Population pharmacokinetic investigation of phenobarbital by mixed effect modelling using routine clinical pharmacokinetic data in Japanese neonates and infants

The population pharmacokinetics of phenobarbital was evaluated using 69 serum concentration measurements obtained from the routine phenobarbital monitoring of 35 neonates and infants. The data were analysed using the nonlinear mixed effects model. A one-compartment open pharmacokinetic model with first-order elimination was used. Covariates screened were current bodyweight (TBW), gestational age, postnatal age (PNA), postconceptional age and gender. The final pharmacokinetic parameters were CL/F (mL/h) = 3.41.TBW (kg) + 1.64. PNA (weeks), Vd/F(L) = 1.09.TBW.(kg) [corrected] and F = 0.406 for oral administration and F = 1 for suppository. Application of the findings in this study to patient care may permit selection of an appropriate initial maintenance dosage to achieve target phenobarbital concentrations, thus enabling the clinician to achieve the desired therapeutic effect in neonates and infants.     

Population pharmacokinetics of phenobarbital by mixed effect modelling using routine clinical pharmacokinetic data in Japanese neonates and infants: an update

What is known and objective: Optimal use of phenobarbital in the neonatal population requires information regarding the drug’s pharmacokinetics and the influence of various factors, such as different routes of administration, on the drug’s disposition. However, because of sampling restrictions, it is often difficult to perform traditional pharmacokinetic studies in neonates and infants. This study was conducted to establish the role of patient characteristics in estimating doses of phenobarbital for neonates and infants using routine therapeutic drug monitoring data. Methods: The population pharmacokinetics of phenobarbital was evaluated using 109 serum concentration measurements obtained from routine phenobarbital monitoring of 70 neonates and infants. The data were analysed using the non‐linear mixed effects model. A one‐compartment pharmacokinetic model with first‐order elimination was used. Covariates screened were current total bodyweight (TBW), gestational age, postnatal age (PNA), post‐conceptional age, gender and neonates‐infants clearance factor (serum concentration of phenobarbital; Conc). Results and discussion: The final pharmacokinetic parameters were CL/F (mL/h) = (5·95·TBW (kg) +1·41·PNA (weeks)) Conc (serum phenobarbital concentration >50 μg/mL)^?0·221,Vd/F (L) =1·01·TBW (kg), and F = 0·483 for oral administration and F = 1 was assumed for suppository. Conc^?0·221 is 1 for phenobarbital concentration <50 μg/mL. The important variables for predicting phenobarbital clearance in this study were TBW, PNA and Conc. Phenobarbital clearance increases proportionately with increasing TBW, and an older newborn was expected to have a higher rate of clearance than a younger newborn of equal bodyweight. Moreover, the clearance of phenobarbital decreased nonlinearly with increasing serum concentration of phenobarbital >50 μg/mL (Conc^?0·221). What is new and conclusion: We developed a new model for neonate and infant dosing of phenobarbital with good predictive performance. Clinical application of our model should permit more accurate selection of initial and maintenance doses to achieve target phenobarbital concentrations in Japanese neonates and infants, thereby enabling the clinician to achieve the desired therapeutic effect. A similar approach can be used to validate our model for use in other neonate and infant populations.     

Population pharmacokinetic investigation of low-dose methotrexate in rheumatoid arthritics Japanese patients

OBJECTIVE: To estimate the population pharmacokinetics of low-dose methotrexate (MTX) in Japanese patients using nonmem, a computer program designed for analysing drug pharmacokinetics in study populations through pooling of data. METHOD: A total of 153 serum concentration measurements obtained from the 17 healthy volunteers and 17 patients with rheumatoid arthritis were collected. Analysis of the pharmacokinetics of MTX was accomplished using a two-compartment pharmacokinetic model with first-order absorption. The effect of a variety of developmental and demographic factors on MTX disposition was investigated. RESULTS: The final pharmacokinetic parameters were CL/F (L/kg/h) = 0.177 x 0.394MULT, V1/F (L/kg) = 0.0501, Q/F (L/kg/h) = 0.056, V2/F (L/kg) = 0.368, ka (h-1) = 0.503, where CL is total body clearance, V1 and V2 is apparent volume of distribution in the central and peripheral compartments, k(a) is absorption rate constant, Q is intercompartmental clearance and MULT = 1 for patients received multiple dosing and zero otherwise. The interindividual variabilities in CL, Q and V1 were 25.7%, 22.3% and 217.9%, respectively, and the residual variability was 17.8% as a coefficient of variation. Because of the lake of information on data set we were unable to characterize the interindividual variability for V2 and ka. CONCLUSION: Clinical application of the model to patient care may permit selection of an appropriate dosage to achieve target MTX concentrations, thus enabling the clinician to achieve the desired therapeutic effect in Japanese patients. However, the MTX dosage regimen for the individual patient should be based on a careful appraisal of their clinical need for the drug.     

