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Epidural sufentanil is widely used for intraoperative analgesia and is recognized as a safe and effective route of administration when carefully monitored and administered at the lowest effective doses. Optimizing drug dosing requires a precise understanding of its pharmacokinetics. While it is known that sufentanil concentrations in the blood decline more slowly after epidural administration compared with intravenous administration, and the flip-flop phenomenon has been observed in this context, a pharmacokinetic model accounting for this phenomenon has not yet been described.
Methods
This study aimed to develop a pharmacokinetic model of sufentanil following epidural administration, with a focus on its absorption from the epidural space. The study was performed in 23 patients, aged 30–83 years with an American Society of Anesthesiologists (ASA) classification of 2–3, undergoing major abdominal (oncologic and non-oncologic) surgery under general anesthesia and epidural analgesia. Blood samples were collected, and sufentanil concentrations were measured at time points ensuring coverage of the absorption phase. Population analysis was performed using a full Bayesian approach implemented in Stan/Torsten programs with the “cmdstanr” and “bbr.bayes” packages in RStudio.
Results
The Bayesian approach helped incorporate prior information about sufentanil disposition. A two-compartment disposition model with two-compartmental absorption best described the data, featuring a fast absorption rate constant into plasma and the peripheral compartment, and a slow redistribution process from the epidural fat compartment.
Conclusions
This study mechanistically describes the flip-flop pharmacokinetics of sufentanil and allows for more precise dosing of epidural sufentanil (NCT06069219).
Full Bayesian pharmacokinetic model for epidural sufentanil.
Slow redistribution from epidural fat prolongs sufentanil blood concentrations compared with intravenous administration.
1 Introduction
Sufentanil is an analgesic opioid primarily used during anesthesia and for postoperative analgesia in adults and occasionally in pediatric patients [1]. It is one of the most potent opioids. Compared with fentanyl, it exhibits 5–10 times greater potency after intravenous administration, with a very rapid onset and short duration of action [2]. In addition, sufentanil is associated with better hemodynamic stability compared with other opioids in some studies [3].
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The pharmacokinetics of sufentanil have predominantly been studied following intravenous administration [4‐10]. However, several reports describe the use of epidurally administered sufentanil in children, including one study that provides individual pharmacokinetic parameter estimates for each patient [11]. Our research group previously developed a population-based model to characterize the pharmacokinetics of epidurally administered sufentanil in the pediatric population [12]. In this study it was observed that sufentanil absorption from the epidural space likely follows flip-flop kinetics. This means that the rate of sufentanil absorption from the epidural space into systemic circulation may be slower than the rate of decline in the terminal phase of sufentanil disposition. Consequently, plasma concentrations decline more slowly after administration cessation compared with intravenous administration. Sufentanil is highly lipophilic, leading to slow redistribution from epidural fat, which supports this hypothesis.
The published analysis was retrospective and based on data from two observational studies with suboptimal sampling schemes for concentration measurements. As a result, the population pharmacokinetic parameters estimated from this analysis may be biased. An especially critical factor for accurately determining the absorption rate constant is the sampling schedule, which in this case included only one concentration measurement during the absorption phase.
To better understand the absorption of sufentanil from the epidural space, a study with an improved experimental design incorporating more frequent sampling during the absorption phase was necessary. Given the scarcity of scientific reports on population pharmacokinetic studies of epidurally administered sufentanil in adult patients, conducting a study in this population is strongly recommended. Some literature suggests rapid systemic absorption of sufentanil after epidural administration [13], while other authors report low systemic absorption [14] and lower plasma concentrations compared with intravenous administration during the initial hours post-dose [15]. The discrepancies in available studies highlight the need for a study that can precisely characterize the absorption profile of sufentanil from the epidural space.
The aim of this study was to describe changes in plasma concentrations of sufentanil after epidural administration in adults using a population pharmacokinetic analysis. The main focus was to characterize sufentanil absorption from the epidural space and confirm the occurrence of flip-flop kinetics, as suggested in a previous study conducted in the pediatric population [12]. In addition, the study investigated relationships between body weight, age, and sex, and the individual parameters describing sufentanil pharmacokinetics.
