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Population pharmacokinetic modeling and Monte Carlo simulation of varying doses of intravenous metronidazole
Diagnostic Microbiology and Infectious Disease 55 (2006) 303 – 309 www.elsevier.com/locate/diagmicrobio
Pharmacology
Population pharmacokinetic modeling and Monte Carlo simulation of varying doses of intravenous metronidazole Kelly A. Sprandela, George L. Drusanoc, David W. Hecht d, John C. Rotschafer e, Larry H. Danziger a,b, Keith A. Rodvolda,b,4 a
College of Pharmacy, University of Illinois, Chicago, IL 60612, USA College of Medicine, University of Illinois, Chicago, IL 60612, USA c Ordway Research Institute, Albany, NY 12208, USA d Department of Medicine, Loyola University Medical Center, Maywood, IL 60153, USA e Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA b
Abstract Population pharmacokinetic modeling and Monte Carlo simulation (MCS) are approaches used to determine probability of target attainment (PTA) of antimicrobial therapy. The objectives of this study were 1) to determine a population pharmacokinetic model (PPM) using metronidazole and hydroxy-metronidazole concentrations from healthy subjects and critically ill patients, and 2) to determine the probability of attaining the pharmacodynamic target area under the plasma concentration (AUC)/MIC ratio z 70 against 218 clinical isolates of Bacteroides fragilis using MCS. Eighteen healthy subjects were randomized to 3 dosages of intravenous metronidazole (500 mg every 8 h, 1000 mg day1, 1500 mg day1) in an open-label 3-way crossover fashion. Serial blood samples were collected over 25.5 h on the 3rd day of each study period. An additional of 8 critically ill patients received intravenous metronidazole 500 mg every 8 h. Serial blood samples were collected over 8 h after the 2nd day of dosing. Plasma metronidazole and hydroxy-metronidazole concentrations were analyzed using a highperformance liquid chromatographic assay. The 834 plasma concentrations from 62 data sets were simultaneously modeled with NonParametric Adaptive Grid population modeling program. A 4-compartment model with a metabolite and zero-order infusion into the central compartment was used. The mean parameter vector and covariance matrix from PPM were inserted into the simulation module of ADAPT II. A 10 000-subject MCS was performed to determine the probability of PTA for a total drug AUC to MIC ratio z 70 against 218 isolates of B. fragilis (MIC range, 0.125–2.0 mg L1). Mean parameter values were CLnon-OH, 3.08 L h1; V c, 35.4 L; K OH, 0.04 h1; CLOH, 2.78 L h1; and V OH, 9.66 L. The regression values of the observed versus predicted concentrations (r 2) of metronidazole and hydroxymetronidazole were 0.972 and 0.980, respectively. The PTA for metronidazole 1500 mg day1 or 500 mg every 8 h (taken together) and 1000 mg day1 were 99.9% and 99.8%, respectively, over the reported MIC distribution range. For an MIC of 4 mg L1, the predicted PTA decreased to 80.0% and 28.5%, respectively. A PPM was determined by comodeling metronidazole and hydroxy-metronidazole concentrations from healthy subjects and critically ill patients. Based on this model, attainment of the target pharmacodynamic parameter (AUC/MIC ratio z 70) against B. fragilis isolates is N 99% when MICs are b 2 mg L1, irrespective of the dosing interval of 24 h. D 2006 Elsevier Inc. All rights reserved.
1. Introduction Metronidazole is a nitroimidazole antibacterial agent that exhibits rapid bactericidal activity against some anaerobic organisms such as members of the Bacteroides fragilis group (Lewis et al., 2000; Nix et al., 1995; Tally et al.,
4 Corresponding author. Pharmacy Practice, College of Pharmacy, University of Illinois at Chicago, Chicago, IL 60612, USA. Tel.: +1-312996-3341; fax: +1-312-413-1797. E-mail address: [email protected] (K.A. Rodvold). 0732-8893/$ – see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.diagmicrobio.2006.06.013
1978). In addition, the in vitro activity of the hydroxymetabolite is approximately 65% of that observed with the parent compound (Haller, 1982; Selkon, 1981). Similar to the aminoglycosides and fluoroquinolones, it has been suggested that metronidazole exhibits concentration-dependent killing against susceptible bacteria (Lewis et al., 2000; Nix et al., 1995; Haller, 1982; Craig and Ebert, 1991). Therefore, the goal of dosing regimens for antibacterial agents with concentration-dependent activity is to maximize the concentration. The maximum plasma concentration to minimum inhibitory concentration (peak/MIC) and/or the area under the plasma concentration (AUC) to MIC ratios
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are the pharmacokinetic/pharmacodynamic parameters best correlated with efficacy (Craig, 1998; Drusano, 2004). The purpose of this analysis was to determine a population pharmacokinetic model (PPM) using metronidazole and hydroxy-metabolite concentrations from healthy subjects and critically ill patients. Using this PPM, a Monte Carlo simulation (MCS) was also performed to determine the probability of attaining the target pharmacodynamic parameter (AUC/MIC z 70) against 218 clinical isolates of B. fragilis.
