTK) and one-compartment chemostat in vitro infection models. H. influenzae 3113 was exposed to PD-0162819 concentrations of 0.5 to 16× the MIC (MIC = 0.125 μg/ml) and area-under-the-curve (AUC)/MIC ratios of 1 to 1,100 in SCTK and chemostat experiments, respectively. Serial samples were collected over 24 h. For efficacy driver analysis, a sigmoid maximum-effect (E(max)) model was fitted to the relationship between bacterial density changes over 24 h and corresponding PK/PD indices. A semimechanistic PK/PD model describing the time course of bacterial growth and death was developed. The AUC/MIC ratio best explained efficacy (r(2) = 0.95) compared to the peak drug concentration (C(max))/MIC ratio (r(2) = 0.76) and time above the MIC (T>MIC) (r(2) = 0.88). Static effects and 99.9% killing were achieved at AUC/MIC values of 500 and 600, respectively. For time course analysis, the net bacterial growth rate constant, maximum bacterial density, and maximum kill rate constant were similar in SCTK and chemostat studies, but PD-0162819 was more potent in SCTK than in the chemostat (50% effective concentration [EC(50)] = 0.046 versus 0.34 μg/ml). In conclusion, basic PK/PD relationships for PD-0162819 were established using in vitro dynamic systems. Although the bacterial growth parameters and maximum drug effects were similar in SCTK and the chemostat system, PD-0162819 appeared to be more potent in SCTK, illustrating the importance of understanding the differences in preclinical models. Additional studies are needed to determine the in vivo relevance of these results.

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.

Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model

We have previously described a general semimechanistic pharmacokinetic-pharmacodynamic (PKPD) model that successfully characterized the time course of antibacterial effects seen in bacterial cultures when exposed to static concentrations of five antibacterial agents of different classes. In this PKPD model, the total bacterial population was divided into two subpopulations, one growing drug-susceptible population and one resting drug-insensitive population. The drug effect was included as an increase in the killing rate of the drug-susceptible bacteria with a maximum-effect (E(max)) model. The aim of the present study was to evaluate the ability of this PKPD model to describe and predict data from in vitro experiments with dynamic concentration-time profiles. Dynamic time-kill curve experiments were performed by using an in vitro kinetic system, where cultures of Streptococcus pyogenes were exposed to benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, or vancomycin using different starting concentrations (2 and 16 times the MIC) and elimination conditions (human half-life, reduced half-life, and constant concentrations). The PKPD model was applied, and the observations for the static as well as dynamic experiments were compared to model predictions based on parameter estimation using (i) static data, (ii) dynamic data, and (iii) combined static and dynamic data. Differences in experimental settings between static and dynamic experiments did not affect the growth kinetics of the bacteria significantly. With parameter reestimation, the structure of our previously proposed PKPD model could well characterize the bacterial growth and killing kinetics when exposed to dynamic concentrations with different elimination rates of all five investigated antibiotics. Furthermore, the model with parameter estimates based on data from only the static time-kill curve experiments could predict the majority of the time-kill curves from the dynamic experiments reasonably well. Adding data from dynamic experiments in the estimation improved the model fit for cefuroxime and vancomycin, indicating some differences in sensitivity to experimental conditions among the antibiotics studied.

A novel approach to pharmacodynamic assessment of antimicrobial agents: new insights to dosing regimen design

Pharmacodynamic modeling has been increasingly used as a decision support tool to guide dosing regimen selection, both in the drug development and clinical settings. Killing by antimicrobial agents has been traditionally classified categorically as concentration-dependent (which would favor less fractionating regimens) or time-dependent (for which more frequent dosing is preferred). While intuitive and useful to explain empiric data, a more informative approach is necessary to provide a robust assessment of pharmacodynamic profiles in situations other than the extremes of the spectrum (e.g., agents which exhibit partial concentration-dependent killing). A quantitative approach to describe the interaction of an antimicrobial agent and a pathogen is proposed to fill this unmet need. A hypothetic antimicrobial agent with linear pharmacokinetics is used for illustrative purposes. A non-linear functional form (sigmoid Emax) of killing consisted of 3 parameters is used. Using different parameter values in conjunction with the relative growth rate of the pathogen and antimicrobial agent concentration ranges, various conventional pharmacodynamic surrogate indices (e.g., AUC/MIC, Cmax/MIC, %T>MIC) could be satisfactorily linked to outcomes. In addition, the dosing intensity represented by the average kill rate of a dosing regimen can be derived, which could be used for quantitative comparison. The relevance of our approach is further supported by experimental data from our previous investigations using a variety of gram-negative bacteria and antimicrobial agents (moxifloxacin, levofloxacin, gentamicin, amikacin and meropenem). The pharmacodynamic profiles of a wide range of antimicrobial agents can be assessed by a more flexible computational tool to support dosing selection.

