Target-controlled Infusion of Remimazolam in Healthy Volunteers Shows Some Acute Tolerance.

BACKGROUND: Remimazolam exhibits sedative properties by binding to γ-aminobutyric acid type A receptors. Remimazolam is administered as a bolus dose or continuous infusion, but has not been studied using target-controlled infusion (TCI). The study quantified the relationship between the remimazolam concentration, Modified Observer's Assessment of Alertness and Sedation (MOAAS) score, and bispectral index (BIS) using TCI. METHODS: The authors performed a three-period, crossover, dose-ranging clinical trial in 24 healthy volunteers using age and sex stratification. Data collected in the first period, where remimazolam was administered alone using a step-up and step-down TCI protocol, were used for this analysis. Remimazolam concentrations, MOAAS scores, and BIS values were collected at each step at steady state. Data were analyzed using nonlinear mixed-effects modeling methodology. RESULTS: The relationship between remimazolam, BIS, and MOAAS differed between step-up and step-down infusions at similar remimazolam target concentrations. Tolerance, driven by remimazolam or CNS7054, significantly improved overall model fit (P < 0.01) for both BIS and MOAAS models. After 30 min of repeated bolus dosing, mimicking the regimen in the label for procedural sedation, the BIS and probability of MOAAS 2/3 were predicted to be 54 (95% prediction interval, 44 to 67) and 2% (95% prediction interval, 0 to 32%) versus 58 (95% prediction interval, 48 to 70) and 8% (95% prediction interval, 0 to 36%) in a model without and with tolerance, respectively. After 60 min of continuous infusion, mimicking the regimen in the label for general anesthesia, the BIS and probability of MOAAS 0 were predicted to be 40 (95% prediction interval, 33 to 50) and 87% (95% prediction interval, 18 to 100%) versus 50 (95% prediction interval, 41 to 60) and 59% (95% prediction interval, 6 to 99%) in a model without and with tolerance, respectively. CONCLUSIONS: In this study, it was shown that remimazolam-induced sedation is prone to tolerance development, which is potentially mediated by the CNS7054 concentration. The clinical consequences are, however, limited in situations where remimazolam is titrated to effect.

Pharmacodynamic mechanism-based interaction model for the haemodynamic effects of remifentanil and propofol in healthy volunteers.

BACKGROUND: Propofol and remifentanil are frequently combined for the induction and maintenance of general anaesthesia. Both propofol and remifentanil cause vasodilation and potentially reduce arterial BP. We aimed to develop a mechanism-based model that characterises the haemodynamic interactions between remifentanil and propofol. METHODS: Data from two clinical trials in healthy volunteers were analysed using remifentanil-alone, propofol-alone, and combination groups. We evaluated remifentanil effects on haemodynamics using a previously developed mechanism-based haemodynamic model of propofol. The interaction between propofol and remifentanil was explored using the principles of the general pharmacodynamic interaction (GPDI) model. RESULTS: Remifentanil alone increased the dissipation rate of total peripheral resistance by 50% at 3.0 ng ml-1. Additionally, the dissipation rates of HR and stroke volume were attenuated by 4.8% and 4.9% per 1 ng ml-1 increase in remifentanil concentration, respectively. The maximal effect of propofol alone in decreasing the production rate of total peripheral resistance was 78%, which decreased to 32% when combined with remifentanil 4 ng ml-1. The effects of remifentanil on HR and stroke volume were attenuated by propofol with maximum decreases of 11.9% and 21.2%, respectively. Goodness-of-fit plots and prediction-corrected visual predictive check plots showed good predictive performance of the models. CONCLUSIONS: The structure of the previous mechanism-based haemodynamic model for propofol was able to describe the effects of remifentanil alone on haemodynamic variables. The GPDI model provided a good framework for characterising the pharmacodynamic interaction between remifentanil and propofol on haemodynamic properties. CLINICAL TRIAL REGISTRATION: NCT02043938; NCT03143972.

General purpose models for intravenous anesthetics, the next generation for target-controlled infusion and total intravenous anesthesia?