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2 Methods
2.1 Study Design
The study was performed in accordance with the approval of the Institutional Bioethics Committee (no. 1144/18) and was registered on ClinicalTrials.gov under the identifier NCT06069219. Male and female patients aged 18–70 years, undergoing epidural anesthesia for abdominal surgery and classified as American Society of Anesthesiologists (ASA) physical status classification 1–3 for perioperative risk, were eligible for inclusion. The clinical study was conducted at the Department of Anesthesiology, Intensive Therapy and Pain Management at H. Święcicki Clinical Hospital of Poznan University of Medical Sciences. Patients undergoing anesthesia for surgery had an epidural catheter placed, through which an initial dose of sufentanil and a local anesthetic from the amide group (e.g., ropivacaine, bupivacaine) were administered into the epidural space. The dose was adjusted on the basis of the type of surgery, the patient’s age, demographic data [weight, height, and body mass index (BMI)], overall health status, and any comorbidities. Subsequently, a continuous infusion of sufentanil into the epidural space was maintained, tailored to the clinical response. The infusion rate was adjusted to optimize analgesia while minimizing potential adverse hemodynamic effects. Administration continued during the immediate postoperative period and for the following 3 days to manage acute postoperative pain effectively. During the procedure, throughout the postoperative pain management, and after the cessation of drug administration, blood samples were collected from patients to measure drug concentrations. The blood sampling schedule was as follows: 5 min after the start of drug administration, then at 0.5, 1, 2, 4, 6, and 12 h after initiation, followed by every 24 h, just before the infusion ended, and after the infusion ended at 3, 5, 20, and 40 min, as well as 1, 2, 6, 12, 36, and 72 h. Blood samples of 2 mL were centrifuged immediately after collection, and the resulting plasma was stored in a freezer at − 80 °C.
2.2 Plasma Sample Collection and Preparation
Samples were thawed at room temperature directly before analysis. Plasma samples were prepared daily after vortex mixing using an IKA MS3 Vortex (IKA®, Staufen, Germany) and centrifuging with a Beckman Coulter centrifuge (Microfuge 16, Beckman-Coulter, Mississauga, Ontario, Canada) to ensure homogeneity and absence of suspended particles. Plasma samples were thawed at room temperature and centrifuged (5 min, 4000 rpm). A total of 500 µL of plasma was transferred to glass tubes, to which 20 µL of sufentanil-D5 (5 ng/mL) was added, along with 900 µL of a 2% formic acid (FA) solution in water. In the next step, the tubes were shaken for 10 min at 1500 rpm and centrifuged (5 min, 4000 rpm). Bond Elut Plexa PCX, 30 mg, 1 mL (Agilent Technologies, Inc.) solid phase extraction (SPE) cartridges were preconditioned with 1 mL of MeOH and 1 mL of water. The plasma samples were applied to the SPE cartridges and left for 1 min, followed by the addition of 1 mL of 2% FA in water, and 1 mL of MeOH:ACN (1:1, v/v). Samples were passed through the extraction cartridges, which were then washed with 1 mL of 5% NH3 in MeOH:ACN (1:1, v/v). The eluates were evaporated under vacuum using a miVac Quatro Sample Concentrator (Genevac, Suffolk, UK). The residue was dissolved in 50 μL of MeOH, vortex-mixed, transferred to glass inserts, centrifuged (15 min, 14,500 rpm), and injected into the liquid chromatography (LC) system.
