New Semiphysiological Absorption Model To Assess the

ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Aug. 2009, p. 3462–3471 Vol. 53, No. 80066-4804/09/$08.00�0 doi:10.1128/AAC.00054-09Copyright © 2009, American Society for Microbiology. All Rights Reserved.

New Semiphysiological Absorption Model To Assess the PharmacodynamicProfile of Cefuroxime Axetil Using Nonparametric and Parametric

Population Pharmacokinetics�†J. B. Bulitta,1‡ C. B. Landersdorfer,1‡ M. Kinzig,1 U. Holzgrabe,2 and F. Sorgel1,3*

Institute for Biomedical and Pharmaceutical Research (IBMP), Nurnberg-Heroldsberg, Germany1; Institute of Pharmacy andFood Chemistry, University of Wurzburg, Wurzburg, Germany2; and Department of Pharmacology, University of

Duisburg—Essen, Essen, Germany3

Received 14 January 2009/Returned for modification 26 March 2009/Accepted 3 June 2009

Cefuroxime axetil is widely used to treat respiratory tract infections. We are not aware of a populationpharmacokinetic (PK) model for cefuroxime axetil. Our objectives were to develop a semiphysiological popu-lation PK model and evaluate the pharmacodynamic profile for cefuroxime axetil. Twenty-four healthy volun-teers received 250 mg oral cefuroxime as a suspension after a standardized breakfast. Liquid chromatography-tandem mass spectrometry was used for drug analysis, NONMEM and S-ADAPT (results reported) were usedfor parametric population PK modeling, and NPAG was used for nonparametric population PK modeling.Monte Carlo simulations were used to predict the duration for which the non-protein-bound-plasma concen-tration was above the MIC (fT>MIC). A model with one disposition compartment, a saturable and time-dependent drug release from the stomach, and fast drug absorption from the intestine yielded precise (r >0.992) and unbiased curve fits and an excellent predictive performance. The apparent clearance was 21.7liters/h (19.8% coefficient of variation [CV]) and the volume of distribution 38.7 liters (18.3% CV). Robust(>90%) probabilities of target attainment (PTAs) were achieved by 250 mg cefuroxime given every 12 h (q12h)or q8h for MICs of <0.375 mg/liter or <0.5 mg/liter, respectively, for the bacteriostasis target fT>MIC of >40%and for MICs of <0.094 mg/liter or <0.375 mg/liter, respectively, for the near-maximal-killing target fT>MICof >65%. For the >40% fT>MIC target, the PTAs for 250 mg cefuroxime q12h were >97.8% for Streptococcuspyogenes and penicillin-susceptible Streptococcus pneumoniae. Cefuroxime at 250 mg q12h or q8h achieved PTAsbelow 73% or 92%, respectively, for Haemophilus influenzae, Moraxella catarrhalis, and penicillin-intermediateS. pneumoniae for susceptibility data from various countries. Depending on the MIC distribution, 250 mg oralcefuroxime q8h instead of q12h should be considered, especially for more-severe infections that requirenear-maximal killing by cefuroxime.

Cefuroxime axetil is the acetoxyethyl-ester prodrug of cefu-roxime. Cefuroxime axetil is reliably absorbed and can be takenwith or without a meal, although its extent of bioavailability isenhanced under the influence of food (20, 54). Cefuroxime hasbeen successfully used in the treatment of upper and lowerrespiratory tract infections as well as genitourinary tractinfections (45) and is active against Haemophilus influenzae,Moraxella catarrhalis, Streptococcus pyogenes, Klebsiella pneu-moniae, Neisseria gonorrhoeae, penicillin-susceptible Strepto-coccus pneumoniae, and some isolates of penicillin-intermedi-ate S. pneumoniae (6, 7, 25–27, 34, 35, 37, 38, 41, 55).

A susceptibility breakpoint of �1 mg/liter has been determinedfor cefuroxime by national organizations in Britain (BSAC) (8)and Germany (DIN) (17). The susceptibility breakpoints fromthe Clinical and Laboratory Standards Institute (CLSI) (11)

are �1 mg/liter for S. pneumoniae and �4 mg/liter for Hae-mophilus spp., Enterobacteriaceae, and Staphylococcus spp.

Several authors (31, 37, 40) determined the pharmacoki-netic-pharmacodynamic (PK-PD) MIC breakpoint for cefu-roxime axetil on the basis of the average plasma concentrationprofiles but did not incorporate between-subject variability(BSV) in their analysis. Ambrose et al. (2) determined thePK-PD MIC breakpoint for intravenous cefuroxime via MonteCarlo simulation (MCS) based on literature data, and Viberget al. (51, 52) developed a population PK model for intrave-nous cefuroxime. We are not aware of a population PK modelor MCS for cefuroxime axetil.

Population PK and the MCS methodology account for theBSV in PK parameters and for the variability in the bacterialsusceptibility. A PK-PD target is used as a surrogate measureto predict successful microbiological or clinical outcome (13,18, 22, 28, 29, 48). For beta-lactams, the duration for which thenon-protein-bound-plasma concentration exceeds the MIC(fT�MIC) best predicts these outcomes (3, 14, 18). For cepha-losporins, data from animal infection models showed that atarget fT�MIC of 40% correlates with bacteriostasis at 24 h andthat an fT�MIC of 60 to 70% is required for near-maximalbactericidal activity at 24 h (3, 12, 14, 18). On the basis of thesePK-PD targets, MCS can predict the probability of target at-tainment (PTA) at various MICs. If the PTA-versus-MIC pro-

* Corresponding author. Mailing address: Institute for Biomedicaland Pharmaceutical Research (IBMP), Paul-Ehrlich-Str. 19, D-90562Nurnberg-Heroldsberg, Germany. Phone: 49-911-518290. Fax: 49-911-5182920. E-mail: [email protected].

† This article is dedicated to Ulrich Stephan, the cofounder ofIBMP, who passed away on 6 February 2009. Without his inspirationand support, IBMP would not exist, and neither would the presentresearch have been performed. We keep him in our hearts.

‡ Present address: Ordway Research Institute, Albany, NY 12208.� Published ahead of print on 15 June 2009.

3462

on April 1, 2018 by guest

http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

file is combined with the expected MIC distribution of thepathogen(s) of interest in a local hospital, the probability ofsuccessful microbiological or clinical outcome can be pre-dicted.

In addition to increasing the extent of bioavailability (20,54), administration after a meal may cause a lower rate ofcefuroxime absorption due to a prolonged gastric transit time.The rate of gastric emptying after a high-fat meal is likely to bevariable and may change over time. Parameters describing theabsorption phase may also not be normally or log-normallydistributed. As MCS based on parametric population PK mod-els uses parametric distributions to describe the variability inPK parameters, we additionally applied nonparametric popu-lation PK modeling. The latter offers the advantage that it doesnot assume any shape for the multivariate distribution of PKparameters. However, sample sizes larger than 24 subjects maybe required to adequately describe the shape of the multivar-iate distribution by a nonparametric variability model.

Our first objective was to develop a semiphysiologicalpopulation PK model for cefuroxime axetil by using para-metric and nonparametric population PK methods. Second,we sought to determine the PTA-versus-MIC profiles andthe PTAs for specific MIC distributions of various patho-gens for dosage regimens with oral cefuroxime given every12 h (q12h) or q8h.

(This work was presented in part at the 46th InterscienceConference on Antimicrobial Agents and Chemotherapy, 2006[8a].)

MATERIALS AND METHODS

Subjects. Twenty-four healthy male Caucasian volunteers participated in thestudy after they had given their written informed consent. Their demographicdata were as follows (means � standard deviations [SD]): age, 24.5 � 3.3 years(range, 18 to 31 years); weight, 73.8 � 9.2 kg (range, 58.2 to 93.6 kg); and height,179 � 8.0 cm (range, 166 to 193 cm). The subjects’ health statuses were assessedby physical examination, electrocardiography, and laboratory tests, includingurinalysis and screening for drugs of abuse. Intake of food and fluid was strictlystandardized during the study days. Consumption of tobacco, methylxanthines,and alcohol in any form was prohibited from 12 h before administration of thestudy drug until acquisition of the last sample. The volunteers were closelyobserved by physicians for the occurrence of adverse events on the study days.The study protocol had been approved by the local ethics committee, and thestudy was conducted by following the revised version of the Declaration ofHelsinki.

