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Anesthetic Pharmacology: Research Report

A Recirculatory Model for Pharmacokinetics and the Effects on Bispectral Index After Intravenous Infusion of the Sedative and Anesthetic AZD3043 in Healthy Volunteers

Björnsson, Marcus A. MSc Pharm, PhD*†; Norberg, Åke MD, PhD‡§; Kalman, Sigridur MD, PhD‡§; Simonsson, Ulrika S. H. MSc Pharm, PhD

Author Information
doi: 10.1213/ANE.0000000000000814
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An easily controllable depth of anesthesia is an important property for a new anesthetic compound. This could be achieved with a short half-life, resulting from rapid metabolism and a low volume of distribution, and rapid equilibration between blood and the site of action. Pharmacokinetic (PK)/pharmacodynamic (PD) modeling is a useful tool to assess these properties.1 A quantitative measure of the depth of anesthesia, to be used for guiding the dosing of the anesthetic, can be obtained by the bispectral index (BIS), which is derived from the electroencephalogram.2

AZD3043, originally called THRX-918661, is a positive allosteric modulator of the γ-aminobutyric acid type A receptor, with sedative and anesthetic properties.3 It is metabolized by esterases in plasma and the liver, leading to a high systemic clearance and a short half-life. In 2 clinical studies in healthy volunteers, AZD3043 induced sedation and anesthesia in a dose-dependent manner, and the subjects recovered rapidly after the end of the AZD3043 infusions.4,5

The aims of this analysis were to describe the population PK of arterial and venous AZD3043 concentrations with a recirculatory model and the relationship between AZD3043 concentrations and the effect on BIS in healthy volunteers, including potential covariate (COV) effects of body weight, sex, age, and esterase activity.

METHODS

Study Designs

This analysis was performed with data from the first 2 clinical studies of AZD3043 in healthy volunteers (clinicaltrials.gov identifiers NCT00918515 and NCT00984880).4,5 Both studies were performed at Karolinska University Hospital, Huddinge, Sweden, in accordance with the Declaration of Helsinki and Good Clinical Practice. The studies were approved by the Regional Independent Ethics Committee in Stockholm, Sweden, and the Swedish Medical Products Agency, and written informed consent was obtained from all subjects.

The first study4 was a dose-escalation study, where 30-minute constant rate IV infusions of 1, 3, 6, 12, 18, 27, 36, 54, or 81 mg/kg/h were given to 53 healthy male volunteers. There were 6 subjects per group except for the 1-mg group, where there were 5 volunteers. All subjects had normal plasma butyrylcholinesterase activity, as measured with the dibucaine number test.6,7 Subject demographics are presented in Table 1. Arterial and venous plasma samples were obtained before start of infusion and at 2, 5, 15, 29, 31, 32, 35, 37, 40, 45, 60, 75, 90, and 120 minutes after start of infusion. In addition, venous plasma samples were obtained at 4.5, 8, and 24 hours after start of infusion.

Table 1
Table 1:
Descriptive Statistics of Demographics and Covariates

The second study5 was divided into 2 parts. In part A, each subject received a 1-minute IV bolus of 1, 1.5, 2, 4, or 6 mg/kg. In part B, each subject received 0.8 + 10, 1 + 15, 3 + 30, or 4 mg/kg bolus + 40 mg/kg/h 30-minute infusion. Different subjects were included in the 2 parts, i.e., each subject received only 1 treatment. Seventy-two healthy volunteers (men or women of nonchildbearing potential, with normal plasma butyrylcholinesterase activity), 8 subjects per group, were included in the study (Table 1). Arterial and venous plasma samples were collected and analyzed for AZD3043 as described previously, before start of infusion, at 50 to 55 seconds (before stop of 1-minute infusion); at 1, 4, 14, and 29 minutes during the 30-minute infusion (part B); and at 1, 4, 6, 9, 14, 29, 44, 59, and 119 minutes after the end of the 1-minute (part A) or 30-minute (part B) infusion. In addition, venous samples were also collected at 4 hours after stop of infusion and at 8 and 24 hours after start of infusion.

The number of patients included in these exploratory studies was not determined based on any statistical considerations but rather on previous experience with similar studies. In a survey of 105 first-in-human studies published between 1995 and 2004,8 the most common number of subjects on active treatment per cohort was 6, as in the first study of this work. In the second study included in this work, the number of subjects was increased to 8.