Mathematical modeling to characterize the inoculum effect

Killing by beta-lactams is well known to be reduced against a dense bacterial population, commonly known as the inoculum effect. However, the underlying mechanism of this phenomenon is not well understood. We proposed a semi-mechanistic mathematical model to account for the reduced in vitro killing observed. Time-kill studies were performed with 4 baseline inocula (ranging from approximately 1 × 10(5) to 1 × 10(8) CFU/ml) of Escherichia coli ATCC 25922 (MIC, 2 mg/liter). Constant but escalating piperacillin concentrations used ranged from 0.25× to 256× MIC. Serial samples were taken over 24 h to quantify viable bacterial burden, and all the killing profiles were mathematically modeled. The inoculum effect was attributed to a reduction of effective drug concentration available for bacterial killing, which was expressed as a function of the baseline inoculum. Biomasses associated with different inocula were examined using a colorimetric method. Despite identical drug-pathogen combinations, the baseline inoculum had a significant impact on bacterial killing. Our proposed mathematical model was unbiased and reasonable in capturing all 28 killing profiles collectively (r(2) = 0.88). Biomass was found to be significantly more after 24 h with a baseline inoculum of 1 × 10(8) CFU/ml, compared to one where the initial inoculum was 1 × 10(5) CFU/ml (P = 0.002). Our results corroborated previous observations that in vitro killing by piperacillin was significantly reduced against a dense bacterial inoculum. This phenomenon can be reasonably captured by our proposed mathematical model, and it may improve prediction of bacterial response to various drug exposures in future investigations.

Compensation of fitness costs and reversibility of antibiotic resistance mutations

Strains of bacterial pathogens that have acquired mutations conferring antibiotic resistance often have a lower growth rate and are less invasive or transmissible initially than their susceptible counterparts. However, fitness costs of resistance mutations can be ameliorated by secondary site mutations. These so-called compensatory mutations may restore fitness in the absence and/or presence of antimicrobials. We review literature data and show that the fitness gains in the absence and presence of antibiotic treatment need not be correlated. The aim of this study is to gain a better conceptual grasp of how compensatory mutations with different fitness gains affect evolutionary trajectories, in particular reversibility. To this end, we developed a theoretical model with which we consider both a resistance and a compensation locus. We propose an intuitively understandable parameterization for the fitness values of the four resulting genotypes (wild type, resistance mutation only, compensatory mutation only, and both mutations) in the absence and presence of treatment. The differential fitness gains, together with the turnover rate and the mutation rate, strongly affected the success of antibacterial treatment, reversibility, and long-term abundance of resistant strains. We therefore propose that experimental studies of compensatory mutations should include fitness measurements of all possible genotypes in both the absence and presence of an antibiotic.

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.

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.

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.

Exploring the role of the immune response in preventing antibiotic resistance

For many bacterial infections, drug resistant mutants are likely present by the time antibiotic treatment starts. Nevertheless, such infections are often successfully cleared. It is commonly assumed that this is due to the combined action of drug and immune response, the latter facilitating clearance of the resistant population. However, most studies of drug resistance emergence during antibiotic treatment focus almost exclusively on the dynamics of bacteria and the drug and neglect the contribution of immune defenses. Here, we develop and analyze several mathematical models that explicitly include an immune response. We consider different types of immune responses and investigate how each impacts the emergence of resistance. We show that an immune response that retains its strength despite a strong drug-induced decline of bacteria numbers considerably reduces the emergence of resistance, narrows the mutant selection window, and mitigates the effects of non-adherence to treatment. Additionally, we show that compared to an immune response that kills bacteria at a constant rate, one that trades reduced killing at high bacterial load for increased killing at low bacterial load is sometimes preferable. We discuss the predictions and hypotheses derived from this study and how they can be tested experimentally.