2.3 Analytical Methods
The analyses were performed using a liquid chromatography tandem mass spectrometry (LC–MS/MS) system consisting of an Agilent 1260 series LC system (Agilent Technologies, CA, USA), composed of a degasser (G1322A), a binary pump (G1312B0), and a thermostated autosampler (G1329B) coupled with a mass spectrometer, the 6430 Series Triple Quadrupole (Agilent Technologies) equipped with an electrospray ionization (ESI) source. MassHunter Workstation (Agilent Technologies) was employed for analytical data acquisition and processing. Data acquisition was carried out in positive ion mode using dynamic multiple reaction monitoring mode (dMRM). The following MS parameters were set: nebulizer pressure: 35 psi, capillary voltage: 2500 V, drying gas flow: 12 L/min, collision gas temperature: 330 °C, and fragmentor voltage: 135 V. Mass Hunter Optimizer from Agilent Technologies v. B.07.01 (Agilent Technologies Inc., Santa Clara, CA, USA) was used for the individual values selection of the fragmentor and the collision energy voltages for sufentanil and sufentanil-D5 (IS). The collision energy for quantitative and qualitative ion mass transitions were set at 15 and 17; 30 V for sufentanil, respectively, whereas for IS the collision energy voltage was 17 V. The quantitative ion mass transitions were: m/z 387.1 → 238.1 for sufentanil and m/z 392.1 → 360.1 for IS, whereas the qualitative ion mass transitions were: m/z 387.1 → 355.1 and m/z 387.1 → 140 for sufentanil. The chromatographic separation was performed on an InfinityLab Poroshell 120 EC-C18 column (3.0 × 100 mm, 2.7 µm) from Agilent Technologies and was carried out using 40% of eluent A (0.1% FA in water) and 60% of eluent B (0.1% FA in MeOH) under isocratic conditions. The injection volume was 3 μL, and the column temperature and flow rate were set at 40 °C and 0.35 mL/min, respectively. The total analysis time was 4.0 min.
2.4 Statistical Methods
Owing to the lack of sufentanil pharmacokinetic (PK) data after intravenous administration, additional knowledge from the literature on sufentanil disposition was incorporated, and Bayesian methods were employed to enhance the modeling process. The full Bayesian approach was implemented using Stan/Torsten (https://metrumresearchgroup.github.io/Torsten) programs with the “cmdstanr” and “bbr.bayes” packages in RStudio. For the inference, we used four Markov chains with 1000 iterations after 1000 warmup iterations. Convergence diagnostics were performed using Gelman–Rubin statistics and trace plots, and the results indicated that the model did not diverge. The R code, data, and Stan code used to analyze the data are publicly available in the GitHub repository (https://github.com/wiczling/sufentanil-epidural).
Following an initial visual inspection of the data, a two-compartment disposition model with first-order absorption was fitted to the concentration–time profiles. However, this model exhibited a significant lack of fit, suggesting a more complex absorption process. Ultimately, we adopted a two-compartment model with first-order absorption, which provided a satisfactory description of the data. The final model equations are as follows:
where A1, denotes an absorption compartment, A4 denotes a distribution compartment within the absorption site, and C1 and C2 denote concentrations in the central and peripheral compartments. The observed concentrations were modeled assuming a proportional error model (additive on a log scale) using a t-distribution to ensure robustness to outliers.
where Pi = [CLi, Qi, V1i, V2i, KAi, KA14i, KA41i] is a vector of subject-specific parameters, f(.) corresponds to the solution of the above ordinary differential equations (ODE) with respect to C1, N is a multivariate normal distribution, and θP is a vector of typical values of Pi. In turn, σ is the scale, ν is the normality constant, and Ω is the scale matrix for the random effects related to the residual and unexplained between-subject variability. For convenience Ω was decomposed as follows:
where ρ = LL′ denotes the correlation, L is a lower triangular Cholesky factor for a correlation matrix, and ω denotes the standard deviation for between-subject variability.
The Bayesian model requires the specification of priors, which define a plausible range for model parameters based on expectations before observing the data. Priors also establish appropriate scales for the analysis and introduce regularization to enhance model robustness. In this study, weakly informative priors were chosen to align with known facts about sufentanil disposition [16]. In addition, the prior mean for epidural-plasma absorption rate and for the epidural distribution rate was assumed to be equal to the sufentanil plasma distribution rate constant (4.85/h), and the epidural redistribuiton rate was assumed to be equal to the sufentanil plasma redistribution rate constant (0.08/h). The standard deviations for disposition- and absorption-related prior parameters were assumed to ensure a coefficient of variation (CV) of about 25% and 50%, respectively. The priors were employed to both between-subject variability and residual variability, assuming an average CV of approximately 40% and 20%, respectively, with an uncertainty of 25%. In this work, the prior uncertainty was intentionally high and primarily served to provide marginal guidance on the model parameters. This approach helped to stabilize the estimation process of the model parameters by taking the historical data into account [17].