2. Materials and methods 2.1. Healthy subjects Eighteen healthy nonsmoking adult male subjects (age, 18 –40 years) were enrolled in a randomized, 3-way crossover, open-label study after obtaining written informed consent. Subjects were randomized to receive 1 of 3 regimens of intravenous metronidazole for 72 h (500 mg every 8 h, 1000 mg once daily, and 1500 mg once daily). Each subject was then crossed over to receive the opposite 2 regimens. There was a minimum 4-day washout period before starting the next study period. In addition to metronidazole, all subjects received intravenous levofloxacin 750 mg once daily for 72 h during all the 3 study periods. Only the data for metronidazole were analyzed in this work. Exclusion criteria included a history or clinical evidence of hepatic, renal, or gastrointestinal disease; presence of any clinically significant baseline laboratory abnormalities, ingestion of any fluoroquinolone antibiotic, metronidazole, antacids, theophylline, or other drugs known to interact with fluoroquinolone antibiotics or metronidazole during the 2 weeks before study drug administration; a history of allergic, hypersensitivity, or other serious adverse reactions to metronidazole, levofloxacin, ofloxacin, or any fluoroquinolone antibiotic; a history of any seizure disorder; and the use of any tobacco products during the 12 months before study drug administration. Females were excluded if they had not undergone medical sterilization, hysterectomy, or tubal ligation. Blood samples for pharmacokinetic and pharmacodynamic analysis were obtained through an indwelling peripheral catheter placed in the arm contralateral to that used for the infusion of metronidazole. Blood and urine samples were collected for a 25.5-h period starting on the 3rd day of each study period (48.0 – 73.5 h) for the measurement of metronidazole and hydroxy-metronidazole. Blood samples for pharmacokinetic analysis were obtained at the following times for dosing regimens involving oncedaily administration of metronidazole: 48, 49.5, 50, 50.5, 50.68, 50.86, 51.22, 52.5, 57.5, 60.5, 72.5, and 73.5 h. For the dosing regimen with metronidazole administered every 8 h, blood samples for pharmacokinetic analysis were obtained at the following times: 48, 49.5, 50, 50.5, 50.68,
50.86, 51.22, 52.5, 57.5, 58.65, 60.5, 62, 65.5, 66.5, 70, 72.5, and 73.5 h. Plasma was separated and stored at 70 8C until the assay was performed. Serial urine samples were collected over 25.5 h for each study period between 48 and 73.5 h at the following intervals: 48 to 52, 52 to 56, 56 to 60, 60 to 64, and 64 to 73.5 h. The total volume of urine was recorded after each interval collection, and a 15-mL aliquot was removed for the measurement of metronidazole and hydroxy-metronidazole. All urine samples were stored in polypropylene cryogenic vials and frozen at 70 8C until analysis. 2.2. Patients Eight adult critically ill patients (age range, 32 –82 years) at the University of Illinois Medical Center, Chicago, IL, received intravenous metronidazole 500 mg every 8 h. Exclusion criteria included pregnancy, a known hypersensitivity to metronidazole, consumption of alcohol within 48 h before study enrollment, concomitant medications known to interact with metronidazole, renal dysfunction (blood urea nitrogen N 35 mg dL1, serum creatinine N 3 mg dL1, or creatinine clearance b20 mL min1), or hepatic dysfunction (serum glutamic-oxaloacetic transaminase N 80 U L1, serum glutamic-pyruvic transaminase N 70 U L1, lactate dehydrogenase N 250 U L1, alkaline phosphatase N 150 U L1, total bilirubin N3 mg dL1, or direct bilirubin N 1 mg dL1). Blood samples were obtained on at least the 2nd day of dosing at the following times: 0 (just before start of the infusion), 0.5, 0.58, 0.67, 0.75, 1, 1.5, 2, 2.5, 4.5, 6.5, and 8 h after the end of the infusion. Plasma was separated and stored at 70 8C until the assay was performed. 2.3. Metronidazole assay Metronidazole and hydroxy-metronidazole concentrations in plasma and urine were analyzed by a reversedphase high-performance liquid chromatographic assay at the University of Illinois Clinical Research Laboratory based on a modified version of previously established methods (Gibson et al., 1984; Jensen and Gugler, 1983;
Fig. 1. Four-compartment open pharmacokinetic model.
K.A. Sprandel et al. / Diagnostic Microbiology and Infectious Disease 55 (2006) 303 – 309
Sprandel et al., 2004). The interday coefficients of variation for metronidazole and the hydroxy-metabolite in plasma and urine were less than 5%. The intraday coefficient of variation for the 2 compounds was less than 2.2% for the urine samples. 2.4. Population pharmacokinetic modeling All plasma metronidazole and hydroxy-metronidazole concentrations were subjected to simultaneous population analysis using Non-Parametric Adaptive Grid (NPAG) population modeling program of Leary et al. (2001). There were 834 plasma concentrations available from 62 data sets (18 healthy subjects undergoing 3 study periods and 8 critically ill patients). A 4-compartment open model with a metabolite and zero-order infusion into the central compartment was used (Fig. 1). The differential equations for the model and the output equations are displayed below: dxð1Þ=dt ¼ Rð1Þ ð K12 Txð1ÞÞ ðKmet Txð1ÞÞ ðSCLnonOH =Vc ÞTxð1Þ þ ð K21 Txð2ÞÞ
ð1Þ
dxð2Þ=dt ¼ ð K12 Txð1ÞÞ ð K21 Txð2ÞÞ
ð2Þ
dxð3Þ=dt ¼ ðKOH Txð1ÞÞ þ ð K43 Txð4ÞÞ ðK34 Txð3ÞÞ
ð3Þ
ððSCLOH =VOH ÞTxð3ÞÞ dxð4Þ=dt ¼ ð K34 Txð3ÞÞ ð K43 Txð4ÞÞ
ð4Þ
For model outputs, metronidazole plasma concentration = x(1)/V c and hydroxy-metronidazole plasma concentration = x(3)/V OH. The differential equations provide the rate of change of the amount of metronidazole in the plasma central compartment (Eq. 1) and the peripheral compartment (Eq. 2), and the rate of change of hydroxy-metronidazole in the plasma (Eq. 3) and peripheral compartment (Eq. 4). R (hour) is the input function for metronidazole intravenous administration, SCLnon-OH (liters per hour) is the non– hydroxy-metabolite clearance for the parent compound,
305