All samples from both studies were collected in the presence of stabilizers to avoid ester-induced hydrolysis of AZD3043 and analyzed for total AZD3043 concentrations by Huntingdon Life Sciences Ltd, Huntingdon, United Kingdom, by the use of liquid chromatography with tandem mass spectrometry. The method was validated over the concentration range 0.01 to 20 µg/mL, and the overall mean precision (percent coefficient of variation, or %CV) and accuracy (%Bias) for the QC samples at 3 concentrations in plasma were 8.4% and ±4.6%, respectively. Unbound AZD3043 concentrations were measured in the venous samples obtained at 29 and 40 minutes after start of infusion in the first study. In both studies, BIS (BIS VISTA, Aspect Medical Systems, Inc., Norwood, MA) was electronically captured every minute until the subjects were awake according to the clinical signs, including Modified Observer’s Assessment of Alertness/Sedation scale.9

Data Analysis

Nonlinear mixed effects modeling was performed with NONMEM 7.2 (ICON Development Solutions, Ellicott City, MD).10 The first-order conditional estimation method with interaction was used for all estimations. Perl speaks NONMEM (PsN)11 was used for automating and controlling runs, and Xpose 412 and R (http://www.r-project.org) were used to produce goodness-of-fit graphics, including visual predictive checks (VPCs).

The modeling was performed sequentially, starting with the PK model, followed by the analysis of BIS. Models were selected based on the goodness-of-fit plots, precision in parameter estimates, and statistically using the objective function value (OFV). A decrease in OFV by 6.63 (corresponding to a P value <0.01) was required for a parameter to be included in the model. Confidence intervals of the parameters were obtained from nonparametric bootstraps with 1000 samples, implemented in PsN. Because the range of doses was large and every individual was dosed based on their body weight, VPC was normalized by the population predictions (PREDs) for each individual via the PRED-corrected VPC (PC-VPC) in PsN.13 In a PC-VPC, each observed or simulated value is divided by the corresponding PRED for that individual at that time and multiplied by the median PRED of all individuals, regardless of dose, in that time-bin. That way all data are normalized to fit on the same scale while the shape of the curve is maintained. Log-likelihood profiles for the PK parameters were created by fixing the parameters, 1 at a time, to different values and re-estimating the other parameters. The difference in OFV was then plotted versus the value of the fixed parameter.

Log-normal distribution of the parameters around the typical value was assumed:

where Pi is the value of the parameter in individual i, P is the typical value of the parameter in the population, and ηi is the normally distributed interindividual random variability with mean 0 and variance ω2. Additive, proportional, and combined additive and proportional residual error models were investigated.

Median prediction error (MDPE) and median absolute performance error (MDAPE) were calculated for the final models according to Varvel et al.14 Plasma concentrations that were less than the limit of quantification (0.01 µg/mL) were excluded from the analysis. In case the concentrations increased after observations below the limit of quantification, these samples were also excluded. In 4 subjects in study 1 and in 16 subjects in study 2, unexpected, and sometimes large, increases in venous concentrations were observed at late time points, likely due to sampling errors (sample contamination by initial dose) because the corresponding metabolite concentrations did not increase. These samples were regarded as artifacts and were not included in the analysis. In 2 subjects, there were BIS values that suddenly decreased to 0 at the exact times of several arterial blood pressure measurements. These values were regarded as artifacts and were excluded from the analysis. Two subjects in the second study were excluded from all analyses because there were interruptions in the infusions, and therefore, the exact dosing history was not known. This means that the total number of subjects included in the analysis was 123. In 2 subjects, sudden transient increases in BIS to high values were seen during infusion while the subjects were deeply anesthetized. These increased values were regarded as artifacts and were excluded from the analysis.