Lewandowski–Kurowicka–Joe (LKJ) is the Stan default prior for correlation matrices.
The prior information was evaluated using prior predictive checks. Prior predictive checking works by simulating new replicated datasets based on the assumed prior information and comparing statistics applied to the replicated dataset with the same statistic applied to the original dataset. The individual prior predictions are shown in Fig. 1. This figure confirms that priors are well-shaped and located. It also indicates that the published findings of sufentanil disposition leads to prior predictions that are close to the observed data.
Fig. 1
Individual prior predictive check. Plots of observed (symbols) and prior predicted (red) concentrations versus time
The raw data is graphically summarized in Fig. 2 and Fig. S1. The demographic characteristics of patients are presented in Tables S1 and S2.
Fig. 2
Raw data. Two black squares denote outliers (very high concentrations just after beginning the infusion). Lines connect observations of every individual
The summary of the posterior distribution of model parameters is shown in Table 1. Figure S2 and Fig. S3 summarize the marginal posterior distributions of population-level parameters, between-subject variability (BSV), and residual variability, and compare them with the prior assumptions.
Table 1
Summary of the Markov chain Monte Carlo (MCMC) simulations of the marginal posterior distributions of population-level model parameters
Parameter
Mean
Median
SD
90% Confidence Interval
Shrinkage
rhat
θCL, L/h
64.9
64.5
7.85
(52.4, 78.2)
–
1.00
θQ, L/h
48.2
47.1
11
(32.5, 68.5)
–
1.00
θV1, L
10.2
9.97
2.4
(6.72, 14.4)
–
1.00
θV2, L
506
495
110
(345, 700)
–
1.00
θKA, 1/h
3.27
3.13
0.901
(2.04, 4.98)
–
1.00
θKA14, 1/h
14.9
14.3
4.03
(9.33, 22.4)
–
1.00
θKA41, 1/h
0.14
0.134
0.023
(0.0987, 0.175)
–
1.00
ωCL
0.49
0.482
0.073
(0.383, 0.623)
11.0
1.00
ωQ
0.45
0.433
0.115
(0.286, 0.653)
54.6
1.00
ωV1
1.51
1.5
0.225
(1.13, 1.88)
11.4
1.00
ωV2
0.41
0.393
0.107
(0.26, 0.609)
75.9
1.00
ωKA
0.36
0.355
0.082
(0.241, 0.504)
62.0
1.00
ωKA14
0.38
0.373
0.086
(0.259, 0.546)
48.2
1.00
ωKA41
0.5
0.496
0.112
(0.331, 0.702)
28.1
1.00
σ
0.31
0.305
0.027
(0.266, 0.354)
10.4
1.00
ν
3.81
3.55
0.862
(3.04, 5.44)
–
1.00
3.2 Model Performance
The model performance was assessed by means of visual posterior predictive checks (VPC). The VPC was calculated on the basis of 1000 datasets simulated using posterior parameters. In this study the 10th, 50th, and 90th percentiles were used to summarize the data and VPC prediction. The VPC enables the comparison of the confidence intervals obtained from prediction with the observed data over time. When the corresponding percentile from the observed data falls outside the 95% confidence interval derived from the predictions, it indicates a model misspecification. The individual and population predictions are shown in Fig. 3. The final model VPCs and prediction-corrected VPCs are shown in Fig. 4 and Fig. S4. The model fittings show that the final PK model accurately described the measured concentrations, contrary to the model assuming a first-order classical absorption as also illustrated in Fig. 4. The one compartment absorption model leads to overpredictions during the infusion and underpredictions after the infusion ends.
Fig. 3
Individual predictions. Plots of observed (symbols), population predicted (blue), and individual predicted (red) concentrations versus time
The individual and population predictions versus observed concentrations are relatively symmetrically distributed around the line of identity, indicating good model performance in quantifying the available PK data. The typical goodness-of-fit diagnostic plots for the final model are presented in Fig. 5.