Study design and drug administration. The study was a single-dose, single-center study. All subjects received an oral suspension of 300.72 mg cefuroximeaxetil (equivalent to 250 mg cefuroxime) with 240 ml low-carbonated, calcium-poor mineral water at room temperature. The study drug was administereddirectly after intake of a standardized breakfast with a significant amount of fat.This breakfast contained four slices of crisp bread (50 g), 20 g margarine, 2 slicesof cheese (40 g; 30% fat content), 25 g jam, 100 ml fruit tea, and 100 ml milk(3.5% fat content).

Blood sampling. All blood samples were drawn in heparinated tubes from aforearm vein via an intravenous catheter. Blood samples were drawn immedi-ately before administration and at 30, 60, and 90 min and at 2, 2.33, 2.67, 3, 3.33,3.67, 4, 4.5, 5, 6, 8, 10, and 12 h after administration of the study drug. Sampleswere immediately centrifuged and immediately frozen and stored at �70°C untilanalysis.

Drug analysis. Samples were analyzed by means of a liquid chromatography-tandem mass spectrometry method validated for 0.1-ml samples of humanplasma. Plasma samples (0.1 ml) were diluted with buffer containing the internalstandard and deproteinized by addition of 400 �l of acetonitrile. After thoroughmixing, the samples were centrifuged for 5 min at 3,600 rpm at approximately�4°C, and acetonitrile was removed by extraction with 1 ml dichloromethane.The mixture was centrifuged again, and 30 �l of the aqueous phase of eachsample was then chromatographed on a reversed-phase column (Waters Sym-

metry C8), eluted with an isocratic solvent system consisting of ammoniumacetate buffer and acetonitrile (70%-30%, vol/vol), and monitored by liquidchromatography-tandem mass spectrometry with a multiple-reaction-monitoringmethod as follows: precursor3 product ion for cefuroxime (m/z 4233 m/z 207)and an internal standard (m/z 426 3 m/z 156). Both analyses were in negativemode. Under these conditions, cefuroxime and the internal standard were elutedafter approximately 1.4 and 1.5 min. The Mac Quan software program (version1.5; PE Sciex, Thornhill, Ontario, Canada [1991 to 1997]) was used for evaluationof chromatograms.

The linearity of the cefuroxime calibration curve was shown from 0.00900mg/liter to 10.2 mg/liter. The coefficient of correlation for all measured se-quences of cefuroxime was at least 0.999. The lowest calibration standard of0.00900 mg/liter was set as the lower limit of quantification of the assay forcefuroxime in human plasma. There was no observation below this quantificationlimit. For the spiked quality control standards of cefuroxime, the interday pre-cision ranged from 3.2 to 5.0%, with interday accuracy (relative error) between�4.3 and 2.1%. The intraday precision and relative error of the cefuroxime assayranged from 0.7 to 4.0% and from �0.1 to 3.4%, respectively.

Population PK analysis. (i) Computation. We applied the first-order condi-tional estimation method with the interaction estimation option in NONMEM,version VI, release 1.2 (NONMEM Project Group, University of California, SanFrancisco) (5). Initial models were developed in NONMEM V. Model develop-ment was primarily performed in NONMEM. The final population PK modelwas additionally estimated in S-ADAPT (version 1.55; parallelized on a com-puter cluster), using the importance-sampling parametric Monte Carlo expecta-tion maximization method (with a pmethod value of 8 in S-ADAPT) (4) and bythe nonparametric adaptive grid (NPAG) algorithm implemented in theUSC*PACK program (version 12.00) (33). WinNonlin Professional (version4.0.1; Pharsight Corp., Mountain View, CA) was used for noncompartmentalanalysis and statistics.

Parameter uncertainty was assessed by standard asymptotic formulas in S-ADAPT (4). As NONMEM could not compute asymptotic standard errors in the$COV step for the final model, nonparametric bootstrap methods with 100replicates were used to calculate standard errors in NONMEM as describedpreviously (9).

(ii) Structural model. We considered one- and two-compartment dispositionmodels with first-order, zero-order, or mixed-order (Michaelis-Menten) absorp-tion with or without a lag time. Additionally, a semiphysiological model with atime-dependent release from the stomach to the intestine and subsequent ab-sorption into the central compartment was developed (Fig. 1). The differentialequations for this model are as follows:

dA1

dt� �

Vmax � A1

Km � A1(1)

dA2

dt�

Vmax � A1

Km � A1� kabs � A2 (2)

dA3

dt� kabs � A2 �

CLV

� A3 (3)

where A1 is the amount of drug in the stomach, A2 is the amount of drug in theintestine, and A3 is the amount in the central compartment (see Table 2 for

FIG. 1. Structure of the final PK model. For the simulation ofVmax/Vmax 0 profiles, Emax values of 3 (long dashed line), 1 (continuousline), or �0.5 (dotted line); a TC50 of 2.5 h; and a Hill coefficient of 10were used. CL, clearance. (See Table 2 and Materials and Methods forfurther explanations of the parameters.)

VOL. 53, 2009 POPULATION PK AND PD OF CEFUROXIME AXETIL 3463


http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

parameter explanations). All initial conditions for all three compartments werezero. The stomach compartment (A1) received a bolus dose of 250 mg cefu-roxime at zero hour. The model is simplified, as it did not contain a specificcompartment for the prodrug cefuroxime axetil. It was assumed that cefuroximeaxetil is converted to cefuroxime before the ester prodrug reaches the peripheralsampling site. This assumption is considered justifiable for an ester prodrug. Themaximum rate of release (Vmax) of drug from the stomach compartment wasdescribed by a time-dependent function:

Vmax�TPM� � Vmax 0 � �1 �Emax � TPM�

TC50� � TPM�� (4)

Time is denoted as time past meal (TPM). Starting from a Vmax at time zero(Vmax 0), the Hill coefficient (�) modifies the Vmax over time, with TC50 denotingthe time past the meal at which Vmax changed by 50% and Emax the extent of thechange in Vmax. For TPM values of ��TC50, the Vmax approaches Vmax 0 � (1 �Emax). Therefore, an Emax of �1 represents complete inhibition of gastric re-lease, an Emax of 0 an unchanged maximum rate of gastric release, and an Emax

of 1 a maximum rate of gastric release twice as fast.Competing models were discriminated by their predictive performance as-

sessed via visual predictive checks (VPCs), their objective function (or log like-lihood), and standard diagnostic plots.

(iii) Parameter variability and observation model. For parametric populationPK modeling in NONMEM and S-ADAPT, we estimated the BSV of PK pa-rameters by assuming a log-normal distribution. The maximum extent of inhibi-tion or stimulation of gastric release for the ith subject (Emax i) was constrainedto a lower bound of �1 by the following logit transformation:

Emax i � �1 � �10 �exp�Lg_Emax � BSV Emax i�

1 � exp�Lg_Emax � BSV Emax i�� (5)

Lg_Emax is the estimated population mean (arithmetic mean) on the transformedscale and BSV Emax i the random deviate for the ith subject on transformed scale.This transformation ensures that all Emax i values range from �1 to 9. A sensi-tivity analysis showed that the upper limit of 9 did not affect the curve fits orpredictive performance of this model.

Plasma concentration time profiles were simulated for at least 4,800 subjectsfor each competing model to calculate the median and nonparametric 80%prediction interval (10th to 90th percentile) for the predicted concentrations.The same percentiles were calculated for the observations to visually assesswhether the simulated percentiles closely matched the percentiles of the obser-vations. For nonparametric population PK models in NPAG, this VPC wasperformed either based on the nonparametric distribution of PK parameterscharacterized by the support point matrix or based on a parametric, multivariatelog-normal distribution of PK parameters. In all three programs full variance-covariance matrices were estimated and used for MCS.

The residual unidentified variability was described by a combined additive andproportional error model. We used the adaptive gamma option in NPAG toestimate the residual error described by the assay error polynomial.

(iv) MCS. A target fT�MIC of 60 to 70% has been identified for near-maximalbactericidal activity of cephalosporins, and a target of 40% is required forbacteriostasis (14, 18). Therefore, we used PK-PD target fT�MICs of 65% fornear-maximal bactericidal activity and 40% for bacteriostasis. A range of MICsfrom 0.031 to 64 mg/liter was considered. As the protein binding levels forcefuroxime have been reported to range between 33 and 50% (21, 23, 45), weassumed an average protein binding of 42% for cefuroxime.