PK Analysis

A recirculatory PK model was developed and fitted to arterial and venous concentrations simultaneously. The model consisted of an arterial and a venous compartment, peripheral distribution compartments, a tanks-in-series transit representing the transport of drug through the heart and lungs, and a nondistributive pathway between arterial and venous plasma (Fig. 1). First, the structural model was identified by increasing the number of compartments in the central circulation, the nondistributive pathway, and the peripheral distribution until no further improvement of the fit was found. Drug elimination was assumed to occur from the arterial compartment, but elimination from the venous compartment also was assessed. Thereafter, the effect of dose, accumulated administered dose, and concentration on the clearance and apparent volume of distribution were evaluated. The influence of body weight on clearance and volume parameters was then assessed by having them not scaled to body weight, directly proportional to body weight, or scaled allometrically to body weight (normalized to the median value 77 kg), where the volume parameters were assumed to be directly proportional to body weight, while the elimination and intercompartmental clearance parameters were related to body weight raised to the power 0.75.15 Finally, the effects of esterase activity, sex, and age on clearance were investigated. COVs were modeled as linear relations, centered at the median value of the COV:

Figure 1
Figure 1:
Schematic representation of the final recirculatory pharmacokinetic model. CL = elimination clearance; ke0 = rate constant for equilibration between plasma and effect-site concentration; QCO = cardiac output (plasma flow); QND = plasma flow of nondistributive pathway; QP1 = plasma flow through peripheral compartment 1; QP2 = plasma flow through peripheral compartment 2; VA = arterial volume; VV = venous volume; VC = volume of central circulation; VP1 = volume of peripheral compartment 1; VP2 = volume of peripheral compartment 2.

where θ1 is the typical value of parameter P for an individual with the median value of a COV (COVmedian), and θ2 is the fractional change in the parameter for each unit the COV differs from the median. For the categorical COV, sex, the effect on clearance was modeled as a fractional change between male and female volunteers.

PD Analysis

When developing the BIS model, the PK parameters (both fixed and random effects) were fixed to their estimates from the final PK model and the PK data were retained in the dataset.16 This approach conditions the PD analysis not only on the PK parameters but also on the PK data, accounting for uncertainty in the individual predictions of the PK parameters. An effect-compartment model was used to describe the delay of effects on BIS in relation to the arterial plasma concentrations of AZD3043. The rate constant of the effect delay was described by the parameter ke0. A 2-compartment effect-site model17 also was tested, in which distribution was assumed to occur from the effect-site compartment to peripheral parts of the brain. The effect-site concentrations were related to BIS by a linear model, a maximum effect (Emax) model, and a sigmoid Emax model, according to the following equation:

where Baseline is the BIS before start of drug administration, Emax is the maximum effect (in this analysis fixed to 1 as very low BIS values were observed), EC50 is the effect-site concentration needed to achieve 50% of the maximum effect, Ce is the concentration at the effect site, and γ is a shape factor. The effects of sex, age, and body weight on EC50 were investigated. The criteria for including a COV in the model were the same as for the PK analysis.

RESULTS

Pharmacokinetics

Observed and predicted arterial and venous concentrations of AZD3043, stratified by the different dose groups, are presented in Figures 2 and 3, respectively. During the infusions, arterial concentrations were greater than venous concentrations, whereas after the end of the infusions, venous concentrations were greater than arterial concentrations (Fig. 4). The unbound fraction of AZD3043 increased with increasing concentrations, from 5% to 12% in the lowest dose group to 18% to 30% in the greatest dose group.

Figure 2
Figure 2:
Individual observed (blue) and typical predicted (red) arterial plasma concentrations of AZD3043 versus time, stratified for different dosing regimen.
Figure 3
Figure 3:
Individual observed (blue) and typical predicted (red) venous plasma concentrations of AZD3043 versus time, stratified for different dosing regimens.
Figure 4
Figure 4:
Arterial (red) and venous (blue) concentrations for a typical individual after a 30-minute infusion of 36 mg/kg/h.

