Previously, we studied the influence of midazolam on the pharmacokinetics of propofol.1 The most important finding of that study was that midazolam increased blood propofol concentrations by 25% through a reduction in the metabolic, rapid, and slow distribution clearances of propofol. In addition, a reduction in mean arterial blood pressure was associated with propofol pharmacokinetic alterations that increased the blood concentrations of propofol even further. In that study, the plasma midazolam concentration, as controlled by target-controlled infusion (TCI), was increased when administered in the presence of propofol, indicative of a possible influence of propofol on the pharmacokinetics of midazolam.
In clinical practice, midazolam is used to reduce preoperative anxiety, to assure sedation during regional anesthesia and during ventilation in the intensive care unit, and during prolonged procedures to induce and maintain surgical hypnosis perioperatively. In these settings, midazolam is occasionally combined with other sedatives and/or opioids to obtain the desired effect (hypnosis) and limit the side effects (hemodynamic or respiratory depression).2–5 Various combinations of hypnotic drugs and/or opioids have been shown to exhibit both pharmacokinetic and pharmacodynamic interactions,6 often increasing the effect of the combination.7–9
Researchers studying the propofol-midazolam interaction predominantly evaluated the pharmacodynamic interaction.3,4,10,11 Only 1 study described the pharmacokinetic interaction between propofol and midazolam and reported that propofol affected the clearance of midazolam through a possible competitive inhibition of hepatic CYP 3A4.8 However, in that study, clearance was determined on the basis of the influence of just a 1-hour infusion of propofol on the pharmacokinetics of midazolam that was given as just a single bolus dose. Because propofol and midazolam are at times combined for prolonged periods of time, e.g., for intensive care unit sedation,10 we evaluated this interaction during prolonged infusion.
We hypothesized that prolonged infusion of propofol would affect the pharmacokinetics of midazolam and that hemodynamic factors might play a role in this pharmacokinetic interaction. Therefore, we studied the influence of a 7-hour infusion of propofol on the pharmacokinetics of midazolam and evaluated the influence of various hemodynamic variables.
Volunteers and Study Protocol
After obtaining approval of the Medical Ethics Committee of the Leiden University Medical Centre and written informed consent, 8 healthy male volunteers were studied. All volunteers were within 30% of ideal body weight, had no history of renal or hepatic disease, and were not taking medication within 6 months before or during the investigation. All volunteers denied smoking or consumption of >20 g of alcohol per day. Before the investigation, a blood sample was taken for screening of renal or hepatic disease in accordance with Leiden University Medical Centre hospital standards.
Volunteers were studied in a randomized crossover manner during 2 sessions. During the first session, volunteers received a midazolam bolus dose of 0.035 to 0.05 mg · kg−1 in 1 minute followed by an infusion of 0.035 to 0.05 mg · kg−1 · h−1 for 59 minutes (session A, control). During the second study session (session B), the volunteers received the same midazolam infusion scheme as during session A but now in the presence of a TCI of propofol for 7 hours at a constant target propofol concentration (CT) of 0.6 or 1.0 μg · mL−1 using the Diprifusor® (Master TCI infusion system, Fresenius-Vial, Brezins, France). The TCI of propofol was started 15 minutes before the start of the midazolam administration to ensure a semi–steady-state concentration of propofol at the beginning of the midazolam infusion.
The 2 sessions (control and TCI propofol) were separated by at least 2 weeks. Both the CT (0.6 or 1.0 μg · mL−1) and the order of the 2 sessions were randomized, such that in half of the volunteers the control session preceded the other session and half of the volunteers received a CT of 0.6 μg · mL−1 and the other half 1 μg · mL−1. Volunteers fasted from midnight on the night before the study until the last blood sample had been collected. During the administration of propofol, they breathed 30% oxygen in air. When indicated, ventilation was assisted using a facemask to maintain the end-tidal CO2 partial pressure at <50 mm Hg. After termination of sessions A and B, the subjects were monitored for another 4 hours during which they could recover from residual sedation and then received a light meal before they were escorted to their homes.