Absence of a pharmacokinetic interaction between digoxin and levofloxacin

BACKGROUND: Levofloxacin, a broad-spectrum fluoroquinolone, may enhance digoxin bioavailability by eliminating intestinal flora that metabolize digoxin. Moreover, levofloxacin, which is eliminated primarily by glomerular filtration and active tubular secretion, may alter the elimination rate of digoxin. Because of the narrow therapeutic index of digoxin, it is important to evaluate the potential for interaction with levofloxacin when administered concomitantly. METHODS: This was a placebo-controlled, randomized, double-blind, two-phase crossover study. Twelve healthy subjects (six males and six females) received 500 mg twice/day oral doses of levofloxacin or placebo for 6 days and a single oral dose of 0.4 mg digoxin on the morning of study day 5 along with levofloxacin or placebo. RESULTS: There was no significant effect of levofloxacin on the pharmacokinetics (Cmax, AUC, and other disposition parameters) of oral digoxin. Steady-state levofloxacin absorption and disposition kinetics were also similar in the presence or absence of digoxin. CONCLUSIONS: Results of this study suggest that an important pharmacokinetic interaction between levofloxacin and digoxin is unlikely to occur when administered concomitantly.     

Population pharmacokinetic investigation of disopyramide by mixed effect modelling using routine clinical pharmacokinetic data in Japanese patients

OBJECTIVE: To estimate the population pharmacokinetic parameters of disopyramide using non-linear mixed effects modelling. METHOD: A total of 148 serum levels from 109 patients (61 males and 48 females) receiving disopyramide were collected. RESULTS: The final pharmacokinetic model was Cl (L/h)=3.75.TBW0.567.AGE-0.374.Conc(-0.719).1.48(DOSE>or=5), Vd (L/kg)=4.13 and k(a) (h-1)=0.363, where Cl is total body clearance, Vd is apparent volume of distribution, k(a) is absorption rate constant, TBW is total bodyweight (kg), AGE is age (years), Conc is the concentration of disopyramide (microg/mL), and DOSE>or=5=1 for patient received 5 mg/kg/day of disopyramide dosage or over and 0 otherwise. CONCLUSION: Application of the findings in this study to patient care may permit selection of an appropriate initial maintenance dosage to achieve target disopyramide concentrations and the desired therapeutic effect.     

Population pharmacokinetics of cefepime in neonates with severe nosocomial infections

Objective: To define the pharmacokinetic behaviour of cefepime in neonates with severe nosocomial infections using a mixed effects model. Patients and methods: Thirty‐one newborn infants were included in the study; 10 additional infants participated in the validation of the pharmacokinetic model. Cefepime CL and V were determined using an open monocompartmental model with first‐order elimination. The influence of demographic and clinical characteristics on the model was evaluated. The non‐linear mixed effect model (nonmem ) program was used to determine the pharmacokinetic population model. Results: The mean corrected gestational age for infants participating in the construction and validation of the model were 35 and 33 weeks, respectively. Factors included in the final pharmacokinetic model were body surface area (BSA) and calculated CL_CR. The final population model was CL (L/h) = 0·457 BSA (m²) + 0·243 CL_CR (L/h) and V(L) = 4·12 BSA (m²). This model explains 33·3% of the interindividual variability for CL and 12·8% for V. This model was validated in ten neonates with nosocomial infections by assessing the predictive capacity of plasma cefepime concentrations using a priori and Bayesian strategies. Conclusions: The predictive performance of this population model for cefepime plasma concentrations was adequate for clinical purposes and can be used for individualizing cefepime therapy in newborn infants with severe infections. Cefepime plasma concentrations can be predicted based on BSA and calculated CL_CR. Cefepime therapy using a 250 mg/m² dose administered every 12 h is adequate to achieve plasma concentrations greater than 8 μg/mL during more than 60% of the dosing interval and is expected to be effective in the treatment of bloodstream infections caused by most gram negative organisms in newborn infants. A dose of 550 mg/m² would be required for the treatment of infections caused by Pseudomonas sp.     

