Population pharmacokinetics of panipenem in neonates and retrospective evaluation of dosage

Toshimi Kimuraa,*, Hideya Kokubuna, Masahiko Nowatarib, Nobuo Matsuurab, Keisuke Sunakawac and Hiroaki Kubod

a Department of Pharmacy, b Division of Neonatology and c Division of Infectious Disease, Kitasato University Hospital 1-15-1 Kitasato, Sagamihara-shi, Kanagawa 228-8555, Japan; d Laboratory of Analytical Chemistry, School of Pharmaceutical Sciences, Kitasato University, 108-8641 Shirogane, Minatoku, Tokyo, Japan


    Abstract
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The population pharmacokinetics of panipenem was studied in 23 neonates. Their postconceptional age (PCA) was 24.7–42.6 weeks and their body weight was 530–4455 g at initiation of therapy. Panipenem was infused over a period of 60 min in a dose of 10.2–34.7 mg/kg bd in 21 patients, tid in one patient and four times daily in one patient for a mean of 10.7 days. Blood samples were obtained just before the infusion and 1–2 h after and again 6 h after the infusion. All the data for the 108 serum panipenem concentrations were evaluated with a non-linear mixed-effect model (NONMEM with first-order method), a computer program designed for population pharmacokinetic analysis. One- and two-compartment population pharmacokinetic parameters were measured. The two-compartment parameters were as follows: panipenem clearance CL = 0.150 L/h, central volume of distribution = 0.54 L, intercompartmental clearance = 0.014 L/h and peripheral volume of distribution = 0.28 L. The one-compartment parameters were CL = 0.175 L/h and volume of distribution = 0.55 L. In the fitting process using the one-compartment model, significantly fixed effects related to CL were PCA, postnatal age (PNA), gestational age (GA), body weight (BW) and serum creatinine, and that for the distribution volume (V) was BW. CL showed a logarithmic rise with PCA (CL = 0.00176 x exp0.14 x PCA). The CL levels in the patients with PCA < 33 weeks (0.098 L/h) were significantly lower (P < 0.001) than those with PCA 33 weeks (0.25 L/h). The final formulae for the population pharmacokinetic parameters are as follows: CL = 0.0832 (PCA < 33 weeks), CL = 0.179 x BW (PCA 33 weeks), V = 0.53 x BW (coefficient of variation; 23.9% for CL, 28.5% for V). Based on these data, a simulated time–concentration curve was compared with that for adult data in a clinical Phase I study. Our findings suggest that the panipenem dosage regimen of 10–20 mg/kg every 12 h should yield concentrations within the accepted therapeutic range.


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Bacterial infection causing neonatal sepsis and low birth weight in neonates are important factors affecting both mortality and long-term morbidity. These factors have resulted in the increasing use of broad-spectrum antibiotics such as carbapenems in the neonatal intensive care unit. Panipenem, a drug used in combination with betamipron, is one of the injectable carbapenem antibiotics developed in Japan. Concomitant iv doses of betamipron reduce the nephrotoxicity of panipenem without any change in the pharmacokinetic parameters.1 Panipenem/betamipron is used for the treatment of severe infections and sepsis because of its wide antibacterial activity against both Gram-positive and -negative bacteria. The Ministry of Health and Welfare, Japan, approved its use for infectious paediatric diseases, and neonatologists use it empirically for neonates. Although panipenem/betamipron is an important antibiotic for the neonatal intensive care unit owing to the high-risk situation, there are no published data on the pharmacokinetics of panipenem in the neonatal population, because of its chemical instability. Various changes in renal function and body composition with increases in the postconceptional age (PCA) in neonates make the pharmacokinetic parameters of panipenem all the more complicated. We have developed a method to measure the serum concentration of panipenem, and the non-linear mixed-effect model (NONMEM with first-order method), population pharmacokinetic program2 was used to assess the intra- and inter-individual variabilities in neonates.

