Evaluation of population pharmacokinetic models for amikacin dosage individualization in critically ill patients

Objectives The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature‐retrieved amikacin population pharmacokinetic models in patients who were critically ill. Methods Four population pharmacokinetic models, three of them customized for critically‐ill patients, were applied using pharmacokinetic software to fifty‐one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient‐specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration‐time curve (AUC) and maximum serum drug concentration (C_max) targets was obtained. Key findings Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and C_max values. Conclusions Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.     

Population pharmacokinetics of ceftriaxone in critically ill septic patients: a reappraisal

AIMS

To investigate the population pharmacokinetics of ceftriaxone in critically ill patients suffering from sepsis, severe sepsis or septic shock.

METHODS

Blood samples were collected at preselected times in 54 adult patients suffering from sepsis, severe sepsis or septic shock in order to determine ceftriaxone concentrations using high-performance liquid chromatography-ultraviolet detection. The pharmacokinetics of ceftriaxone were assessed on two separate occasions for each patient: on the second day of ceftriaxone therapy and 48 h after catecholamine withdrawal in patients with septic shock, or on the fifth day in patients with sepsis. The population pharmacokinetics of ceftriaxone were studied using nonlinear mixed effects modelling.

RESULTS

The population estimates (interindividual variability; coefficient of variation) for ceftriaxone pharmacokinetics were: a clearance of 0.88 l h⁻¹ (49%), a mean half-life of 9.6 h (range 0.83–28.6 h) and a total volume of distribution of 19.5 l (range 6.48–35.2 l). The total volume of distribution was higher than that generally found in healthy individuals and increased with the severity of sepsis. However, the only covariate influencing the ceftriaxone pharmacokinetics was creatinine clearance. Dosage simulations showed that the risk of ceftriaxone concentrations dropping below the minimum inhibitory concentration threshold was low.

CONCLUSIONS

Despite the wide interpatient variability of ceftriaxone pharmacokinetic parameters, our results revealed that increasing the ceftriaxone dosage when treating critically ill patients is unnecessary. The risk of ceftriaxone concentrations dropping below the minimum inhibitory concentration threshold is limited to patients with high glomerular filtration rates or infections with high minimum inhibitory concentration pathogens (>1 mg l⁻¹).     

Relevance of pharmacokinetic and pharmacodynamic modeling to clinical care of critically ill patients

Efficacious therapy is of utmost importance to save lives and prevent bacterial resistance in critically ill patients. This review summarizes pharmacokinetic (PK) and pharmacodynamic (PD) modeling methods to optimize clinical care of critically ill patients in empiric and individualized therapy. While these methods apply to all therapeutic areas, we focus on antibiotics to highlight important applications, as emergence of resistance is a significant problem. Nonparametric and parametric population PK modeling, multiple-model dosage design, Monte Carlo simulations, and Bayesian adaptive feedback control are the methods of choice to optimize therapy. Population PK can estimate between patient variability and account for potentially increased clearances and large volumes of distribution in critically ill patients. Once patient- specific PK data become available, target concentration intervention and adaptive feedback control algorithms can most precisely achieve target goals such as clinical cure of an infection or resistance prevention in stable and unstable patients with rapidly changing PK parameters. Many bacterial resistance mechanisms cause PK/PD targets for resistance prevention to be usually several-fold higher than targets for near-maximal killing. In vitro infection models such as the hollow fiber and one-compartment infection models allow one to study antibiotic-induced bacterial killing and emergence of resistance of mono- and combination therapies over clinically relevant treatment durations. Mechanism-based (and empirical) PK/PD modeling can incorporate effects of the immune system and allow one to design innovative dosage regimens and prospective validation studies. Mechanism-based modeling holds great promise to optimize mono- and combination therapy of anti-infectives and drugs from other therapeutic areas for critically ill patients.     