A recirculatory model was used to fit arterial and venous plasma concentrations of AZD3043 simultaneously (Fig. 1). The final model consisted of a series of a 5 tanks-in-series transit of drug from venous plasma, where the drug was administered, to arterial plasma. This represents the central circulation, described by the cardiac output (QCO) and a central apparent volume of distribution (VC). The cardiac output was not measured in the studies but rather estimated as a plasma flow based on the limited sampling of AZD3043. From the arterial plasma, with the volume VA, the drug is transferred to the venous plasma, with the volume VV, through 2 peripheral distribution compartments with the apparent volumes VP1 and VP2 and 1 compartment for the nondistributive transit. The plasma flow through the nondistributive and distribution compartments is represented by QND, QP1, and QP2. In the final model, elimination clearance occurred from the arterial compartment. Plasma clearance was high (2.2 L/min), which was as much as 76% of the estimated cardiac output (plasma flow), with low between-subject variability (14%). The effect of body weight on the parameters was best described by an allometric model. No significant effects (P > 0.1) of dose, esterase activity, sex, or age on clearance were found. The arterial and the peripheral apparent volumes of distribution increased with increasing administered dose. Total apparent volume of distribution (the sum of the distribution volumes in the arterial, venous, central, and peripheral compartments at the end of infusion) was 15 L in the lowest dose group (receiving a total dose of 0.5 mg/kg) and increased with increasing infused dose up to 37 L in the greatest dose group (40.5 mg/kg). Shrinkage was <30% for all PK parameters except VV (31%) and QCO (49%). A high shrinkage implies that there is limited information of a parameter in some individuals, and therefore, their individual estimates of the parameters are shrunk toward the population mean. For example, in the case of oral absorption, a patient with only samples late in the elimination phase will provide no information on the absorption parameters and that patient’s individual predictions of the absorption parameters will be the population mean. The population parameters for absorption may still be well estimated if other individuals provide data on the absorption phase. If shrinkage is high, goodness-of-fit plots using individual predictions may be less useful.18

Table 2
Table 2:
Key Modeling Steps in the Pharmacokinetic and Pharmacodynamic Analysis
Table 3
Table 3:
Parameter Estimates of the Final Recirculatory PK Model for AZD3043
Table 4
Table 4:
Median Prediction Error and Median Absolute Performance Error of Observed Data in Comparison with the Final Pharmacokinetic and Pharmacodynamic Models
Figure 5
Figure 5:
PRED-corrected visual predictive check for the arterial (top row) and venous (bottom row) concentrations versus time based on the final pharmacokinetic model. Solid line is the PRED-corrected observed median, and the dashed lines are the 5th and 95th percentiles of the PRED-corrected observations. The blue- and red-shaded areas represent the 95% confidence interval for the median and 5th and 95th percentiles, respectively, of the simulated data (n = 1000). Left, 30-minute infusions; middle, bolus; right, bolus + 30-minute infusions. As data were PRED corrected, all doses are shown on the same scale, and the PRED-corrected observations are not the same as the uncorrected observations. PRED = population predictions.
Figure 6
Figure 6:
Observed divided by population-predicted concentrations over time. Top row, arterial concentrations; bottom row, venous concentrations; left, 30-minute infusion; middle, bolus; right, bolus + 30-minute infusion.

Key modeling steps are listed in Table 2. The parameter estimates for the final PK model are presented in Table 3. The PC-VPC for the final PK model, stratified for arterial and venous plasma and the 3 different dosing regimens, is shown in Figure 5, and the plots of observed divided by population predicted concentrations over time are shown in Figure 6. MDPE and MDAPE are summarized in Table 4.

Pharmacodynamics

Table 5
Table 5:
Parameter Estimates of the Final Pharmacodynamic Model for AZD3043
Figure 7
Figure 7:
Individual observed (blue) and typical predicted (red) BIS versus time, stratified for different dosing regimens. BIS = bispectral index.
Figure 8
Figure 8:
PRED-corrected visual predictive check for the BIS model. Solid black line is the PRED-corrected median, and the dashed black lines are the 5th and 95th percentiles of the PRED-corrected observations. The blue- and red-shaded areas represent the 95% confidence interval for the median and 5th and 95th percentiles, respectively, of the simulated data (n = 1000). Left, 30-minute infusions; middle, bolus; right, bolus + 30-minute infusions. As data were PRED corrected, all doses are shown on the same scale, and the PRED-corrected observations are not the same as the uncorrected observations. BIS = bispectral index; PRED = population predictions.
Figure 9
Figure 9:
Observed dependent variable (DV) minus population-predicted (PRED) BIS over time. Left, 30-minute infusion; middle, bolus; right, bolus + 30-minute infusion. BIS = bispectral index.