This study was conducted in order to determine the population pharmacokinetic parameters and the various indices of maturation in neonates of various PCA. Furthermore, the correct dosage of panipenem/betamipron for neonates was evaluated.


    Materials and methods
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Patients

Patients with clinically suspected or confirmed severe infection in the neonatal intensive care unit at the Kitasato University Hospital, Japan, were eligible for participation in this study. The study group comprised 23 patients (13 females, 10 males) and the patients' PCA ranged from 24.7 to 42.6 weeks, with a mean (± s.d.) of 31.8 (± 6.3) weeks at the time of therapy initiation. The mean patient weight was 1.5 ± 1.2 kg. The serum creatinine (Scr) concentration was 0.9 ± 0.6 mg/dL. Table IGo shows the patient characteristics. Informed consent was obtained from the parents of the patients.


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Table I. Patient demographic data at the time of initiation of study
 
Dosing and sampling procedures

Panipenem/betamipron (Carbenin; Sankyo Co., Ltd, Tokyo, Japan) was infused over 60 min using a syringe pump at a dose of 10–20 mg/kg given bd (actual dosages were between 10.2 and 34.7 mg/kg; mean 18.6 ± 5.3 mg/kg). The dose recommendation was based on the paediatric dosage; 30–60 mg/kg divided into three times a day, which the Ministry of Health and Welfare, Japan, approved. The mean duration of the treatment was 10.7 ± 5.7 days ranging over 3–28 days in this study.

Blood samples obtained by capillary heel prick were drawn just before the infusion 1–2 h post-infusion, and again 6 h after the infusion, for determination of panipenem concentrations. This sampling strategy was applied twice: on the third and sixth day of panipenem therapy, at least in principle.

Panipenem concentrations in serum were determined by high-performance liquid chromatography using a Multisolvent Delivery System 600 (Nihon Waters K.K., Tokyo, Japan) as follows: detector, UV-970 (Jusco Co., Ltd, Tokyo, Japan); wavelength, 300 nm; column, ODS-2 (GL Science Co., Ltd, Tokyo, Japan); mobile phase, 35% (v/v) methanol in 5 mM sodium phosphate buffer containing 5 mM dodecylsulphate sodium salt (pH 5.8) at a flow rate of 0.8 mL/min. Panipenem is a very unstable antibiotic in solution; therefore, 10 µL of MOPS [200 mM 3-(N-morpholino)propanesulphonic acid] was added to 10 µL of the plasma sample as a stabilizer immediately after blood sampling. The lower limit of quantification was 0.5 mg/L. A linear regression analysis of the standard curve from 6.25 to 100 mg/L yielded the following equation: y = 0.11126 x –0.0075 (r = 1.000). The coefficient of variation (CV%) of within-runs and between-runs for the assay of panipenem were less than 2.5 and 2.7%, respectively. The recoveries of panipenem were 98.4%. (Panipenem was a gift from Sankyo Co., Ltd, Tokyo, Japan.)

Data analysis

The concentration–time study and the physiological data analysis were performed using the NONMEM with first-order method software (double precision NONMEM Version V, Level 1.0) on a PC/AT computer operating under Windows 95 (Microsoft Co., Ltd, Tokyo, Japan).

The pharmacokinetic model of the individual serum panipenem concentrations was examined by the oneand two-compartment models using an ADVAN1 and ADVAN3 supplied PREDPP subroutine, part of the NONMEM program. The design of the one-compartment model (ADVAN1) allowed determination of the components of the population mean parameters, the panipenem clearance (CL) and the volume of distribution (V) of the panipenem. The two-compartment model (ADVAN3) determined CL, the central volume of distribution (Vc), the intercompartmental clearance (Q) and the peripheral volume of distribution (Vp) of the panipenem.

Proportional interindividual variability models were invoked for each clearance and the volume of distribution as follows:


in which and are the population parameters; {eta}CL and {eta}V are random variables with a mean of zero and a variance of {omega}2; CLi and Vi are the individualized estimated parameters.