The studies were performed in an operating room. An IV cannula was inserted into a large forearm vein for the infusion of propofol and midazolam, and an arterial cannula was inserted into a radial artery for collection of hemodynamic data and blood samples. The electrocardiogram, respiratory rate, peripheral oxygen saturation, bispectral index, and intraarterial blood pressure were monitored continuously throughout the study. Furthermore, the cardiac output was determined using the pulse contour methodology on the basis of the intraarterial blood pressure curve with the LiDCO monitor (LiDCO Group Plc, London, UK). The LiDCO monitor was calibrated before each experiment. For this purpose, a lithium sensor was connected to the arterial cannula. After 0.2 mmol lithium was injected IV, the LiDCO monitor was calibrated on the basis of the noninvasive online-determined arterial lithium concentration-time curve, and the cardiac output was calculated. The LiDCO has been found reliable for cardiac output monitoring when compared with traditional thermodilution cardiac output monitoring for up to 8 hours after calibration (LiDCO versus thermodilution: r = 0.86).12 After calibration of the LiDCO, blood samples were obtained from the arterial cannula.
Heart rate; cardiac output; cardiac index; systemic vascular resistance; the systolic, mean, and diastolic arterial blood pressure were all recorded online and saved for further analysis. All volunteers received an infusion of saline of 2 mL · kg−1 · h−1 during each session.
Blood Samples and Assays
During session A, a blank blood sample (10 mL) was obtained. This sample was used for calibration purposes. Additional arterial blood samples (5 mL) for the determination of the plasma midazolam concentration were taken 1, 3, 5, 10, 20, 30, 45, and 60 minutes after the start of the midazolam infusion, and 1, 2, 3, 5, 10, 20, 30, 45, 60, 90, 120, 180, 240, 300, and 360 minutes after termination of the midazolam infusion. Blood samples were taken into heparinized syringes for determination of the plasma midazolam concentration. These samples were centrifuged to obtain plasma that was subsequently stored at −20°C until analysis. The concentration of midazolam in plasma was determined by reversed-phase high-performance liquid chromatography–ultraviolet detection at 216 nm.13 The intra- and interassay coefficients of variation were 2.2% and 2.0%, respectively, for midazolam in plasma in the concentration range of 9.7 to 1120 ng · mL−1. Midazolam assays were conducted within 12 weeks.
During session B, in addition to the sample scheme in session A, an additional arterial blood sample (3 mL) was taken every 60 minutes for determination of the whole blood propofol concentration. These blood samples were stored at 4°C. Propofol assays were performed within 12 weeks. Propofol concentrations in blood were measured by high-performance liquid chromatography–fluorescence at 276 nm.14 The intra- and interassay coefficients of variation were 4.3% and 3.7%, respectively, for propofol in blood in the concentration range of 0.06 to 6.8 μg · mL−1. The assays of midazolam and propofol did not interfere because the fluorescence wavelengths of midazolam (217 nm) and propofol (276 nm) do not overlap. This allows a distinct and accurate estimation of the 2 drug concentrations. Measured and predicted propofol concentrations were compared using the Wilcoxon signed rank test.
A first exploratory analysis of hemodynamic differences between sessions 1 and 2 was performed using the Wilcoxon signed rank test (SPSS version 12.5 for Windows; SPSS, Chicago, IL). A probability level <0.05 was considered significant. The aim of this analysis was to explore the significance of the hemodynamic changes by propofol and limit the number of hemodynamic variables to be tested as covariate in the population pharmacokinetic analysis by NONMEM (version VI 1.2) (Nonlinear Mixed-Effects Modeling; Globomax, Ellicott City, MD).
