Objective
The goals of the study were to estimate the pharmacokinetic parameters of standard dose betamethasone in a large obstetrics population and evaluate the effect of maternal body size and multiple gestation on the pharmacokinetic parameters and their observed variability.
Study Design
This was a prospective pharmacokinetic study. Liquid chromatography mass spectrometry was used to measure betamethasone plasma concentrations. Pharmacokinetic parameters and significant clinical covariates were estimated with mixed effect modeling. Bootstrap analysis confirmed validity of the model.
Results
Two hundred seventy-four blood samples from 77 patients were obtained. The greatest effect on pharmacokinetic variability was observed with maternal lean body weight (LBW). The relationship between the pharmacokinetic parameters and LBW remained linear over a wide range of maternal body sizes. Multiple gestations did not affect the pharmacokinetic parameters.
Conclusion
Individualization of betamethasone dosing by maternal LBW reduces variability in drug exposure. Mutiple gestations do not require betamethasone dosing adjustment, because pharmacokinetics are the same as singleton gestations.
Pregnancy is characterized by many physiologic changes that may alter the disposition of drugs and the pregnant woman’s response. To date pharmacokinetics during pregnancy are known only for a few drugs.
Antenatal steroid therapy has been demonstrated to improve neonatal outcomes in premature neonates. This treatment decreases the risk of respiratory distress syndrome, reduces cerebroventricular hemorrhage, and improves overall neonatal survival. In multiple gestations, there may be a less beneficial effect of antenatal corticosteroid in reducing respiratory distress syndrome and death. Ballabh et al reported a more rapid elimination half-life of betamethasone in twin, compared with singleton, pregnancies and believe a decrease in betamethasone exposure may explain the reduced effectiveness in twins. However, the lack of difference in this medication’s clearance and volume of distribution conflicts with this interpretation. Few data exist about whether betamethasone that is given to obese women has the same beneficial neonatal effects as in normal-weight women. Recently, a retrospective investigation of >1000 pregnant women who were receiving antenatal steroid treatment and stratified by body mass index (BMI) found no difference among groups in composite neonatal morbidity and mortality rates. Similar results were noted by another investigator.
Aims of this study were to (1) examine the pharmacokinetics of standard dose intramuscular betamethasone in women between 24 and 34 weeks of gestation, (2) determine whether body size indicators influence the variability in betamethasone volume of distribution and clearance, and (3) determine whether multiple gestations affect these pharmacokinetic parameters.
Materials and Methods
Patients
A prospective population pharmacokinetic study was conducted from March, 2008 to November, 2008 at the University of Illinois at Chicago. All pregnant women, between 24 and 34 weeks’ gestation, >18 years old, and clinically eligible for an inpatient antenatal corticosteroid treatment course were approached for enrollment. Participating subjects received the standard regimen of betamethasone (equal amounts of betamethasone sodium phosphate and betamethasone acetate at a dose of 12 mg every 24 hours for a total of 2 injections intramuscularly). A blood sample of 8 mL was collected from participants within each of the 5 sampling windows: 5-20 minutes, 1-3 hours, 5-8 hours, and 22-24 hours after the first intramuscular dose and 2-6 hours after the second intramuscular dose. Sampling windows were constructed from the D-optimal sample times that were determined with the ADAPT II software (Biomedical Simulations Resource, Los Angeles, CA). Enrollment occurred 7 days a week, 24 hours daily. Participation in the study was limited to the inpatient population to ensure compliance with blood draws. Fewer than 5 patients refused to participate in the study; the main reason for refusal was discomfort with the research-related venous punctures. The study was approved by the University of Illinois at Chicago Institutional Review Board, and written informed consent was obtained before any study-related interventions.
Demographic and clinical characteristics were recorded for each participant along with betamethasone dosing and sampling times. Blood samples were collected in evacuated tubes and were centrifuged; the plasma was separated. To minimize hydrolysis of the esters, the plasma was transferred immediately to tubes that contained 100 mmol/L sodium arsenate and potassium fluoride and was stored at –20°C. Of note, common definitions used in pharmacology language and utilized in this work are summarized in Appendix I for readers’ convenience.
Betamethasone assay
Betamethasone plasma concentrations were measured with a validated liquid chromatography–tandem mass spectrometry assay, which had been adapted from previously published procedures. Quality control data for the assay is described in Appendix II .
