Phenobarbitone population pharmacokinetics from routine clinical data: role of patient characteristics for estimating dosing regimens.

Routine clinical pharmacokinetic data collected from patients receiving phenobarbitone have been analysed to evaluate the role of patient characteristics for estimating dosing regimens. The data were analysed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. The pharmacokinetic model of phenobarbitone was described using a one-compartment steady-state model. The effect of a variety of developmental and demographic factors on clearance was investigated. NONMEM estimates indicated a nonlinear function of total body weight as the optimum adjustment of phenobarbitone clearance. Concomitant administration of phenobarbitone and other antiepileptic drugs showed a decrease of phenobarbitone clearance in young children. The dosing method based on clearance values obtained by NONMEM analysis allowed the prediction of the steady-state concentration as a function of maintenance dose with acceptable error for therapeutic drug monitoring.     

Estimation of population characteristics of pharmacokinetic parameters from routine clinical data

A general data analysis technique estimates average population values of pharmacokinetic parameters and their interindividual variability from clinical pharmacokinetic data gathered during the routine care of patients. Several drug concentration values from each individual, along with dosage information and the values of other routinely assessed variables suffice for purposes of analysis. The Maximum Likelihood principle estimates underlying population values without the necessity for the intermediate estimation of individual parameter values. The approach is quite general, permitting the use of nonlinear statistical models with both fixed and random effects. Complex expressions involving physiological variables can be used to define the pharmacokinetic parameters. Thus, the relationship of physiological factors to parameter values can be assessed. The generality and appropriateness of the analysis technique are demonstrated by analysis of a set of data derived from 141 patients receiving the drug digoxin.This work was supported in part by NIH Grant GM 16496.     

Population pharmacokinetics of procainamide from routine clinical data

Routine clinical pharmacokinetic data collected from patients receiving procainamide were analysed to estimate population pharmacokinetic parameters. 116 plasma concentration determinations for procainamide and 14 timed urine collections for the drug and its major metabolite N-acetylprocainamide (NAPA) were obtained from 39 patients, mostly males. The data were analysed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data from many individuals. Estimates of the influence of weight, height, renal function, and the presence of congestive heart failure (CHF) on the renal clearance (CLR), acetylation clearance (CLA), miscellaneous metabolic clearance (CLO), and volume of distribution (Vd) of procainamide were obtained. The mean (SE) CLR, CLA, CLO and Vd for procainamide in a 70kg patient with normal renal function were estimated to be 14.4 (2.3) L/h, 10.1 (1.7) L/h, 1.2 (1.3) L/h, and 136.0 (20.0) L, respectively. These pharmacokinetic parameters vary linearly with bodyweight; height adds no information if weight is known. The presence of CHF has no significant effect on either CLO or Vd, but reduces CLA and CLR by 11% (p less than 0.01). Even after adjustments for CHF, renal function and weight, the total clearance and Vd of procainamide vary unpredictably among individuals, with a coefficient of variation between 30 and 40%, and less than 50%, respectively.     

Digoxin pharmacokinetics: multicompartmental analysis and its clinical implications.

The kinetics of digoxin have been investigated in healthy volunteers using an isotopic tracer technique. A three compartment open kinetic model has been proposed as the simplest model consistent with the plasma, urinary and faecal data obtained. The renal clearance of digoxin (mean +/- s.d.) was found to be 119+/-10 ml/min, which did not differ significantly from the glomerular filtration rate (110+/-14 ml/min). Digoxin extra-renal clearance (mean+/-s.d.) was found to be 47+/-7 ml/min. The model predicts that the tissue concentration attained after four 0.25 mg oral doses spread over 24 h can be achieved within a period of 4 h following a single oral loading dose of 1 mg. Maintenance doses can be derived from a simple formula based on the glomerular filtration rate, extra-renal clearance and bioavailability of the digoxin preparation used.     

The integration of pharmacokinetics and pathogen susceptibility data in the design of rational dosing regimens

The integration of pharmacokinetic and pathogen susceptibility data has had an increasing impact on the design of dosage regimens. Pathogen susceptibility is often described by the minimum inhibitory concentration (MIC). While the MIC is an indicator of drug potency, it does not predict pharmacologic response in vivo when drug concentrations are fluctuating. To this end, three pharmacokinetic/pharmacodynamic (PK/PD) parameters that result from indexing pharmacokinetics to MIC have proven quite useful. A number of experimental models of infection have determined the magnitude of each parameter, AUC/MIC, Cmax/MIC and %T>MIC, required for the optimal treatment of specific pathogens. These measurements have proven to be predictive of clinical outcomes as well. As a result, PK/PD breakpoints have been determined based on the likelihood that the pharmacokinetic profile of a given dose will achieve a target PK/PD parameter value. These breakpoints correlate well with treatment success or failure, particularly evident in infections conducive to microbiologic sampling such as otitis media. Therefore, PK/PD assessments have fostered a much more targeted approach to the treatment of patients with infectious diseases, and have proven useful in the selection of antimicrobial therapy and the development of novel dosing strategies.     