We compared dosage regimens of 250 mg and 500 mg oral cefuroxime givenq12h and q8h at steady state. For the final population PK models in NONMEM,S-ADAPT, and NPAG, we simulated 10,000 subjects for each dosage regimen inthe absence of residual error. NONMEM was used to simulate the full concen-tration time profiles at steady state with very frequent sampling based on the finalpopulation PK model and the estimated full variance-covariance matrix. ThefT�MIC values were calculated by linear interpolation between simulated timepoints as previously described (9).

The PTA was estimated by calculating the fraction of subjects who attained thePK-PD target at each MIC. The highest MIC with a PTA of at least 90% wasused as the PK-PD MIC breakpoint.

To put these PTAs into clinical perspective, we calculated the PTA expecta-tion value (39) for successful treatment against pathogens from specific MICdistributions as described previously (9). The PTA expectation value is the PTAfor treatment of infections caused by bacteria from a specific MIC distribution(ideally the MIC distribution of each local hospital).

The PTA expectation value was calculated based on published MIC distribu-tions. We used susceptibility data from the United Kingdom (38) collected in

2002 and 2003 for H. influenzae (n 581), M. catarrhalis (n 269), and S.pneumoniae (n 519); susceptibility data from Canada (55) collected in 2001and 2002 for H. influenzae (n 1,350); and susceptibility data from Germany (7)collected in 2002 for H. influenzae (n 300), M. catarrhalis (n 308), S.pneumoniae (n 331), and S. pyogenes (n 340). Additionally, we used sus-ceptibility data from a global surveillance study (6) collected between 1997 and2000 for penicillin-susceptible S. pneumoniae (n 2,102) and penicillin-inter-mediate S. pneumoniae (n 1,024); susceptibility data from a European surveil-lance study (26) collected between 1997 and 1999 for S. pneumoniae (n 2,018)and S. pyogenes (n 662); and susceptibility data from North America (25)collected between 1997 and 1999 for S. pyogenes (n 119), S. pneumoniae (n 417), H. influenzae (n 300), and M. catarrhalis (n 231). The PTA expectationvalues were also calculated for the MIC distributions for H. influenzae (n 66,947), K. pneumoniae (n 34,629), M. catarrhalis (n 14,308), Staphylococcusaureus (n 10,620), N. gonorrhoeae (n 655), and Neisseria meningitidis (n 257) on the basis of the multinational database of the European Committee onAntimicrobial Susceptibility Testing (EUCAST) (http://www.eucast.org/mic_distributions_of_wild_type_microorganisms/).

RESULTS

The PK parameters from noncompartmental analysis (Table1) were in good agreement with the literature (41, 45). Wefound (average � SD) a terminal half-life of 1.34 � 0.13 h anda peak concentration of 2.64 � 0.64 mg/liter between 2 and 5 hpostdose. The variability in terminal half-life (9.4% coefficientof variation) was lower than the variabilities in apparent clear-ance (20%), peak concentration (24%), and time of peak(24%).

A biphasic absorption pattern was found for 5 of 24 subjectsand a “plateau-like” peak for 8 of 24 subjects (Fig. 2). Theseshapes could not be described by standard first-order or zero-order absorption models that included a lag time. Compared tothe objective function value for the final semiphysiologicalmodel, the objective function differences in NONMEM were721 points for the first-order absorption model with a lag time,359 points for the zero-order absorption model with a lag time,and 189 points for the Michaelis-Menten absorption modelwith a lag time (likelihood ratio test: P 0.0001 for all com-parisons). The semiphysiological absorption model with a two-compartment disposition model had a 25-point-lower objective

TABLE 1. PK parameters for noncompartmental analysis for250 mg oral cefuroxime given as cefuroxime axetil

Parameter Avg � SDa Median (range)

Area under the curve fromtime zero to infinity(mg/h/liter)

11.9 � 2.49 11.6 (8.49–18.1)

Peak concn (mg/liter) 2.64 � 0.64 2.54 (1.65–3.90)Time of peak concn (h) 2.98 � 0.73 2.83 (2.00–5.00)Terminal half-life (h) 1.34 � 0.13 1.35 (1.08–1.54)Apparent total clearance

(liters/h)21.8 � 4.29 21.5 (13.8–29.4)

Apparent vol of distribution atsteady statea (liters)

54.1 � 15.7 51.2 (34.7–102)

Apparent vol of distributionduring the terminal phase(liters)

41.7 � 7.64 44.4 (27.4–54.9)

Time of total concn above1 mg/liter (h)

4.97 � 0.79 4.90 (3.43–7.08)

Time of total concn above0.5 mg/liter (h)

6.90 � 0.77 6.75 (5.13–9.02)

a To calculate the mean residence time after intravenous bolus administration,mean input time was estimated as 0.5 times the time of peak concentration,assuming approximately zero-order kinetics of drug input.

3464 BULITTA ET AL. ANTIMICROB. AGENTS CHEMOTHER.


http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

function than the same absorption model with one dispositioncompartment. As the latter model showed precise curve fitsand an excellent predictive performance, we chose the simplermodel as the final model.

The individual curve fits for the semiphysiological model(Fig. 1) were excellent in all three programs (Fig. 2). Themodel was flexible enough to fit profiles with “sharp” peaks,“plateau-like” peaks, and dual peaks. No estimation algorithm(program) provided the best fit for every subject. The linearregression plot of individual fitted versus observed concentra-tions had a slope of 1.007 and an intercept of �0.014 mg/literin NONMEM (r 0.9941), a slope of 1.011 and an intercept of�0.011 mg/liter in S-ADAPT (r 0.9928), and a slope of 1.003and an intercept of �0.011 mg/liter in NPAG (r 0.9935).

The final estimates (Table 2 and Table 3) were precise. Therelative standard errors were 31% or less for all populationmeans (except for Km, 70% in NONMEM and 39% in S-ADAPT) and 41% or less for all BSV estimates. The estimatesfor apparent clearance and volume of distribution were similarfor all three programs. For the absorption parameters, thedifferences were more apparent. The low Km/dose value(mean, 0.433%) from NONMEM indicated that the releasefrom the stomach was estimated to be essentially a zero-orderprocess and that Vmax was inhibited in some subjects and stim-ulated in others, as indicated by the negative or positive indi-vidual Emax value.

In S-ADAPT, the release from the stomach to the intestinehad partial first-order and zero-order properties, as indicatedby the estimate of 42.6% for Km/dose. This rate of release was

more notably stimulated, since the median (90th percentile) ofindividual Emax values was 1.82 (5.55). NPAG estimated therelease from the stomach to the intestine primarily as a first-order process (Km/dose, 343%), and stimulation of gastricemptying was more pronounced than for S-ADAPT. The meanTC50 values for the rate of gastric emptying after a standard-ized breakfast were 1.61 h in NONMEM and S-ADAPT and2.08 h in NPAG. Individual TC50 estimates were variable (Ta-ble 2).

The VPCs (Fig. 3) indicated that the nonparametric simu-lation based on the support points from NPAG yielded theclosest match between predicted and observed concentrations.This was expected, since this simulation is based on the non-parametric distribution of PK parameters that yielded preciseand unbiased fits for all 24 concentration-time profiles. Theparametric simulations based on the estimates from S-ADAPTand NPAG matched the median and 10th to 90th percentilesfor the observations more closely between approximately 1 and4 h than those based on NONMEM (Fig. 3). For the threeparametric simulations, S-ADAPT yielded the best represen-tation of the observations during the terminal phase, followedby NONMEM. The predicted variability was slightly too wideduring the terminal phase for the parametric simulation (Fig.3) using the variance-covariance matrix derived from the sup-port point matrix in NPAG.

As the VPCs showed that the nonparametric simulationbased on NPAG and the parametric simulation based on S-ADAPT had the best predictive performance, breakpoints forMCS are reported for these two models. The breakpoints for

FIG. 2. Individual curve fits from NONMEM (dashed line), S-ADAPT (dotted line), and NPAG (continuous line) overlaid on observations(markers).



http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

TA

BL

E2.

