Miho Yukawa

Pharmacoepidemiologic investigation of a clonazepam-valproic acid interaction by mixed effect modeling using routine clinical pharmacokinetic data in japanese patients

Non‐linear Mixed Effects Modeling (NONMEM) was used to estimate the effects of clonazepam–valproic acid interaction on clearance values using 576 serum levels collected from 317 pediatric and adult epileptic patients (age range, 0·3–32·6 years) during their clinical routine care. Patients received the administration of clonazepam and/or valproic acid. The final model describing clonazepam clearance was CL = 144·0 TBW−0·172 1·14VPA, where CL is total body clearance (mL/kg/h); TBW is total body weight (kg); VPA = 1 for concomitant administration of valproic acid and VPA = zero otherwise. The final model describing valproic acid clearance was CL (mL/kg/h) = 17·2 TBW−0·264 DOSE0·159 0·821CZP 0·896GEN, where DOSE is the daily dose of valproic acid (mg/kg/day); CZP = 1 for concomitant administration of clonazepam and CZP = zero otherwise; GEN = 1 for female and GEN = zero otherwise. Concomitant administration of clonazepam and valproic acid resulted in a 14% increase in clonazepam clearance, and a 17·9% decrease in valproic acid clearance.

Population pharmacokinetics of digoxin in Japanese patients: a 2-compartment pharmacokinetic model.

ObjectiveTo clarify the observed variability of digoxin disposition by performing a population pharmacokinetic analysis in a Japanese population.DesignRetrospective analysis of clinical pharmacokinetic data.Patients and participantsData were obtained from 106 patients with heart failure and atrial fibrillation (43 males and 63 females).MethodsDigoxin concentrations in serum were measured by fluorescence polarisation immunoassay. Population pharmacokinetic analysis was performed using a 2-compartment open pharmacokinetic model with the computer program NONMEM.Results246 serum concentrations were obtained. Final pharmacokinetic parameters were: CL (L/h) = (0.036 □TBW + 0.112 □CLCR) □0.77SPI □0.784CCB, V1 = 1.83 L/kg, V2 = 22.6 L/kg and Q = 0.629 L/h/kg, where CL is total body clearance, V1 and V2 are the apparent volumes of distribution in the central and peripheral compartments, Q is intercompartmental clearance, TBW is total bodyweight (in kg), CLCR is creatinine clearance (in ml/min), SPI = 1 for concomitant administration of spironolactone (and zero otherwise) and CCB = 1 for concomitant administration of calcium antagonists (and zero otherwise). Concomitant administration of digoxin and spironolactone resulted in a 23% decrease in digoxin clearance. Concomitant administration of digoxin and calcium antagonists (diltiazem, nicardipine, nifedipine or verapamil) resulted in a 21.6% decrease in digoxin clearance.ConclusionsThe estimated population parameter values may assist clinicians in the individualisation of digoxin dosage regimens.

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.

Population pharmacokinetics of haloperidol using routine clinical pharmacokinetic data in Japanese patients.