Population pharmacokinetics of perhexiline from very sparse, routine monitoring data.

Using NONMEM, the population pharmacokinetics of perhexiline were studied in 88 patients (34 F, 54 M) who were being treated for refractory angina. Their mean +/- SD (range) age was 75 +/- 9.9 years (46-92), and the length of perhexiline treatment was 56 +/- 77 weeks (0.3-416). The sampling time after a dose was 14.1 +/- 21.4 hours (0.5-200), and the perhexiline plasma concentrations were 0.39 +/- 0.32 mg/L (0.03-1.56). A one-compartment model with first-order absorption was fitted to the data using the first-order (FO) approximation. The best model contained 2 subpopulations (obtained via the $MIXTURE subroutine) of 77 subjects (subgroup A) and 11 subjects (subgroup B) that had typical values for clearance (CL/F) of 21.8 L/h and 2.06 L/h, respectively. The volumes of distribution (V/F) were 1470 L and 260 L, respectively, which suggested a reduction in presystemic metabolism in subgroup B. The interindividual variability (CV%) was modeled logarithmically and for CL/F ranged from 69.1% (subgroup A) to 86.3% (subgroup B). The interindividual variability in V/F was 111%. The residual variability unexplained by the population model was 28.2%. These results confirm and extend the existing pharmacokinetic data on perhexiline, especially the bimodal distribution of CL/F manifested via an inherited deficiency in hepatic and extrahepatic CYP2D6 activity.     

Important considerations for the clinical evaluation of drugs: appropriate dosing regimens

A major aspect of clinical trials is the determination of a dosing regimen that will be effective and reasonably safe. The author presents a brief overview of the major considerations that govern the choice of dosage regimens for clinical trials and dosage recommendations for the labeling of marketed products. They include: (1) determination of the lowest effective dose, (2) determination of a safe dose for patients with liver or renal disease, (3) recommendations for doses in the elderly, (4) determination of the effect of concomitant drugs on drug disposition, and (5) examination of dose proportionality. Association of blood levels with desired therapeutic effect also is discussed.     

Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: Routine clinical pharmacokinetic data

Individual pharmacokinetic parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual kinetic variability, and residual variability, including intraindividual variability and measurement error. Individual pharmacokinetics are estimated by fitting a pharmacokinetic model to individual data. Population pharmacokinetic parameters have traditionally been estimated by doing this separately for each individual, and then combining the individual parameter estimates, the Standard Two Stage (STS) approach. Another approach, NONMEM, appropriately pools data across individuals and is therefore less dependent on individual parameter estimates. This study provides further evidence of NONMEM's validity and usefulness by comparing both approaches on simulated routine-type pharmacokinetic data arising from a monoexponential model. The estimates of population parameters (notably those describing interindividual variability) provided by the STS method are poorer than those provided by NONMEM, especially when there is considerable residual error. Further, NONMEM's estimates of population parameters do not require that the data be restricted to special types of routine data such as those obtained only at steady state, or only at peak or trough, nor do the estimates improve with such data. NONMEM's estimates do improve, however, when a data set is enhanced by the addition of single-observation-per-individual type data. Thus, population parameters can be estimated efficiently from data that simulate real clinical pharmacokinetic conditions.     

Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis-menten model: Routine clinical pharmacokinetic data

Individual pharmacokinetic parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual variability, and residual intraindividual variability plus measurement error. Individual pharmacokinetics are estimated by fitting individual data to a pharmacokinetic model. Population pharmacokinetic parameters are estimated either by fitting all individual's data together as though there were no individual kinetic differences (the naive pooled data approach), or by fitting each individual's data separately, and then combining the individual parameter estimates (the two-stage approach). A third approach, NONMEM, takes a middle course between these, and avoids shortcomings of each of them. A data set consisting of 124 steady-state phenytoin concentration-dosage pairs from 49 patients, obtained in the routine course of their therapy, was analyzed by each method. The resulting population parameter estimates differ considerably (population mean Km, for example, is estimated as 1.57, 5.36, and 4.44 g/ml by the naive pooled data, two-stage, and NONMEM approaches, respectively). Simulations of the data were analyzed to investigate these differences. The simulations indicate that the pooled data approach fails to estimate variabilities and produces imprecise estimates of mean kinetics. The two-stage appproach produces good estimates of mean kinetics, but biased and imprecise estimates of interindividual variability. NONMEM produces accurate and precise estimates of all parameters, and also reasonable confidence intervals for them. This performance is exactly what is expected from theoretical considerations and provides empirical support for the use of NONMEM when estimating population pharmacokinetics from routine type patient data.Work supported in part by NIH Grants GM 26676 and GM 26691.     