Population pharmacokinetic analysis of meropenem in Japanese adult patients

What is known and Objective: Meropenem is frequently employed as an empirical treatment for serious infections, but there has been no report on its population pharmacokinetic parameters for Japanese patients. Our aim is to undertake a population pharmacokinetic analysis of meropenem using non‐linear mixed effects model (NONMEM). Methods: Data from 68 patients were analysed via NONMEM with the first‐order method. The participants’ covariates, including gender, age, actual body weight, serum creatinine, serum albumin, serum total protein and creatinine clearance, were analyzed by the forward inclusion and backward elimination method to identify their potential influence on meropenem pharmacokinetics. The adequacy of the constructed model was assessed by goodness‐of‐fit plots and the precision of the parameter estimated at each step of the model development. To assess the robustness of the estimated parameter, bootstrap analysis was performed. Results and Discussion: The data were best described by a one‐compartment model. The serum creatinine values modified by the below normal limit in our hospital (mSCR) were an influential covariate for clearance (CL): CL (L/h) = 11·1 × (mSCR/0·7)^?1. The volume of distribution was estimated as 33·6 L. The coefficient of variation of the inter‐individual variability of CL and the residual variability were 52·1% and 0·827% μg/mL, respectively. A comparison of the population pharmacokinetic parameters of meropenem in the final model estimated in NONMEM with original data, and 1000 bootstrap samples shows that both sets of estimates were comparable, thereby indicating the robustness of the proposed model. What is new and Conclusion: A population pharmacokinetic model that satisfactorily described the disposition and variability of meropenem in our Japanese population is described. NONMEM analysis showed that the clearance of meropenem depended on modified serum creatinine. The results of this study should help Japanese patients on meropenem by improving the prediction accuracy of dosing using the Bayesian method.     

Effect of CYP2C19 genetic polymorphism on pharmacokinetics of phenytoin and phenobarbital in Japanese epileptic patients using Non-linear Mixed Effects Model approach

OBJECTIVE: To clarify the effect of genetic polymorphism of CYP2C19 on pharmacokinetics of phenytoin and phenobarbital using a Non-linear Mixed Effects Modelling analysis in Japanese epileptic patients. METHOD: A total of 326 serum phenytoin concentrations were collected from 132 patients, and a total of 144 serum phenobarbital concentrations were collected from 74 patients during their clinical routine care. RESULT: The maximal elimination rate of phenytoin decreased by 10.2% in patients with CYP2C19*1/*2 compared with patients with normal CYP2C19. The Michaelis-Menten constants in the patients with CYP2C19*1/*3 and the poor metabolizers of (CYP2C19*2/*2 or *2/*3 or *3/*3) were 27% and 54% higher than those for the patients with normal CYP2C19, respectively. The total body clearance of phenobarbital decreased by 19.3% in patients with CYP2C19*1/*3 or the poor metabolizers of CYP2C19 compared with patients with normal CYP2C19 or with CYP2C19*1/*2. CONCLUSION: These findings indicated that the genetic polymorphisms of CYP2C19 contribute to the pharmacokinetic variability of phenytoin and phenobarbital, the poor metabolizers of CYP2C19, which are relatively common in Asian groups.     