Popu

latio

nPK

para

met

ers

for

250

mg

oral

cefu

roxi

me

give

nas

cefu

roxi

me

axet

ilaf

ter

abr

eakf

ast

with

asi

gnifi

cant

amou

ntof

fat

Para

met

erSy

mbo

l

Val

uefo

r:

NO

NM

EM

S-A

DA

PTN

PAG

Popu

latio

nm

ean

(%SE

i )M

edia

n(1

0–90

perc

entil

e)of

indi

vidu

ales

timat

esPo

pula

tion

mea

n(%

SEi )

Med

ian

(10–

90pe

rcen

tile)

ofin

divi

dual

estim

ates

Popu

latio

nm

ean

(%SE

i )M

edia

n(1

0–90

perc

entil

e)of

indi

vidu

ales

timat

es

Fix

edef

fect

sA

ppar

ent

clea

ranc

e(l

iters

/h)

CL

/F23

.0(8

.1)

21.7

(16.

8–27

.9)

21.7

(4.1

)21

.5(1

7.1–

27.8

)21

.8c

21.8

(17.

0–28

.3)

App

aren

tvo

lof

dist

ribu

tion

(lite

rs)

V/F

40.7

(6.3

)39

.7(2

8.8–

47.6

)38

.7(4

.1)

40.2

(30.

4–45

.8)

34.8

35.6

(25.

5–48

.0)

Max

imum

rate

ofre

leas

efr

omst

omac

hto

inte

stin

eat

time

zero

divi

ded

bydo

se(1

/h)

Vm

ax0

/dos

e0.

381j

(12)

a0.

308

(0.2

00–0

.471

)0.

505

j(2

2)a

0.41

8(0

.164

–1.4

8)1.

53a

,j1.

35(0

.500

–2.6

6)

Fra

ctio

nof

dose

asso

ciat

edw

ith50

%of

Vm

ax0

(%)

Km

/dos

e0.

433

(70)

a0.

488

(0.1

80–3

.09)

42.6

(39)

a38

.1(4

.70–

478)

343a

363

(193

–498

)

Fas

test

half-

life

ofga

stri

cre

leas

e,if

frac

tion

ofdo

sein

stom

ach

is

Km

/dos

e(m

in)

Ln(

2)/V

max

0�

Km

0.53

4(0

.270

–6.3

6)30

.4(1

3.0–

120)

104

(49.

3–15

5)

Abs

orpt

ion

half-

life

from

inte

stin

eto

cent

ral

com

part

men

t(m

in)

Tab

sf9.

00(2

9)14

.2(3

.48–

30.9

)9.

34(1

5)9.

20(4

.70–

16.2

)16

.917

.5(4

.00–

32.2

)

Tim

epa

stm

eala

tw

hich

Vm

axch

ange

dby

50%

(h)

TC

501.

61(1

4)2.

06(1

.09–

3.26

)1.

61(2

0)1.

53(0

.619

–5.0

5)2.

082.

33(1

.14–

5.31

)

Max

imum

frac

tiona

lcha

nge

ontr

ansf

orm

edsc

ale

Lg_

Em

ax�

3.59

(8.9

)�

3.14

(�4.

15to

�1.

79)

�0.

762

(31)

�0.

937

(�1.

96to

0.64

1)0.

515c

�0.

50(�

3.44

to4.

95)

Max

imum

frac

tiona

lcha

nge

Em

ax�

0.58

2(�

0.84

5to

0.42

6)1.

82(0

.242

–5.5

5)2.

78(�

0.58

2to

8.93

)H

illco

effic

ient

�10

g10

g10

g

Ran

dom

effe

ctsb

BSV

(CL

/F)

0.20

2b(1

8)h

0.19

8b(3

1)h

0.19

3e

BSV

(V/F

)0.

201

(16)

0.18

3(3

2)0.

258

BSV

(Vm

ax0

/dos

e)0.

401

(22)

0.90

0(3

0)0.

518

BSV

(Km

/dos

e)1.

23(3

7)1.

80(2

7)0.

403

BSV

(Tab

s)0.

867

(23)

0.60

0(4

1)0.

634

BSV

(TC

50)

0.53

6(2

5)0.

946

(31)

0.58

0B

SV(L

g_E

max

)1.

09d

(30)

0.99

0d(2

9)3.

03d

Prop

ortio

nalr

esid

uale

rror

0.07

54(1

5)0.

0851

(5.5

)0.

0620

Add

itive

resi

dual

erro

r(m

g/lit

er)

0.00

65(2

4)0.

0029

(48)

0.00

62

aV

max

0an

dK

mw

ere

estim

ated

and

are

repo

rted

asfr

actio

nsof

the

cefu

roxi

me

dose

.Thi

sas

sum

esth

atth

era

teof

drug

rele

ase

from

the

stom

ach

ispr

imar

ilyde

term

ined

byth

em

eala

ndno

tby

the

amou

ntof

cefu

roxi

me

axet

il.T

his

assu

mpt

ion

yiel

dsa

linea

rin

crea

sein

peak

conc

entr

atio

nsan

dno

chan

gein

time

tope

akw

ithdo

se,w

hich

isco

nsis

tent

with

liter

atur

eda

ta.

bT

hese

estim

ates

for

rand

omef

fect

sre

pres

entt

heap

pare

ntco

effic

ient

ofva

riat

ion

for

the

BSV

.All

vari

abili

tyes

timat

esar

ere

port

edas

squa

rero

ots

ofth

ees

timat

edva

rian

ce,s

ince

this

squa

rero

otis

anap

prox

imat

ion

ofth

eco

effic

ient

ofva

riat

ion

ofa

norm

aldi

stri

butio

non

natu

rall

ogar

ithm

icsc

ale.

cF

orN

PAG

,the

med

ians

ofth

esu

ppor

tpo

int

mat

rix

are

prov

ided

.For

Lg_

Em

ax,t

hear

ithm

etic

mea

nis

repo

rted

,sin

ceth

edi

stri

butio

nof

Lg_

Em

axw

asm

ore

sym

met

ric

and

sinc

eth

isch

oice

for

Lg_

Em

axyi

elde

da

bett

erpr

edic

tive

perf

orm

ance

inth

eV

PC.

dT

heva

riab

ility

estim

ate

for

Lg_

Em

axis

repo

rted

asth

eSD

onth

etr

ansf

orm

edsc

ale.

eE

stim

ates

are

coef

ficie

nts

ofva

riat

ion

calc

ulat

edba

sed

onth

em

eans

�SD

ofre

sults

from

the

supp

ort

poin

tm

atri

x.fT

heco

rres

pond

ing

rate

cons

tant

k ab

s(i

nlit

ers/

h)is

calc

ulat

edas

ln(2

)/(T

abs/6

0).

gT

heH

illco

effic

ient

was

initi

ally

estim

ated

,and

estim

ates

rang

edbe

twee

n10

and

15.T

oim

prov

em

odel

stab

ility

,the

Hill

coef

ficie

ntw

assu

bseq

uent

lyfix

edat

10.T

his

had

nono

tabl

eef

fect

onth

ecu

rve

fits

and

the

obje

ctiv

efu

nctio

n.h

The

valu

ein

pare

nthe

ses

repr

esen

tsth

eun

cert

aint

yof

the

resp

ectiv

eB

SVes

timat

ean

dis

the

rela

tive

stan

dard

erro

rof

the

estim

ated

vari

ance

(rep

orte

das

%).

iSt

anda

rder

rors

are

deri

ved

from

ano

npar

amet

ric

boot

stra

pfo

rN

ON

ME

Man

dar

eas

ympt

otic

stan

dard

erro

rsfo

rS-

AD

APT

.jT

his

estim

ate

repr

esen

tsth

em

axim

umfr

actio

nof

stom

ach

cont

ent

that

can

bere

leas

edin

toth

ein

test

ine

per

hour

.Thi

sm

axim

umfr

actio

nca

nch

ange

over

time

asde

fined

inth

em

odel

.



http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

the other two simulation models were within one 1.5-fold di-lution. The PTA-versus-MIC profiles were similar for thesetwo models (Fig. 4). For the bacteriostasis target fT�MIC of�40%, the PK-PD MIC breakpoints in NPAG and S-ADAPTwere 0.5 and 0.375 mg/liter, respectively, for 250 mg q12h and0.5 and 0.5 mg/liter, respectively, for 250 mg q8h. As these PKmodels are linear with dose, 500 mg q12h achieved breakpointsof 1 and 0.75 mg/liter and 500 mg q8h achieved breakpoints of1 and 1 mg/liter in NPAG and S-ADAPT, respectively, for the�40% fT�MIC target. For the near-maximal-killing targetfT�MIC of �65%, the PK-PD MIC breakpoints for S-ADAPTand NPAG were identical (0.094 mg/liter for 250 mg q12h,

0.375 mg/liter for 250 mg q8h, 0.188 mg/liter for 500 mg q12h,and 0.75 mg/liter for 500 mg q8h). Additional simulations witha hypothetical higher rate of absorption showed that thePK-PD MIC breakpoints were lower if the rate of absorptionwas faster. The decrease in breakpoints was most pronouncedfor the 65% fT�MIC target and q12h dosing.