SCLOH (liters per hour) is the hydroxy-metabolite clearance, V c and V OH (liters) are volume terms for metronidazole and hydroxy-metronidazole in plasma, respectively, K OH (per hour) is the 1st-order rate constant for the conversion of metronidazole to the hydroxy-metabolite, and the remaining K terms (per hour) represent the 1st-order intercompartmental transfer rate constants. We assumed that assay variance was the major component of total observation variance. Bayesian parameter estimates for each subject and patient were calculated and used to simulate AUC0-l of metronidazole and the hydroxy-metabolite after a single dose in the ADAPT II package of D’Argenio and Schumitzky (1997). 2.5. Renal clearance The renal clearance (CLR) of metronidazole and hydroxy-metronidazole in the healthy adult subjects was calculated from the cumulative amount excreted in the urine (Ae) divided by AUC0-l. 2.6. Monte Carlo simulation The mean parameter vector and full covariance matrix from the output of NPAG were embedded in SUBROUTINE PRIOR of the ADAPT II package of D’Argenio and Schumitzky (1997). A 10 000-subject MCS was performed for the 3 dosage regimens of metronidazole. Both normal and lognormal distributions were evaluated. The lognormal distribution was chosen because this recreated the original parameter values and their dispersions with the greatest fidelity. The pharmacodynamic target that was used in this analysis was a total drug AUC/MIC ratio z 70. This target was based on data from an in vitro pharmacodynamic model where the administration of metronidazole 3 times daily or as a PULSYSR dose profile (multiple peaks) had similar time-kill curves against B. fragilis (Ibrahim et al., 2004). The pulse dose profile has a shorter time above the MIC (time N MIC) relative to the more fractionated schedule of administration. Consequently, if time N MIC were linked to
Table 1 Pharmacokinetic parameter values and covariance matrix values from population analysis SCLnon-OH (L h1) V c (L) Pharmacokinetic parameter Mean F SD 3.08 1.00
35.4 21.63
K 12 (h1) 2.55 4.17
K 21 (h1) 7.55 10.73
K OH (h1) 0.04 0.01
SCLOH (L h1) V OH (L) 2.78 2.25
Covariance matrix pharmacokinetic parameter Parameter SCLnon-OH (L h1) 0.999 204 V c (L) 4.221 66 467.814 K 12 (h1) 1.152 66 3.344 08 17.4093 K 21 (h1) 4.192 57 103.863 16.8359 115.115 0.000 732 328 0.010 65 0.010 834 0.005 197 87 0.000 033 713 K OH (h1) SCLOH (L h1) 0.359 968 14.5944 1.091 78 6.9043 0.001 324 47 0.621 233 V OH (L) 5.081 35 32.3963 6.601 28 32.1722 0.007 402 22 1.691 87 0.493 279 145.824 2.693 12 5.8857 .003 289 19 1.518 11 K 34 (h1) K 43 (h1) 16.4953 6.319 72 15.6516 23.8627 0.008 701 08 0.058 927 5
9.66 12.06
K 34 (h1) 5.79 3.96
K 43 (h1) 3.84 6.70
0.764 053 64.3582 1.015 49 33.2828 2.5428 44.8929
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the outcome, the fractionated schedule should have been the best. If peak/MIC was linked to the outcome, the PULSYSR dose profile would have been superior. Using the data from the in vitro pharmacodynamics model and taking into consideration the protein binding of metronidazole (~10%), it was determined that a conservative total drug AUC/MIC target of 70 would provide maximum killing. This target value reflects only the activity of metronidazole and does not incorporate any in vitro activity of the hydroxy-metabolite. The MIC distribution of the 218 isolates of B. fragilis (MIC range, 0.125–2.0 mg L1) was obtained from Dr. David Hecht (Loyola University Hospital, Maywood, IL). The simulated AUC/MIC ratio data for the subjects were plotted for the MICs of the B. fragilis isolates. To further determine how well the metronidazole dosage regimens
would perform over the distribution of MIC values, we also evaluated 2 additional breakpoint values (AUC/MIC ratio z 30 and 100).
3. Results 3.1. Demographics Heights and weights of the 18 healthy adult males ranged from 68 to 75 in. and from 135 to 220 lb, respectively. The subjects were white (n = 15), Hispanic (n = 1), or Asian (n = 2). Recorded weights of the critically ill patients ranged from 140 to 194 lb. The patients were African American (n = 5), white (n = 2), or Hispanic (n = 1). The mean (FSD) APACHE II score (calculated within 24 h of admission to the intensive care unit) was 17.7 F 2.9.
Fig. 2. (A) Plot of observed versus predicted values for metronidazole plasma concentrations. (B) Plot of observed versus predicted values for hydroxymetronidazole plasma concentrations.
K.A. Sprandel et al. / Diagnostic Microbiology and Infectious Disease 55 (2006) 303 – 309
3.2. Pharmacokinetic parameter values The mean pharmacokinetic parameter values with SDs and the full covariance matrix from the output are displayed in Table 1. The mean (FSD) SCLnon-OH was 3.08 F 1.00 L h1, and the V c of metronidazole was 35.4 F 21.63 L. The mean (FSD) SCLOH was 2.78 F 2.25 L h1. The CLR of metronidazole and hydroxy-metronidazole were 0.84 F 0.23 and 2.77 F 0.78 L h1, respectively. The results of the Bayesian estimation showed that the plot of observed versus predicted values of metronidazole had a regression line with an intercept of 0.021 and a slope of 1.012 (Fig. 2A). The r 2 of this regression is 0.972 (r = .986), P .001. The predicted versus observed regression values for the hydroxy-metabolite have an intercept of 0.094 and a slope of 1.024 (Fig. 2B). The r 2 of this regression is 0.980 (r = .99), P .001. Comparison of Bayesian posterior parameter value estimates for volunteers and critically ill patients revealed that SCLnon-OH, V c, SCLOH, and V OH were significantly lower ( P b .05) in the critically ill patients. There was no difference between the 2 groups in terms of the 1st-order rate constants, including K OH, the 1st-order rate constant of formation of the hydroxy-metabolite. 3.3. Monte Carlo simulation Utilizing the above pharmacokinetic model, the 10 000subject MCS predicted that the attainment of the target pharmacodynamic parameter of 70 was N 99% for all 3 dosing regimens of metronidazole (500 mg every 8 h, 1000 mg day1, and 1500 mg day1) when MIC values were less than 2 mg L1 (Fig. 3). Among the 218 B. fragilis isolates, 213 of the isolates had an MIC b 1.0 mg L1 and the distribution of MIC values being 0.125 mg L1 (n = 2), 0.25 mg L1 (n = 18), 0.5 mg L1 (n = 90), and 1.0 mg L1 (n = 103). The remaining 5 isolates had a reported MIC of 2.0 mg L1.
Fig. 3. Probability of target attainment for 10 000 simulated subjects for metronidazole administered as 500 mg every 8 h, 1000 mg day1, and 1500 mg day1 at a steady state. The AUC/MIC ratio of z 70 was chosen as the target for the analysis.