Population pharmacokinetic parameters of vancomycin in critically ill patients

BACKGROUND: Intensive care unit patients are a highly heterogeneous population. Accurate dosing for this population requires characterization of the appropriate pharmacokinetic parameters. OBJECTIVE: To estimate population pharmacokinetic parameters of vancomycin (VAN) in adult critically ill patients and to establish the predictive performance of the resulting model. Patients and method: Fifty critically ill patients with suspected or documented infection with VAN-sensitive micro-organisms were included. Thirty patients and 234 serum concentration-time sets obtained during clinical routine monitoring were used to estimate the pharmacokinetic parameters (group A). An open bicompartimental model with intermittent intravenous administration was used to adjust the data. Data were evaluated using a nonlinear mixed effects model (nonmem software). Forty plasma concentration-time data sets from 20 patients were used for validation using the Bayesian method (group B). RESULTS: There was a linear relationship between creatinine clearance (Cl(cr)) and VAN clearance (Cl(VAN)). The inclusion of the non-renal clearance (Cl(nr)) (intercept of Cl(VAN) vs. Cl(cr) relationship) improved the model significantly (Cl(nr) 17 mL/min). The volume of distribution seems to be larger than previously reported: volume of the central compartment (V(c)) was 0.41 L/kg and volume of the peripheral compartment was (V(p)), 1.32 L/kg. The mean error (bias) and mean absolute error (precision) for predicting subsequent peak concentrations were -2.16 and 9.28 mg/L and for trough concentrations, -0.22 and 3.87 mg/L respectively. CONCLUSION: The use of population-specific pharmacokinetic parameters and Bayesian forecasting improves dosage-regimen design.     

A Population Pharmacokinetic Analysis of the Influence of Nutritional Status of Digoxin in Hospitalized Korean Patients

Background

Safe and effective use of digoxin in hospitalized populations requires information about the drug’s pharmacokinetics and the influence of various factors on drug disposition. However, no attempts have been made to link an individual’s digoxin requirements with nutritional status.

Objectives

The main goal of this study was to estimate the population pharmacokinetics of digoxin and to identify the nutritional status that explains pharmacokinetic variability in hospitalized Korean patients.

Methods

Routine therapeutic drug-monitoring data from 106 patients who received oral digoxin at Seoul National University Bundang Hospital were retrospectively collected. The pharmacokinetics of digoxin were analyzed with a 1-compartment, open-label pharmacokinetic model by using a nonlinear mixed-effects modeling tool (NONMEM) and a multiple trough screening approach.

Results

The effect of demographic characteristics and biochemical and nutritional indices were explored. Estimates generated by using NONMEM indicated that the CL/F of digoxin was influenced by renal function, serum potassium, age, and percentage of ideal body weight (PIBW). These influences could be modeled by following the equation CL/F (L/h) = 1.36 × (creatinine clearance/50)^1.580 × K^0.835 × 0.055 × (age/65) × (PIBW/100)^0.403. The interindividual %CV for CL/F was 34.3%, and the residual variability (SD) between observed and predicted concentrations was 0.225 μg/L. The median estimates from a bootstrap procedure were comparable and within 5% of the estimates from NONMEM. Correlation analysis with the validation group showed a linear correlation between observed and predicted values.

Conclusions

The use of this model in routine therapeutic drug monitoring requires that certain conditions be met which are consistent with the conditions of the subpopulations in the present study. Therefore, further studies are needed to clarify the effects of nutritional status on digoxin pharmacokinetics. The present study established important sources of variability in digoxin pharmacokinetics and highlighted the relationship between pharmacokinetic parameters and nutritional status in hospitalized Korean patients.     

Effect of obesity on serum amiodarone concentration in Japanese patients: population pharmacokinetic investigation by multiple trough screen analysis