PURPOSE OF REVIEW: There are various pharmacokinetic-dynamic models available, which describe the time course of drug concentration and effect and which can be incorporated into target-controlled infusion (TCI) systems. For anesthesia and sedation, most of these models are derived from narrow patient populations, which restricts applicability for the overall population, including (small) children, elderly, and obese patients. This forces clinicians to select specific models for specific populations. RECENT FINDINGS: Recently, general purpose models have been developed for propofol and remifentanil using data from multiple studies and broad, diverse patient groups. General-purpose models might reduce the risks associated with extrapolation, incorrect usage, and unfamiliarity with a specific TCI-model, as they offer less restrictive boundaries (i.e., the patient "doesn't fit in the selected model") compared with the earlier, simpler models. Extrapolation of a model can lead to delayed recovery or inadequate anesthesia. If multiple models for the same drug are implemented in the pump, it is possible to select the wrong model for that specific case; this can be overcome with one general purpose model implemented in the pump. SUMMARY: This article examines the usability of these general-purpose models in relation to the more traditional models.

Mechanism-based pharmacodynamic model for propofol haemodynamic effects in healthy volunteers☆.

BACKGROUND: The adverse haemodynamic effects of the intravenous anaesthetic propofol are well known, yet few empirical models have explored the dose-response relationship. Evidence suggests that hypotension during general anaesthesia is associated with postoperative mortality. We developed a mechanism-based model that quantitatively characterises the magnitude of propofol-induced haemodynamic effects during general anaesthesia. METHODS: Mean arterial pressure (MAP), heart rate (HR) and pulse pressure (PP) measurements were available from 36 healthy volunteers who received propofol in a step-up and step-down fashion by target-controlled infusion using the Schnider pharmacokinetic model. A mechanistic pharmacodynamic model was explored based on the Snelder model. To benchmark the performance of this model, we developed empirical models for MAP, HR, and PP. RESULTS: The mechanistic model consisted of three turnover equations representing total peripheral resistance (TPR), stroke volume (SV), and HR. Propofol-induced changes were implemented by Emax models on the zero-order production rates of the turnover equations for TPR and SV. The estimated 50% effective concentrations for propofol-induced changes in TPR and SV were 2.96 and 0.34 µg ml-1, respectively. The goodness-of-fit for the mechanism-based model was indistinguishable from the empirical models. Simulations showed that predictions from the mechanism-based model were similar to previously published MAP and HR observations. CONCLUSIONS: We developed a mechanism-based pharmacodynamic model for propofol-induced changes in MAP, TPR, SV, and HR as a potential approach for predicting haemodynamic alterations. CLINICAL TRIAL REGISTRATION: NCT02043938.

Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists.

This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we can update our initial belief about data with the evidence obtained from any kind of acquired data. It provides a theoretical framework and a statistical method to use pre-existing information within the context of new evidence. Several authors have described the Bayesian approach as capable of dealing with uncertainty in medical decision-making. This review describes the Bayes theorem and how it is used in clinical studies in anesthesia and critical care. It starts with a general introduction to the theorem and its related concepts of prior and posterior probabilities. Second, there is an explanation of the basic concepts of the Bayesian statistical inference. Last, a summary of the applicability of some of the Bayesian statistics in current literature is provided, such as Bayesian analysis of clinical trials and PKPD modeling.

Modeling the Effect of Excitation on Depth of Anesthesia Monitoring in γ-Aminobutyric Acid Type A Receptor Agonist ABP-700.

BACKGROUND: γ-Aminobutyric acid type A (GABAA) receptor agonists are known to cause involuntary muscle movements. The mechanism of these movements is not known, and its relationship to depth of anesthesia monitoring is unclear. We have explored the effect of involuntary muscle movement on the pharmacokinetic-pharmacodynamic model for the GABAA receptor agonist ABP-700 and its effects on the Bispectral Index (BIS) as well as the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) scores. METHODS: Observations from 350 individuals (220 men, 130 women) were analyzed, comprising 6,312 ABP-700 concentrations, 5,658 ABP-700 metabolite (CPM-acid) concentrations, 25,745 filtered BIS values, and 6,249 MOAA/S scores, and a recirculatory model developed. Various subject covariates and pretreatment with an opioid or a benzodiazepine were explored as covariates. Relationships between BIS and MOAA/S models and involuntary muscle movements were examined. RESULTS: The final model shows that the pharmacokinetics of ABP-700 are characterized by small compartmental volumes and rapid clearance. The BIS model incorporates an effect-site for BIS suppression and a secondary excitatory/disinhibitory effect-site associated with a risk of involuntary muscle movements. The secondary effect-site has a threshold that decreases with age. The MOAA/S model did not show excitatory effects. CONCLUSIONS: The GABAA receptor agonist ABP-700 shows the expected suppressive effects for BIS and MOAA/S, but also disinhibitory effects for BIS associated with involuntary muscle movements and reduced by pretreatment. Our model provides information about involuntary muscle movements that may be useful to improve depth of anesthesia monitoring for GABAA receptor agonists.