To evaluate the relationship between individual pharmacokinetic parameters and covariates such as age, body weight, height, catheter placement, and sex, ETA values (the random, individual deviations of a parameter from its typical population value) were plotted against these variables (Fig. S5–S9). This exploratory analysis was limited by the small sample size and substantial uncertainty in individual parameter estimates, leading to high shrinkage.
4 Conclusions
Historical data can be seamlessly incorporated using Bayesian inference, allowing for the accommodation of expected between-study variations by extending prior variances of literature-based parameters. In addition, it enables robust analyses that account for outliers, which are common in clinical studies.
The primary finding of this work is that the absorption rate constant (to the plasma and to the absorption peripheral compartment) is fast, whereas the redistribution within the epidural space is slow. Since the mean half-life of the beta phase of absorption is higher than the mean half-life of the beta phase of sufentanil disposition (29.7 ± 5.8 h versus 13.7 ± 3.4 h), it also suggests the presence of flip-flop kinetics. Thus, the rate of decline of sufentanil in the plasma after epidural administration is governed by the slow redistribution process within the epidural space. Burm et al. [18] observed that the systemic absorption of drugs from the epidural space likely follows a two-compartment model by studying radiolabeled bupivacaine, with the slow phase attributed to the redistribution of the drug from the epidural fat. A similar pattern of absorption can be expected for sufentanil, which is likely to be well distributed into the epidural fat owing to its high lipophilicity.
The higher mean posterior clearance compared with prior assumptions (64.9 L/h versus 46.8 L/h) suggests an increased clearance of the drug in the studied population. Since other disposition parameters are also elevated, this increase can be attributed to the fact that only a fraction of the sufentanil dose is absorbed from the epidural space. While the estimated values could be influenced by reduced bioavailability, this is unlikely, as it would require sufentanil to be eliminated within the epidural space—an implausible scenario from a mechanistic perspective.
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The presence of a two-compartment absorption model suggests that steady-state concentrations in the epidural space could be achieved through an induction dose and a time-dependent infusion rate. To establish theoretical initial epidural sufentanil concentrations equivalent to those expected at steady state for a given infusion rate (Rss), the induction dose should be Rss/KA (≈ Rss/3.3), while the maintenance dose should follow the equation Rss + KA14/KA ∙ Rss ∙ exp(−KA41 ∙ t) (≈ Rss + 4.5 ∙ Rss ∙ exp(− 0.7 ∙ t/5.1). This implies that the initial dose rate must be about 5.5 higher and should progressively decline with a half-life of 5.1 h for a typical subject to the desired Rss (Fig. S10).
Recognizing the occurrence of flip-flop pharmacokinetics is clinically important, as it helps prevent the miscalculation or misinterpretation of pharmacokinetic parameters [19] and avoids introducing spurious covariate relationships in population analyses [20]. Accurate identification of this phenomenon is also crucial for ensuring patient safety, particularly during continuous epidural drug infusions, as it facilitates the prediction of drug accumulation in the epidural space and in plasma. This phenomenon has previously been described in anesthetic practice for other drugs administered epidurally, such as ropivacaine [21]. Neurotoxicity is a possible side effect of opioids, whereas systemic absorption from the epidural space may lead to hypotension, sedation, nausea, vomiting, pruritus, urinary retention, and delayed respiratory depression [22, 23]. Although epidural administration of fentanyl and sufentanil is generally considered safe when standard opioid precautions are used, it is recommended to use the lowest effective dose and ensure adequate monitoring [22‐24]. Particular attention is required for the risk of delayed respiratory depression, which is associated with the cephalad migration of the drug within the cerebrospinal fluid (CSF) [13, 15, 24]. Studies have shown that sufentanil concentrations in CSF are higher following epidural administration compared with intravenous administration and appear to vary in a manner dependent on epidural fat content [13]. Therefore, incorporating redistribution from fat tissue and the flip-flop phenomenon into pharmacokinetic (PK) modeling is highly relevant for ensuring drug safety. This issue was first highlighted in the 1990s, when the popularity of epidural anesthesia revealed the phenomenon of time-dependent increases in the bioavailability of sufentanil. These observations coincided with studies on the drug’s distribution in the CSF [13, 15]. Despite the critical importance of this issue, it has remained largely unaddressed, and the flip-flop phenomenon of sufentanil following epidural administration has yet to be incorporated into population PK models. In fact, flip-flop pharmacokinetics are often overlooked in population PK analyses and rarely described using a mechanistic approach, which is essential for accurate parameter estimation and interpretation [20]. In our study, we utilized a first-order absorption model with two compartments to account for the mechanistic hypothesis regarding systemic absorption of sufentanil from the epidural space. To our knowledge, this is the first study to address absorption of epidural sufentanil in a population PK analysis.