High PTA expectation values (�97.8% for the 40% fT�MIC

target) were achieved by all three dosage regimens shown inTable 4 for S. pyogenes, penicillin-susceptible S. pneumoniae,N. gonorrhoeae, and N. meningitidis (results not shown for thelatter two pathogens). High (�90%) PTA expectation valueswere achieved for some but not all MIC distributions for S.pneumoniae. The PTA expectation values were notably lowerfor penicillin-intermediate S. pneumoniae, H. influenzae, M.catarrhalis, S. aureus, and K. pneumoniae (results not shown forthe latter two pathogens).

DISCUSSION

Cefuroxime axetil has been used widely for treatment ofcommunity-acquired upper and lower respiratory tract infec-tions (including community-acquired pneumonia) (45) that areoften caused by S. pneumoniae, H. influenzae, M. catarrhalis,and S. pyogenes. The reported MIC90s of cefuroxime against H.

TABLE 3. Variance-covariance matrix for the final populationPK model in S-ADAPTa

Parameter CL/F V/F Vmax 0/dose Km/dose Tabs TC50 Lg_Emax

CL/F 0.0393V/F 0.0326 0.0333Vmax 0/dose 0.1143 0.0877 0.8100Km/dose 0.2693 0.2066 1.5925 3.2560Tabs 0.0083 0.0142 �0.1036 �0.1640 0.3604TC50 0.0931 0.0389 0.5324 1.0200 0.0459 0.8945Lg_Emax 0.1530 0.1131 0.5702 1.1824 0.0414 0.8197 0.9793

a V/F, apparent volume of distribution in the central compartment. See Table2 for explanations of other parameters.

FIG. 3. VPC for a single oral dose of 250 mg cefuroxime as cefuroxime axetil after a breakfast with a significant amount of fat for the final modelin each program. The continuous lines represent the 10th, 50th, and 90th percentiles for the simulated concentrations, the broken lines representthe same percentiles for the observations, and the markers show the observations. Ideally, the continuous and corresponding broken lines shouldfall onto each other. The insets show the terminal phase on a semilogarithmic scale.



http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

influenzae and M. catarrhalis are often 2 or 4 mg/liter (41, 45).Whereas most isolates would be deemed susceptible by theCLSI breakpoint of �4 mg/liter for those pathogens, a signif-icant number of isolates would be considered resistant accord-ing to the BSAC and DIN susceptibility breakpoint of �1mg/liter. To evaluate which breakpoint is in better agreementwith the predicted PK-PD MIC breakpoint, we applied para-metric and nonparametric population PK modeling and MCSfor cefuroxime axetil.

The fT�MIC best predicts the clinical and microbiological suc-cess for cephalosporins. As the prolonged absorption phase ofcefuroxime axetil has a notable influence on the fT�MIC values, itwas critical to develop a population PK model that adequatelycaptures the rates of absorption and BSVs that were observed inour studies and in the literature. We intensively qualified thepredictive performance of our population PK model to ensurethat the model-predicted PK-PD MIC breakpoints are sound. An8-fold increase (from 125 to 1,000 mg) in the oral cefuroximeaxetil dose after a meal causes a 7.5-fold increase in the areaunder the curve and a 6.5-fold increase in peak concentrations(20) and causes no systematically altered time of peak concentra-tion (Tmax) (20). Data from rats suggest a saturable componentfor the rate of absorption (42–44).

We found a range of complex absorption patterns in healthyvolunteers (Fig. 2). Models with first-order or zero-order ab-sorption with or without a lag time can describe the doseproportionality in area under the curve and peak concentration(20) but cannot describe a mixed-order rate of absorption andthe complex absorption profiles observed in our study. Amixed-order absorption model with a lag time could describethe plasma concentration time profiles of cefuroxime axetil at

one dose level (results not shown). However, such a mixed-order absorption model would predict a notable increase inTmax with cefuroxime doses. This is in disagreement with thedata reported by Finn et al. (20). A mixed-order absorptionmodel cannot describe profiles with a dual peak.

Food increases the extent of bioavailability of cefuroximeaxetil from 36% in fasting subjects to 52% after a meal (20).Similar results were found by Williams and Harding (54). Inboth studies (20, 54), Tmax is prolonged by approximately 0.6 to0.7 h for administration with food. Potential reasons for theincreased bioavailability and slightly longer Tmax observed un-der fed conditions include a more complete dissolution ofcefuroxime axetil due to a longer residence time in the stomachand due to bile acid secretion stimulated by the presence oflipids in the intestine (36, 50).

The proposed absorption model (Fig. 1) is in agreement withthe observations of literature studies for various dose levels ofcefuroxime axetil (20, 54). One limitation of our study is thatwe had data only for 250 mg oral cefuroxime. Therefore, oursimulation results for 500 mg oral cefuroxime q12h should beinterpreted conservatively. The saturable rate of absorption isdescribed by a mixed-order release of drug from the stomachto the intestine that is primarily saturated due to the presenceof food and not due to the cefuroxime axetil dose. In ourmodel, Vmax and Km are expressed as fractions of dose, and thiscauses Tmax to be independent of dose. The second peak insome profiles was described by an increase in the rate of gastricrelease over time.

This semiphysiological model proved to be robust (Table 2)and to yield excellent individual curve fits (Fig. 2) for all threepopulation PK algorithms and programs. This absorptionmodel was able to capture relevant features of complex oralabsorption profiles (24, 32, 53) which showed that the rate ofgastric emptying is important for the absorption of amoxicillin(amoxicilline) and clavulanic acid (53).

Estimation of a full variance-covariance matrix and its useduring simulations yielded the best predictive performance,caused no instability during estimation, and did not prolongrun times in S-ADAPT and NPAG. This saves modeling time,since there are fewer decisions about the choice of the param-eter variability model in S-ADAPT and NPAG than inNONMEM. NPAG does not directly estimate the variance-covariance matrix but always derives this full matrix from theestimated support points. Estimating a full variance-covariancematrix in NONMEM tended to cause model instability (i.e.,unsuccessful termination messages and the inability ofNONMEM to obtain asymptotic standard errors) and notablyincreased estimation times in NONMEM.

The most important difference between the parametric andnonparametric approaches is that the former describes BSV bya parametric, multivariate distribution (often a multivariatelog-normal distribution). In contrast, nonparametric methodsuse a discrete set of support points to exactly store the BSVand correlation structure of all estimated PK parameters in thestudied patient population. In the simplest case, each supportpoint essentially represents a complete set of PK parametersfor one patient and has a probability of 1 divided by thenumber of subjects.

As the variability of individual PK parameter estimates inthe studied subject population is “exactly” represented by the

FIG. 4. PTA-versus-MIC profiles for the PK-PD target fT�MICs of�40% (top) and �65% (bottom) for the nonparametric MCS basedon NPAG (continuous lines) and the parametric MCS based on S-ADAPT (broken lines).



http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

set of support points, it was expected that the nonparametricVPC had the best predictive performance (Fig. 3). The para-metric VPC using estimates from S-ADAPT had the best pre-dictive performance among the parametric VPCs. We reportedthe results from a parametric MCS in S-ADAPT with 10,000virtual patients and from a nonparametric MCS in NPAG. Asevery support point had the same probability for this study withfrequent sampling, the latter MCS was identical to simulationfrom the individual PK parameter estimates of the 24 subjects.

The PTA-versus-MIC profiles from S-ADAPT and NPAGwere similar (Fig. 4). For 250 mg oral cefuroxime q12h or q8h,the PK-PD MIC breakpoints fell between 0.375 and 0.5 mg/liter for the bacteriostasis target fT�MIC of 40% but wereapproximately fourfold lower (0.094 mg/liter) for dosing of 250mg q12h and the near-maximal-killing target fT�MIC of 65%.Dosing of 250 mg or 500 mg q8h increased the latter break-points to 0.375 mg/liter or 0.75 mg/liter, respectively. Koeth etal. (31) determined a PK-PD MIC breakpoint of �1 mg/literfor susceptibility for standard cefuroxime dosage regimensbased on an fT�MIC of �40 to 50%. This higher breakpoint isexpected, since Koeth et al. (31) used average PK parametersfor simulation and did not include BSV.