The additive error model was used for residual variability:

in which Cij is the ith measured serum panipenem concentration in the jth individual, Cij* is the estimated serum panipenem concentration, as observed for the pharmacokinetic data, and {epsilon}ij is the independently identical distributed statistical error with a mean of zero and a variance of {delta}2.

Effects of patient characteristics on the population mean value

In the fitting process using the one-compartment model, many covariates were evaluated as follows for the influence on the population mean values for the CL of the panipenem: PCA, gestational age (GA), postnatal age (PNA), Apgar score, body weight (BW) and Scr.

where {theta}1 and {theta}2 are the intercept and slope parameters. The glomerular filtration rate (GFR) standardized for body weight correlated strongly with the inverse of the Scr, so that the equation below was used in the pharmacokinetic model.

Coulthard3 and Al-Dahhan et al.4 reported that GFR in infants shows a logarithmic rise with PCA so that an exponential model was also used in the pharmacokinetic model.

Because the maturation of GFR in infants is increasing from a PCA of 34 to 35 weeks, the differences in the CL for a PCA < or >= 32, 33, 34 and 35 weeks were also used in the pharmacokinetic model.

The volume of distribution was assessed by the equation shown below.

where the physiological factor was either PCA, GA, PNA or BW.

During the regression of the above equations, NONMEM computes the estimates of the population parameters and the standard error for all samples, and this standard error can be used to define the confidence intervals for the true parameter values. NONMEM provides the minimum value of the objective function (negative value of twice the log-likelihood difference: –2 l.l.d.) in this regression. The changes in the objective function >6.635 were determined to be significant ({chi}2, degree of freedom = 1, P < 0.01) based on a {chi}2 distribution with 1 degree of freedom. A P value <0.01 was considered to be statistically significant. Based on these results, all significant factors were used to construct the full regression equation. After deletion of each factor in the full model, the objective function value of this reduced model was compared with the full model. The final estimates of the population pharmacokinetic parameters of panipenem were then defined.

Retrospective evaluation of a panipenem dosage for neonates

Based on the NONMEM analysis, we simulated a concentration–time curve with the use of one-compartment parameter estimates and compared that simulation with that of the parameters in a Phase I multiple dose study,5 which we re-analysed using NONMEM. The Phase I study was conducted in nine healthy adult male volunteers who received 500 or 1000 mg of panipenem by constant iv infusion for 60 min every 12 h for 5 days. The Phase I study was comparable and had a study design similar to our study; it was concluded that 2000 mg panipenem daily was well tolerated and safe.


    Results
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The serum concentration–time curve of panipenem for all subjects (23 cases; 108 serum concentrations) is shown in Figure 1Go. No specific findings that indicated the toxicity of panipenem were detected. All cases were analysed for the pharmacokinetic parameters by various factors.



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Figure 1. All the data of observed serum concentration of panipenem at the time after injection (n = 108).

 
The two-compartment parameters were as follows: CL = 0.150 L/h, Vc = 0.54 L, Q = 0.014 L/h and Vp = 0.28 L. One-compartment parameters were CL = 0.175 L/h and V = 0.55 L. The two-compartment model described the population mean significantly better than the onecompartment model (P < 0.01). However, further analysis to estimate the covariates for CL is not possible using the two-compartment model owing to the lack of samples, and the two-compartment model is inherently much more difficult to use in a clinical situation, so that the model-building process was conducted with only the one-compartment model.

