Pharmacodynamic modeling of the in vivo interaction between cefotaxime and ofloxacin by using serum ultrafiltrate inhibitory titers

Population pharmacokinetics of cefotaxime in intensive care patients

PURPOSE

To characterise the pharmacokinetics and associated variability of cefotaxime in adult intensive care unit (ICU) patients and to assess the impact of patient covariates.

METHODS

This work was based on data from cefotaxime-treated patients included in the ACCIS (Antibiotic Concentrations in Critical Ill ICU Patients in Sweden) study. Clinical data from 51 patients at seven different ICUs in Sweden, given cefotaxime (1000-3000 mg given 2-6 times daily), were collected from the first day of treatment for up to three consecutive days. In total, 263 cefotaxime samples were included in the population pharmacokinetic analysis.

RESULTS

A two-compartment model with linear elimination, proportional residual error and inter-individual variability (IIV) on clearance and central volume of distribution best described the data. The typical individual was 64 years, with body weight at ICU admission of 92 kg and estimated creatinine clearance of 94 mL/min. The resulting typical value of clearance was 11.1 L/h, central volume of distribution 5.1 L, peripheral volume of distribution 18.2 L and inter-compartmental clearance 14.5 L/h. The estimated creatinine clearance proved to be a significant covariate on clearance (p < 0.001), reducing IIV from 68 to 49%.

CONCLUSION

A population pharmacokinetic model was developed to describe cefotaxime pharmacokinetics and associated variability in adult ICU patients. The estimated creatinine clearance partly explained the IIV in cefotaxime clearance. However, the remaining unexplained IIV is high and suggests a need for dose individualisation using therapeutic drug monitoring where the developed model, after evaluation of predictive performance, may provide support.

Combined immunomodulator and antimicrobial therapy eliminates polymicrobial sepsis and modulates cytokine production in combined injured mice

Purpose: A combination therapy for combined injury (CI) using a non-specific immunomodulator, synthetic trehalose dicorynomycolate and monophosphoryl lipid A (STDCM-MPL), was evaluated to augment oral antimicrobial agents, levofloxacin (LVX) and amoxicillin (AMX), to eliminate endogenous sepsis and modulate cytokine production.

Materials and methods: Female B6D2F₁/J mice received 9.75 Gy cobalt-60 gamma-radiation and wound. Bacteria were isolated and identified in three tissues. Incidence of bacteria and cytokines were compared between treatment groups.

Results: Results demonstrated that the lethal dose for 50% at 30 days (LD_50/30) of B6D2F₁/J mice was 9.42 Gy. Antimicrobial therapy increased survival in radiation-injured (RI) mice. Combination therapy increased survival after RI and extended survival time but did not increase survival after CI. Sepsis began five days earlier in CI mice than RI mice with Gram-negative species predominating early and Gram-positive species increasing later. LVX plus AMX eliminated sepsis in CI and RI mice. STDCM-MPL eliminated Gram-positive bacteria in CI and most RI mice but not Gram-negative. Treatments significantly modulated 12 cytokines tested, which pertain to wound healing or elimination of infection.

Conclusions: Combination therapy eliminates infection and prolongs survival time but does not assure CI mouse survival, suggesting that additional treatment for proliferative-cell recovery is required.

Comparison of antimicrobial pharmacokinetic/pharmacodynamic breakpoints with EUCAST and CLSI clinical breakpoints for Gram-positive bacteria

This study compared the susceptibility breakpoints based on pharmacokinetic/pharmacodynamic (PK/PD) models and Monte Carlo simulation with those defined by the Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) for antibiotics used for the treatment of infections caused by Gram-positive bacteria. A secondary objective was to evaluate the probability of achieving the PK/PD target associated with the success of antimicrobial therapy. A 10,000-subject Monte Carlo simulation was executed to evaluate 13 antimicrobials (47 intravenous dosing regimens). Susceptibility data were extracted from the British Society for Antimicrobial Chemotherapy database for bacteraemia isolates. The probability of target attainment and the cumulative fraction of response (CFR) were calculated. No antibiotic was predicted to be effective (CFR≥90%) against all microorganisms. The PK/PD susceptibility breakpoints were also estimated and were compared with CLSI and EUCAST breakpoints. The percentages of strains affected by breakpoint discrepancies were calculated. In the case of β-lactams, breakpoint discrepancies affected <15% of strains. However, higher differences were detected for low doses of vancomycin, daptomycin and linezolid, with PK/PD breakpoints being lower than those defined by the CLSI and EUCAST. If this occurs, an isolate will be considered susceptible based on CLSI and EUCAST breakpoints although the PK/PD analysis predicts failure, which may explain treatment failures reported in the literature. This study reinforces the idea of considering not only the antimicrobial activity but also the dosing regimen to increase the probability of clinical success of an antimicrobial treatment.

