a Department of Clinical Pharmacology, Division of Clinical Pharmacokinetics, b Department of Internal Medicine I, Division of Infectious Diseases and Chemotherapy, c Department of Internal Medicine II, Division of Angiology, University of Vienna Medical School, Allgemeines Krankenhaus, Währinger Gürtel 1820, A-1090 Vienna, Austria; d Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA
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Abstract |
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Introduction |
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Although several in vitro models have been developed that expose bacteria to changing drug concentrations,13 such attempts to mimic more closely an in vivo situation and to predict clinical response4 have been hampered by the inability to measure pharmacokinetics (PK) at the site of infection.4,5 Although antibiotic tissue concentrations may readily be measured from tissue biopsies, total concentration measurements from biopsies may be misleading for several reasons. Most importantly, it needs to be kept in mind that tissue samples do not represent a homogeneous matrix, but rather an aqueous dispersion of biological material. However, only the freely available drug concentration in the aqueous space of the tissue compartment that is in contact with the infection site was claimed by several authors to exert anti-infective efficacy.68 Whether any such intracellular aqueous spaces are excluded from the distribution also needs to be considered. Thus, if overall concentrations are measured, effect site concentrations of drugs that equilibrate exclusively with the extracellular space, such as ß-lactams, may be underestimated.810 This, in turn, will also lead to an overestimation of effect site concentrations of drugs that accumulate intracellularly.
Recently, a new technique, microdialysis, was introduced to clinical drug studies, which allows for the on-line measurement of unbound drug concentrations in the interstitial space,8,11,12 an important target compartment for antimicrobial chemotherapy.10,13 Since microdialysis monitors free antibiotic concentrations in the fluid that directly surrounds the infective agents, the antimicrobial effect, linked to the time versus drug concentration profile obtained by microdialysis, may easily be simulated in vitro on bacterial cultures. This dynamic simulation may provide a rational approach to the description and prediction of pharmacodynamics at the interstitial target site.
In the present study we set out to demonstrate the feasibility of this tandem in vivo PKin vitro PD approach. For this purpose, the effects of individual time versus interstitial concentration profiles of a model drug, ciprofloxacin, were measured in vitro on cultures of Pseudomonas aeruginosa.
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Materials and methods |
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The PK data of the clinical study in healthy male volunteers were reported previously.9 Briefly, in vivo microdialysis was employed to measure unbound interstitial drug concentrations in the subcutaneous adipose layer.8,9,14,15 A commercially available microdialysis probe (CMA 10, Stockholm, Sweden) with an outer diameter of 500 µm, a membrane length of 16 mm and a molecular cut-off of 20 kDa was inserted in each subject into the subcutaneous adipose layer of the thigh. The microdialysis system was perfused with Ringer's solution at a flow rate of 1.5 µL/min by a microinfusion pump (Precidor; Infors-AG, Basel, Switzerland). Following in vivo probe calibration,8,9,12 ciprofloxacin (Ciproxin, Bayer, Leverkusen, Germany) was administered as a single iv dose of 200 mg over 10 min. Sampling of microdialysates and corresponding blood samples was continued at 20 min intervals for up to 480 min.
Ciprofloxacin concentrations in microdialysates and in serum were analysed by high performance liquid chromatography.16 No extraction was required before analysis of the microdialysates. The inter- and intra-assay coefficients of variation were <5%.
In vitro PD study
Based on the PK data obtained from the in vivo experiments we simulated the time versus concentration profile of ciprofloxacin in the interstitial space fluid and in serum in an in vitro setting in order to generate a PKPD model,17,18 which allows for the description of the antibacterial activity of ciprofloxacin at the target site. MuellerHinton broth, kept in a water bath at 37°C, was inoculated with select strains of P. aeruginosa, at an approximate concentration of 108 cfu/mL. Subsequently, the time versus ciprofloxacin concentration profiles obtained in vivo from the interstitial fluid and from serum following administration of ciprofloxacin 200 mg were simulated in vitro by changing ciprofloxacin concentrations in broth by adding the appropriate amount of MuellerHinton broth at 20 min intervals. To strengthen the model we performed similar experiments for virtual PK profiles following 400 mg and 800 mg doses by multiplying the 200 mg profiles by factors of 2 and 4, respectively. Samples for determination of bacterial counts were drawn at defined time points (control, 0, 40, 80, 140, 200, 260, 320, 380, 440 and 480 min).