Population pharmacokinetic analysis of clopidogrel in healthy Jordanian subjects with emphasis optimal sampling strategy

Aim: Clopidogrel is metabolized primarily into an inactive carboxyl metabolite (clopidogrel‐IM) or to a lesser extent an active thiol metabolite. A population pharmacokinetic (PK) model was developed using NONMEM® to describe the time course of clopidogrel‐IM in plasma and to design a sparse‐sampling strategy to predict clopidogrel‐IM exposures for use in characterizing anti‐platelet activity.Methods: Serial blood samples from 76 healthy Jordanian subjects administered a single 75 mg oral dose of clopidogrel were collected and assayed for clopidogrel‐IM using reverse phase high performance liquid chromatography. A two‐compartment (2‐CMT) PK model with first‐order absorption and elimination plus an absorption lag‐time was evaluated, as well as a variation of this model designed to mimic enterohepatic recycling (EHC). Optimal PK sampling strategies (OSS) were determined using WinPOPT based upon collection of 3–12 post‐dose samples.Results: A two‐compartment model with EHC provided the best fit and reduced bias in C_max (median prediction error (PE%) of 9.58% versus 12.2%) relative to the basic two‐compartment model, AUC_0‐24 was similar for both models (median PE% = 1.39%). The OSS for fitting the two‐compartment model with EHC required the collection of seven samples (0.25, 1, 2, 4, 5, 6 and 12 h). Reasonably unbiased and precise exposures were obtained when re‐fitting this model to a reduced dataset considering only these sampling times.Conclusions: A two‐compartment model considering EHC best characterized the time course of clopidogrel‐IM in plasma. Use of the suggested OSS will allow for the collection of fewer PK samples when assessing clopidogrel‐IM exposures. Copyright © 2013 John Wiley & Sons, Ltd.     

Linezolid pharmacokinetic/pharmacodynamic profile in critically ill septic patients: intermittent versus continuous infusion

Pharmacokinetics and pharmacodynamics are significantly altered in critically ill septic patients and the risk of prolonged periods with concentrations below the minimum inhibitory concentration (MIC) and of low area under the serum concentration-time curve/MIC (AUC/MIC) ratios is of concern. We compared the pharmacokinetic/pharmacodynamic (PK/PD) profile of linezolid administered by intermittent or continuous infusion in critically ill septic patients. Patients were divided into two groups: intermittent infusion (Group I) (600mg/12h); or continuous infusion (Group C) (300mg intravenous loading dose +900mg continuous infusion on Day 1, followed by 1200mg/daily from Day 2). Linezolid serum levels were monitored for 72h and microbiological data were collected. The clinical outcome was monitored. Sixteen patients completed the study. MICs of susceptible pathogens were 2mg/L for 80% of the isolates. In Group I, linezolid trough serum levels (C(min)) varied widely and were below the susceptibility breakpoint (4mg/L) during the study period; in 50% of patients C(min) was <1mg/L. In Group C, mean linezolid serum levels were more stable and, starting from 6h, were significantly higher than C(min) levels observed in Group I and were always above the susceptibility breakpoint. Time that the free drug concentration was above the MIC (T(free)>MIC) of>85% was more frequent in Group C than in Group I (P<0.05). Finally, with continuous infusion it was possible to achieve AUC/MIC values of 80-120 more frequently than with intermittent infusion (P<0.05). According to PK/PD parameters, continuous infusion has theoretical advantages over intermittent infusion in this population of patients.     

Clinical pharmacokinetic monitoring of midazolam in critically ill patients

Midazolam is a commonly used sedative in critically ill, mechanically ventilated patients in intensive care unit (ICU) settings worldwide. We used a nine-step decision-making algorithm to determine whether therapeutic monitoring of midazolam in the ICU is warranted. Midazolam has a higher clearance and shorter half-life than other benzodiazepines, and prolonged sedation is achieved with continuous infusion. There appears to be very good correlation between plasma concentrations of both midazolam and its active metabolite, alpha1-hydroxymidazolam, and the degree of sedation. However, due to high interpatient variability, it is not possible to predict the level of sedation in any given individual based on plasma concentration of midazolam or its metabolites. Moreover, no simple and practical assay is available to quantitate midazolam plasma concentrations in the acute ICU setting. Many scales are available to assess the sedative effects of midazolam. Because the plasma concentration of midazolam required to achieve a constant level of sedation is highly variable, it is usually more prudent for the clinician to monitor for sedation with a validated clinical scale than by plasma concentrations alone. Various physiologic parameters, including age-related effects, compromised renal function, and liver dysfunction affect the pharmacokinetics of midazolam and alpha1-hydroxymidazolam. Although routine drug monitoring for all critically ill patients receiving midazolam is not recommended, this practice is likely beneficial in patients with neurologic damage in whom sedation cannot be assessed and in patients who have renal failure with a prolonged time to awakening.     