We did not manually increase the BSV in clearance andvolume of distribution to mirror the higher variability in criti-cally ill patients, as the relatively low PK-PD MIC breakpointsfor oral cefuroxime do not support treatment of critically illpatients. A higher variability in PK parameters and lower av-erage extent of bioavailability after intake of cefuroxime in thefasting state (20, 54) are expected to result in lower PK-PDMIC breakpoints than those reported here. As the sample sizeof 24 subjects probably did not allow us to obtain preciseestimates of the BSV in the whole patient population, theresults of our MCS should be interpreted conservatively. Theprobability of clinical success of the simulated cefuroxime ax-etil dosage regimens will ultimately depend on the MIC distri-bution of the pathogen(s) of interest in the local hospital.Dosing at 250 mg cefuroxime q8h instead of q12h had only asmall benefit for S. pyogenes and penicillin-susceptible S. pneu-moniae, since 250 mg q12h achieved high PTA expectationvalues, especially for the bacteriostasis target (Table 4).

Administering cefuroxime axetil q8h yielded notably higherPTA expectation values for some but not all MIC distributionsof H. influenzae and M. catarrhalis. Although 500 mg oralcefuroxime q8h is above the typically recommended oral cefu-

TABLE 4. PTA expectation values for various cefuroxime axetil dosage regimens and PK-PD targets for the parametricMCS based on S-ADAPT

Pathogen, source, and yr (no. of isolates)a Source or reference

PTA expectation value (%) for indicated PK-PD target dosage regimen

fT�MIC �40% fT�MIC �65%

250 mgq12h

250 mgq8h

500 mgq8h

250 mgq12h

250 mgq8h

500 mgq8h

S. pyogenesUnited States and Canada, 1997-1999 (119) 25 99.2 99.2 99.2 98.9 99.2 99.2Germany, 2002 (340) 7 99.6 99.8 100 99.3 99.6 99.9Europe, 1997-1999 (662) 26 98.9 99.3 99.6 96.7 98.9 99.4

S. pneumoniaeUnited States and Canada, 1997-1999 (417) 25 65.9 67.6 70.2 56.4 65.6 68.1United Kingdom, 2002-2003 (519) 38 94.4 95.0 96.2 90.6 94.2 95.2Germany, 2002 (331) 7 97.3 98.0 98.2 93.4 97.0 98.0Europe, 1997-1999 (2,018) 26 71.5 74.1 77.8 63.2 70.8 74.7

PSSPUnited States and Canada, 1997-1999 (249) 25 97.8 98.5 99.3 91.4 97.6 98.6Global, 1997-2000 (2,102) 6 98.2 99.0 99.6 92.7 98.0 99.2Europe, 1997-1999 (1,274) 26 98.4 99.1 99.6 93.5 98.2 99.2

PISPUnited States and Canada, 1997-1999 (70) 25 44.5 52.5 65.1 10.9 43.4 55.0Global, 1997-2000 (1,024) 6 46.0 53.9 64.4 19.9 44.0 55.7Europe, 1997-1999 (458) 26 39.9 48.8 60.8 18.0 37.8 50.9

H. influenzaeUnited States and Canada, 1997-1999 (300) 25 18.5 45.3 83.8 1.7 16.0 53.4Canada, 2001-2002 (1,350) 55 19.3 44.4 81.6 4.8 17.3 52.2United Kingdom, 2002-2003 (581) 38 41.1 67.6 88.8 3.9 33.7 72.4Germany, 2002 (300) 7 72.4 91.6 98.1 13.2 64.7 93.6EUCAST (66,947) EUCAST MIC

distribution websiteb41.5 72.7 99.1 4.9 34.9 79.3

M. catarrhalisUnited States and Canada, 1997-1999 (231) 25 26.2 49.5 82.5 3.6 22.7 56.0United Kingdom, 2002-2003 (269) 38 35.1 63.0 93.0 3.6 30.0 69.7Germany, 2002 (308) 7 11.8 35.5 77.4 1.5 10.5 43.6

a PSSP, penicillin-susceptible S. pneumoniae; PISP, penicillin-intermediate S. pneumoniae.b Available at http://www.eucast.org/mic_distributions_of_wild_type_microorganisms/ (last accessed on 20 December 2008).



http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

roxime dose, parenteral doses of up to 6,000 mg split into fourdaily doses are recommended for severe infections.

To put the results of our MCS into a clinical perspective, wecompared our PTA expectation values to the microbiologicaland clinical outcomes in clinical studies. Clinical data for chil-dren with pneumococcal acute otitis media suggest a break-point of about 0.5 mg/liter for oral cefuroxime (15). Shah et al.(47) studied hospitalized patients and outpatients in 14 coun-tries in Europe, Africa, and South America with acute exacer-bation of chronic bronchitis (AECB) and found a 60% bacte-riological overall cure rate for 250 mg oral cefuroxime giventwice daily (BID). This cure rate is comparable to the placeboresponse rate for AECB (30, 46), depending on the severity ofdisease. Interestingly, 56% (22 of 39) of the H. influenzaeisolates were eradicated. Using per-protocol and intention-to-treat analyses, the authors reported clinical cure rates of 66%and 61%, respectively, at the clinical endpoint (5 to 14 daysposttreatment) and of 53% and 39%, respectively, at the fol-low-up 3 to 4 weeks posttreatment. Although the authors didnot report the MICs in those patients, the failures for thetreatment of H. influenzae show a suboptimal effectiveness oforal cefuroxime against this pathogen.

Chodosh et al. (10) found a significantly lower microbiolog-ical eradication rate for 500 mg oral cefuroxime BID (82%)versus 500 mg oral ciprofloxacin BID (96%) in an outpatienttrial with AECB patients. Cefuroxime eradicated S. pneu-moniae in 100% of the cases (13/13) but had eradication ratesof only 76% (19/25) for M. catarrhalis and 86% (19/22) for H.influenzae. In an outpatient trial with AECB patients, de Abateet al. (16) found a clinical cure rate of 77% for 250 mg oralcefuroxime BID, which was significantly lower than the clinicalcure rate of 89% for 400 mg gatifloxacin once daily. The mi-crobiological eradication rates were 77% for cefuroxime and90% for gatifloxacin.

A trial using patients with community-acquired pneumonia(19) showed a significantly lower rate of microbiological erad-ication of H. influenzae for combinations of intravenous ceftri-axone (1 to 2 g once daily or BID) and/or oral cefuroximeaxetil (500 mg BID) than for intravenous and/or oral levofloxa-cin (500 mg once daily). The former regimen had an eradica-tion rate of 79% and the latter 100%. Upchurch et al. (49)found a clinical cure rate of 74.5% for treatment of acutebacterial sinusitis with 250 mg oral cefuroxime for 10 days butdid not document the bacterial etiology. Alvarez-Sala et al. (1)found a clinical success rate of approximately 82% for patientswith H. influenzae and S. pneumoniae for 250 mg oral cefu-roxime q12h.

In conclusion, we developed a semiphysiological populationPK model for oral cefuroxime which provided precise andunbiased individual curve fits for complex absorption profilesand had an excellent predictive performance. The nonpara-metric VPC based on NPAG showed a better predictive per-formance than the best parametric VPC in S-ADAPT. ThePK-PD MIC breakpoints were 0.375 to 0.5 mg/liter for 250 mgoral cefuroxime q12h and 0.5 mg/liter for 250 mg oral cefu-roxime q8h for the bacteriostasis target fT�MIC of �40%.Dosing at 250 mg cefuroxime q8h instead of q12h increased thebreakpoint for the near-maximal-killing target fT�MIC of 65%from 0.094 mg/liter to 0.375 mg/liter. These breakpoints were(slightly) lower than the susceptibility breakpoint of �1 mg/

liter provided by the BSAC and DIN, whereas the CLSI break-point of �4 mg/liter for most pathogens is higher than thePK-PD MIC breakpoints predicted by MCS in this study. Oralcefuroxime (250 mg q12h or q8h) achieved high PTA expec-tation values for S. pyogenes (�96.7%) and penicillin-suscep-tible S. pneumoniae (�91.4%) but notably lower PTA expec-tation values for M. catarrhalis, penicillin-intermediate S.pneumoniae, and H. influenzae for most studied MIC distribu-tions for various countries. Administering 250 mg oral cefu-roxime q8h instead of q12h was most beneficial for the near-maximal-killing target for MICs between 0.094 and 0.375 mg/liter. Future clinical studies that assess the MIC of thecausative pathogen are warranted to validate these predictionsfor the clinical and microbiological success for q12h and q8hcefuroxime axetil dosage regimens.