Population pharmacokinetic parameters were estimated using the first-order conditional estimation method with η-ε interaction for a 3-compartment model (ADVAN11). A proportional error model was used with variance σ2 of the intraindividual variability terms (ε). The interindividual variability of each model parameter was specified using a log-normal variance model:
where Φi is the population value and ΦTV i(t) is the typical value with fixed effects taken into account of the pharmacokinetic parameter in individual i at time t. ηi is the Bayesian estimate of the normally distributed random variable η (with mean 0 and variance ω2) in the individual i (which is estimated by NONMEM), m is the number of covariates considered, αj is the value of a coefficient parameter describing the dependence of the pharmacokinetic parameter on covariate j, and MDcovj is the median of the covariate j in the population. MDcovj is the median of 16 observations (8 volunteers × 2 sessions), except for the propofol concentration (only session B).
Coefficients of variation (CV%) were calculated as 100 times the square root of the variance ω2 of η and, parameter distributions being asymmetric, are only approximately the coefficients of variation as usually defined.
Pharmacokinetic Data Analysis and Inclusion of Covariates
1. A pharmacokinetic parameter set was determined on the basis of the plasma midazolam concentration-time data alone (without covariates) of the 16 sessions. Three compartment models were fitted to the data (number of components based on literature, inspection of concentration-time data, and experiment design) with parameters V1–V3 and Cl1–Cl3 (central volume of distribution [V1], shallow peripheral volume of distribution [V2], deep peripheral volume of distribution [V3], elimination clearance [Cl1], rapid distribution clearance [Cl2], and slow distribution clearance [Cl3]).
2. To determine the influence of propofol on the 6 midazolam pharmacokinetic parameters, all 64 possible combinations for the covariate propofol were evaluated (64 = 26, 2 referring to presence or absence of the covariate, 6 referring to the 6 possible pharmacokinetic parameters). Propofol was included as the mean blood propofol concentration over the study period of 420 minutes in each individual volunteer and was treated as a time-independent covariate. The model with the lowest Akaike's information-theoretic criterion (AIC) value was considered best.15
3. The hemodynamic parameters that differed significantly between sessions A and B were evaluated as potential covariates to further improve the predictability of the midazolam pharmacokinetic parameter set. The arithmetic means of these hemodynamic parameters of the time periods before a blood sample was taken for plasma midazolam concentration analysis were calculated. These data then were treated as time-dependent variables in the analysis. For each hemodynamic parameter, another 64 analysis runs were performed on the basis of the pharmacokinetic parameter set of midazolam with propofol as covariate included. Again, the combination with the lowest AIC value was considered best.
4. To assess the accuracy of the model, we calculated the weighted residual (WR) and the absolute weighted residual (AWR) for each sample.
in which Cmeas, ij is the jth measured concentration of the ith individual and the Cpred,j denotes the corresponding population-predicted values. The median values of the weighted residuals (MDWRs) and the absolute weighted residuals (MDAWRs) were used as overall measures of goodness of fit as well as the residual error (σ2).
The likelihood profile method was used to assess statistical significance of the covariate coefficients. In this method, each coefficient is fixed to a range of values at which the −2 log likelihood (−2LL) is determined (by optimizing the remaining parameters). The 2 values of each coefficient that yield an increase of 3.84 in −2LL constitute the 95% confidence intervals. Finally, internal model selection validation was performed using the bootstrap.16 In this approach, 1000 bootstrap data sets were subjected to analysis with a set of models and the number of times each model was selected was counted to assess replication stability.17 The set of models consisted of those with 1 covariate added at a time in order of importance according to the objective function values. The final model parameter estimates were also used to obtain 95% confidence intervals (using the percentiles method).
The clinical consequences of the influence of propofol on midazolam pharmacokinetics were explored by computer simulation using the final midazolam pharmacokinetic parameter with propofol and heart rate as covariates in an 85-kg male.