危重症患者替加环素的群体药动学

目的: 建立替加环素在危重症患者中的群体药动学模型,探究该类人群中影响替加环素药动学的因素。方法: 收集静脉使用替加环素的危重症患者的血样,使用高效液相色谱-质谱联用技术测定替加环素的血药浓度。利用NONMEM软件估算替加环素的药动学参数,通过向前纳入法和逆向剔除法建立替加环素群体药动学模型,并对该模型进行验证和评价。结果: 收集54名患者的143个血药浓度建立替加环素的群体药动学模型,静脉给药的一室模型较好地描述替加环素的药动学特征,替加环素的清除率(CL)、表观分布容积(V_d)的群体典型值分别为11.3 L·h^-1和105 L,患者的APACHE Ⅱ评分和年龄对模型有显著影响。结论: 建立的替加环素群体药动学模型预测性能稳定良好,APACHE Ⅱ评分影响替加环素CL,年龄影响替加环素V_d,可为临床替加环素在危重症患者中的个体化给药提供参考。     

Immune-modulating interventions in critically ill septic patients: pharmacological options

Critically ill patients with severe sepsis and septic shock are characterized by a systemic inflammatory response consisting of pro- and anti-inflammatory mediators. Owing to the high mortality of severe sepsis, great efforts have been undertaken within the last 30 years to develop an immune-modulating therapy to improve survival. Relatively few pharmacological immune-modulating interventions have demonstrated a beneficial impact on survival, while other studies have shown a detrimental effect of such interventions. Among the immune-modulating interventions tested, activated protein C and intensive insulin therapy have been shown to improve survival in septic patients. However, in later studies, it has been difficult to reproduce these beneficial effects. There appears to be a discrepancy between the promising effects of immune-modulating interventions in animal studies and the effects seen in the clinical setting. In the future, the onset of the proinflammatory versus the anti-inflammatory response must be better defined and the timing of treatment with immune-modulating agents should be better managed.     

Ciprofloxacin use in critically ill patients: pharmacokinetic and pharmacodynamic approaches

The objective of this study was to evaluate the properties of ciprofloxacin in intensive care patients using a population approach. Seventy patients received ciprofloxacin. On Day 1, three to eight blood samples were taken over a 12-h period. Peak drug concentration (Cmax) and 24-h area under the concentration-time curve (AUC) were compared with the French breakpoint defining antibiotic susceptibility. A population pharmacokinetic modelling approach was then carried out. A two-compartment open model with a proportional error model best fitted the data. A relationship between the elimination constant rate and the Cockcroft creatinine clearance was found. Ciprofloxacin clearance was 13.6+/-5.8L/h, the volume of distribution was 62.0+/-10.7 L and the ciprofloxacin half-life was 3.7+/-1.8h. When the minimum inhibitory concentration (MIC) was equal to 1mg/L the inhibitory ratio (IR) was > or = 8 in only 10.8% of cases, and the AUC/MIC ratio (AUIC) was 42.0+/-36. In conclusion, this study highlights that the Cockcroft clearance significantly influences ciprofloxacin elimination. Target plasma concentrations for ciprofloxacin, the IR and AUIC were rarely reached with a standard dosing regimen. In critically ill patients, the observed pharmacokinetic variability is mainly responsible for the overly frequent low concentrations of ciprofloxacin, emphasising the need for therapeutic monitoring.     

Population pharmacokinetic measures, their estimation and selection of sampling times

In pharmacokinetic (PK) studies, including bioavailability assessment, various population PK measures, such as area under the curve (AUC), maximal concentration (C(max)) and time to maximal concentration (T(max)) are estimated. In this paper we compare a model-based approach, where parameters of a compartmental model are estimated and the explicit formulae for PK measures are used, and a model-independent approach, where numerical integration algorithms are used for AUC and sample estimates for C(max) and T(max). Since regulatory agencies usually require the model-independent estimation of PK measures, we focus on the empirical approach while using the model-based approach and corresponding measures as a benchmark. We show how to "split" a single sampling grid into two or more subsets, which substantially reduces the number of samples taken for each patient, but often has little effect on the precision of estimation of PK measures in terms of mean squared error (MSE). We give explicit formulae for the MSE of the empirical estimator of AUC for a simple example and discuss how costs may be taken into account.     