Do Vancomycin Pharmacokinetics Differ Between Obese and Non-obese Patients? Comparison of a General-Purpose and Four Obesity-Specific Pharmacokinetic Models.

BACKGROUND: Over the past decade, numerous obesity-specific pharmacokinetic (PK) models and dosage regimens have been developed. However, it is unclear whether vancomycin PKs differ between obese and other patients after accounting for weight, age, and kidney function. In this study, the authors investigated whether using obesity-specific population PK models for vancomycin offers any advantage in accuracy and precision over using a recently developed general-purpose model. METHODS: Vancomycin plasma concentrations in a cohort of 49 obese patients (body mass index [BMI] >30 kg/m2), not previously used in the development of any of the evaluated models, were used to validate the performance of 4 obesity-specific models and a general model. Bias and imprecision were calculated for the a priori and a posteriori predictive performance. RESULTS: The bias of the a priori prediction was lowest for one of the obesity-specific models (-1.40%) and that of the general model was a close second (-7.0%). The imprecision was lowest for the general model (4.34 mg/L). The predictive performance for the a posteriori predictions was best for the general model, both for bias (1.96%) and imprecision (2.75 mg/L). CONCLUSIONS: The results of the external validation of vancomycin PK in obese patients showed that currently available obesity-specific models do not necessarily outperform a broadly supported general-purpose model. Based on these results, the authors conclude that there is no advantage in using vancomycin PK models specifically tailored to obese patients over the general-purpose model reported by Colin et al.

Prospective clinical validation of the Eleveld propofol pharmacokinetic-pharmacodynamic model in general anaesthesia.

BACKGROUND: Target-controlled infusion (TCI) systems incorporating pharmacokinetic (PK) or PK-pharmacodynamic (PK-PD) models can be used to facilitate drug administration. Existing models were developed using data from select populations, the use of which is, strictly speaking, limited to these populations. Recently a propofol PK-PD model was developed for a broad population range. The aim of the study was to prospectively validate this model in children, adults, older subjects, and obese adults undergoing general anaesthesia. METHODS: The 25 subjects included in each of four groups were stratified by age and weight. Subjects received propofol through TCI with the Eleveld model, titrated to a bispectral index (BIS) of 40-60. Arterial blood samples were collected at 5, 10, 20, 30, 40, and 60 min after the start of propofol infusion, and every 30 min thereafter, to a maximum of 10 samples. BIS was recorded continuously. Predictive performance was assessed using the Varvel criteria. RESULTS: For PK, the Eleveld model showed a bias < ±20% in children, adults, and obese adults, but a greater bias (-27%) in older subjects. Precision was <30% in all groups. For PD, the bias and wobble were <5 BIS units and the precision was close to 10 BIS units in all groups. Anaesthetists were able to achieve intraoperative BIS values of 40-60 using effect-site target concentrations about 85-140% of the age-adjusted Ce50. CONCLUSIONS: The Eleveld propofol PK-PD model showed predictive precision <30% for arterial plasma concentrations and BIS predictions with a low (population) bias when used in TCI in clinical anaesthesia practice.

Target-controlled-infusion models for remifentanil dosing consistent with approved recommendations.