ACKNOWLEDGMENT

We thank George Drusano for fruitful discussions about this project.

REFERENCES

1. Alvarez-Sala, J. L., P. Kardos, J. Martinez-Beltran, P. Coronel, and L.Aguilar. 2006. Clinical and bacteriological efficacy in treatment of acuteexacerbations of chronic bronchitis with cefditoren-pivoxil versus cefu-roxime-axetil. Antimicrob. Agents Chemother. 50:1762–1767.

2. Ambrose, P. G., S. M. Bhavnani, R. N. Jones, W. A. Craig, and M. N. Dudley.2004. Use of pharmacokinetic-pharmacodynamic and Monte-Carlo simula-tion as decision support for the re-evaluation of NCCLS cephem suscepti-bility breakpoints for Enterobacteriaceae. Abstr. 44th Intersci. Conf. Antimi-crob. Agents Chemother., abstr. A-138.

3. Ambrose, P. G., S. M. Bhavnani, C. M. Rubino, A. Louie, T. Gumbo, A.Forrest, and G. L. Drusano. 2007. Pharmacokinetics-pharmacodynamics ofantimicrobial therapy: it’s not just for mice anymore. Clin. Infect. Dis. 44:79–86.

4. Bauer, R. J. 2007. S-ADAPT/MCPEM user’s guide (version 1.55). Softwarefor pharmacokinetic, pharmacodynamic and population data analysis. Bio-medical Simulations Resource, Berkeley, CA.

5. Beal, S. L., L. B. Sheiner, and A. J. Boeckmann. 2006. NONMEM user’sguides (1989 to 2006). Icon Development Solutions, Ellicott City, MD.

6. Bouchillon, S. K., D. J. Hoban, J. L. Johnson, B. M. Johnson, D. L. Butler,K. A. Saunders, L. A. Miller, and J. A. Poupard. 2004. In vitro activity ofgemifloxacin and contemporary oral antimicrobial agents against 27,247Gram-positive and Gram-negative aerobic isolates: a global surveillancestudy. Int. J. Antimicrob. Agents 23:181–196.

7. Brauers, J., S. Bagel, and M. Kresken. 2005. Aktuelle Resistenzsituation beibakteriellen Erregern von ambulant erworbenen Atemwegsinfektionen.Chemother. J. 14:45–53.

8. British Society for Antimicrobial Chemotherapy. 2008. BSAC methods forantimicrobial susceptibility testing, version 7.1. British Society for Antimi-crobial Chemotherapy, Birmingham, United Kingdom.

8a.Bulitta, J., C. Landersdorfer, M. Kinzig-Schippers, V. Jakob, M. Rodamer,G. L. Drusano, U. Thyroff-Friesinger, U. Holzgrabe, and F. Sorgel. 2006.Abstr. 46th Intersci. Conf. Antimicrob. Agents Chemother., poster A-1119.

9. Bulitta, J. B., S. B. Duffull, M. Kinzig-Schippers, U. Holzgrabe, U. Stephan,G. L. Drusano, and F. Sorgel. 2007. Systematic comparison of the populationpharmacokinetics and pharmacodynamics of piperacillin in cystic fibrosispatients and healthy volunteers. Antimicrob. Agents Chemother. 51:2497–2507.

10. Chodosh, S., J. McCarty, S. Farkas, M. Drehobl, R. Tosiello, M. Shan, L.Aneiro, S. Kowalsky, et al. 1998. Randomized, double-blind study of cipro-floxacin and cefuroxime axetil for treatment of acute bacterial exacerbationsof chronic bronchitis. Clin. Infect. Dis. 27:722–729.

11. Clinical and Laboratory Standards Institute. 2007. Performance standardsfor antimicrobial susceptibility testing; 17th informational supplement, vol.27. Clinical and Laboratory Standards Institute, Wayne, PA.

12. Craig, W. A. 2003. Basic pharmacodynamics of antibacterials with clinicalapplications to the use of beta-lactams, glycopeptides, and linezolid. Infect.Dis. Clin. N. Am. 17:479–501.

13. Craig, W. A. 1998. Choosing an antibiotic on the basis of pharmacodynamics.Ear Nose Throat J. 77:7–12.

14. Craig, W. A. 1998. Pharmacokinetic/pharmacodynamic parameters: rationalefor antibacterial dosing of mice and men. Clin. Infect. Dis. 26:1–12.

15. Dagan, R., O. Abramson, E. Leibovitz, D. Greenberg, R. Lang, S. Goshen, P.Yagupsky, A. Leiberman, and D. M. Fliss. 1997. Bacteriologic response tooral cephalosporins: are established susceptibility breakpoints appropriate inthe case of acute otitis media? J. Infect. Dis. 176:1253–1259.



http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

16. de Abate, C. A., R. A. McIvor, P. McElvaine, et al. 1999. Smokers treatedwith gatifloxacin had a high clinical cure rate: gatifloxacin vs. cefuroximeaxetil in patients with acute exacerbations of chronic bronchitis. J. Respir.Dis. 20(Suppl.):S23–S29.

17. Deutsches Institut fur Normung e.V. 2004. Medizinische Mikrobiologie—Empfindlichkeitsprufung von mikrobiellen Krankheitserregern gegen Che-motherapeutika—Teil 4: Bewertungsstufen fur die minimale Hemmkonzen-tration—MHK-Grenzwerte von antibakteriellen Wirkstoffen. DIN 58940-4.Beuth Verlag, Berlin, Germany.

18. Drusano, G. L. 2004. Antimicrobial pharmacodynamics: critical interactionsof ‘bug and drug’. Nat. Rev. Microbiol. 2:289–300.

19. File, T. M., Jr., J. Segreti, L. Dunbar, R. Player, R. Kohler, R. R. Williams,C. Kojak, and A. Rubin. 1997. A multicenter, randomized study comparingthe efficacy and safety of intravenous and/or oral levofloxacin versus ceftri-axone and/or cefuroxime axetil in treatment of adults with community-acquired pneumonia. Antimicrob. Agents Chemother. 41:1965–1972.

20. Finn, A., A. Straughn, M. Meyer, and J. Chubb. 1987. Effect of dose and foodon the bioavailability of cefuroxime axetil. Biopharm. Drug Dispos. 8:519–526.

21. Foord, R. D. 1976. Cefuroxime: human pharmacokinetics. Antimicrob.Agents Chemother. 9:741–747.

22. Garraffo, R., H. B. Drugeon, and D. Chiche. 1997. Pharmacokinetics andpharmacodynamics of two oral forms of cefuroxime axetil. Fundam. Clin.Pharmacol. 11:90–95.

23. Hara, T., H. Saeki, A. Yamamoto, K. Ohta, T. Suzuki, M. Ara, M. Sugawara,Y. Sano, H. Sakuma, and K. Satoh. 1988. Concentrations of cefuroxime inthe skin. Jpn. J. Antibiot. 41:10–17.

24. Higaki, K., S. Y. Choe, R. Lobenberg, L. S. Welage, and G. L. Amidon. 2008.Mechanistic understanding of time-dependent oral absorption based on gas-tric motor activity in humans. Eur. J. Pharm. Biopharm. 70:313–325.

25. Hoban, D. J., S. K. Bouchillon, J. L. Johnson, G. G. Zhanel, D. L. Butler,K. A. Saunders, L. A. Miller, and J. A. Poupard. 2003. Comparative in vitropotency of amoxycillin-clavulanic acid and four oral agents against recentNorth American clinical isolates from a global surveillance study. Int. J.Antimicrob. Agents 21:425–433.

26. Hoban, D. J., S. K. Bouchillon, J. L. Johnson, G. G. Zhanel, D. L. Butler,K. A. Saunders, L. A. Miller, and J. A. Poupard. 2003. Comparative in vitrosurveillance of amoxicillin-clavulanic acid and four oral comparators against21232 clinical isolates from europe. Eur. J. Clin. Microbiol. Infect. Dis.22:261–267.