307
The target attainment rate of the AUC/MIC ratio breakpoint of 30 was N 99% for all 3 dosing regimens of metronidazole when MIC values were 4 mg L1 or less. When a breakpoint of 100 was evaluated, the two 1500-mg day1 regimens (500 mg every 8 h and 1500 mg day1) achieved a target attainment rates of 99%, 97%, and 34% for MIC values of b 1, 2, and 4 mg L1, respectively. In comparison, the respective target attainment rates were 99%, 72%, and 3% for MIC values of b 1, 2, and 4 mg L1 for the metronidazole dosage regimen of 1000 mg day1 and AUC/ MIC ratio breakpoint of 100. 4. Discussion Healthy volunteer’s data tend to provide a more conservative estimate of drug exposure compared with what might be observed in an actual patient setting as regards the estimates of the pharmacokinetic parameters (e.g., clearances are frequently lower and half lives longer in patients versus volunteers). However, volunteers are often more homogeneous than patients, leading to smaller estimates of between-patient differences in pharmacokinetic parameter values. This will actually make the inferences drawn from volunteer data less conservative in most instances. Therefore, we chose to use data from both healthy volunteers and critically ill patients who received metronidazole in our population pharmacokinetic analysis. In addition, a recent study has demonstrated that calculations of target attainment rates from healthy volunteers were predictive for patients receiving ceftazidime and meropenem (Kuti et al., 2005). The mean pharmacokinetic parameters of metronidazole and hydroxy-metronidazole from the model are within the range of parameters reported for both healthy volunteers and critically ill patients (Tally et al., 1978; Sprandel et al., 2004; Plaissance et al., 1988). The lower mean values for SCLnon-OH and V c in our critically ill patients compared with the healthy volunteers have also been previously reported for metronidazole in surgical and critically ill patients without renal and/or hepatic impairment. Most studies in patients have not performed pharmacokinetic comodeling of metronidazole and its hydroxy-metabolite. Our findings demonstrate that mean values for SCLOH and V OH are also lower in the critically ill patients compared with healthy volunteers. Because critically ill patients often exhibit changes in drug clearance, elimination half-life, and volume of distribution (Bodenham et al., 1988; Power et al., 1998), it was not surprising that the volunteers and patients had significantly different values for clearance and volumes of distribution, but, importantly, not for the formation rate constant for hydroxy-metronidazole from metronidazole. Despite statistically significant differences in some of the Bayesian parameters between the groups, the data from the healthy subjects and critically ill patients fit the pharmacokinetic model well, with r 2 values of .972 (Fig. 2A) and .980 (Fig. 2B) for the observed versus predicted values of metronidazole and hydroxy-metronidazole concentrations,
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respectively. Our application of the comodeling technique allowed both metronidazole and hydroxy-metronidazole plasma data to be accurately fitted to a 4-compartment model for the estimation of pharmacokinetic parameters. Using this PPM, a 10 000-subject MCS was performed to determine the probability of attaining the pharmacodynamic target ratio AUC/MIC z 70 over a given MIC distribution of clinical isolates of B. fragilis. Although there are no established pharmacokinetic/pharmacodynamic parameters correlated with the efficacy for metronidazole in the treatment of anaerobic infections, we chose to use the AUC/MIC ratio because this parameter is often one of the parameters correlated with the efficacy of antimicrobials with similar pharmacokinetic and pharmacodynamic properties (Craig, 1998; Drusano, 2004). Also, data from an in vitro pharmacodynamic model of anaerobic infection indicated that AUC/MIC ratio was highly likely to be the dynamically linked variable (Ibrahim et al., 2004). Taking into consideration the protein binding of metronidazole (approximately 10%) and the published data from an in vitro pharmacodynamic model (Ibrahim et al., 2004), it was decided that a total drug AUC/MIC z 70 would provide a conservative pharmacodynamic target. The data from this in vitro pharmacodynamics model demonstrated in vitro eradication at exposures of this value (AUC/MIC = 70) or greater. Since 1981, national surveys on the susceptibility of the B. fragilis group have reported all isolates to be susceptible to metronidazole at concentrations less than 8 mg L1 (Snydman et al., 2002). The MIC was less than or equal to 2.0 mg L1 for all of the 218 clinical isolates used in this analysis. Using this distribution, the probability of attaining the proposed pharmacodynamic target was greater than 99% for all 3 dosing regimens. If an isolate were to have a reported MIC of 4 mg L1, the chance of attaining the proposed target would decrease to 28.5% for the 1000-mg once-daily regimen and to 80% for the 1500-mg once-daily and the 500-mg every 8-h regimens (Fig. 3). Based on this simulation, it appears that a dose of 1000 mg once daily would be less likely to achieve the target parameter against isolates with a higher MIC compared with regimens of 1500 mg once daily or 500 mg every 8 h. This observation correlates well with 2 recent reports involving in vitro bactericidal activity for anaerobic infections and 24-h areas under the inhibitory and bactericidal curves (Lewis et al., 2000; Sprandel et al., 2004). It is important to emphasize that a daily dosing regimen for metronidazole is likely to be effective on the basis of this evaluation and the findings from the in vitro model system of infection (Ibrahim et al., 2004). It should also be emphasized, however, that clinical trials of once-daily metronidazole therapy are required before once-daily regimens can be used in the clinic. Because there are so few isolates resistant to metronidazole, it will likely remain one of the drugs of choice for mixed infections involving anaerobic pathogens (Hecht, 2004). Traditional dosing of metronidazole was determined before our advances in the study of pharmacodynamics
(Anonymous, 1996). The administration of higher oncedaily doses of metronidazole may offer an effective and more convenient regimen. Because of the lack of Gramnegative coverage, metronidazole is often used in combination with other antimicrobial agents for the treatment of mixed aerobic–anaerobic infections. There are some choices of antibiotics that could be combined with metronidazole, including fluoroquinolones, aminoglycosides, and broadspectrum h-lactams without significant antianaerobic activity, which would offer convenient once-daily regimens for the treatment of mixed aerobic–anaerobic infections. Further clinical investigation of once-daily administration of metronidazole for the treatment of anaerobic infections or in combination with other agents for the treatment of mixed infections is warranted. In summary, a PPM was determined by comodeling metronidazole and hydroxy-metronidazole concentrations from healthy subjects and critically ill patients. The MCS, combined with a B. fragilis clinical isolate MIC distribution, demonstrated that varying doses of metronidazole would achieve the target pharmacodynamic parameter AUC/MIC z 70 at a rate greater than 99% when MICs are less than or equal to 2.0 mg L1.
Acknowledgments The authors greatly appreciate the analytical and technical assistance of Kelly Deyo and Carolyn Sibley. This work was funded in part by a research grant from Ortho-McNeil Pharmaceutical (Raritan, NJ). This work was supported in part by the General Clinical Research Center at the University of Illinois at Chicago, which is funded by NIH grant M01-RR-13987. The authors would like to thank the Clinical Research Center staff for help and support of this study.