Pharmacokinetic analysis
The pharmacokinetic methods are summarized below and described in greater detail in Appendix II . Population pharmacokinetic analysis of betamethasone plasma concentrations was performed by nonlinear mixed effect modeling with the use of the first-order conditional estimation method of the NONMEM software (version VI; Icon Development Solutions, Ellicott City, MD). Several alternative pharmacokinetic models were evaluated to describe the disposition of intramuscular betamethasone. Model selection was guided by visual inspection of diagnostic plots, standard error of the parameter estimates, and minimum value of the objective function (OFV).
Covariate analysis
Bayesian estimates of the pharmacokinetic parameters for individual patients were obtained from the pharmacokinetic model without covariates (base model), then graphic methods were used to screen for potential relationships between covariates and pharmacokinetic parameters, with the use of the software S-Plus (version 6.1; Insightful Corporation, Seattle, WA). Variables that were evaluated were total body weight (TBW), lean body weight (LBW), body surface area (BSA), BMI, gestational age (calculated by last menstrual period, if available and confirmed by first- or second-trimester ultrasound scans), race, age, twin or multiple gestation, concurrent liver or kidney disease, and presence of preeclampsia. The LBW was calculated as described by Janmahasatian et al :
L B W ( k g ) = 9270 × T B W ( k g ) 8780 + 244 × B M I ( k g / m 2 )
Validation of the model
The validity of the final population pharmacokinetic model was evaluated by bootstrap analysis. Resampling with replacement from the original dataset was used to construct 1000 bootstrap datasets. Each dataset was fit to the final population model.
Power analysis
To determine the power of the design that was implemented in this study for the identification of important differences in the pharmacokinetic parameters between women with singleton and multifetal pregnancies, a simulation approach was used. These simulations used a modified form of the final population model that involved the addition of a covariate in the expression for the betamethasone apparent clearance (CL/F). This covariate represented a proportional increase (from 0-40% in increments of 5%) in the apparent clearance in women with multifetal pregnancies. For each incremental increase, 200 replicate datasets were simulated and fit to the modified model. The percentage of the 200 replications at each incremental increase that showed a significantly higher apparent clearance in multifetal pregnancies represented the power of the study to detect such a difference.
Simulation of betamethasone plasma concentrations
The effect on betamethasone systemic exposure of different body size–adjusted dosing schemes was examined by simulation. Using the final population model, betamethasone plasma concentrations from time 0-24 hours after the dose were simulated for 77 patients for each of the following doses: 12 mg (standard), 12 mg per 45 kg LBW (LBW-adjusted dose), and 12 mg per 70 kg TBW (TBW-adjusted dose). Covariate values from the pharmacokinetic dataset were reproduced in the simulation dataset. At each dose, 1000 replicates of the dataset were simulated. The area under betamethasone plasma concentrations time curve from time 0 to infinity (AUC) was calculated at each simulated plasma concentration profile. The findings were examined graphically by the construction of box-plots of the AUCs, categorized by dosage regimen and BMI (<25 kg/m 2 , 25-30 kg/m 2 , 31- 40 kg/m 2 , >40 kg/m 2 ).
Results
Eighty-four pregnant women were enrolled in the study. However, 7 patients had no evaluable betamethasone plasma concentrations and were not included in the population analysis. Isolated plasma samples from other patients were excluded from the dataset for the following reasons: 13 samples were outside the quantifiable limit of the assay; 3 samples had incomplete labeling, and 2 samples were frozen before the red cells and plasma were separated. Thus, the final pharmacokinetic dataset consisted of 77 pregnant women who contributed 274 plasma samples for analysis. Of the 274 plasma samples, 233 samples (3.5 samples per patient) were from a singleton pregnancy, and 51 samples (3.9 per patient) were from multifetal pregnancies. The demographic and clinical characteristics of subjects are summarized in Table 1 . Sixty-four women were carrying singleton pregnancies; 12 women were carrying twin pregnancies, and 1 woman was carrying a set of triplets. Gestational age ranged from 21-34 weeks. One patient received steroids at 21 weeks’ gestation because of an error in the determination of her gestational age at time of admission.