Modeling the mechanism of postantibiotic effect and determining implications for dosing regimens

A stochastic model is proposed to explain one possible underlying mechanism of the postantibiotic effect (PAE). This phenomenon, of continued inhibition of bacterial growth after removal of the antibiotic drug, is of high relevance in the context of optimizing dosing regimens. One clinical implication of long PAE lies in the possibility of increasing intervals between drug administrations. The model describes the dynamics of synthesis, saturation and removal of penicillin binding proteins (PBPs). High fractions of saturated PBPs are in the model associated with a lower growth capacity of bacteria. An analytical solution for the bivariate probability of saturated and unsaturated PBPs is used as a basis to explore optimal antibiotic dosing regimens. Our finding that longer PAEs do not necessarily promote for increased intervals between doses, might help for our understanding of data provided from earlier PAE studies and for the determination of the clinical relevance of PAE in future studies.

Pharmacodynamics of non-replicating viruses, bacteriocins and lysins

The pharmacodynamics of antibiotics and many other chemotherapeutic agents is often governed by a 'multi-hit' kinetics, which requires the binding of several molecules of the therapeutic agent for the killing of their targets. In contrast, the pharmacodynamics of novel alternative therapeutic agents, such as phages and bacteriocins against bacterial infections or viruses engineered to target tumour cells, is governed by a 'single-hit' kinetics according to which the agent will kill once it is bound to its target. In addition to requiring only a single molecule for killing, these agents bind irreversibly to their targets. Here, we explore the pharmacodynamics of such 'irreversible, single-hit inhibitors' using mathematical models. We focus on agents that do not replicate, i.e. in the case of phage therapy, we deal only with non-lytic phages and in the case of cancer treatment, we restrict our analysis to replication of incompetent viruses. We study the impact of adsorption on dead cells, heterogeneity in adsorption rates and spatial compartmentalization.

Pharmacodynamic model to describe the concentration-dependent selection of cefotaxime-resistant Escherichia coli

Antibiotic dosing regimens may vary in their capacity to select mutants. Our hypothesis was that selection of a more resistant bacterial subpopulation would increase with the time within a selective window (SW), i.e., when drug concentrations fall between the MICs of two strains. An in vitro kinetic model was used to study the selection of two Escherichia coli strains with different susceptibilities to cefotaxime. The bacterial mixtures were exposed to cefotaxime for 24 h and SWs of 1, 2, 4, 8, and 12 h. A mathematical model was developed that described the selection of preexisting and newborn mutants and the post-MIC effect (PME) as functions of pharmacokinetic parameters. Our main conclusions were as follows: (i) the selection between preexisting mutants increased with the time within the SW; (ii) the emergence and selection of newborn mutants increased with the time within the SW (with a short time, only 4% of the preexisting mutants were replaced by newborn mutants, compared to the longest times, where 100% were replaced); and (iii) PME increased with the area under the concentration-time curve (AUC) and was slightly more pronounced with a long elimination half-life (T(1/2)) than with a short T(1/2) situation, when AUC is fixed. We showed that, in a dynamic competition between strains with different levels of resistance, the appearance of newborn high-level resistant mutants from the parental strains and the PME can strongly affect the outcome of the selection and that pharmacodynamic models can be used to predict the outcome of resistance development.

Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens

There is a complex quantitative relationship between the concentrations of antibiotics and the growth and death rates of bacteria. Despite this complexity, in most cases only a single pharmacodynamic parameter, the MIC of the drug, is employed for the rational development of antibiotic treatment regimens. In this report, we use a mathematical model based on a Hill function-which we call the pharmacodynamic function and which is related to previously published E(max) models-to describe the relationship between the bacterial net growth rates and the concentrations of antibiotics of five different classes: ampicillin, ciprofloxacin, tetracycline, streptomycin, and rifampin. Using Escherichia coli O18:K1:H7, we illustrate how precise estimates of the four parameters of the pharmacodynamic function can be obtained from in vitro time-kill data. We show that, in addition to their respective MICs, these antibiotics differ in the values of the other pharmacodynamic parameters. Using a computer simulation of antibiotic treatment in vivo, we demonstrate that, as a consequence of differences in pharmacodynamic parameters, such as the steepness of the Hill function and the minimum bacterial net growth rate attained at high antibiotic concentrations, there can be profound differences in the microbiological efficacy of antibiotics with identical MICs. We discuss the clinical implications and limitations of these results.