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Pharmacology
Population pharmacokinetic modeling and Monte Carlo simulation of varying doses of intravenous metronidazole Kelly A. Sprandela, George L. Drusanoc, David W. Hecht d, John C. Rotschafer e, Larry H. Danziger a,b, Keith A. Rodvolda,b,4 a
College of Pharmacy, University of Illinois, Chicago, IL 60612, USA College of Medicine, University of Illinois, Chicago, IL 60612, USA c Ordway Research Institute, Albany, NY 12208, USA d Department of Medicine, Loyola University Medical Center, Maywood, IL 60153, USA e Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA b
Abstract Population pharmacokinetic modeling and Monte Carlo simulation (MCS) are approaches used to determine probability of target attainment (PTA) of antimicrobial therapy. The objectives of this study were 1) to determine a population pharmacokinetic model (PPM) using metronidazole and hydroxy-metronidazole concentrations from healthy subjects and critically ill patients, and 2) to determine the probability of attaining the pharmacodynamic target area under the plasma concentration (AUC)/MIC ratio z 70 against 218 clinical isolates of Bacteroides fragilis using MCS. Eighteen healthy subjects were randomized to 3 dosages of intravenous metronidazole (500 mg every 8 h, 1000 mg day1, 1500 mg day1) in an open-label 3-way crossover fashion. Serial blood samples were collected over 25.5 h on the 3rd day of each study period. An additional of 8 critically ill patients received intravenous metronidazole 500 mg every 8 h. Serial blood samples were collected over 8 h after the 2nd day of dosing. Plasma metronidazole and hydroxy-metronidazole concentrations were analyzed using a highperformance liquid chromatographic assay. The 834 plasma concentrations from 62 data sets were simultaneously modeled with NonParametric Adaptive Grid population modeling program. A 4-compartment model with a metabolite and zero-order infusion into the central compartment was used. The mean parameter vector and covariance matrix from PPM were inserted into the simulation module of ADAPT II. A 10 000-subject MCS was performed to determine the probability of PTA for a total drug AUC to MIC ratio z 70 against 218 isolates of B. fragilis (MIC range, 0.125–2.0 mg L1). Mean parameter values were CLnon-OH, 3.08 L h1; V c, 35.4 L; K OH, 0.04 h1; CLOH, 2.78 L h1; and V OH, 9.66 L. The regression values of the observed versus predicted concentrations (r 2) of metronidazole and hydroxymetronidazole were 0.972 and 0.980, respectively. The PTA for metronidazole 1500 mg day1 or 500 mg every 8 h (taken together) and 1000 mg day1 were 99.9% and 99.8%, respectively, over the reported MIC distribution range. For an MIC of 4 mg L1, the predicted PTA decreased to 80.0% and 28.5%, respectively. A PPM was determined by comodeling metronidazole and hydroxy-metronidazole concentrations from healthy subjects and critically ill patients. Based on this model, attainment of the target pharmacodynamic parameter (AUC/MIC ratio z 70) against B. fragilis isolates is N 99% when MICs are b 2 mg L1, irrespective of the dosing interval of 24 h. D 2006 Elsevier Inc. All rights reserved.
1. Introduction Metronidazole is a nitroimidazole antibacterial agent that exhibits rapid bactericidal activity against some anaerobic organisms such as members of the Bacteroides fragilis group (Lewis et al., 2000; Nix et al., 1995; Tally et al.,
4 Corresponding author. Pharmacy Practice, College of Pharmacy, University of Illinois at Chicago, Chicago, IL 60612, USA. Tel.: +1-312996-3341; fax: +1-312-413-1797. E-mail address: [email protected] (K.A. Rodvold). 0732-8893/$ – see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.diagmicrobio.2006.06.013
1978). In addition, the in vitro activity of the hydroxymetabolite is approximately 65% of that observed with the parent compound (Haller, 1982; Selkon, 1981). Similar to the aminoglycosides and fluoroquinolones, it has been suggested that metronidazole exhibits concentration-dependent killing against susceptible bacteria (Lewis et al., 2000; Nix et al., 1995; Haller, 1982; Craig and Ebert, 1991). Therefore, the goal of dosing regimens for antibacterial agents with concentration-dependent activity is to maximize the concentration. The maximum plasma concentration to minimum inhibitory concentration (peak/MIC) and/or the area under the plasma concentration (AUC) to MIC ratios
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are the pharmacokinetic/pharmacodynamic parameters best correlated with efficacy (Craig, 1998; Drusano, 2004). The purpose of this analysis was to determine a population pharmacokinetic model (PPM) using metronidazole and hydroxy-metabolite concentrations from healthy subjects and critically ill patients. Using this PPM, a Monte Carlo simulation (MCS) was also performed to determine the probability of attaining the target pharmacodynamic parameter (AUC/MIC z 70) against 218 clinical isolates of B. fragilis.