ObjectiveTo clarify the observed variability of haloperidol disposition in patients with psychiatric disorders.DesignRetrospective population pharmacokinetic study.Participants218 Japanese patients aged 16 to 82 years who provided 391 serum haloperidol concentrations.MethodsRoutine clinical pharmacokinetic data gathered from patients receiving haloperidol were analysed to estimate population pharmacokinetic parameters with the nonlinear mixed effects model (NONMEM) computer program.ResultsThe final pharmacokinetic model was CL = 42.4 • (TBW/60)0.655 • 0.814AGE≥55 • (DOSE/200)0-236 • 1.32ANTIEP and Vd = 34.4 • TBW • 0.336 AGE≥65, where CL is total body clearance (L/h), Vd is apparent volume of distribution (L), TBW is total bodyweight (kg), DOSE is daily dosage (μg/kg/day), ANTIEP = 1 for concomitant administration of antiepileptic drugs (phenobarbital, phenytoin or carbamazepine) and 0 otherwise, AGE≥55 = 1 for patient aged 55 years or over and 0 otherwise, and AGE≥65 = 1 for patient aged 65 years or over and 0 otherwise. Concomitant administration of haloperidol and antiepileptic drugs resulted in a 32% increase in haloperidol clearance. Patients aged 55 years or over showed an 18.6% reduction in clearance, and elderly patients aged 65 years or over showed a 66.4% reduction in apparent volume of distribution. Inclusion of terms for the concomitant administration of haloperidol and anti-parkinsonian drugs (amantadine, bromocriptine, biperiden, trihexyphenidyl or mazaticol) or cytochrome P450 (CYP) 2D6 substrates (levomepromazine, perphenazine, thioridazine, amitriptyline or clomipramine) did not significantly improve the estimate of haloperidol clearance.ConclusionApplication of the findings in this study to patient care may permit selection of an appropriate initial maintenance dosage to achieve target haloperidol serum concentrations, thus enabling the clinician to achieve the desired therapeutic effect.

Population pharmacokinetic investigation of digoxin in Japanese neonates

Objective:  To establish the role of patient characteristics in estimating doses of digoxin for neonates using routine therapeutic drug monitoring data.

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), 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.

Pharmacoepidemiologic investigation of a clonazepam-carbamazepine interaction by mixed effect modeling using routine clinical pharmacokinetic data in Japanese patients.

Nonlinear mixed effects modeling was used to estimate the effects of clonazepam-carbamazepine interaction on clearance values using 359 serum levels gathered from 183 pediatric and adult epileptic patients (age range, 0.3–26.8 years) during their clinical routine care. Patients received the administration of clonazepam and/or carbamazepine. The final model describing clonazepam clearance was CL = 179.0 · TBW −0.231 · 1.22 CBZ , where CL is total body clearance (mL/kg/h) and TBW is total body weight (kg); CBZ = 1 for concomitant administration of carbamazepine and CBZ = zero otherwise. The final model describing carbamazepine clearance was CL = 92.7 · TBW −0.394 · DOSE 0.397 · 0.795 CZP , where DOSE is the daily dose of carbamazepine (mg/kg/day); CZP = 1 for concomitant administration of clonazepam and CZP = zero otherwise. Concomitant administration of clonazepam and carbamazepine resulted in a 22% increase in clonazepam clearance and a 20.5% decrease in carbamazepine clearance.

Population Pharmacokinetic Investigation of Digoxin in Japanese Infants and Young Children

To establish the role of patient characteristics in estimating doses of digoxin for infants and young children using routine therapeutic drug monitoring data, the steady‐state blood‐level data (n = 245) after repetitive oral administration in 117 hospitalized infants and young children were analyzed using nonlinear mixed effects modeling (NONMEM), a computer program designed for analyzing drug pharmacokinetics in study populations through pooling of data. Analysis of the pharmacokinetics of digoxin was accomplished using a 1‐compartment pharmacokinetic model. Estimates generated by NONMEM indicated that the clearance of digoxin (CL/F; L/h) was influenced by the following demographic variables: total body weight (TBW), presence of congestive heart failure (CHF), and infant‐young children clearance factor (trough serum concentration of digoxin; Conc). These influences could be modeled by the equation CL/F (L/h) = 0.302 · TBW (kg)1.17 · 0.905CHF · Conc (trough serum digoxin concentration >1.7 ng/mL)−0.540; F = 0.754, where CHF is 1 for presence of congestive heart failure, 0 otherwise; F is bioavailability, 1 for elixirs, 0.754 for powders; and Conc−0540 is 1 for digoxin concentration <1.7 ng/mL. Clinical application of the model to patient care may permit selection of an appropriate initial maintenance dose, thus enabling the clinician to achieve the desired therapeutic effect. However, the digoxin dosage regimen for the individual patient should be based on a careful appraisal of his or her clinical need for the drug.