System for real-time monitoring of mutation-in-progress

AIMS

There has been an increasing number of pathogens becoming resistant to multiple classes of antibiotics. The study on how mutation emerges is therefore crucial to promote further understanding in this area. Conventional methods for such studies involve the monitoring of growth by standard plate count and biomolecular sequencing. This is however tedious and not cost effective. The aim of this paper is thus to introduce a novel system that enables real-time monitoring of bacterial 'mutation-in-progress'.

METHODS AND RESULTS

This system provides real-time data, thus enabling confirmatory and further work to be performed at the important points when mutation is initiated. The system integrates spectroscopic techniques as the detection system and various supporting systems, such as a nutrient replenishing system, a pH control system and a waste system to allow for extended monitoring. In this paper, the feasibility of monitoring the emergence of ciprofloxacin resistance in Staphylococcus aureus was demonstrated as an initial example. The integrated system was found to require significantly less material resource and manpower compared with conventional techniques.

CONCLUSIONS

The novel system to monitor bacterial mutation-in-progress is presented. The work reported herein demonstrates such a system to be effective and efficient in performing real-time monitoring of mutation-in-progress, especially in extended time frames for mutation into the weeks and months.

SIGNIFICANCE AND IMPACT OF THE STUDY

With the successful optimization of this system, researchers can learn about the dynamics of antibiotic resistance and further understand how the mutation of bacteria occurs.

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.

Modeling of microbial population responses to time-periodic concentrations of antimicrobial agents

We present the development and first experimental validation of a mathematical modeling framework for predicting the eventual response of heterogeneous (distributed-resistance) microbial populations to antimicrobial agents at time-periodic (hence pharmacokinetically realistic) concentrations. Our mathematical model predictions are validated in a hollow-fiber in vitro experimental infection model. They are in agreement with the threshold levofloxacin exposure necessary to suppress resistance development of Pseudomonas aeruginosa in a murine thigh infection model. Predictions made by the proposed mathematical modeling framework can offer guidance for targeted testing of promising regimens. This can aid the development and clinical use of antimicrobial agents that combat microbial resistance.

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.

Pharmacodynamic modeling of the evolution of levofloxacin resistance in Staphylococcus aureus

Previously, we demonstrated the importance of low-level-resistant variants to the evolution of resistance in Staphylococcus aureus exposed to ciprofloxacin in an in vitro system and developed a pharmacodynamic model which predicted the emergence of resistance. Here, we examine and model the evolution of resistance to levofloxacin in S. aureus exposed to simulated levofloxacin pharmacokinetic profiles. Enrichment of subpopulations with mutations in grlA and low-level resistance varied with levofloxacin exposure. A regimen producing average steady-state concentrations (Cavg ss) just above the MIC selected grlA mutants with up to 16-fold increases in the MIC and often additional mutations in grlA/grlB and gyrA. A regimen providing Cavg ss between the MIC and the mutant prevention concentration (MPC) suppressed bacterial numbers to the limit of detection and prevented the appearance of bacteria with additional mutations or high-level resistance. Regimens producing Cavg ss above the MPC appeared to eradicate low-level-resistant variants in the cultures and prevent the emergence of resistance. There was no relationship between the time concentrations remained between the MIC and the MPC and the degree of resistance or the presence or type of mutations that appeared in grlA/B or gyrA. Our pharmacodynamic model described the growth and levofloxacin killing of the parent strains and the most resistant grlA mutants in the starting cultures and correctly predicted conditions that enrich subpopulations with low-level resistance. These findings suggest that the pharmacodynamic model has general applicability for describing fluoroquinolone resistance in S. aureus and further demonstrate the importance of low-level-resistant variants to the evolution of resistance.