2. Materials and methods 2.1. Healthy subjects Eighteen healthy nonsmoking adult male subjects (age, 18 –40 years) were enrolled in a randomized, 3-way crossover, open-label study after obtaining written informed consent. Subjects were randomized to receive 1 of 3 regimens of intravenous metronidazole for 72 h (500 mg every 8 h, 1000 mg once daily, and 1500 mg once daily). Each subject was then crossed over to receive the opposite 2 regimens. There was a minimum 4-day washout period before starting the next study period. In addition to metronidazole, all subjects received intravenous levofloxacin 750 mg once daily for 72 h during all the 3 study periods. Only the data for metronidazole were analyzed in this work. Exclusion criteria included a history or clinical evidence of hepatic, renal, or gastrointestinal disease; presence of any clinically significant baseline laboratory abnormalities, ingestion of any fluoroquinolone antibiotic, metronidazole, antacids, theophylline, or other drugs known to interact with fluoroquinolone antibiotics or metronidazole during the 2 weeks before study drug administration; a history of allergic, hypersensitivity, or other serious adverse reactions to metronidazole, levofloxacin, ofloxacin, or any fluoroquinolone antibiotic; a history of any seizure disorder; and the use of any tobacco products during the 12 months before study drug administration. Females were excluded if they had not undergone medical sterilization, hysterectomy, or tubal ligation. Blood samples for pharmacokinetic and pharmacodynamic analysis were obtained through an indwelling peripheral catheter placed in the arm contralateral to that used for the infusion of metronidazole. Blood and urine samples were collected for a 25.5-h period starting on the 3rd day of each study period (48.0 – 73.5 h) for the measurement of metronidazole and hydroxy-metronidazole. Blood samples for pharmacokinetic analysis were obtained at the following times for dosing regimens involving oncedaily administration of metronidazole: 48, 49.5, 50, 50.5, 50.68, 50.86, 51.22, 52.5, 57.5, 60.5, 72.5, and 73.5 h. For the dosing regimen with metronidazole administered every 8 h, blood samples for pharmacokinetic analysis were obtained at the following times: 48, 49.5, 50, 50.5, 50.68,
50.86, 51.22, 52.5, 57.5, 58.65, 60.5, 62, 65.5, 66.5, 70, 72.5, and 73.5 h. Plasma was separated and stored at 70 8C until the assay was performed. Serial urine samples were collected over 25.5 h for each study period between 48 and 73.5 h at the following intervals: 48 to 52, 52 to 56, 56 to 60, 60 to 64, and 64 to 73.5 h. The total volume of urine was recorded after each interval collection, and a 15-mL aliquot was removed for the measurement of metronidazole and hydroxy-metronidazole. All urine samples were stored in polypropylene cryogenic vials and frozen at 70 8C until analysis. 2.2. Patients Eight adult critically ill patients (age range, 32 –82 years) at the University of Illinois Medical Center, Chicago, IL, received intravenous metronidazole 500 mg every 8 h. Exclusion criteria included pregnancy, a known hypersensitivity to metronidazole, consumption of alcohol within 48 h before study enrollment, concomitant medications known to interact with metronidazole, renal dysfunction (blood urea nitrogen N 35 mg dL1, serum creatinine N 3 mg dL1, or creatinine clearance b20 mL min1), or hepatic dysfunction (serum glutamic-oxaloacetic transaminase N 80 U L1, serum glutamic-pyruvic transaminase N 70 U L1, lactate dehydrogenase N 250 U L1, alkaline phosphatase N 150 U L1, total bilirubin N3 mg dL1, or direct bilirubin N 1 mg dL1). Blood samples were obtained on at least the 2nd day of dosing at the following times: 0 (just before start of the infusion), 0.5, 0.58, 0.67, 0.75, 1, 1.5, 2, 2.5, 4.5, 6.5, and 8 h after the end of the infusion. Plasma was separated and stored at 70 8C until the assay was performed. 2.3. Metronidazole assay Metronidazole and hydroxy-metronidazole concentrations in plasma and urine were analyzed by a reversedphase high-performance liquid chromatographic assay at the University of Illinois Clinical Research Laboratory based on a modified version of previously established methods (Gibson et al., 1984; Jensen and Gugler, 1983;
Fig. 1. Four-compartment open pharmacokinetic model.
K.A. Sprandel et al. / Diagnostic Microbiology and Infectious Disease 55 (2006) 303 – 309
Sprandel et al., 2004). The interday coefficients of variation for metronidazole and the hydroxy-metabolite in plasma and urine were less than 5%. The intraday coefficient of variation for the 2 compounds was less than 2.2% for the urine samples. 2.4. Population pharmacokinetic modeling All plasma metronidazole and hydroxy-metronidazole concentrations were subjected to simultaneous population analysis using Non-Parametric Adaptive Grid (NPAG) population modeling program of Leary et al. (2001). There were 834 plasma concentrations available from 62 data sets (18 healthy subjects undergoing 3 study periods and 8 critically ill patients). A 4-compartment open model with a metabolite and zero-order infusion into the central compartment was used (Fig. 1). The differential equations for the model and the output equations are displayed below: dxð1Þ=dt ¼ Rð1Þ ð K12 Txð1ÞÞ ðKmet Txð1ÞÞ ðSCLnonOH =Vc ÞTxð1Þ þ ð K21 Txð2ÞÞ
ð1Þ
dxð2Þ=dt ¼ ð K12 Txð1ÞÞ ð K21 Txð2ÞÞ
ð2Þ
dxð3Þ=dt ¼ ðKOH Txð1ÞÞ þ ð K43 Txð4ÞÞ ðK34 Txð3ÞÞ
ð3Þ
ððSCLOH =VOH ÞTxð3ÞÞ dxð4Þ=dt ¼ ð K34 Txð3ÞÞ ð K43 Txð4ÞÞ
ð4Þ
For model outputs, metronidazole plasma concentration = x(1)/V c and hydroxy-metronidazole plasma concentration = x(3)/V OH. The differential equations provide the rate of change of the amount of metronidazole in the plasma central compartment (Eq. 1) and the peripheral compartment (Eq. 2), and the rate of change of hydroxy-metronidazole in the plasma (Eq. 3) and peripheral compartment (Eq. 4). R (hour) is the input function for metronidazole intravenous administration, SCLnon-OH (liters per hour) is the non– hydroxy-metabolite clearance for the parent compound,