Hypothesis testing for the pharmacokinetic parameters of the panipenem is summarized in Table IIGo. The objective function (–2 l.l.d.) values in the CL evaluations were significantly decreased in PCA, exponential PCA, GA, PNA, BW, inverted Scr and the complex factor of BW and inverted Scr. The Apgar score was not found to influence significantly the objective function value. The values of –2 l.l.d. for the effect of different PCAs on the CL were minimal from 33 weeks. The CL levels in the patients with PCA < 33 weeks (0.098 L/h) were significantly lower (P < 0.001) than that with PCA >= 33 weeks (0.250 L/h). The influence of PCA < 33 weeks or >= 33 weeks on the CL was chosen for the next analysis step. Based on the results of this study, strong and highly significant relationships between the pharmacokinetic parameters and the physiological factors are presented graphically in Figures 2–4GoGoGo.


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Table II. Hypothesis testing for fixed effect on panipenem pharmacokinetics parameter
 


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Figure 2. Relationship between post-conceptional age and panipenem clearance; clearance of panipenem in patient of PCA < 33 weeks ({circ}) and PCA >= 33 weeks (•).

 


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Figure 3. Relationship between body weight and panipenem clearance; panipenem clearance in patients of PCA < 33 weeks ({circ}) and PCA >= 33 weeks (•).

 


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Figure 4. Relationship between body weight and the distribution volume of panipenem; distribution volume of panipenem in patients of PCA < 33 weeks ({circ}) and of PCA >= 33 weeks (•).

 
BW, inverted Scr and (exponential) PCA were selected for the full regression analysis. In the preliminary analyses, the formula of the CL with PCA was not an important factor when PCA and BW were analysed at the same time. It should be noted that PCA also showed a very strong correlation with BW (r = 0.88). We surveyed the quantitative relationship between the three kinds of full model for CL. Statistically significant parameters were added to the full regression model, and each parameter was eliminated from the full model (Table IIIGo).


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Table III. Hypothesis tested concerning intersubject valiability using reduced models of the full model
 
First, the full regression model 1 for the estimated CL and V was as follows:


The full regression model 1 using the above factors for the fixed effect showed that only BW was found to significantly affect CL (P < 0.001) and V (P < 0.001) (OBJ = 478.539).


The full regression model 2 reduced (P < 0.01) the OBJ (471.899) significantly more than the full model 1. Similar to the full model 1, the inverted Scr and PCA were not found to significantly affect CL and V, respectively. Data from the patients of full-term PCA showed significant correlation with the logarithmic rise in CL.

The full regression model 3 was constructed with the data separated by <33 weeks of PCA from >=33 weeks of PCA.



The effect of BW on CL for >=33 weeks of PCA and on V was significant in the full model 3.

The final model using PCA and BW was examined, and the final regression equations, parameter estimates and 95% confidence intervals are shown in Table IVGo. The final equations for full-term PCA were CL = 0.00176 x exp0.141 x PCA (L/h) and V = 0.53 x BW (L). The interindividual variability (CV%) of CL and V was 26.9% and 29.0%, respectively. The standard deviation in the intraindividual variability was determined to be 4.3 mg/L. Other final equations from the group of divided terms of PCA were CL = 0.0832 (PCA < 33), CL = 0.179 x BW (PCA >= 33) and V = 0.53 x BW (L). The interindividual variability (CV%) of CL and V was 23.9 and 28.5%, respectively. The standard deviation of the intraindividual variability was determined to be 4.2 mg/L.


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Table IV. Final estimates for the population pharmacokinetics parameters of panipenem in neonates
 
Fitting performance using population means

To evaluate the predictive performances of the population parameters, we also obtained the individual Bayesian estimates of clearance and the volume of distribution of panipenem in these patients using the POSTHOC option in the NONMEM software package. The derived pharmacokinetic parameter of panipenem in this study was evaluated by the distribution against the linear regression of the predicted versus the observed serum panipenem concentrations (Figure 5Go).



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Figure 5. Comparison of observed concentration with estimated concentration based on our NONMEM analysis.