基于治疗药物监测的重症患者头孢吡肟PK/PD研究

目的探索临床使用头孢吡肟的重症感染患者首次治疗药物监测(TDM)后的谷浓度及药动学/药效学(PK/PD)参数达标情况。方法采用高效液相色谱法测定患者头孢吡肟血药浓度,以谷浓度值为参考,计算f_T>MIC100%的比例,并分析不同MIC值下各PK/PD目标值的达标情况。结果 117例患者的首次谷浓度为(24.31±19.38)μg·mL~(-1),个体间差异大;f_T>MIC100%的达标率为65.81%;有20例(17.09%)患者首次谷浓度超过5 MIC,患者个体间变异大;其中37例肾功能重度减退患者(CLcr<30 mL·min~(-1))中有13例(35.14%)患者浓度超过5 MIC;117例患者中18例(15.38%)确认为头孢吡肟耐药病原菌感染(MIC>8μg·mL~(-1)),其f_T>MIC 100%的达标率仅为5.56%。结论在重症患者中,对于高MIC患者以及肾功能损伤患者,经验性给药往往达不到合理的PK/PD参数指标,有必要在重症感染患者中开展基于TDM的头孢吡肟个体化给药方案设计。     

Population pharmacokinetic parameters for bayesian monitoring of amikacin therapy in intensive care unit patients

Summary The pharmacokinetics of amikacin has been studied in 40 intensive care unit patients using the bayesian estimation method implemented in the USC PC PACK program of Jelliffe.The volume of the central compartment was significantly higher in these patients than in the reference population, while other pharmacokinetic parameters did not differ significantly from the reference values.The population values maybe employed, in addition to those supplied with the software, to adapt dosage regimens of amikacin in ICU patients.     

A dose-response study of phenylephrine in critically ill, septic surgical patients

Objective: To determine the response of haemodynamic and oxygen-transport parameters to phenylephrine in a dose-response fashion in septic non-hypotensive, vasodilated surgical intensive care unit (ICU) patients. Design: Prospective study. Setting: Surgical ICU of a tertiary care, university medical centre. Patients: Ten septic non-hypotensive, vasodilated surgical ICU patients. Interventions: Routine ICU monitoring, including pulmonary and radial artery catheters. Measurements: Haemodynamic and oxygen-transport measurements were taken at baseline and during therapy. Phenylephrine was infused intravenously for 3 h at progressively increasing doses of 0.5, 1.0, 2.0, 3.0, 4.0, and 8.0 μg · kg⁻¹ · min⁻¹ at 30-min intervals. Measurements were taken after each dose. Results: Mean arterial pressure (MAP) and systemic vascular resistance (SVRI) increased linearly with phenylephrine dose. Cardiac index and pulmonary artery occlusion pressures did not change. Statistically significant changes were observed in heart rate, MAP, stroke index, and systemic and pulmonary vascular resistance. Eight patients had a clinically significant increase (>15%) in oxygen consumption (VO₂I). Oxygen delivery (D₂OI) increased in only three patients. Serum lactate concentrations were unchanged or lower at the end of the study in all eight pateints, who displayed a 15% increase in VO₂I. Conclusions: Treatment with phenylephrine increased expected haemodynamic parameters in a linear fashion; however, clinical changes in VO₂I occurred at variable doses. Dose-response trials are needed to determine the optimal dose of phenylephrine. Further study is needed to evaluate the clinical effects of phenylephrine in septic patients. Received: 12 December 1995 / Accepted in revised form: 19 August 1996     

Random sparse sampling strategy using stochastic simulation and estimation for a population pharmacokinetic study

The purpose of this study was to use the stochastic simulation and estimation method to evaluate the effects of sample size and the number of samples per individual on the model development and evaluation. The pharmacokinetic parameters and inter- and intra-individual variation were obtained from a population pharmacokinetic model of clinical trials of amlodipine. Stochastic simulation and estimation were performed to evaluate the efficiencies of different sparse sampling scenarios to estimate the compartment model. Simulated data were generated a 1000 times and three candidate models were used to fit the 1000 data sets. Fifty-five kinds of sparse sampling scenarios were investigated and compared. The results showed that, 60 samples with three points and 20 samples with five points are recommended, and the quantitative methodology of stochastic simulation and estimation is valuable for efficiently estimating the compartment model and can be used for other similar model development and evaluation approaches.     