Three computer simulations were performed. (1) A computer simulation exploring the influence of the blood propofol concentrations of 0 or 1.5 μg · mL−1 (1.5 μg · mL−1 was the maximal blood propofol concentration measured in this study) on the midazolam concentration-time profile in the presence of a steady heart rate of 63 bpm. (2) A computer simulation to evaluate the effect of heart rate on the midazolam concentration-time relationship. For this purpose, we explored the influence of a heart rate of 40 and 90 bpm on the midazolam concentration-time profile in the absence of propofol. (3) A computer simulation evaluating the influence of propofol on the 50% (the context-sensitive half-time) and the 80% decrement time of midazolam. For this purpose, we used the final midazolam pharmacokinetic parameters in the presence of a blood propofol concentration of 0 or 1.5 μg · mL−1 with a stable heart rate of 63 bpm.
All volunteers completed the study without adverse events. Volunteers who received propofol in addition to midazolam were sedated for a longer period of time after the end of the study. All volunteers stayed in the hospital for 4 hours after the end of the study, and after this period, they were fit to leave the hospital. The age, weight, and height of the volunteers (mean ± SD) were 25.5 ± 5.8 years, 85.0 ± 8.2 kg, and 188 ± 5 cm, respectively.
Blood propofol concentration analyses and plasma midazolam concentration analyses were performed within 12 weeks after the end of the study. Blood propofol concentrations were stable in each participant (Fig. 1) and were similar as predicted (+2%, P = 0.378) in those who received a CT of 0.6 μg · mL−1 and significantly higher than predicted (+23%, P < 0.001) in those who received a CT of 1.0 μg · mL−1. None of the volunteers experienced significant respiratory depression and the end-tidal partial CO2 pressure never exceeded 50 mm Hg.
During the 16 study sessions, 470 blood samples were collected for both midazolam and propofol concentration determination. The analysis of the pharmacokinetics of midazolam in this study is based on 368 measured plasma midazolam concentrations. In the presence of propofol, mean arterial blood pressure, cardiac output, and stroke volume were significantly lower and heart rate and systemic vascular resistance higher than when midazolam was given as a sole drug (Table 1). Because of a power failure, hemodynamic data were lost in 1 session. Consequently, the pharmacokinetic parameter sets of midazolam without covariates (the naively pooled) and with propofol as covariate are based on the concentration-time data of 16 sessions, whereas those with an additional hemodynamic parameter as covariate are based on hemodynamic data of 15 sessions.
Figure 2 shows the measured plasma midazolam concentrations in the presence and absence of propofol when targeted at a CT of 0.6 and 1 μg · mL−1, respectively. In the presence of a CT of 0.6 μg · mL−1 (mean measured blood propofol concentration of 0.62 μg · mL−1) and 1.0 μg · mL−1 (mean measured blood propofol concentration of 1.2 μg · mL−1), the plasma midazolam concentrations were 5.0% ± 14.7% and 26.9% ± 9.4% higher than when midazolam was given as a sole drug (P = 0.115 and <0.001, respectively).
The addition of propofol as covariate significantly improved the pharmacokinetic model of midazolam according to the AIC (Table 2). The pharmacokinetic parameters of midazolam that were influenced by propofol were V1, Cl1, and Cl2. With a blood propofol concentration increasing from 0 to 1.2 μg · mL−1, V1 of midazolam decreased from 5.37 to 2.98 L, Cl1 decreased from 0.39 to 0.31 L · min−1, and Cl2 decreased from 2.77 to 2.11 L · min−1 (Fig. 3). Various hemodynamic parameters, when included in the midazolam pharmacokinetic model, reduced the AIC and residual error (σ2) significantly. Of these hemodynamic covariates, heart rate contributed most according to the AIC. Midazolam pharmacokinetic parameters influenced by heart rate were V3, Cl1, and Cl2 (Table 2, Fig. 4). Figure 5 shows the results of the optimization process and displays the measured versus predicted midazolam concentrations for the model without any covariates (Fig. 5A) and the population-predicted (Fig. 5B) and individual-predicted (Fig. 5C) midazolam concentrations for the model with propofol and heart rate as covariates. In Figure 6, the log likelihood profiles are shown. The plots contain lines that denote a 3.84 increase in −2LL from which the 95% confidence intervals for the parameters can be read.