27. Ison, C. A., J. W. Mouton, K. Jones, K. A. Fenton, and D. M. Livermore.2004. Which cephalosporin for gonorrhoea? Sex. Transm. Infect. 80:386–388.

28. Jacobs, M. R. 2003. How can we predict bacterial eradication? Int. J. Infect.Dis. 7(Suppl. 1):S13–S20.

29. Jacobs, M. R., S. Bajaksouzian, A. Zilles, G. Lin, G. A. Pankuch, and P. C.Appelbaum. 1999. Susceptibilities of Streptococcus pneumoniae and Hae-mophilus influenzae to 10 oral antimicrobial agents based on pharmacody-namic parameters: 1997 U.S. surveillance study. Antimicrob. Agents Che-mother. 43:1901–1908.

30. Jorgensen, A. F., J. Coolidge, P. A. Pedersen, K. P. Petersen, S. Waldorff, andE. Widding. 1992. Amoxicillin in treatment of acute uncomplicated exacer-bations of chronic bronchitis. A double-blind, placebo-controlled multicen-tre study in general practice. Scand. J. Prim. Health Care 10:7–11.

31. Koeth, L. M., D. Felmingham, M. R. Jacobs, and F. Rossi. 2004. Antimicro-bial resistance of Streptococcus pneumoniae and Haemophilus influenzae inSao Paulo, Brazil from 1996 to 2000. Int. J. Antimicrob. Agents 23:356–361.

32. Kong, F., and R. P. Singh. 2008. A model stomach system to investigatedisintegration kinetics of solid foods during gastric digestion. J. Food Sci.73:E202–E210.

33. Leary, R., R. W. Jelliffe, A. Schumitzky, and M. Van Guilder. 2001. Anadaptive grid non-parametric approach to pharmacokinetic and dynamic(PK/PD) models. IEEE Computer Society, Bethesda, MD.

34. Lesmana, M., C. I. Lebron, D. Taslim, P. Tjaniadi, D. Subekti, M. O. Wasfy,J. R. Campbell, and B. A. Oyofo. 2001. In vitro antibiotic susceptibility ofNeisseria gonorrhoeae in Jakarta, Indonesia. Antimicrob. Agents Chemother.45:359–362.

35. Llanes, R., J. Sosa, D. Guzman, A. Llop, E. A. Valdes, I. Martinez, S. Palma,and M. I. Lantero. 2003. Antimicrobial susceptibility of Neisseria gonor-rhoeae in Cuba (1995-1999): implications for treatment of gonorrhea. Sex.Transm. Dis. 30:10–14.

36. Mackay, J., A. E. Mackie, J. L. Palmer, A. Moult, and N. S. Baber. 1992.

Investigations into the mechanism for the improved oral systemic bioavail-ability of cefuroxime from cefuroxime axetil when taken after food (proceed-ings of the BPS, Sept. 1991). Br. J. Clin. Pharmacol. 33:226P–227P.

37. Mason, E. O., Jr., L. B. Lamberth, N. L. Kershaw, B. L. Prosser, A. Zoe, andP. G. Ambrose. 2000. Streptococcus pneumoniae in the USA: in vitro sus-ceptibility and pharmacodynamic analysis. J. Antimicrob. Chemother. 45:623–631.

38. Morrissey, I., M. Robbins, L. Viljoen, and D. F. Brown. 2005. Antimicrobialsusceptibility of community-acquired respiratory tract pathogens in the UKduring 2002/3 determined locally and centrally by BSAC methods. J. Anti-microb. Chemother. 55:200–208.

39. Mouton, J. W., M. N. Dudley, O. Cars, H. Derendorf, and G. L. Drusano.2005. Standardization of pharmacokinetic/pharmacodynamic (PK/PD) ter-minology for anti-infective drugs: an update. J. Antimicrob. Chemother.55:601–607.

40. Peric, M., F. A. Browne, M. R. Jacobs, and P. C. Appelbaum. 2003. Activityof nine oral agents against gram-positive and gram-negative bacteria encoun-tered in community-acquired infections: use of pharmacokinetic/pharmaco-dynamic breakpoints in the comparative assessment of beta-lactam and mac-rolide antimicrobial agents. Clin. Ther. 25:169–177.

41. Perry, C. M., and R. N. Brogden. 1996. Cefuroxime axetil. A review of itsantibacterial activity, pharmacokinetic properties and therapeutic efficacy.Drugs 52:125–158.

42. Ruiz-Balaguer, N., A. Nacher, V. G. Casabo, and M. Merino. 1997. Nonlin-ear intestinal absorption kinetics of cefuroxime axetil in rats. Antimicrob.Agents Chemother. 41:445–448.

43. Ruiz-Balaguer, N., A. Nacher, V. G. Casabo, and M. Merino Sanjuan. 2002.Intestinal transport of cefuroxime axetil in rats: absorption and hydrolysisprocesses. Int. J. Pharm. 234:101–111.

44. Ruiz-Carretero, P., M. Merino-Sanjuan, A. Nacher, and V. G. Casabo. 2004.Pharmacokinetic models for the saturable absorption of cefuroxime axetiland saturable elimination of cefuroxime. Eur. J. Pharm. Sci. 21:217–223.

45. Scott, L. J., D. Ormrod, and K. L. Goa. 2001. Cefuroxime axetil: an updatedreview of its use in the management of bacterial infections. Drugs 61:1455–1500.

46. Sethi, S., and T. F. Murphy. 2004. Acute exacerbations of chronic bronchitis:new developments concerning microbiology and pathophysiology—impacton approaches to risk stratification and therapy. Infect. Dis. Clin. N. Am.18:861–882.

47. Shah, P. M., F. P. Maesen, A. Dolmann, N. Vetter, E. Fiss, and R. Wesch.1999. Levofloxacin versus cefuroxime axetil in the treatment of acute exac-erbation of chronic bronchitis: results of a randomized, double-blind study.J. Antimicrob. Chemother. 43:529–539.

48. Stoeckel, K., W. L. Hayton, and D. J. Edwards. 1995. Clinical pharmacoki-netics of oral cephalosporins. Antibiot. Chemother. 47:34–71.

49. Upchurch, J., M. Rosemore, R. Tosiello, S. Kowalsky, and R. Echols. 2006.Randomized double-blind study comparing 7- and 10-day regimens of faro-penem medoxomil with a 10-day cefuroxime axetil regimen for treatment ofacute bacterial sinusitis. Otolaryngol. Head Neck Surg. 135:511–517.

50. Vasu, S., C. Adithan, C. H. Shashindran, M. Asad, K. Koumaravelou, and I.Topno. 2000. Effect of two types of Indian breakfast on bioavailability ofcefuroxime axetil. Indian J. Med. Res. 112:104–108.

51. Viberg, A., O. Cars, M. O. Karlsson, and S. Jonsson. 2008. Estimation ofcefuroxime dosage using pharmacodynamic targets, MIC distributions, andminimization of a risk function. J. Clin. Pharmacol. 48:1270–1281.

52. Viberg, A., A. Lannergard, A. Larsson, O. Cars, M. O. Karlsson, and M.Sandstrom. 2006. A population pharmacokinetic model for cefuroxime usingcystatin C as a marker of renal function. Br. J. Clin. Pharmacol. 62:297–303.

53. Weitschies, W., C. Friedrich, R. S. Wedemeyer, M. Schmidtmann, O. Kosch,M. Kinzig, L. Trahms, F. Sorgel, W. Siegmund, S. Horkovics-Kovats, F.Schwarz, J. Raneburger, and H. Monnikes. 2008. Bioavailability of amoxi-cillin and clavulanic acid from extended release tablets depends on intragas-tric tablet deposition and gastric emptying. Eur. J. Pharm. Biopharm. 70:641–648.

54. Williams, P. E., and S. M. Harding. 1984. The absolute bioavailability of oralcefuroxime axetil in male and female volunteers after fasting and after food.J. Antimicrob. Chemother. 13:191–196.

55. Zhanel, G. G., L. Palatnick, K. A. Nichol, D. E. Low, and D. J. Hoban. 2003.Antimicrobial resistance in Haemophilus influenzae and Moraxella catarrhalisrespiratory tract isolates: results of the Canadian Respiratory OrganismSusceptibility Study, 1997 to 2002. Antimicrob. Agents Chemother. 47:1875–1881.



http://aac.asm.org/

Dow

nloaded from

http://aac.asm.org/

Documents

New Semiphysiological Absorption Model To Assess the