Antimicrobial management strategies for Gram-positive bacterial resistance in the intensive care unit

This article summarizes the current situation with Gram-positive infections, including the two primary consequences-failure to cure and resistance-relevant to the intensive care unit. The past few years have seen Enterococcus faecium resistance to vancomycin increase from 10% of strains to approaching 60% of strains in some centers. Failure is now so frequent that vancomycin can no longer be safely used. This has lead to use of two new antibiotics, quinupristin/dalfopristin (Synercid), first marketed in the United States in September 1999, and linezolid (Zyvox), which reached the U.S. market in May 2000. Both of these agents are being used to treat culture-proven vancomycin-resistant E. faecium. The calculated areas under the inhibitory curve (AUIC) values of vancomycin, even when its minimal inhibitory concentration (MIC) is 4.0 microg/mL, show almost all vancomycin-resistant E. faecium have AUICs <125. This explains failure, as well as the further selection of this bacteria into subpopulations with progressively higher MICs. Less well defined, but potentially an even greater problem, is the poor efficacy of vancomycin against multiresistant Staphylococcus aureus. Here, there is evidence of clinical failure in lower respiratory tract infection patients, but in most cases the MIC values of the organism have not risen to the point where AUICs are <125. However, the minimum bactericidal concentration of this organism may be considerably higher than its MIC, and in other cases there may be a high inoculum effect or a protein-binding effect to explain the failure of vancomycin to kill multiresistant S. aureus. Besides the increasing use of the new agents, strategies to manage these two increasingly resistant Gram-positive infections include cephalosporin restriction, switch and streamlining when cultures come back from the lab, combination regimens, and cycling in selected intensive care units.

Antimicrobial action and pharmacokinetics/pharmacodynamics: the use of AUIC to improve efficacy and avoid resistance

In in-vitro and in animal models, antibiotics show good relationships between concentration and response, when response is quantified as the rate of bacterial eradication. The strength of these in-vitro relationships promises their utility for dosage regimen design and predictable cure of human infections. Resistance is also predictable from these parameters, fostering a rational means of using dosing adjustments to avoid or minimize the development of resistant organisms. Newly developed computerized methods for the quantitation of susceptibility allow testing of integrated kinetic-susceptibility models in patients. Our attention has focused recently on fluoroquinolones, since they are relatively non-toxic and provide the necessary range of dosage needed to elucidate correlations between concentration and response in the Intensive Care Unit patient. Studies conducted in patients with nosocomial gram-negative pneumonia reveal good correlations between bacterial eradication and integration of concentration with bacterial susceptibility. In patients, the best correlation parameters are time over MIC, and the ratio of 24-hour AUC to MIC (AUIC). Patients with serious infections like nosocomial pneumonia require bactericidal antimicrobial activity. Studies in our laboratory demonstrate that the minimum effective antimicrobial action is an area under the inhibitory titer (AUIC) of 125, where AUIC is calculated as the 24-hour serum AUC divided by the MIC of the pathogen. This target AUIC may be achieved with either a single antibiotic or it can be the sum of AUIC values of two or more antibiotics. There is considerable variability in the actual AUIC value for patients when antibiotics are given in their usually recommended dosages. Examples of this variance will be provided using aminoglycosides, fluoroquinolones, beta-lactams, macrolides and vancomycin. The achievement of minimally effective antibiotic action, consisting of an AUIC of at least 125, is associated with bacterial eradication in about 7 days for beta-lactams and quinolones. When AUIC is increased to 250, the quinolone ciprofloxacin (which displays in vivo concentration dependent bacterial killing) can eliminate the bacterial pathogen in 1-2 days. Beta lactams, even when dosed to an AUIC of 250, often require longer treatment duration to eliminate the bacterial pathogen, because the in vivo bacterial killing rate is slower with beta-lactams than with the quinolones. This remains true even at AUIC values of 250 for both compounds, which is theoretically identical dosing. Antibiotic activity indices allow clinicians to evaluate individualized patient regimens. Furthermore, antibiotic activity is a predictable clinical endpoint with predictable clinical outcome. This value is also highly predictive of the development of bacterial resistance. Antimicrobial regimens that do not achieve an AUIC of at least 125 cannot prevent the selective pressure that leads to overgrowth of resistant bacterial sub-populations. Indeed, there is considerable anxiety that conventional respiratory tract infection management strategies, which prescribe antibacterial dosages that may attain AUIC values below 125, are contributing to the pandemic rise in bacterial resistance levels.

Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: concepts and perspectives

Pharmacokinetic/pharmacodynamic (PK/PD)-modeling links dose-concentration relationships (PK) and concentration-effect relationships (PD), thereby facilitating the description and prediction of the time course of drug effects resulting from a certain dosing regimen. PK/PD-modeling approaches can basically be distinguished by four major attributes. The first characterizes the link between measured drug concentration and the response system, direct link versus indirect link. The second considers how the response system relates effect site concentration to the observed outcome, direct versus indirect response. The third regards what clinically or experimentally assessed information is used to establish the link between concentration and effect, hard link versus soft link. And the fourth considers the time dependency of pharmacodynamic model parameters, distinguishing between time-variant versus time-invariant. Application of PK/PD-modeling concepts has been identified as potentially beneficial in all phases of preclinical and clinical drug development. Although today predominantly limited to research, broader application of PK/PD-concepts in clinical therapy will provide a more rational basis for patient-specific dosage individualization and may thus guide applied pharmacotherapy to a higher level of performance.