Objective:  To evaluate the influence of obesity on pharmacokinetics of amiodarone (AMD) using Non-Linear Mixed Effects Modelling ( nonmem ) in Japanese patients treated with oral therapy.
Method:  Serum concentrations of AMD were determined by high performance liquid chromatography. One hundred and fifty-one trough concentrations from 23 patients receiving repetitive oral AMD were collected. Body mass index (BMI) and body fat percentage were measured.
Results:  Estimates generated using nonmem indicated that the clearance of AMD was influenced by BMI, age and daily dosage of AMD. The final pharmacokinetic model was CL (L/h) = 0·16 · TBW · 0·53^AGE ≥ 65 · 0·78^BMI ≥ 25 · DD^0·51, V _d (L) = 10·2 · TBW, where CL is total body clearance, TBW is total body weight (kg), DD (mg/kg/day) is daily dosage of AMD, AGE (years) ≥65 = 1 for patient was 65 years old or over and 0 otherwise, BMI (kg/m²) ≥25 = 1 for patient was 25 kg/m² or over and 0 otherwise and V _d is apparent volume of distribution. The clearance of AMD decreased significantly by 22·3% with a BMI higher than 25 kg/m². The clearance of AMD also decreased significantly by 46·9% when patient age was more than 65 years.
Conclusion:  Population pharmacokinetic analysis confirms that obesity affects the pharmacokinetics of AMD.     

Population pharmacokinetic analysis of indinavir in HIV-infected patient treated with a stable antiretroviral therapy

The objectives of this study were to build a population pharmacokinetic model that describe plasma concentrations of indinavir in human immunodeficiency virus (HIV)-infected patients with sustained virological response under a stable antiretroviral combination, and to characterize the effect of covariates and co-medications on indinavir pharmacokinetics. Data were obtained from 45 patients who received different dosages of indinavir: either indinavir alone t.i.d. (mostly 800 mg), either indinavir b.i.d. (mostly 800 mg) with a booster dose of 100 mg of ritonavir. Patients were required to have a baseline plasma HIV RNA <200 copies/mL and to have unchanged antiretroviral treatment for 6 months. Indinavir concentrations were measured at a first visit (one sample before drug administration and five after) and at a second visit 3 months later (before and 1 or 3 h after drug administration). A one-compartment model with first-order absorption and first-order elimination best described indinavir pharmacokinetics. For patients treated with indinavir alone, absorption rate constant was estimated to be 0.43/h, and oral clearance Cl/F was 33 L/h. For patients treated with indinavir plus ritonavir these estimates were 0.25/h and 19 L/h, respectively. Cl/F was found to increase by 1.45-fold in men and by 1.18-fold in patients also receiving zidovudine. Oral volume of distribution (V/F) was 24 L. The inter-individual and intra-individual variability were 117 and 205% for V/F, 42 and 58% for Cl/F, respectively. This population analysis in patients with sustained virological response, quantified the effect of ritonavir on the absorption rate constant and on the clearance of indinavir, showed an increase of Cl/F in men and can be used to draw reference curve for therapeutic drug monitoring.     

Population pharmacokinetic analysis of vancomycin in patients with gram-positive infections and the influence of infectious disease type

Objective:  To describe the population pharmacokinetics of vancomycin in patients with gram-positive infections and to investigate the influence of type of infectious disease.
Methods:  A two-compartment open model was adopted as a pharmacokinetic model. The nonlinear mixed-effects model was used to analyze the population pharmacokinetic models.
Results:  We propose one general model and one infectious disease type-specific model. The general model showed that vancomycin clearance (CL) was linearly correlated with estimated creatinine clearance (CL_CR) when CL_CR was less than 85 mL/min, as expressed by CL(L/h) = 0·0322 × CL_CR + 0·32. The distribution volumes of the central and peripheral compartment were different in healthy volunteers and patients with gram-positive infections. The infectious disease type-specific model showed that these differences were more pronounced in patients with pneumonia.
Conclusion:  The population pharmacokinetic parameters of vancomycin obtained here can be used to individualize the dosage of vancomycin in institutions with similar patient population characteristics.     