The bootstrap model selection validation resulted in 0%, 10.2%, 25.6%, and 64.1% selection frequencies for propofol as covariate on no parameters, V1, V1 and Cl1, and V1, Cl1, and Cl2, and 0%, 13.2%, 36.6%, and 50.1% with, in addition, heart rate on no parameters, Cl2, Cl2 and V3, and Cl2, V3, and Cl1. The 95% confidence intervals obtained from the bootstrap and likelihood profiles were similar to those that would be obtained by the normal approximations using values and SEs from Table 2.
The 3 computer simulations using the final pharmacokinetic parameter set offer a clear view of the consequences of the propofol-midazolam interaction on the midazolam dose-concentration relationship (Table 3) (online supplemental data, see Supplement 1, http://links.lww.com/AA/A120). In the presence of a blood propofol concentration of 1.5 μg · mL−1, midazolam concentrations are increased (Fig. 7). The simulations revealed that in the presence of propofol, the bolus dose of midazolam should be reduced by 25% for short-term midazolam dosing schemes to obtain a similar midazolam plasma concentration-time profile as in the absence of propofol. When midazolam is given for an infusion of several hours, the simulations suggest that an additional reduction of 15% in the midazolam infusion rate is required to obtain equal midazolam concentrations in the presence and absence of propofol.
In Figure 8, the influence of heart rate on midazolam pharmacokinetics is explored. The computer simulations show that by varying the heart rate from 40 to 90 bpm, the predicted midazolam concentration changes only to a limited degree. Heart rate affects predominantly the initial distribution of midazolam. The influence of propofol on the pharmacokinetics of midazolam furthermore becomes evident in Figure 9. The concomitant administration of propofol at a blood concentration of 1.5 μg · mL−1 (the maximal blood propofol concentration measured in this study) leads to a slight increase in the context-sensitive half-time and a significant lengthening of the 80% decrement time of midazolam.
We studied the influence of propofol on the pharmacokinetics of midazolam. We hypothesized that propofol would alter the pharmacokinetics of midazolam. The results of this study confirmed our hypothesis. The most important finding of this study is that propofol (Cblood: 1.2 μg · mL−1) increased midazolam concentrations by 26.9%. In the presence of propofol, midazolam is administered in a smaller central compartment from which midazolam is cleared and distributed less rapidly to peripheral tissues.
Next to the primary findings of this study, we identified heart rate as the hemodynamic parameter that further improved the pharmacokinetic dataset of midazolam. Although heart rate improved the pharmacokinetic model of midazolam (as based on the AIC), computer simulations revealed this effect to be of limited clinical importance.
The pharmacokinetic parameter set of midazolam without covariates described in this study corresponds well with midazolam pharmacokinetic parameter sets in the literature.18–21 Our pharmacokinetic parameter set corresponds most closely with that by Bührer et al.,19 probably because of similarities in the study design and the population studied, with a comparable midazolam dose regimen.
Midazolam, with its metabolism through cytochromes CYP 3A3, CYP 3A4, and CYP 3A5,22–24 is subject to pharmacokinetic interactions on the basis of enzyme induction and inhibition in the liver and possibly the kidneys.25–27 The concentration shifts caused by these CYP 450 interactions that affect the clearance of midazolam are huge (up to 1000%), but in practice these interactions occur infrequently.28 The interactions between anesthetic drugs, however, occur more frequently, even daily, but induce concentration shifts that are less significant (20%–50%).8,27
In general, interactions between anesthetics lead to an increase in the concentrations of the drugs combined. For example, alfentanil29 has been shown to increase blood propofol concentrations through a reduction in the elimination and distribution clearance of propofol. Propofol has been shown to increase alfentanil concentrations by decreasing the elimination, rapid, and slow distribution clearances of alfentanil.30 Coadministration of propofol increased remifentanil concentrations through both a decrease in the central volume of distribution and distributional clearance of remifentanil by 41% and elimination clearance by 15%.31
The results of our study follow the above-described pattern such that in the presence of propofol (Cblood: 1.2 μg · mL−1), plasma midazolam concentrations were increased (26.9%). Both hemodynamic and enzymatic factors may be responsible for this interaction.