 
Retrospective evaluation of the panipenem dosage for neonates

The population means of the re-analysis for the data in the Phase I multiple-dose study were as follows: CL = 14.1 L/h and V = 13.1 L. The simulation of the concentration–time curve with the use of the Phase I study was compared with that of the parameters in neonates: CL = 0.175 L/h, V = 0.55 L. The plot assumes a 10 or 20 mg/kg dose given to a 1.5 kg neonate every 12 h, and 500 mg or 1000 mg of panipenem given to an adult by constant iv infusion for 60 min every 12 h (Figure 6Go).



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Figure 6. Comparison of the simulation of the concentration– time curves in neonate and adult. Slow elimination was observed in neonates compared with adults (thin solid line, 2000 mg in adult; thick solid line, 1000 mg in adult; thin dotted line, 20 mg/kg in 1.5 kg neonate; thick dotted line, 10 mg/kg in 1.5 kg neonate).

 

    Discussion
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Previous studies show that the values of PCA, serum creatinine and BW are very important factors when determining the initial panipenem dosing regimen in neonates. Panipenem is stable against the renal brush border enzyme, dehydropeptidase-1 (DHP-1), but it still exists as a metabolite (R-976-2) in the urine.6 Panipenem and R-976-2 are almost totally excreted with the urine. Therefore, the factors affecting the clearance of panipenem are thought to be (i) the inactivity by DHP-1 in the renal brush border, (ii) the extrarenal pathway of panipenem metabolism,7 and (iii) the glomerular filtration, reflecting the same results as the ‘pharmacokinetics of imipenem–cilastatin in neonates’, report.8 Clinically, renal function is the most important factor, because the area under the concentration–time curve of panipenem in patients with severe renal dysfunction (creatinine clearance < 30) was apparently increased compared with that in those with a moderate renal dysfunction.9 For these reasons, the maturation of GFR and systemic renal function are the most important factors in estimating the CL. There are many studies of the developmental changes with PCA and GA in the renal function of neonates. Fawer et al.10 and Arant11 reported that the rate of rise of GFR in infants with gestational age has been shown to increase immediately from 34 to 35 weeks. Coulthard3 has also reported that the rate of glomerular maturation has been shown to increase steeply with an increase in the PCA from 34 to 35. Also, there was no difference between babies of up to 1 week of postnatal age and older babies. Based on this association, it is suggested that the proposed adjustment in the initial dosage regimen for panipenem be based on PCA.

When we evaluated CL by the NONMEM analysis, we found that the mean CL of the patients with PCA < 33 weeks was significantly lower than that of the patients with a PCA >= 33 weeks, and CL showed a logarithmic rise with PCA. Many reports support our conclusion that CL is correlated strongly with PCA. In the final analysis, CL in a patient over 33 weeks PCA was strongly correlated with BW, but we could not find any significant covariates related to CL in a group with PCA < 33 weeks. In a group with PCA < 33 weeks, the 5 min values of the Apgar score also did not correlate with CL. In the very low birth weight neonates, especially for a PCA < 33 weeks, it is very difficult to estimate the maturation of renal function and CL, because of the high interpatient variability and rapid changes in the renal function. Arant11 has also described previously that renal maturation and improving GFR appear to correlate best with PCA, but there was a high degree of interpatient variation and a relative lack of maturation in the functional capacity of the kidney in infants with a PCA of < 34 weeks.

Based on the final analysis of this research, the population means analysed showed a good predictive performance. Distribution of the plot versus the CL formula line displays a bilateral symmetry around the regression so that the observed population mean was assumed to be an unprejudiced parameter.

When we attempted to determine the correct dosage of antibiotics, the time above MIC, peak concentration and area under the concentration–time curve are the major parameters correlating with efficacy. In a neonatal intensive care unit, nosocomial infections are an important cause of morbidity and mortality. Infections due to Listeria spp., group B ß-haemolytic streptococci, Gram-negative bacilli and methicillin-resistant staphylococci all have a high mortality. We compared neonatal pharmacokinetic parameters with adult parameters. Based on the simulated concentrations for neonates and adults, the predicted concentrations in neonates resulted in an adequate serum concentration. Actually, we observed the 1 h serum concentrations of 18.59–54.90 mg/L and mean trough concentrations of 1.15–9.45 mg/L with a mean dosage of 18.6 ± 5.3 mg/kg given every 12 h. The peak concentrations observed were similar to those of most adult populations and the trough concentrations did not become too high even in the group with PCA < 33 weeks. The AUC in neonates was greater than in adults.