Evolution of ciprofloxacin-resistant Staphylococcus aureus in in vitro pharmacokinetic environments

The development of novel antibacterial agents is decreasing despite increasing resistance to presently available agents among common pathogens. Insights into relationships between pharmacodynamics and resistance may provide ways to optimize the use of existing agents. The evolution of resistance was examined in two ciprofloxacin-susceptible Staphylococcus aureus strains exposed to in vitro-simulated clinical and experimental ciprofloxacin pharmacokinetic profiles for 96 h. As the average steady-state concentration (C(avg ss)) increased, the rate of killing approached a maximum, and the rate of regrowth decreased. The enrichment of subpopulations with mutations in grlA and low-level ciprofloxacin resistance also varied depending on the pharmacokinetic environment. A regimen producing values for C(avg ss) slightly above the MIC selected resistant variants with grlA mutations that did not evolve to higher levels of resistance. Clinical regimens which provided values for C(avg ss) intermediate to the MIC and mutant prevention concentration (MPC) resulted in the emergence of subpopulations with gyrA mutations and higher levels of resistance. A regimen producing values for C(avg ss) close to the MPC selected grlA mutants, but the appearance of subpopulations with higher levels of resistance was diminished. A regimen designed to maintain ciprofloxacin concentrations entirely above the MPC appeared to eradicate low-level resistant variants in the inoculum and prevent the emergence of higher levels of resistance. There was no relationship between the time that ciprofloxacin concentrations remained between the MIC and the MPC and the degree of resistance or the presence or type of ciprofloxacin-resistance mutations that appeared in grlA or gyrA. Regimens designed to eradicate low-level resistant variants in S. aureus populations may prevent the emergence of higher levels of fluoroquinolone resistance.

Guide to selection of fluoroquinolones in patients with lower respiratory tract infections

Newer fluoroquinolones such as levofloxacin, moxifloxacin, gatifloxacin and gemifloxacin have several attributes that make them excellent choices for the therapy of lower respiratory tract infections. In particular, they have excellent intrinsic activity against Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis and the atypical respiratory pathogens. Fluoroquinolones may be used as monotherapy to treat high-risk patients with acute exacerbation of chronic bronchitis, and for patients with community-acquired pneumonia requiring hospitalisation, but not admission to intensive care. Overall, the newer fluoroquinolones often achieve clinical cure rates in > or =90% of these patients. However, rates may be lower in hospital-acquired pneumonia, and this infection should be treated on the basis of anticipated organisms and evaluation of risk factors for specific pathogens such as Pseudomonas aeruginosa. In this setting, an antipseudomonal fluoroquinolone may be used in combination with an antipseudomonalbeta-lactam. Concerns are now being raised about the widespread use, and possibly misuse, of fluoroquinolones and the emergence of resistance among S. pneumoniae, Enterobacteriaceae and P. aeruginosa. A number of pharmacokinetic parameters such as the peak concentration of the antibacterial after a dose (C(max)), and the 24-hour area under the concentration-time curve (AUC24) and their relationship to pharmacodynamic parameters such as the minimum inhibitory and the mutant prevention concentrations (MIC and MPC, respectively) have been proposed to predict the effect of fluoroquinolones on bacterial killing and the emergence of resistance. Higher C(max)/MIC or AUC24/MIC and C(max)/MPC or AUC24/MPC ratios, either as a result of dose administration or the susceptibility of the organism, may lead to a better clinical outcome and decrease the emergence of resistance, respectively. Pharmacokinetic profiles that are optimised to target low-level resistant minor subpopulations of bacteria that often exist in infections may help preserve fluoroquinolones as a class. To this end, optimising the AUC24/MPC or C(max)/MPC ratios is important, particularly against S. pneumoniae, in the setting of lower respiratory tract infections. Agents such as moxifloxacin and gemifloxacin with high ratios against this organism are preferred, and agents such as ciprofloxacin with low ratios should be avoided. For agents such as levofloxacin and gatifloxacin, with intermediate ratios against S. pneumoniae, it may be worthwhile considering alternative dose administration strategies, such as using higher dosages, to eradicate low-level resistant variants. This must, of course, be balanced against the potential of toxicity. Innovative approaches to the use of fluoroquinolones are worth testing in further in vitro experiments as well as in clinical trials.