BACKGROUND: Target-controlled infusion (TCI) systems use pharmacokinetic (PK) models to predict the drug infusion rates necessary to achieve a desired target plasma or effect-site concentration. As new PK models are developed and implemented in TCI systems, there can be uncertainty as to which target concentrations are appropriate. Existing dose recommendations can serve as a point of reference to identify target concentrations suitable for clinical applications. METHODS: Simulations of remifentanil TCI were performed using three PK models (Minto, Eleveld, and Kim). We sought to identify models and target concentrations for remifentanil administration in children, adult, older people, and severely obese individuals, consistent with the remifentanil product label. In a typical adult this is an induction dose of 0.5-1 µg kg-1 and starting maintenance infusion rate of 0.25 µg kg-1 min-1. RESULTS: For the Minto, Eleveld, and Kim remifentanil models, a plasma target concentration of ∼ 4 ng ml-1 achieves drug administration consistent with product label recommended initial doses for all groups with minor exceptions. With effect-site targeting in older individuals, a target concentration of ∼2 ng ml-1 is required for induction and ∼4 ng ml-1 for starting maintenance to achieve drug dosages close to product label recommendations. CONCLUSIONS: We identified remifentanil TCI target concentrations that resulted in drug administration similar to product label dosing recommendations. This approach did not necessarily identify target concentrations that achieve desired clinical effect, only those that are consistent with the product label recommended doses. We estimate that plasma target concentrations of 3.1-5.3 ng ml-1 are suitable for initial dosing.

An Allometric Model of Remifentanil Pharmacokinetics and Pharmacodynamics.

BACKGROUND: Pharmacokinetic and pharmacodynamic models are used to predict and explore drug infusion schemes and their resulting concentration profiles for clinical application. Our aim was to develop a pharmacokinetic-pharmacodynamic model for remifentanil that is accurate in patients with a wide range of age and weight. METHODS: Remifentanil pharmacokinetic data were obtained from three previously published studies of adults and children, one of which also contained pharmacodynamic data from adults. NONMEM was used to estimate allometrically scaled compartmental pharmacokinetic and pharmacodynamic models. Weight, age, height, sex, and body mass index were explored as covariates. Predictive performance was measured across young children, children, young adults, middle-aged, and elderly. RESULTS: Overall, 2,634 remifentanil arterial concentration and 3,989 spectral-edge frequency observations from 131 individuals (55 male, 76 female) were analyzed. Age range was 5 days to 85 yr, weight range was 2.5 to 106 kg, and height range was 49 to 193 cm. The final pharmacokinetic model uses age, weight, and sex as covariates. Parameter estimates for a 35-yr-old, 70-kg male (reference individual) are: V1, 5.81 l; V2, 8.82 l; V3, 5.03 l; CL, 2.58 l/min; Q2, 1.72 l/min; and Q3, 0.124 l/min. Parameters mostly increased with fat-free mass and decreased with age. The pharmacodynamic model effect compartment rate constant (ke0) was 1.09 per minute (reference individual), which decreased with age. CONCLUSIONS: We developed a pharmacokinetic-pharmacodynamic model to predict remifentanil concentration and effect for a wide range of patient ages and weights. Performance exceeded the Minto model over a wide age and weight range.

A Phase 1, Single-center, Double-blind, Placebo-controlled Study in Healthy Subjects to Assess the Safety, Tolerability, Clinical Effects, and Pharmacokinetics-Pharmacodynamics of Intravenous Cyclopropyl-methoxycarbonylmetomidate (ABP-700) after a Single Ascending Bolus Dose.

BACKGROUND: Cyclopropyl-methoxycarbonylmetomidate (ABP-700) is a new "soft" etomidate analog. The primary objectives of this first-in-human study were to describe the safety and efficacy of ABP-700 and to determine its maximum tolerated dose. Secondary objectives were to characterize the pharmacokinetics of ABP-700 and its primary metabolite (cyclopropyl-methoxycarbonyl acid), to assess the clinical effects of ABP-700, and to investigate the dose-response and pharmacokinetic/pharmacodynamic relationships. METHODS: Sixty subjects were divided into 10 cohorts and received an increasing, single bolus of either ABP-700 or placebo. Safety was assessed by clinical laboratory evaluations, infusion-site reactions, continuous monitoring of vital signs, physical examination, adverse event monitoring, and adrenocorticotropic hormone stimulation testing. Clinical effects were assessed with modified observer's assessment of alertness/sedation and Bispectral Index monitoring. Pharmacokinetic parameters were calculated. RESULTS: Stopping criteria were met at 1.00 mg/kg dose. No serious adverse events were reported. Adverse events were dose-dependent and comprised involuntary muscle movement, tachycardia, and ventilatory effects. Adrenocorticotropic hormone stimulation evoked a physiologic cortisol response in all subjects, no different from placebo. Pharmacokinetics were dose-proportional. A three-compartment pharmacokinetic model described the data well. A rapid onset of anesthesia/sedation after bolus administration and also a rapid recovery were observed. A quantitative concentration-effect relationship was described for the modified observer's assessment of alertness/sedation and Bispectral Index. CONCLUSIONS: This first-in-human study of ABP-700 shows that ABP-700 was safe and well tolerated after single-bolus injections up to 1.00 mg/kg. Bolus doses of 0.25 and 0.35 mg/kg were found to provide the most beneficial clinical effect versus side-effect profile.