Bayesian pharmacokinetic estimation of vinorelbine in non-small-cell lung cancer patients

Objective: To develop a population pharmacokinetics of vinorelbine in a population of non-small-cell lung cancer (NSCLC) patients using a Bayesian estimation in order to calculate for any further patient, individual pharmacokinetic parameters from few blood samples. Methods: Vinorelbine was given by a 15-min infusion (30 mg · m⁻²) to eight patients with NSCLC. Its serum concentration was determined by HPLC and its pharmacokinetics was described by a three-compartment open model with elimination from the central compartment. Volume of the central compartment (V₁) and rate constants (k₁₀, k₁₂, k₂₁, k₁₃, k₃₁) were selected as population pharmacokinetic parameters and computed by non-linear regression (two-step approach) from 14 to 18 concentration measurements per course. Subsequently, these parameters were used by the Bayesian estimator to calculate individual pharmacokinetics from only 2 or 3 measured concentrations. Results: The population mean values (CV%) of V₁, k₁₀, k₁₂, k₂₁, k₁₃, k₃₁, CL, t _1/2 were respectively 21 l (55%), 3.2 h⁻¹ (29%), 7.7 h⁻¹ (74%), 1.3 h⁻¹ (67%), 4.7 h⁻¹ (53%), 0.04 h⁻¹ (20%), 57 l · h⁻¹ (31%) and 43 h (36%). The comparison of results obtained from the Bayesian estimator and from the three-compartment model showed that CL and t _1/2 were well predicted (relative deviation: ±12 to 22%) by the Bayesian method using only two blood samples. Conclusion: We demonstrated that Bayesian estimation allows, at minimal cost and minimal disturbance for the patient, the determination of several vinorelbine pharmacokinetic parameters and therefore dose adaptation from as few as two drug concentrations, measured at 6 h and 24 h after infusion. Received: 4 July 1997 / Accepted in revised form: 15 November 1997     

Population pharmacokinetics of continuous infusion of piperacillin in critically ill patients

Dosing recommendations for continuous infusion of piperacillin, a broad-spectrum beta-lactam antibiotic, are mainly guided by outputs from population pharmacokinetic models constructed with intermittent infusion data. However, the probability of target attainment in patients receiving piperacillin by continuous infusion may be overestimated when drug clearance estimates from population pharmacokinetic models based on intermittent infusion data are used, especially when higher doses (e.g. 16?g/24?h or more) are simulated. Therefore, the purpose of this study was to describe the population pharmacokinetics of piperacillin when infused continuously in critically ill patients. For this analysis, 270 plasma samples from 110 critically ill patients receiving piperacillin were available for population pharmacokinetic model building. A one-compartment model with linear clearance best described the concentration–time data. The mean?±?standard deviation parameter estimates were 8.38?±?9.91?L/h for drug clearance and 25.54?±?3.65?L for volume of distribution. Creatinine clearance improved the model fit and was supported for inclusion as a covariate. In critically ill patients with renal clearance higher than 90?mL/min/1.73?m², a high-dose continuous infusion of 24?g/24?h is insufficient to achieve adequate exposure (pharmacokinetic/pharmacodynamic target of 100% fT_{>4 x MIC}) against susceptible Pseudomonas aerginosa isolates (MIC ≤16?mg/L). These findings suggest that merely increasing the dose of piperacillin, even with continuous infusion, may not always result in adequate piperacillin exposure. This should be confirmed by evaluating piperacillin target attainment rates in critically ill patients exhibiting high renal clearance.     