The results of this study will improve the prediction of plasma concentrations in patients and, consequently, its analgesic efficacy and potential side effects following epidural administration. This knowledge will enable the optimization of sufentanil dosing for epidural administration and help prevent adverse effects associated with sufentanil use, which are closely tied to plasma concentration levels.
Declarations
Funding
Support was provided solely from institutional and/or departmental sources
Ethics Approval/Consent to Participate/Consent for Publication
The study was performed in accordance with the approval of the Institutional Bioethics Committee (no. 1144/18) and was registered on ClinicalTrials.gov (NCT06069219). Written informed consent was obtained from all patients prior to enrolment.
Authors’ Contributions
P.W., A.B., and A.B-d.M. contributed to writing; A.B. and A.B-d.M. to conceptualization and methodology; D.S. and M.W. to analytical method development and assays; J.B. to data curation; P.W. and P.O. to statistical methodology, data analysis, and visualization; and K.K., T.B., J.S., A.K., and T.K. to clinical care of patients and data collection.
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Pharmacokinetics of Sufentanil After Epidural Administration During the Course of Extensive Abdominal Surgery
Verfasst von
Agnieszka Bienert
Agnieszka Borsuk-De Moor
Paulina Okuńska
Justyna Ber
Danuta Siluk
Małgorzata Waszczuk
Krzysztof Kusza
Tomasz Bartkowiak
Jakub Szrama
Anna Kluzik
Tomasz Koszel
Paweł Wiczling
Lundeberg S, Roelofse JA. Aspects of pharmacokinetics and pharmacodynamics of sufentanil in pediatric practice. Paediatr Anaesth. 2011;21:274–9.CrossRefPubMed
2.
Maciejewski D. Sufentanil in anaesthesiology and intensive therapy. Anaesthesiol Intensive Ther. 2012;44:35–41.PubMed
3.
Martín-Rodríguez S, Bø K. Is abdominal hypopressive technique effective in the prevention and treatment of pelvic floor dysfunction? Marketing or evidence from high-quality clinical trials? Br J Sports Med. 2019;53:135–6.CrossRefPubMed
4.
Ethuin F, Boudaoud S, Leblanc I, Troje C, Marie O, Levron J-C, Le Moing J-P, Assoune P, Eurin B, Jacob L. Pharmacokinetics of long-term sufentanil infusion for sedation in ICU patients. Intensive Care Med. 2003;29:1916–20. https://doi.org/10.1007/s00134-003-1920-y.CrossRefPubMed
5.
Pokorná P, Šíma M, Koch B, Tibboel D, Slanař O. Sufentanil disposition and pharmacokinetic model-based dosage regimen for sufentanil in ventilated full-term neonates. Pharmacology. 2021;106:384–9. https://doi.org/10.1159/000515787.CrossRefPubMed
6.
Pokorná P, Šíma M, Vobruba V, Bašková M, Posch L, Slanař O. Sufentanil pharmacokinetics in a full-term neonate treated with extracorporeal membrane oxygenation: a case report. Perfusion. 2019;34:433–6. https://doi.org/10.1177/0267659118824011.CrossRefPubMed
7.
Khaowroongrueng V, Son KH, Lee S-M, Lee J, Park C-G, Lee SI, Shin D, Shin K-H. Population pharmacokinetic modeling of sufentanil in adult Korean patients undergoing cardiopulmonary bypass surgery. CPT Pharmacometr Syst Pharmacol. 2024;13:1682–92. https://doi.org/10.1002/psp4.13205.CrossRef
8.