What about confidence intervals? A word of caution when interpreting PTA simulations.

OBJECTIVES: In the field of antimicrobial chemotherapy, readers are increasingly confronted with population pharmacokinetic models and the ensuing simulation results with the purpose to improve the efficiency of currently used therapeutic regimens. One such type of analysis is Monte Carlo (MC) simulations in support of dose selection. At the moment, results of these MC simulations consist of predictions for the typical individual/population only. The uncertainty associated with the parameters, from which the simulations are derived, is completely ignored. Here, we highlight the importance of and the need to include parameter uncertainty in PTA simulations. METHODS: Using MC simulation with parameter uncertainty, we estimated CIs around PTA curves. The added benefit of this approach was illustrated using, on the one hand, a population pharmacokinetic model developed in-house for a ß-lactam antibiotic and, on the other hand, results from a previously published PTA analysis. RESULTS: Our examples illustrate that proper clinical decision-making requires more than the typical PTA curve. Therefore, authors should be encouraged to provide an estimate of the uncertainty along with their simulations and to take this into account when interpreting the results. We feel that CIs around PTA curves provide this information in a comprehensive manner without requiring advanced knowledge on the underlying modelling approaches from the reader. CONCLUSIONS: We believe that this approach should be advocated by all stakeholders in antibiotic stewardship programmes to safeguard the quality of clinical decision-making in the future.

Development of an Optimized Pharmacokinetic Model of Dexmedetomidine Using Target-controlled Infusion in Healthy Volunteers.

BACKGROUND: Several pharmacokinetic models are available for dexmedetomidine, but these have been shown to underestimate plasma concentrations. Most were developed with data from patients during the postoperative phase and/or in intensive care, making them susceptible to errors due to drug interactions. The aim of this study is to improve on existing models using data from healthy volunteers. METHODS: After local ethics committee approval, the authors recruited 18 volunteers, who received a dexmedetomidine target-controlled infusion with increasing target concentrations: 1, 2, 3, 4, 6, and 8 ng/ml, repeated in two sessions, at least 1 week apart. Each level was maintained for 30 min. If one of the predefined safety criteria was breached, the infusion was terminated and the recovery period began. Arterial blood samples were collected at preset times, and NONMEM (Icon plc, Ireland) was used for model development. RESULTS: The age, weight, and body mass index ranges of the 18 volunteers (9 male and 9 female) were 20 to 70 yr, 51 to 110 kg, and 20.6 to 29.3 kg/m, respectively. A three-compartment allometric model was developed, with the following estimated parameters for an individual of 70 kg: V1 = 1.78 l, V2 = 30.3 l, V3 = 52.0 l, CL = 0.686 l/min, Q2 = 2.98 l/min, and Q3 = 0.602 l/min. The predictive performance as calculated by the median absolute performance error and median performance error was better than that of existing models. CONCLUSIONS: Using target-controlled infusion in healthy volunteers, the pharmacokinetics of dexmedetomidine were best described by a three-compartment allometric model. Apart from weight, no other covariates were identified.

A response surface model approach for continuous measures of hypnotic and analgesic effect during sevoflurane-remifentanil interaction: quantifying the pharmacodynamic shift evoked by stimulation.