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

OBJECTIVE: To clarify the observed variability of haloperidol disposition in patients with psychiatric disorders. DESIGN: Retrospective population pharmacokinetic study. PARTICIPANTS: 218 Japanese patients aged 16 to 82 years who provided 391 serum haloperidol concentrations. METHODS: Routine clinical pharmacokinetic data gathered from patients receiving haloperidol were analysed to estimate population pharmacokinetic parameters with the nonlinear mixed effects model (NONMEM) computer program. RESULTS: The final pharmacokinetic model was CL = 42.4 * (TBW/60)(0.655) * 0.814(AGE> or = 55) * (DOSE/200)(0.236) * 1.32(ANTIEP) and Vd = 34.4 * TBW * 0.336( AGE> or = 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 (microg/kg/day), ANTIEP = 1 for concomitant administration of antiepileptic drugs (phenobarbital, phenytoin or carbamazepine) and 0 otherwise, AGE > or = 55 = 1 for patient aged 55 years or over and 0 otherwise, and AGE > or = 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 antiparkinsonian 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. CONCLUSION: Application 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.     

Estimation of the comparative therapeutic superiority of QD and BID dosing regimens, based on integrated analysis of dosing history data and pharmacokinetics

Once-daily dosing almost invariably shows a slightly higher percentage of prescribed doses taken than does twice-daily dosing. Many pharmaceutical scientists, regulators, and prescribers have considered this finding to signify the therapeutic superiority of once-daily dosing. The therapeutically more relevant question, however, is not the percentage of prescribed doses taken but the comparative impact of missed doses on the pharmacologic effects of a drug under the two dosing regimens. A key point in this regard is that the pharmacokinetic equivalent of a single missed once-daily dose is 2–3 sequentially omitted twice-daily doses. Thus, an important parameter in comparing the two regimens is the probability of two or three twice-daily doses being sequentially omitted, versus the probability of missing a single once-daily dose. Our data indicate that the probability of sequential omission of 2–3 twice daily doses is half the probability of omission of a single once-daily dose. For that reason, a twice-daily regimen could prove to be superior to a once-daily regimen in maintaining drug concentrations within a therapeutically desirable range. A more important consideration, however, is to maintain not just the concentration of drug in plasma, but the drug’s therapeutic action. The duration of therapeutic drug action following a last-taken dose is not only drug-specific, but also, for some drug, dependent on the pharmacodynamic properties. Judging the comparative superiority of one dosing regimen over another requires knowledge of the drug’s duration action after a last-taken dose, plus knowledge of the comparative probabilities of the various patterns of dose omission. When applied to HIV protease inhibitors, a twice-daily regimen appears to be better than an once-daily regimen in maintaining therapeutically effective drug actions.     

Steady-state pharmacokinetics of phenytoin from routinely collected patient data

Previously reported routine phenytoin clinical pharmacokinetic data from Japan, England, and Germany were analysed to estimate population pharmacokinetic parameters. There were 780 steady-state phenytoin concentrations and associated dosage rates (mg/day) from 322 patients. The patient group spanned paediatric and adult ages, mean age being 18.4 +/- 17.3 (SD) years; 53% were males. The data were analysed using NONMEM, a computer programme designed for population pharmacokinetic analysis. Estimates of the influence of age, gender, data source, height and weight on the maximum elimination rate (Vm) and Michaelis-Menten constant (Km) were obtained. The Vm and Km of a 70 kg adult male European were estimated to be 415 mg/day and 5.7 mg/L, respectively. Vm is not influenced by gender, age or data source. The parameters of a power function of height and weight were estimated to adjust Vm for body size. The best function adjusts Vm in proportion to weight to the 0.6 power; height contains no useful information. Km is not influenced by gender. The Km for patients less than 15 years old is 43% less than that of older patients. The Km of Japanese patients appears to be 23% less than that for European patients. Even after adjustments for age, etc., apparent Vm and Km vary unpredictably among individuals with a coefficient of variation between 10 to 20% and approximately 50% respectively.     