Hahn J, Yang S, Min KL, Kim D, Jin BH, Park C, Park MS, Wi J, Chang MJ. Population pharmacokinetics of intravenous sufentanil in critically ill patients supported with extracorporeal membrane oxygenation therapy. Crit Care. 2019;23:248. https://doi.org/10.1186/s13054-019-2508-4.CrossRefPubMedPubMedCentral
9.
Davis PJ, Cook DR, Stiller RL, Davin-Robinson KA. Pharmacodynamics and pharmacokinetics of high-dose sufentanil in infants and children undergoing cardiac surgery. Anesth Analg. 1987;66:203–8.CrossRefPubMed
Woloszczuk-Gebicka B, Grabowski T, Borucka B, Karas-Trzeciak M. Pharmacokinetics of sufentanil administered with 0.2% ropivacaine as a continuous epidural infusion for postoperative pain relief in infants. Paediatr Anaesth. 2014;24:962–7.CrossRefPubMed
12.
Borsuk A, Wołoszczuk-Gębicka B, Bartkowska-Śniatkowska A, Rosada-Kurasińska J, Bienert A, Wiczling P. Flip-flop phenomenon in epidural sufentanil pharmacokinetics: a population study in children and infants. J Clin Pharmacol. 2017;57:1194–206. https://doi.org/10.1002/jcph.912.CrossRefPubMed
13.
Taverne RH, Ionescu TI, Nuyten ST. Comparative absorption and distribution pharmacokinetics of intravenous and epidural sufentanil for major abdominal surgery. Clin Pharmacokinet. 1992;23:231–7.CrossRefPubMed
14.
Menigaux C, Guignard B, Fletcher D, Sessler DI, Levron JC, Chauvin M. More epidural than intravenous sufentanil is required to provide comparable postoperative pain relief. Anesth Analg. 2001;93:472–6 (4th contents page).CrossRefPubMed
15.
Geller E, Chrubasik J, Graf R, Chrubasik S, Schulte-Mönting J. A randomized double-blind comparison of epidural sufentanil versus intravenous sufentanil or epidural fentanyl analgesia after major abdominal surgery. Anesth Analg. 1993;76:1243–50.CrossRefPubMed
16.
Bartkowska-Śniatkowska A, Bienert A, Wiczling P, Rosada-Kurasińska J, Zielińska M, Warzybok J, Borsuk A, Tibboel D, Kaliszan R, Grześkowiak E. Pharmacokinetics of sufentanil during long-term infusion in critically ill pediatric patients. J Clin Pharmacol. 2016;56:109–15. https://doi.org/10.1002/jcph.577.CrossRefPubMed
Burm AGL, Vermeulen NPE, van Kleef JW, de Boer AG, Spierdijk J, Breimer DD. Pharmacokinetics of lignocaine and bupivacaine in surgical patients following epidural administration. Clin Pharmacokinet. 1987;13:191–203.CrossRefPubMed
19.
Yáñez JA, Remsberg CM, Sayre CL, Forrest ML, Davies NM. Flip-flop pharmacokinetics – delivering a reversal of disposition: challenges and opportunities during drug development. Ther Deliv. 2011;2:643–72. https://doi.org/10.4155/tde.11.19.CrossRefPubMed
Cusato M, Allegri M, Niebel T, Ingelmo P, Broglia M, Braschi A, Regazzi M. Flip-flop kinetics of ropivacaine during continuous epidural infusion influences its accumulation rate. Eur J Clin Pharmacol. 2011;67:399–406. https://doi.org/10.1007/s00228-010-0927-x.CrossRefPubMed
22.
Fonseca NM, Guimarães GMN, Pontes JPJ, Azi LMTDA, de Ávila Oliveira R. Safety and effectiveness of adding fentanyl or sufentanil to spinal anesthesia: systematic review and meta-analysis of randomized controlled trials. Braz J Anesthesiol (Engl Ed). 2023;73:198–216. https://doi.org/10.1016/j.bjane.2021.10.010.CrossRef