All patients tolerated panipenem therapy very well. In conclusion, we recommend a dosage for neonates of 10–20 mg/kg administered bd. Further studies are needed for neonates from the group with a PCA of < 33 weeks and specific populations such as neonates with intrauterine growth retardation.


    Acknowledgments
 
We appreciate Kouki Oguchi, MD, Shigehiko Shimada, PhD and the medical and nursing staff of the Neonatal Intensive Care Unit, Kitasato University, for their kind cooperation.


    Notes
 
* Corresponding author. Tel: +81-427-78-7580; Fax: +81-427-78-9430; E-mail: gr4t-kmr{at}asahi-net.or.jp Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
1 . Naganuma, H., Tokiwa, H., Hirouchi, Y., Kawahara, Y., Inui, K., Tanigawara, Y. et al. (1991). Nephroprotective effect and its mechanism of betamipron (2)—Relationships of renal excretion. Chemotherapy 39, Suppl. 3, 178–89.

2 . Beal, S. L. & Sheiner, L. B. (1989). NONMEM Users Guides. NONMEM Projects Group, University of California, San Francisco, CA.

3 . Coulthard, M. G. (1985). Maturation of glomerular filtration in preterm and mature babies. Early Human Development 11, 281–92.[ISI][Medline]

4 . Al-Dahhan, J., Haycock, G. B., Chantler, C. & Stimmler, L. (1983). Sodium homeostasis in term and preterm neonates. Archives of Disease in Childhood 58, 335–42.[Abstract]

5 . Nakashima, M., Uematsu, T., Kanamaru, M., Tajima, M., Naganuma, H., Ichikawa, M. et al. (1991). Phase 1 study of panipenem/ betamipron—Multiple dose study. Chemotherapy 39, Suppl. 3, 265–88.

6 . Takahagi, H., Hirota, T., Matsushita, Y., Muramatsu, S., Tanaka, M. & Matsuo, E. (1991). In vitro dehydropeptidase-1 activity and its hydrolytic activity of panipenem in several tissues in animal species and their influence on the disposition of panipenem in vivo. Chemotherapy 39, Suppl. 3, 236–41.

7 . Naganuma, H., Tokiwa, H., Hirouchi, Y., Kawahara, Y., Fukushige, J., Fukami, M. et al. (1991). Nephroprotective effect and its mechanism of betamipron (1)—Relationships of renal transport. Chemotherapy 39, Suppl. 3, 166–77.

8 . Freij, B. J., McCracken, G. H., Olsen, K. D. & Threlkeld, N. (1985). Pharmacokinetics of imipenem–cilastatin in neonates. Antimicrobial Agents and Chemotherapy 27, 431–5.[ISI][Medline]

9 . Aoki, N., Usuda, Y., Koda, Y., Takasawa, T., Wakabayashi, N., Hayashi, S. et al. (1991). Clinical pharmacology and efficacy of panipenem/betamipron. Chemotherapy 39, Suppl. 3, 372–84.

10 . Fawer, C. L., Torrado, A. & Guignard, J. P. (1979). Maturation of renal function in full-term and premature neonates. Helvetica Paediatrica Acta 34, 11–21.[ISI][Medline]

11 . Arant, B. S. (1978). Developmental patterns of renal functional maturation compared in the human neonate. Journal of Pediatrics 92, 705–12.[ISI][Medline]

Received 3 February 2000; returned 26 May 2000; revised 22 June 2000; accepted 1 August 2000