BACKGROUND: The authors studied the interaction between sevoflurane and remifentanil on bispectral index (BIS), state entropy (SE), response entropy (RE), Composite Variability Index, and Surgical Pleth Index, by using a response surface methodology. The authors also studied the influence of stimulation on this interaction. METHODS: Forty patients received combined concentrations of remifentanil (0 to 12 ng/ml) and sevoflurane (0.5 to 3.5 vol%) according to a crisscross design (160 concentration pairs). During pseudo-steady-state anesthesia, the pharmacodynamic measures were obtained before and after a series of noxious and nonnoxious stimulations. For the "prestimulation" and "poststimulation" BIS, SE, RE, Composite Variability Index, and Surgical Pleth Index, interaction models were applied to find the best fit, by using NONMEM 7.2.0. (Icon Development Solutions, Hanover, MD). RESULTS: The authors found an additive interaction between sevoflurane and remifentanil on BIS, SE, and RE. For Composite Variability Index, a moderate synergism was found. The comparison of pre- and poststimulation data revealed a shift of C50SEVO for BIS, SE, and RE, with a consistent increase of 0.3 vol%. The Surgical Pleth Index data did not result in plausible parameter estimates, neither before nor after stimulation. CONCLUSIONS: By combining pre- and poststimulation data, interaction models for BIS, SE, and RE demonstrate a consistent influence of "stimulation" on the pharmacodynamic relationship between sevoflurane and remifentanil. Significant population variability exists for Composite Variability Index and Surgical Pleth Index.

Moxifloxacin dosing in post-bariatric surgery patients.

INTRODUCTION: Given the ever increasing number of obese patients and obesity related bypass surgery, dosing recommendations in the post-bypass population are needed. Using a population pharmacokinetic (PK) analysis and PK-pharmacodynamic (PD) simulations, we investigated whether adequate moxifloxacin concentrations are achieved in this population. METHODS: In this modelling and simulation study we used data from a trial on moxifloxacin PK. In this trial, volunteers who had previously undergone bariatric surgery (at least 6 months prior to inclusion), received two doses (intravenous and oral) of 400 mg moxifloxacin administered on two occasions. RESULTS: In contrast to other papers, we found that moxifloxacin PK were best described by a three compartmental model using lean body mass (LBM) as a predictor for moxifloxacin clearance. Furthermore, we showed that the probability of target attainment for bacterial eradication against a hypothetical Streptococcus pneumoniae infection is compromised in patients with higher LBM, especially when targeting microorganisms with minimum inhibitory concentrations (MICs) of 0.5 mg l(-1) or higher (probability of target attainment (PTA) approaching zero). When considering the targets for suppression of bacterial resistance formation, even at MIC values as low as 0.25 mg l(-1) , standard moxifloxacin dosing does not attain adequate levels in this population. Furthermore, for patients with a LBM of 78 kg or higher, the probability of hitting this target approaches zero. CONCLUSIONS: Throughout our PK-PD simulation study, it became apparent that, whenever optimal bacterial resistance suppression is deemed necessary, the standard moxifloxacin dosing will not be sufficient. Furthermore, our study emphasizes the need for a LBM based individualized dosing of moxifloxacin in this patient population.

A general purpose pharmacokinetic model for propofol.

BACKGROUND: Pharmacokinetic (PK) models are used to predict drug concentrations for infusion regimens for intraoperative displays and to calculate infusion rates in target-controlled infusion systems. For propofol, the PK models available in the literature were mostly developed from particular patient groups or anesthetic techniques, and there is uncertainty of the accuracy of the models under differing patient and clinical conditions. Our goal was to determine a PK model with robust predictive performance for a wide range of patient groups and clinical conditions. METHODS: We aggregated and analyzed 21 previously published propofol datasets containing data from young children, children, adults, elderly, and obese individuals. A 3-compartmental allometric model was estimated with NONMEM software using weight, age, sex, and patient status as covariates. A predictive performance metric focused on intraoperative conditions was devised and used along with the Akaike information criteria to guide model development. RESULTS: The dataset contains 10,927 drug concentration observations from 660 individuals (age range 0.25-88 years; weight range 5.2-160 kg). The final model uses weight, age, sex, and patient versus healthy volunteer as covariates. Parameter estimates for a 35-year, 70-kg male patient were: 9.77, 29.0, 134 L, 1.53, 1.42, and 0.608 L/min for V1, V2, V3, CL, Q2, and Q3, respectively. Predictive performance is better than or similar to that of specialized models, even for the subpopulations on which those models were derived. CONCLUSIONS: We have developed a single propofol PK model that performed well for a wide range of patient groups and clinical conditions. Further prospective evaluation of the model is needed.