Determination of Digoxin Clearance in Japanese Elderly Patients for Optimization of Drug Therapy

AbstractBackground: Optimal use of digoxin in the elderly population requires information about the drug’s pharmacokinetics and the influence of various factors on the drug’s disposition. However, because of sampling restrictions, it is often difficult to perform traditional pharmacokinetic studies in elderly patients. Objective: This study was conducted to determine the apparent total clearance of digoxin from serum after oral administration (CL/F) and to establish the role of patient characteristics in estimating doses of digoxin for elderly patients (age ≥65 years), using routine therapeutic drug monitoring data. Methods: Analyses of the pharmacokinetics of digoxin were conducted using the nonlinear mixed-effects modelling (NONMEM®) software, a computer program designed to analyse pharmacokinetics in study populations by allowing pooling of data. Steady-state data (140 observations) obtained by routine therapeutic drug monitoring following repeated oral administration of digoxin in 94 hospitalized elderly patients (age ≥65 years) were analysed to establish the role of patient characteristics in estimating doses of digoxin for elderly patients. Results: Estimates generated by NONMEM® indicated that digoxin CL/F was influenced by the demographic variables of total bodyweight (TBW), serum creatinine (SCr), age (AGE), presence of congestive heart failure (CHF), concomitant administration of the calcium channel antagonists (calcium channel blockers [CCBs]: verapamil, diltiazem or nifedipine), sex (SEX) and elderly clearance factor (trough serum concentration of digoxin; [Ctrough]^θ). The full version of the final NONMEM® model was CL/F[L/h]=(0.588 × TBW [kg])^(0.189) × SCr[mg/dL]^(−0.163) × (AGE [years]/65)^(−0.152) × 0.957^(CCB) × 0.941^(CHF) × 0.965^(SEX) × Ctrough [ng/mL]^ (−0.180), where CCB is 1 for concomitant administration of a CCB and is 0 otherwise; CHF is 1 for patients with CHF and is 0 otherwise; SEX is 0 for male and is 1 for female; and the elderly clearance factor Ctrough−0.180 is 1 for digoxin Ctrough <1.7ng/mL. Conclusions: We developed a new model for elderly patient dosing of digoxin with good predictive performance. Clinical application of the findings of the present study to patient care may permit selection of an appropriate initial digoxin maintenance dose, thus enabling the clinician to achieve a desired therapeutic effect. However, the digoxin dosage regimen should be based on an appraisal of the individual patient’s clinical need for the drug.

Pharmacoepidemiologic Investigation of Clonazepam Relative Clearance by Mixed‐Effect Modeling Using Routine Clinical Pharmacokinetic Data in Japanese Patients

The effects of drug‐drug interactions on clonazepam clearance were examined through a retrospective analysis of serum concentration data from pediatric and adult epileptic patients. Patients received clonazepam as monotherapy or in combination with other antiepileptic drugs. A total of259 serum clonazepam concentrations gathered from 137 patients were used in a population analysis of drug‐drug interactions on clonazepam clearance. Data were analyzed using a nonlinear mixed‐effects modeling (NONMEM) technique. The final model describing clonazepam clearance was CL = 152 · TBW−0.181 · DIF, where CL is clearance (ml/kg/h), TBWis total body weight (kg), and DIF (drug interaction factor) is a scaling factor for concomitant medication with a value of 1 for patients on clonazepam monotherapy, 1.18 for those patients receiving concomitant administration of clonazepam and one antiepileptic drug (carbamazepine or valproic acid), and 2.12 · TBW−0119 for those patients receiving concomitant administration of clonazepam and more than two antiepileptic drugs. Clonazepam clearance decreased in a weight‐related fashion in children, with minimal changes observed in adults. Concomitant administration of clonazepam and carbamazepine resulted in a 22% increase in clonazepam clearance. Concomitant administration of clonazepam and valproic acid resulted in a 12% increase in clonazepam clearance. Concomitant administration of clonazepam with two or more antiepileptic drugs resulted in a 23% to 75% increase in clonazepam clearance.