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SCLOH (liters per hour) is the hydroxy-metabolite clearance, V c and V OH (liters) are volume terms for metronidazole and hydroxy-metronidazole in plasma, respectively, K OH (per hour) is the 1st-order rate constant for the conversion of metronidazole to the hydroxy-metabolite, and the remaining K terms (per hour) represent the 1st-order intercompartmental transfer rate constants. We assumed that assay variance was the major component of total observation variance. Bayesian parameter estimates for each subject and patient were calculated and used to simulate AUC0-l of metronidazole and the hydroxy-metabolite after a single dose in the ADAPT II package of D’Argenio and Schumitzky (1997). 2.5. Renal clearance The renal clearance (CLR) of metronidazole and hydroxy-metronidazole in the healthy adult subjects was calculated from the cumulative amount excreted in the urine (Ae) divided by AUC0-l. 2.6. Monte Carlo simulation The mean parameter vector and full covariance matrix from the output of NPAG were embedded in SUBROUTINE PRIOR of the ADAPT II package of D’Argenio and Schumitzky (1997). A 10 000-subject MCS was performed for the 3 dosage regimens of metronidazole. Both normal and lognormal distributions were evaluated. The lognormal distribution was chosen because this recreated the original parameter values and their dispersions with the greatest fidelity. The pharmacodynamic target that was used in this analysis was a total drug AUC/MIC ratio z 70. This target was based on data from an in vitro pharmacodynamic model where the administration of metronidazole 3 times daily or as a PULSYSR dose profile (multiple peaks) had similar time-kill curves against B. fragilis (Ibrahim et al., 2004). The pulse dose profile has a shorter time above the MIC (time N MIC) relative to the more fractionated schedule of administration. Consequently, if time N MIC were linked to
Table 1 Pharmacokinetic parameter values and covariance matrix values from population analysis SCLnon-OH (L h1) V c (L) Pharmacokinetic parameter Mean F SD 3.08 1.00
35.4 21.63
K 12 (h1) 2.55 4.17
K 21 (h1) 7.55 10.73
K OH (h1) 0.04 0.01
SCLOH (L h1) V OH (L) 2.78 2.25
Covariance matrix pharmacokinetic parameter Parameter SCLnon-OH (L h1) 0.999 204 V c (L) 4.221 66 467.814 K 12 (h1) 1.152 66 3.344 08 17.4093 K 21 (h1) 4.192 57 103.863 16.8359 115.115 0.000 732 328 0.010 65 0.010 834 0.005 197 87 0.000 033 713 K OH (h1) SCLOH (L h1) 0.359 968 14.5944 1.091 78 6.9043 0.001 324 47 0.621 233 V OH (L) 5.081 35 32.3963 6.601 28 32.1722 0.007 402 22 1.691 87 0.493 279 145.824 2.693 12 5.8857 .003 289 19 1.518 11 K 34 (h1) K 43 (h1) 16.4953 6.319 72 15.6516 23.8627 0.008 701 08 0.058 927 5
9.66 12.06
K 34 (h1) 5.79 3.96
K 43 (h1) 3.84 6.70
0.764 053 64.3582 1.015 49 33.2828 2.5428 44.8929
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the outcome, the fractionated schedule should have been the best. If peak/MIC was linked to the outcome, the PULSYSR dose profile would have been superior. Using the data from the in vitro pharmacodynamics model and taking into consideration the protein binding of metronidazole (~10%), it was determined that a conservative total drug AUC/MIC target of 70 would provide maximum killing. This target value reflects only the activity of metronidazole and does not incorporate any in vitro activity of the hydroxy-metabolite. The MIC distribution of the 218 isolates of B. fragilis (MIC range, 0.125–2.0 mg L1) was obtained from Dr. David Hecht (Loyola University Hospital, Maywood, IL). The simulated AUC/MIC ratio data for the subjects were plotted for the MICs of the B. fragilis isolates. To further determine how well the metronidazole dosage regimens
would perform over the distribution of MIC values, we also evaluated 2 additional breakpoint values (AUC/MIC ratio z 30 and 100).
3. Results 3.1. Demographics Heights and weights of the 18 healthy adult males ranged from 68 to 75 in. and from 135 to 220 lb, respectively. The subjects were white (n = 15), Hispanic (n = 1), or Asian (n = 2). Recorded weights of the critically ill patients ranged from 140 to 194 lb. The patients were African American (n = 5), white (n = 2), or Hispanic (n = 1). The mean (FSD) APACHE II score (calculated within 24 h of admission to the intensive care unit) was 17.7 F 2.9.
Fig. 2. (A) Plot of observed versus predicted values for metronidazole plasma concentrations. (B) Plot of observed versus predicted values for hydroxymetronidazole plasma concentrations.
K.A. Sprandel et al. / Diagnostic Microbiology and Infectious Disease 55 (2006) 303 – 309
3.2. Pharmacokinetic parameter values The mean pharmacokinetic parameter values with SDs and the full covariance matrix from the output are displayed in Table 1. The mean (FSD) SCLnon-OH was 3.08 F 1.00 L h1, and the V c of metronidazole was 35.4 F 21.63 L. The mean (FSD) SCLOH was 2.78 F 2.25 L h1. The CLR of metronidazole and hydroxy-metronidazole were 0.84 F 0.23 and 2.77 F 0.78 L h1, respectively. The results of the Bayesian estimation showed that the plot of observed versus predicted values of metronidazole had a regression line with an intercept of 0.021 and a slope of 1.012 (Fig. 2A). The r 2 of this regression is 0.972 (r = .986), P .001. The predicted versus observed regression values for the hydroxy-metabolite have an intercept of 0.094 and a slope of 1.024 (Fig. 2B). The r 2 of this regression is 0.980 (r = .99), P .001. Comparison of Bayesian posterior parameter value estimates for volunteers and critically ill patients revealed that SCLnon-OH, V c, SCLOH, and V OH were significantly lower ( P b .05) in the critically ill patients. There was no difference between the 2 groups in terms of the 1st-order rate constants, including K OH, the 1st-order rate constant of formation of the hydroxy-metabolite. 3.3. Monte Carlo simulation Utilizing the above pharmacokinetic model, the 10 000subject MCS predicted that the attainment of the target pharmacodynamic parameter of 70 was N 99% for all 3 dosing regimens of metronidazole (500 mg every 8 h, 1000 mg day1, and 1500 mg day1) when MIC values were less than 2 mg L1 (Fig. 3). Among the 218 B. fragilis isolates, 213 of the isolates had an MIC b 1.0 mg L1 and the distribution of MIC values being 0.125 mg L1 (n = 2), 0.25 mg L1 (n = 18), 0.5 mg L1 (n = 90), and 1.0 mg L1 (n = 103). The remaining 5 isolates had a reported MIC of 2.0 mg L1.
Fig. 3. Probability of target attainment for 10 000 simulated subjects for metronidazole administered as 500 mg every 8 h, 1000 mg day1, and 1500 mg day1 at a steady state. The AUC/MIC ratio of z 70 was chosen as the target for the analysis.