Integration of data from multiple sources for simultaneous modelling analysis: experience from nevirapine population pharmacokinetics

AIMS

To propose a modelling strategy to efficiently integrate data from different sources in one simultaneous analysis, using nevirapine population pharmacokinetic data as an example.

METHODS

Data from three studies including 115 human immunodeficiency virus-infected South African adults were used. Patients were on antiretroviral therapy regimens including 200 mg nevirapine twice daily and sampled at steady state. A development process was suggested, implemented in NONMEM7 and the final model evaluated with an external data set.

RESULTS

A stepwise approach proved efficient. Model development started with the intensively sampled data. Data were added sequentially, using visual predictive checks for inspecting their compatibility with the existing model. Covariate exploration was carried out, and auxiliary regression models were designed for imputation of missing covariates. Nevirapine pharmacokinetics was described by a one-compartment model with absorption through two transit compartments. Body size was accounted for using allometric scaling. The model included a mixture of two subpopulations with different typical values of clearance, namely fast (3.12 l h⁻¹) and slow metabolizers (1.45 l h⁻¹), with 17% probability of belonging to the latter. Absorption displayed large between-occasion variability, and food slowed the absorption mean transit time from 0.6 to 2.5 h. Concomitant antitubercular treatment including rifampicin typically decreased bioavailability by 39%, with significant between-subject variability. Visual predictive checks of external validation data indicated good predictive performance.

CONCLUSIONS

The development strategy succeeded in integrating data from different sources to produce a model with robust parameter estimates. This work paves the way for the creation of a nevirapine mega-model, including additional data from numerous diverse sources.     

Efficient pharmacokinetic modeling of complex clinical dosing regimens: the universal elementary dosing regimen and computer algorithm EDFAST

Dosing regimens used in clinical practice are often complex, involving several different routes of administration, dose sizes, dosing rates, and dosing intervals and durations. A novel universal elementary dosing regimen (uedr), that allows general pharmacokinetic modeling of these clinical regimens, is presented and developed mathematically. The uedr concept is computationally efficient, mathematically exact, and logically simple, and can replace the disparate standard concepts and equations of elementary infusion, "bolus" injection, multiple injection, oral dosing, zero-order-release dosing, etc. An optimized computer algorithm (EDFAST) based on the uedr approach, that readily permits the creation of a single fast and versatile microcomputer program for the analysis of all complex dosing regimens in any set of linear compartmental models, is presented. This is of particular relevance to three areas of application in clinical pharmacokinetics: compartmental drug concentration or amount versus time course prediction (simulation) with known model parameter values; parameter estimation (curve-fitting) from measured concentration versus time data; and individualized complex dosing regimen calculation (optimization) for target sets of concentration-time points. Application examples that show the versatility of the uedr approach are presented in detail.     

Estimation of the impact of noncompliance on pharmacokinetics: an analysis of the influence of dosing regimens

AIMS

To determine whether, for oxybutynin and risperidone, drug exposure is better with less frequent dosing regimens than with regimens that require more frequent dosing.

METHODS

Pharmacokinetic models of oxybutynin (5 mg twice-daily and 10 mg once-daily) and risperidone (2 mg once-daily orally and 25 mg fortnightly intramuscular injection) were developed. Simulations of multiple doses were performed by use of stochastic models of dose-taking compliance and clinic visit attendance.

RESULTS

At therapeutic concentrations and with typical patterns of noncompliance, intramuscular injections of risperidone resulted in a 41% (SD 12%) greater pharmacokinetic coverage than the oral dose, 76% (SD 10%) vs. 35% (SD 7%). No discernable differences were evident between once- and twice-daily formulations of oxybutynin, 29.2% (SD 10%) vs. 29.0% (SD 13%).

CONCLUSIONS

For equivalent doses for each drug, the longer acting preparation of risperidone, but not oxybutynin, is pharmacokinetically more forgiving of noncompliance than the shorter acting counterparts. Further analysis is required to confirm whether these observations are valid clinically.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• Patient compliance is better with formulations that require less frequent dosing than with formulations that require more frequent dosing.
• Intramuscular risperidone and long-acting oxybutynin are two examples of medicines reformulated for less frequent dosing.
• However, it is not clear whether better compliance with less frequent dosing regimens translates to improved therapeutic outcome.

WHAT THIS STUDY ADDS

• At equivalent daily doses and typical patterns of compliance, fortnightly intramuscular depot administrations of risperidone provide better pharmacokinetic coverage than once-daily oral dosing.
• Once-daily dosing of oxybutynin is no better at maintaining pharmacokinetic exposure than twice-daily dosing at half strength.
• The use of simulated compliance data as input to pharmacokinetic models is useful to assess the impact of noncompliance on internal drug exposure.