In vitro susceptibility tests. The MIC of ciprofloxacin was determined by a two-fold serial MuellerHinton broth microdilution method. P. aeruginosa isolates were precultured overnight in brainheart infusion broth and then introduced into MuellerHinton broth containing ciprofloxacin, at an inoculum of c.105 cfu/mL. The MIC was defined as the lowest concentration of ciprofloxacin that inhibited visible bacterial growth after incubation for 24 h at 37°C. The MBC was determined by subculturing 20 µL of the broth on to antibiotic-free Columbia agar plates. The MBC was defined as the lowest antibiotic concentration that did not show any visible growth after 24 h at 37°C.
Viable counts
Samples for determination of bacterial counts were withdrawn at defined time points. After vortexing the culture tube, two 50 µL samples were pulled and diluted serially with 0.9% sodium chloride; 20 µL of each dilution step was then plated on to Columbia agar plates. The plates were then incubated at 37°C for 24 h. Subsequently, the colonies were counted and back-extrapolated to the original volume.
PKPD modelling
To develop an integrated PKPD model based on our in vitro and in vivo experiments (Figure 1) we determined non-compartmental pharmacokinetic parameters such as the maximum concentration, Cmax, and area under the curve, AUC0480min, according to the trapezoidal rule from the PK data. A detailed description of the PK data based on the use of a commercially available computer program (Topfit 2.0; Gustav Fischer, Stuttgart, Germany), where data for tissue values were fitted according to a onecompartment model to the following equation:
, where K1 represents the elimination rate constant, A and B the back-extrapolated intercepts and Ka the absorption rate constant, was reported by Brunner et al.9 Also, the following non-compartmental PKPD surrogate markers were determined: the Cmax/MIC ratio, the time above MIC during the observation period (t > MIC) and the AUC/MIC ratio. For correlation or comparison between pharmacokinetic parameters and the rate of reduction in the viable count, a linear regression analysis was performed as described previously.17 A P value < 0.05 was considered significant.
The serum concentration (Cp) was fitted to a pharmacokinetic two-compartment model with a short-term infusion rate of 200 mg/10 min (k0). The following equation was used:
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where and ß are hybrid constants for distribution and elimination, and k21 is the rate constant describing the transfer from the peripheral compartment back into the central compartment. Vc represents the volume of distribution of the central compartment, T is the infusion time (10 min) and t is the total time after the infusion was started. The same equation could be used to fit the measured microdialysate concentrations (Cm), corrected by a proportionality factor (f)
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The experimental pharmacodynamic data were fitted to the following model using a non-linear least square regression:1820
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where dN/dt is the change in the number of bacteria as a function of time (PD effect), k is the bacteria generation rate constant in the absence of any drug, kmax is the maximal killing effect, Cm is the concentration of ciprofloxacin at time t and EC50 is the concentration of ciprofloxacin required to produce 50% of the maximal effect. In the absence of drug, bacteria are grown at their normal growth rate and the term (kmax x Cm)/(EC50 + Cm) equals zero. With Cm much higher than EC50, the term Cm in the equations cancels out and the resultant killing rate is kkmax.18
Pharmacokinetic and pharmacodynamic analyses were carried out using the non-linear regression program SCIENTIST (MicroMath, Salt Lake City, UT, USA).
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Results |
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The results of the computer-assisted PKPD fits are depicted in Figure 2. The pharmacokinetic concentrations could be fitted very well to a two-compartment body model. The resulting pharmacokinetic parameters (mean ± s.d.) were
= 0.025 ± 0.014/min, ß = 0.0022 ± 0.0011/min, k21 = 0.007 ± 0.003/min and Vc = 124.6 ± 38.6 L. The measured concentration versus time profile in the interstitial fluid was fitted simultaneously with the same sets of parameters, modified by a constant factor f = 0.51 ± 0.33. This factor is in good agreement with the known unbound fraction of ciprofloxacin, indicating that the unbound concentrations in the interstitial space are approximately equal to unbound serum concentrations. These unbound concentrations were used to determine the anti-infective activity in vitro. The proposed pharmacodynamic Emax model provided excellent fits to the experimental data (Figure 2
). The pharmacodynamic parameters measured were k = 0.0072 ± 0.0001/min, kmax = 0.309 ± 0.132/min and an EC50 of 3.2 ± 1.6 mg/L.