The observations of BIS ranged from 4 to 98. A sigmoid Emax model, where Emax was fixed to 1, i.e., a 100% decrease in BIS from baseline, described the relationship between AZD3043 concentrations and BIS. EC50 was 15.6 µg/mL, with an interindividual variability of 37% (range, 4.1–38.3 µg/mL), and the Hill coefficient (γ) was 1.7. No interindividual variability was estimated in γ. No significant effects (P > 0.1) of body weight, age, or sex on EC50 were found. The rate constant for the effect delay, ke0, was 0.64/min (range, 0.049–10.8), corresponding to a half-life of the effect delay of 1.08 minutes (range, 0.064–14.1). A 2-compartment effect-site model,16 with a distribution compartment off from the effect site, allowing for different onset and offset rates in relation to plasma concentrations, did not significantly improve the fit (P > 0.1). Shrinkage was ≤15% in all PD parameters. The parameter estimates of the final PK-BIS model are presented in Table 5. Key modeling steps are listed in Table 2. Observed and predicted BIS, stratified by dose group, are presented in Figure 7. The PC-VPC for the final PK-BIS model, stratified for the 3 different dosing regimens, is shown in Figure 8. Plots of observed divided by population-predicted concentrations over time are shown in Figure 9. MDPE and MDAPE are summarized in Table 4.

DISCUSSION

A recirculatory model, with 2 peripheral distribution compartments, a 5 tanks-in-series transit from venous to arterial plasma, and 1 transit compartment from arterial to venous plasma, adequately described the PK of AZD3043 in the healthy volunteers in the 2 studies. The elimination of AZD3043 was fast, with an estimated plasma clearance of AZD3043 of 2.2 L/min, which is >75% of the estimated cardiac output (plasma flow) and greater than the anticipated liver blood flow, suggesting esterases not only in the liver but also in blood to be involved in the metabolism. This finding is also supported by in vitro data.3 However, no significant relationship between esterase activity and clearance was found in this analysis, which could have been because of the limited range in esterase activity in this healthy volunteer population, where normal esterase activity was an inclusion criterion for participation in the study. Disease conditions or genetic polymorphism influencing the esterase levels or coadministration of esterase inhibitors might still influence the clearance of AZD3043, and caution should be taken when investigating such patients or patients with unknown esterase activity. The data did not support inclusion of sex or age on clearance, but because the study was not designed to detect such differences and the population was rather small and homogenous, no firm conclusions could be made regarding a broader population. An allometric model was the best descriptor of the relationship of body weight and clearance parameters, whereas those for volumes were directly proportional to weight alone. In this homogenous population, the difference between allometrically scaled parameters and parameters directly proportional to body weight is small, but the allometric model could be an advantage when scaling to children or obese patients. However, further studies in a wider range of body weights are needed to draw any firm conclusions.

The results from this analysis were consistent with the results from noncompartmental analyses performed in the 2 studies. A low but dose-dependent total apparent volume of distribution, ranging from 15 L after the lowest dose to 37 L after the greatest dose in a typical individual was estimated. The reason for dose-dependent distribution is unknown, but because AZD3043 is a lipophilic compound administered as an emulsion, the distribution of the lipids in the emulsion used in the drug formulation could potentially influence the distribution of AZD3043. Similarly, infusion of a lipid emulsion is used as treatment in situations of overdoses of lipophilic local anesthetics19,20 where the local anesthetics distribute to the infused lipids.

Litonius et al.21 showed that plasma concentrations of bupivacaine decreased and volume of distribution increased when patients were treated with an IV lipid emulsion. However, it could be questioned whether such a large increase in apparent volume of distribution could have been because of lipids alone. Another hypothesis is that cardiovascular effects could have influenced the distribution, because tachycardia was seen after greater doses. It is also possible that blood flows to different tissues are altered during anesthesia, which could possibly alter the distribution.22–24 The dose-dependent apparent volume of distribution does not influence the area under the plasma concentration-time curve or the steady-state concentrations, because they are dependent on clearance only, but the half-life is affected, meaning that the greater the dose, the longer time it takes for the concentrations to decrease and for a subject to recover after anesthesia. This could potentially be of importance if the apparent volume of distribution continues to increase after infusions >30 minutes studied. The maximum concentrations after a short infusion are also influenced by the apparent volume of distribution, leading to less than proportional increases in maximum concentrations with increased dose.