     

基于群体药动学模型的万古霉素个体化给药软件研制及应用

目的:研发基于建立的成人和老年群体药动学(population pharmacokinetics,PPK)模型的万古霉素(vancomycin,VCM)个体化给药软件。方法:根据已建立的成人和老年VCM的PPK模型信息,运用MyEclipse、SQL Server、JRE等工具软件研发VCM给药软件。软件开发方案包括需求分析,概要设计,详细设计,软件编码,软件测试以及软件维护和二次开发。结果:研制的VCM给药软件可实现感染患者信息输入和管理,软件通过接口调用非线性混合效应模型(NONMEM)软件,不仅能预测多种具体VCM给药方案下的血药浓度,供临床医师制定初始用药方案参考,而且能结合已有的血药浓度监测信息和贝叶斯反馈法更精准地预测血药浓度,辅助临床医师进一步优化给药方案。软件应用于VCM血药浓度解读,药师向临床做出剂量调整建议。采纳建议组患者复查的血药浓度均达到目标血药浓度范围。结论:本研究基于VCM的PPK模型研制的给药软件能快速方便地辅助成人和老年感染患者VCM的个体化给药。     

移植患者口服环孢素A常规监测的多中心数据群体药代动力学分析

目的 建立群体模型评价多中心的肝、肾移植患者口服环孢素A常规监测的药代动力学特征.方法 收集来自3个不同移植中心的2种器官移植患者的数据(稳态谷浓度),考察人口统计学资料、生化指标、移植中心及器官等协变量对米曼动力学参数Vm、Km的影响,并通过自举验证和拟合优度来评价最终模型的拟合性能.结果 最终模型拟合优度良好,群体拟合的相关系数是83.6%;个体拟合的相关系数是96.9%.Vm、Km的个体间变异系数是13.6%,43.0%;个体内变异系数是7.6%.术后,时间、日剂量、性别、移植中心和器官等协变量对参数有显著性影响.自举验证与最终模型的结果偏差均在1.18%以内.结论 日剂量、术后时间、不同移植中心和器官对参数的影响表明,环孢素A用药要有针对性且应实施个体化用药.     

Application of routine monitoring data for determination of the population pharmacokinetics and enteral bioavailability of phenytoin in neonates and infants with seizures

This study investigated the population pharmacokinetics and the enteral bioavailability of phenytoin (PTN) in neonates and infants with seizures. Data from 83 patients were obtained retrospectively from medical records. A 1-compartment model was fitted to the log-transformed concentration data using NONMEM. Between-subject variability and interoccasion variability were modelled exponentially together with a log transform, both-sides exponential residual unexplained variance model. Covariates in nested models were screened for significance. Model robustness was assessed by bootstrapping with replacement (n = 500) from the study data. The parameters of the final pharmacokinetic model were clearance (L/h) = 0.826.[weight (WT, kg) / 70].[1 + 0.0692.(postnatal age (d) - 11)]; volume of distribution (L) = 74.2.[WT (kg) / 70]; absolute enteral bioavailability = 0.76; absorption rate constant (h) = 0.167. The between-subject variability for clearance and volume of distribution was 74.2% and 65.6%, respectively. The interoccasion variability for clearance was 54.4%. The unexplained variability was 51.1%. Final model parameter values deviated from median bootstrap estimates by less than 9%. Phenytoin disposition in neonates and infants can be described satisfactorily by linear pharmacokinetics. The values of allometrically scaled clearance and volume were similar to adult values, suggesting no major kinetic differences between adults and infants on the basis of size alone. Postnatal age independently influenced clearance. Switching from enteral to intravenous routes may require a dosage adjustment. The results of this study provide a basis for more rational prescribing of phenytoin in infants and neonates.     

Rifampicin combined regimens for Gram-negative infections: data from the literature

Multidrug-resistant (MDR) Gram-negative bacterial infections are associated with high morbidity and mortality. Given the lack of availability of new highly effective antimicrobial drugs against multiresistant strains, combination regimens are administered that include rifampicin for its demonstrated in vitro synergism with multiple drugs. A literature review was performed of clinical studies reporting the use of rifampicin in the treatment of MDR Gram-negative bacterial infections. Nineteen studies were found, including only one randomised controlled study. Data in the literature on combined therapeutic regimens with rifampicin are limited and refer mostly to uncontrolled studies. Therefore, the real clinical benefit of using rifampicin-containing therapies for the treatment of Gram-negative multiresistant bacteria in terms of clinical outcome and survival rates still needs to be assessed.