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Discussion |
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In our study, different isolates of P. aeruginosa were exposed in vitro to individual free concentrationtime profiles of ciprofloxacin obtained in vivo from the interstitial space of subcutaneous adipose tissue following a single 200 mg dose to healthy volunteers. The in vitro simulation led to a mean 1- to 3-log10 decrease in the number of viable organisms after 8 h. Simulations based on interstitial PK after higher doses (400 mg and 800 mg), led to a further 1- and 2-log10 decrease in the number of viable organisms but no eradication could be achieved. One main finding of our experiments was that the interindividual variability observed for in vivo target site PK resulted in corresponding variability in the in vitro PD profile. This is borne out by the fact that, when comparing individual experiments, a range of almost 3-log10 cycles in the reduction of viable organisms was observed. Individual PD data, however, were correlated closely with PK parameters such as AUC (Figure 3). Thus, it appears from our study that PK variability and particularly the variability in distribution kinetics is a key factor in determining PD variability.
Based on previous findings that effect site concentrations of ciprofloxacin should be at least as high as corresponding serum concentrations,21,22 the bactericidal activity of ciprofloxacin in serum was assumed to provide a surrogate for its activity in most human tissues. Relying on ciprofloxacin serum concentrations and derivative PK surrogate parameters such as Cmax/MIC values,23 however, may be inadequate for the assessment of its bactericidal activity in peripheral tissues. This concept is also supported by our experiments, which show that merely relying on serum PK may lead to an overestimation of both target site PK and target site PD (Figure 4). This overestimation might even become more pronounced if penetration from the bloodstream to the target site is impaired, e.g. by diffusion barriers, which evolve in select patient groups, e.g. in intensive care patients.23 Interestingly it was shown that acute inflammatory processes do not lead to an impairment of ciprofloxacin penetration.24 Therefore, our present results may also reflect the situation in patients with soft tissue infections.
We also tried to define in vivo PKPD surrogate parameters, which may prove to be predictive for the antimicrobial effect. In particular, there was a close correlation between the maximum bactericidal effect and the AUIC of the free interstitial concentration but we also observed significant correlations for other PK surrogates, notably for Cmax, time > MIC and also Cmax/MIC ratio (Figure 3). Our combined in vivo PKin vitro PD measurements, further corroborate the concept that crude MIC values may not represent ideal surrogates for the assessment of clinical antimicrobial efficacy.4,25 It is important to consider in this regard that dosage regimens that lead to suboptimal peripheral concentrations may not only fail to kill the pathogens as in our study, but might also rapidly select resistant strains of the initial bacterial population.25 Although 99% killing can be obtained by quinolones at a low Cmax/MIC ratio, i.e. 3 for ciprofloxacin, bacterial regrowth and development of bacterial resistance may occur unless higher ratios, i.e. 8, are reached.25 In our experiments Cmax/MIC ratios for P. aeruginosa (MIC = 0.125 mg/L) of c. 20 and of 6 were attained for serum levels and interstitial space fluid, respectively. Predictions of bacterial eradication based solely on serum measurements may, thus, be misleading, as Cmax/MIC ratios may differ significantly between the central and the peripheral compartments.8,9
PD data generated by an in vitro simulation based on interstitial in vivo PK may particularly lend themselves to computer-based predictions of bacterial eradication employing the Emax model. This approach allows for a much more detailed analysis of the data than simple MIC-based methods. This is shown by the good fit between experimental data and the computer-assisted PKPD simulation (Figure 2). By applying this principle, multiple dose situations even under variable administration times may be simulated, providing rational support for appropriate dose and drug selection.1820 One important limitation of the present approach, however, relates to the fact that in vitro experiments are usually performed in a milieu devoid of cells and humoral factors with potential antimicrobial activity. Thus, if phagocytosis and bacterial destruction by antibiotics accumulating in white blood cells play a subaltern role, in vivo PKin vitro PD approaches reflect only drug-related antimicrobial activity in an immunocompromised host. Accordingly our model may underestimate the overall antimicrobial effect in an immunocompetent host. However, the same is true for any MIC-based noncompartmental PKPD method.
In conclusion, our experiments show that therapeutic success and failure of antimicrobial chemotherapy may be explained by pharmacokinetic variability. Therefore, the in vivo PKin vitro PD approach presented in our study may provide valuable support for drug and dose selection of antimicrobial agents.
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Acknowledgments |
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Notes |
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References |
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Received 9 March 2000; returned 24 May 2000; revised 13 June 2000; accepted 3 July 2000