The short terminal half-life, because of the high clearance and relatively low apparent volume of distribution, provides potential for a rapid recovery after anesthesia. In a study with similar sampling, propofol was found to have a clearance of 1.6 L/min and a steady-state apparent volume of distribution of 91 L,16 compared with 2.2 L/min and 15 to 37 L for AZD3043. The high ke0 and the limited or rapid distribution within the brain, as suggested by the lack of improvement of fit when applying a 2-compartment effect-site model, are consistent with a rapid onset and offset of anesthesia. However, because of increasing volume of distribution with greater doses, the terminal half-life could potentially be prolonged after long infusions. More studies are needed to investigate whether this could affect the recovery after long-term dosing.

The recirculatory model contains a large number of parameters to obtain a physiologically plausible model. The model has a tendency toward overparametrization, resulting in the minimization easily ending up in local minima or not converging successfully. This is seen in the log-likelihood profiling (Fig. 10), where the profiles are not always smooth parabolas. However, simplifications of the model would have the drawback of losing physiologic interpretation.

Figure 10
Figure 10:
Log-likelihood profiles of the pharmacokinetic parameters. CL = elimination clearance; Dose-VA, Dose-VP1, and Dose-VP2 = effect of accumulated dose on VA, VP1, and VP2, respectively; QCO = cardiac output (plasma flow); QND = plasma flow of nondistributive pathway; QP1 = plasma flow through peripheral compartment 1; QP2 = plasma flow through peripheral compartment 2; VA = arterial volume; VV = venous volume; VC = volume of central circulation; VP1 = volume of peripheral compartment 1; VP2 = volume of peripheral compartment 2.

The large range of ke0 was because of a few outlying subjects with very high or low values. The subjects with the lowest ke0 (longest equilibration time) received low doses with a small effect on BIS, and it is possible that lying still in a quiet room also could have influenced BIS in these subjects. γ was estimated to be 1.7, implying a steeper concentration–effect relationship than for an Emax model. The γ is in line with what was found for propofol in a similar setting.16

The PK and PD properties of AZD3043, with its fast onset and offset of effect, suggest it may be suitable for individual titration, which is commonly used in anesthesia practice. In 2 subjects in the greatest dose group, sudden and transient increases in BIS were seen during the infusion, despite the subjects being deeply anesthetized as judged by the investigators. The reason for this is unknown, but could possibly be because of other electroencephalogram signals that distort the transformation into BIS. One subject receiving the greatest dose had a large outlying decrease in BIS from approximately 15 to 30 minutes after start of infusion. The reason for this is unknown.

In conclusion, a recirculatory model, with 2 peripheral distribution compartments, a 5 tanks-in-series transit delay from venous to arterial plasma, and a single compartment transit delay from arterial to venous plasma, described the PK of AZD3043. Clearance was high and volume of distribution was low, resulting in a short elimination half-life. However, the volume of distribution was increasing with increasing dose. The distribution of the drug to the effect site was rapid, and together with the short half-life, the onset and offset of effect was fast. The effect on BIS was described using a sigmoidal Emax model, with the delay in the effect described by a single-effect compartment.

DISCLOSURES

Name: Marcus A. Björnsson, MSc Pharm, PhD.

Contribution: This author helped design the studies, analyze the data, and write the manuscript.

Attestation: Marcus A. Björnsson has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: Marcus A. Björnsson was employed by AstraZeneca and has equity interest in AstraZeneca.

Name: Åke Norberg, MD, PhD.

Contribution: This author helped design the studies, conduct the studies, analyze the data, and write the manuscript.

Attestation: Åke Norberg has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Conflicts of Interest: Åke Norberg received an honorarium from AstraZeneca 3 years ago for participating in an advisory board on AZD3043.

Name: Sigridur Kalman, MD, PhD.

Contribution: This author helped design the studies, conduct the studies, analyze the data, and write the manuscript.

Attestation: Sigridur Kalman approved the final manuscript.

Conflicts of Interest: Sigridur Kalman received an honorarium from AstraZeneca 3 years ago for participating in an advisory board on AZD3043.

Name: Ulrika S. H. Simonsson, MSc Pharm, PhD.

Contribution: This author helped analyze the data and write the manuscript.

Attestation: Ulrika S. H. Simonsson approved the final manuscript.

Conflicts of Interest: Ulrika S. H. Simonsson has equity interest in AstraZeneca.

This manuscript was handled by: Steven L. Shafer, MD.

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