Population pharmacokinetic modelling of carbamazepine in epileptic elderly patients: implications for dosage

BACKGROUND: Proper use of antiepileptic drugs in the elderly involves knowledge of their pharmacokinetics to ensure a patient-specific balance between efficacy and toxicity. However, populations of epileptic patients on chronic carbamazepine (CBZ) therapy which have been studied have included data of relatively few elderly patients. AIMS: The aim of the present study was to evaluate the population pharmacokinetics of CBZ in elderly patients on chronic monotherapy. METHODS: We have used the non-parametric expectation maximization (NPEM) program in the USC*PACK collection of PC programs to estimate individual and population post-induction pharmacokinetics of CBZ in epileptic elderly patients who received chronic CBZ monotherapy. Age-related changes of CBZ population pharmacokinetics were evaluated from routine therapeutic drug monitoring (TDM) data of 37 elderly and 35 younger patients with epilepsy. As a 'historical control' we used previously published population modelling results from 99 young epileptic patients on chronic CBZ monotherapy. In that control group, TDM was performed in the same pharmacokinetic (PK) laboratory, using the same sampling strategy as in the present study, and the same PK population modelling software was used for data analysis. RESULTS AND CONCLUSIONS: A poor correlation was found between daily CBZ dose and serum concentrations in the elderly patients (r=0.2, P=0.25). Probably statistically significant difference in the median values of the CBZ metabolic rate constant (P<0.001) between elderly and relatively young epileptic patients was found. Our results showed that age-related influences in CBZ pharmacokinetics in elderly patients should be considered in the optimal planning of CBZ dosage regimens. Most elderly patients with epilepsy will usually need CBZ dosages lower than those based on the median population PK parameter values obtained from younger patients. The present population model is also uniquely well suited for the new 'multiple model' design of dosage regimens to hit target therapeutic goals with maximum precision.     

Impact of SLCO4C1 Genotypes,Creatinine, and Spironolactone on Digoxin Population Pharmacokinetic Variables in Patients With Cardiac Insufficiency

PurposeThis study aimed to utilize a population pharmacokinetic method to obtain information about the influence of covariates on the in vivo behavior of digoxin in patients with cardiac insufficiency.MethodsA total of 228 therapeutic drug monitoring concentrations were retrospectively collected from 176 inpatients. The patients were randomly divided into a modeling group (n = 126) and a validation group (n = 50). The first-order absorption one-compartment model was used to develop a population pharmacokinetic model from a nonlinear mixed effects modeling approach. Sixteen single nucleotide polymorphisms involved in the pharmacokinetic variables of digoxin were identified by using the MassARRAY system. Various demographic parameters, biochemical test values, concomitant medications, and genetic variants were investigated.FindingsThe typical population value of digoxin CL/F was 5.06 L/h, and the volume of distribution was 211.82 L. The drug CL/F was significantly related to serum creatinine, in a combination of spironolactone and SLCO4C1 genotypes of 2 variants (rs3114660 and rs3114661). Results of model evaluation and internal/external validation indicated a stable and precise performance of the final model.ImplicationsFor the first time, 2 single nucleotide polymorphisms (rs3114660 and rs3114661) in SLCO4C1 were found to significantly affect the elimination of digoxin in vivo. The final population model may be useful for the individualized dosing of digoxin for patients with cardiac insufficiency.     

Physiologically based pharmacokinetic modelling of acute digoxin toxicity and the effect of digoxin-specific antibody fragments

Context: Recommended doses of digoxin-specific antibody fragments (digoxin-Fab) for treatment of acute digoxin poisoning are pharmacokinetically unsubstantiated and theoretically excessive. Physiologically based pharmacokinetic (PBPK) modelling creates clinical simulations which are closely related to physiological and pharmacokinetic behaviour. This paper details the formulation of a PBPK model of digoxin and explores its use as a simulation tool for acute digoxin toxicity and its management.

Materials and methods: A PBPK model of digoxin was constructed and validated for acute digoxin poisoning management by comparing simulations with observed individual acute overdose patients. These simulations were compared with standard two-compartment PK model simulations.

Results: PBPK model simulations showed good agreement with post-absorption plasma concentrations of digoxin measured in 6 acute overdose patients. PBPK predictions were accurate to 1.5-fold or less of observed clinical values, proving to be more accurate than two-compartment simulations of the same patients which produced up to a 4.9-fold change.

Conclusions: Compared to conventional two-compartment modelling, PBPK modelling is superior in generating realistic simulations of acute digoxin toxicity and the response to digoxin-Fab. Simulation capacity provides realistic, continuous data which has the potential to substantiate alternative, less expensive, and safer digoxin-Fab dosing strategies for the treatment of acute digoxin toxicity.