In contrast to propofol that is known for its high hepatic extraction ratio (>0.9),32 midazolam is a drug with an intermediate hepatic extraction ratio of 0.55.23 Therefore, the clearance of midazolam may be affected by changes in hepatic blood flow, free fraction, and intrinsic hepatic enzyme activity. Propofol is generally known for its hemodynamic-depressant effects and may reduce hepatic blood flow.33 In addition, in our study, the mean arterial blood pressure, stroke volume, and cardiac output were reduced in the presence of propofol (Table 1). This suggests that, at least to some extent, the reduction in clearance described in this study (Cl1 from 0.39 to 0.31 L · min−1: −21%) may be caused by a propofol-induced reduction in hepatic blood flow.
In addition, propofol is known as a CYP 3A4 inhibitor.34 In contrast to enzyme induction that may take several weeks to develop, competitive inhibition of CYP activity may occur almost instantaneously because of the competition of 2 drugs (e.g., propofol and midazolam) for the enzyme's active site. A short-term exposure to propofol at a blood concentration of 3 μg · mL−1 reduced the CYP 3A4 activity by approximately 37%.8 Therefore, we conclude that the propofol-induced reduction in the metabolic clearance of midazolam likely is the result of both the hemodynamic depressant and enzymatic inhibitory effects of propofol.
In addition to the propofol-related reduction in midazolam clearance, hemodynamic alterations induced by propofol also influence the distribution pharmacokinetics of midazolam. Next to the influence of heart rate on the initial distribution, the hemodynamic-depressant effects of propofol are also responsible for the reduced transfer of midazolam to peripheral tissues that is expressed by the reduction in Cl2 by 23.8% from 2.77 to 2.11 L · min−1 in the controls. From Table 1, the difference in heart rate between sessions A and B seems obscure and only significantly different between sessionsbecause of the power of paired testing. Nevertheless, the addition of heart rate significantly reduced AIC ([Delta]AIC: 16; Table 2), the residual error (σ2; Table 2), and the interindividual variability (CV%; Table 2). Observation of the raw heart rate data and the residual errors in each individual finally taught us that this apparent discrepancy is explained by the fact that heart rate does not so much reduce the interindividual variability or the variability between sessions A and B but minimizes variability within each individual.
Finally, model selection stability as assessed by the bootstrap showed that the replication stability was robust; in other words, the final models presented have a higher probability of being selected than simpler ones. The 95% confidence intervals as derived from the log likelihood profiles (Fig. 6) for the covariate effects of propofol on Cl2 and heart rate on Cl1 included 0. Although these covariate effects did not attain statistical significance, inclusion of those effects may still be of importance for prediction because they were selected by AIC. This is in agreement with the arguments for predictor selection as described by Steyerberg.35
In conclusion, when midazolam and propofol are combined,3–5,36 propofol increases the midazolam concentration by a reduction in the central volume of distribution and the metabolic and rapid distribution clearances of midazolam in a concentration-dependent manner. Inclusion of heart rate significantly improved the predictive performance of the midazolam pharmacokinetic model affecting the initial distribution of midazolam and reducing intraindividual variability. The propofol-midazolam pharmacokinetic interaction allows for a 25% reduction of the midazolam bolus dose during short-term combined administration (2–3 hours). Although the influence of midazolam on propofol pharmacokinetics is predominantly caused by hemodynamic alterations,1 the results of this study suggest that propofol affects midazolam pharmacokinetics both through enzyme inhibition and hemodynamic alterations.
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