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The target attainment rate of the AUC/MIC ratio breakpoint of 30 was N 99% for all 3 dosing regimens of metronidazole when MIC values were 4 mg L1 or less. When a breakpoint of 100 was evaluated, the two 1500-mg day1 regimens (500 mg every 8 h and 1500 mg day1) achieved a target attainment rates of 99%, 97%, and 34% for MIC values of b 1, 2, and 4 mg L1, respectively. In comparison, the respective target attainment rates were 99%, 72%, and 3% for MIC values of b 1, 2, and 4 mg L1 for the metronidazole dosage regimen of 1000 mg day1 and AUC/ MIC ratio breakpoint of 100. 4. Discussion Healthy volunteer’s data tend to provide a more conservative estimate of drug exposure compared with what might be observed in an actual patient setting as regards the estimates of the pharmacokinetic parameters (e.g., clearances are frequently lower and half lives longer in patients versus volunteers). However, volunteers are often more homogeneous than patients, leading to smaller estimates of between-patient differences in pharmacokinetic parameter values. This will actually make the inferences drawn from volunteer data less conservative in most instances. Therefore, we chose to use data from both healthy volunteers and critically ill patients who received metronidazole in our population pharmacokinetic analysis. In addition, a recent study has demonstrated that calculations of target attainment rates from healthy volunteers were predictive for patients receiving ceftazidime and meropenem (Kuti et al., 2005). The mean pharmacokinetic parameters of metronidazole and hydroxy-metronidazole from the model are within the range of parameters reported for both healthy volunteers and critically ill patients (Tally et al., 1978; Sprandel et al., 2004; Plaissance et al., 1988). The lower mean values for SCLnon-OH and V c in our critically ill patients compared with the healthy volunteers have also been previously reported for metronidazole in surgical and critically ill patients without renal and/or hepatic impairment. Most studies in patients have not performed pharmacokinetic comodeling of metronidazole and its hydroxy-metabolite. Our findings demonstrate that mean values for SCLOH and V OH are also lower in the critically ill patients compared with healthy volunteers. Because critically ill patients often exhibit changes in drug clearance, elimination half-life, and volume of distribution (Bodenham et al., 1988; Power et al., 1998), it was not surprising that the volunteers and patients had significantly different values for clearance and volumes of distribution, but, importantly, not for the formation rate constant for hydroxy-metronidazole from metronidazole. Despite statistically significant differences in some of the Bayesian parameters between the groups, the data from the healthy subjects and critically ill patients fit the pharmacokinetic model well, with r 2 values of .972 (Fig. 2A) and .980 (Fig. 2B) for the observed versus predicted values of metronidazole and hydroxy-metronidazole concentrations,
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respectively. Our application of the comodeling technique allowed both metronidazole and hydroxy-metronidazole plasma data to be accurately fitted to a 4-compartment model for the estimation of pharmacokinetic parameters. Using this PPM, a 10 000-subject MCS was performed to determine the probability of attaining the pharmacodynamic target ratio AUC/MIC z 70 over a given MIC distribution of clinical isolates of B. fragilis. Although there are no established pharmacokinetic/pharmacodynamic parameters correlated with the efficacy for metronidazole in the treatment of anaerobic infections, we chose to use the AUC/MIC ratio because this parameter is often one of the parameters correlated with the efficacy of antimicrobials with similar pharmacokinetic and pharmacodynamic properties (Craig, 1998; Drusano, 2004). Also, data from an in vitro pharmacodynamic model of anaerobic infection indicated that AUC/MIC ratio was highly likely to be the dynamically linked variable (Ibrahim et al., 2004). Taking into consideration the protein binding of metronidazole (approximately 10%) and the published data from an in vitro pharmacodynamic model (Ibrahim et al., 2004), it was decided that a total drug AUC/MIC z 70 would provide a conservative pharmacodynamic target. The data from this in vitro pharmacodynamics model demonstrated in vitro eradication at exposures of this value (AUC/MIC = 70) or greater. Since 1981, national surveys on the susceptibility of the B. fragilis group have reported all isolates to be susceptible to metronidazole at concentrations less than 8 mg L1 (Snydman et al., 2002). The MIC was less than or equal to 2.0 mg L1 for all of the 218 clinical isolates used in this analysis. Using this distribution, the probability of attaining the proposed pharmacodynamic target was greater than 99% for all 3 dosing regimens. If an isolate were to have a reported MIC of 4 mg L1, the chance of attaining the proposed target would decrease to 28.5% for the 1000-mg once-daily regimen and to 80% for the 1500-mg once-daily and the 500-mg every 8-h regimens (Fig. 3). Based on this simulation, it appears that a dose of 1000 mg once daily would be less likely to achieve the target parameter against isolates with a higher MIC compared with regimens of 1500 mg once daily or 500 mg every 8 h. This observation correlates well with 2 recent reports involving in vitro bactericidal activity for anaerobic infections and 24-h areas under the inhibitory and bactericidal curves (Lewis et al., 2000; Sprandel et al., 2004). It is important to emphasize that a daily dosing regimen for metronidazole is likely to be effective on the basis of this evaluation and the findings from the in vitro model system of infection (Ibrahim et al., 2004). It should also be emphasized, however, that clinical trials of once-daily metronidazole therapy are required before once-daily regimens can be used in the clinic. Because there are so few isolates resistant to metronidazole, it will likely remain one of the drugs of choice for mixed infections involving anaerobic pathogens (Hecht, 2004). Traditional dosing of metronidazole was determined before our advances in the study of pharmacodynamics
(Anonymous, 1996). The administration of higher oncedaily doses of metronidazole may offer an effective and more convenient regimen. Because of the lack of Gramnegative coverage, metronidazole is often used in combination with other antimicrobial agents for the treatment of mixed aerobic–anaerobic infections. There are some choices of antibiotics that could be combined with metronidazole, including fluoroquinolones, aminoglycosides, and broadspectrum h-lactams without significant antianaerobic activity, which would offer convenient once-daily regimens for the treatment of mixed aerobic–anaerobic infections. Further clinical investigation of once-daily administration of metronidazole for the treatment of anaerobic infections or in combination with other agents for the treatment of mixed infections is warranted. In summary, a PPM was determined by comodeling metronidazole and hydroxy-metronidazole concentrations from healthy subjects and critically ill patients. The MCS, combined with a B. fragilis clinical isolate MIC distribution, demonstrated that varying doses of metronidazole would achieve the target pharmacodynamic parameter AUC/MIC z 70 at a rate greater than 99% when MICs are less than or equal to 2.0 mg L1.
Acknowledgments The authors greatly appreciate the analytical and technical assistance of Kelly Deyo and Carolyn Sibley. This work was funded in part by a research grant from Ortho-McNeil Pharmaceutical (Raritan, NJ). This work was supported in part by the General Clinical Research Center at the University of Illinois at Chicago, which is funded by NIH grant M01-RR-13987. The authors would like to thank the Clinical Research Center staff for help and support of this study.
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