Limited sampling strategy using Bayesian estimation for estimating individual exposure of the once-daily prolonged-release formulation of tacrolimus in kidney transplant children

Background

A limited sampling strategy (LSS) for estimating the area under the curve (AUC) of the prolonged-release formulation of tacrolimus (tacrolimus^PR) is not available in pediatric patients, although the method is of real benefit to children. The objective of this study was to develop and validate a reliable and clinically applicable LSS using Bayesian estimation for estimating tacrolimus^PR AUC in pediatric kidney transplant patients

Methods

The original tacrolimus pharmacokinetic dataset consisted of 22 full profiles from 22 pediatric kidney transplant patients. The Bayesian estimation method was used to develop the LSS. External validation was performed in an independent validation group which consisted of 20 full pharmacokinetic profiles from 12 pediatric kidney transplant patients.

Results

Bayesian estimator using C_0h C_2h and C_3h gave the best predictive performance with a mean prediction error of 2.2 % in the external validation dataset. There was no correlation between the prediction error and age. The Bland–Altman analysis showed that the mean difference between the reference and Bayesian-estimated AUC_0-24 was 3.5 (95 % confidence interval ?3.5–10.5) ng h/mL

Conclusions

A reliable and clinically applicable LSS for estimating AUC_0–24 of tacrolimus^PR was determined and validated in children. The prediction was unbiased and precise. It can be used as a routine procedure to perform AUC-based tacrolimus^PR dosage optimization in pediatric renal transplant patients.     

Bayesian estimation of variability in adipose tissue blood flow in man by physiologically based pharmacokinetic modeling of inhalation exposure to toluene

Due to the lipophilicity of many xenobiotics, the perfusion of fat tissue is of special interest in physiologically based pharmacokinetic (PBPK) modeling. In order to estimate inter- and intra-individual variability in fat tissue blood flow with exercise, a population PBPK model for toluene was fitted to experimental data from subjects exposed to toluene vapors (Carlsson, A., 1982. Exposure to toluene: uptake, distribution and elimination in man. Scand. J. Work Environ. Health 8, 43-55). Six male volunteers were exposed to 80 ppm toluene for two hours during rest and moderate to heavy exercise (50-150 W). Extensive data collection was made, including sampling of arterial blood, exhaled breath and subcutaneous fat tissue. The model was simultaneously fitted to the time courses of toluene in arterial blood, exhaled breath, and subcutaneous fat in the six individuals by Markov chain Monte Carlo (MCMC) simulation. In order to describe the experimental observations in subcutaneous fat accurately, the fat compartment was split in two. According to the analysis, the increased perfusion of perirenal fat associated with physical workload was best described if it was set to the same, elevated, level during all exercise levels, rather than scaled directly to the increase in oxygen uptake. No increase in subcutaneous fat perfusion could be detected at these exposure conditions.     

Development of a pharmacokinetic and Bayesian optimal sampling model for individualization of oral busulfan in hematopoietic stem cell transplantation

Therapeutic drug monitoring is used to minimize toxicity and maximize the therapeutic efficacy of busulfan, which shows high intra- and interpatient pharmacokinetic variability and erratic oral absorption. This study was designed to develop a pharmacokinetic model that could accommodate the erratic oral absorption of busulfan and to use this model to develop an optimal sparse pharmacokinetic sampling strategy to improve the precision and efficiency of therapeutic drug monitoring. Twenty-one pharmacokinetic profiles were collected from 12 patients receiving oral busulfan before hematopoietic stem cell transplantation. Each pharmacokinetic profile was defined by 5 to 9 plasma concentrations. Candidate pharmacokinetic models were initially fit to the data by maximum likelihood, with model discrimination by Akaike's Information Criterion. Maximum likelihood results were used to derive Bayesian previous parameter estimates, and D-optimal design was used to determine optimal sparse sampling strategies. Each candidate sampling strategy was tested in each patient by comparing the resultant Css obtained from the sparse strategy to the actual Css derived from each patient's full pharmacokinetic dataset. The final model was a 1-compartment model, with oral busulfan absorbed in 1 to 3 phases, and fit the data well. All limited sampling models tested were unbiased in their results, and a 4-sample scheme proved to adequately characterize busulfan pharmacokinetics, and should allow for a reduced sampling frequency for therapeutic drug monitoring.