| Characteristic | Measurement |
|---|---|
| Patients, n | 77 |
| Blood samples, n | 274 |
| Age, y a | 27 (16–45) |
| Gestational age, wk a | 30 (21–34) |
| Total body weight, kg a | 85 (36–159) |
| Lean body weight, kg a | 48 (26–68) |
| Body mass index, kg/m 2 a | 30 (16–53) |
| Distribution, % | |
| <25 kg/m 2 | 18 |
| 25-30 kg/m 2 | 32 |
| 31-39 kg/m 2 | 36 |
| ≥40 kg/m 2 | 13 |
| Race/ethnicity, n | |
| Black | 64 |
| Hispanic | 22 |
| White | 14 |
| Fetuses, n | |
| Singleton | 64 |
| Multiple | 13 |
| Preterm premature rupture of membranes/preterm labor/cervical insufficiency, % | 65 |
| Preeclampsia, % | 28 |
| Nonreassuring fetal well-being, % | <1 |
| Other, % | 6 |
A 2-compartment model with first order of absorption and no lag time fit the betamethasone plasma concentration profile well. The pharmacokinetic parameters that were estimated included absorption rate constant, apparent distribution clearance (Q/F), CL/F, apparent volume of distribution of the central compartment, and volume of distribution at steady state (Vss/F). Data that supported the structural and covariate model selection are provided in Appendix III . Table 2 lists the pharmacokinetic parameters, covariate coefficients, interindividual variability and residual variability for the base, and final population pharmacokinetic models with their relative standard errors. The models included estimates for the interindividual variability for CL/F, Q/F, Vss/F, and absorption rate constant along with a covariance term between CL/F and Vss/F.
| Parameter | Relative standard error | Bootstrap median (2.5th-97.5th percentile) | |
|---|---|---|---|
| Base mode estimate, n (%) | Final model estimate, n (%) | ||
| Fixed effects | |||
| Typical value of the absorption rate constant (h –1 ) | 3.1 (12.8) | 3.0 (16.8) | 2.8 (1.4-4.2) |
| Typical value of apparent distribution clearance (L/h) | 2700 (65.2) | 2480 (63.7) | 2425 (473-3960) |
| Typical value of apparent clearance (L/h) | 17.6 (4.3) | — | — |
| Typical value of apparent clearance in a 45 kg lean body weight pregnant woman: lean body weight of effect on apparent clearance (L/h/45 kg) | — | 17.2 (4.0) | 17.2 (15.8-18.6) |
| Typical value of apparent volume of distribution of the central compartment (L) | 48.5 (17.4) | 43.7 (21.6) | 34.9 (1.2-59.3) |
| Typical value of apparent volume of distribution at steady-state (L) | 205 (7.4) | — | — |
| Typical value of apparent volume of distribution at steady-state in a 45 kg lean body weight woman: lean body weight of effect on apparent volume of distribution at steady-state (L/45 kg) | — | 166 (13.5) | 167 (102-215) |
| Covariate for effect of gestational age on typical value of apparent volume of distribution at steady-state (L/45 kg) | — | 121 (37.6) | 114 (37-196) |
| Interindividual variability | |||
| Typical value of the absorption rate constant (h -1 ) | 1.3 (49.3) | 1.3 (55.3) | 1.2 (0.3-2.2) |
| Typical value of apparent distribution clearance: coefficient of variation (%) | 271 (43.0) | 221 (42.9) | 200 (110-330) |
| Typical value of apparent clearance: coefficient of variation (%) | 35.1 (17.0) | 31.8 (20.0) | 30.9 (25.2-37.0) |
| Typical value of apparent volume of distribution at steady-state: coefficient of variation (%) | 27.0 (17.0) | 19.1 (37.1) | 16.8 (10.1-22.7) |
| Covariance apparent clearance, apparent volume of distribution at steady-state: coefficient of variation (%) | 26.0 (20.6) | 19.3 (27.0) | 22.1 (7.2-37.0) |
| Residual variability: coefficient of variation (%) | 17.5 (20.7) | 17.6 (20.4) | 18.1 (14.6-21.8) |
The covariate analysis identified LBW for CL/F and gestational age and LBW for Vss/F as factors that significantly explained their variability. The influence of LBW on the interindividual variability of CL/F was small, with variability reduced by 10.4%. On the other hand, LBW and gestational age explained nearly 40% of the interindividual variability on Vss/F. No factors that significantly influenced interindividual variability in Q/F or absorption rate constant were identified. The final covariate models are provided in Appendix III .
Other descriptors of body size (TBW, BSA, and BMI), when individually input into the covariate models for CL/F and Vss/F, produced significant decreases in the OFV. However, the decrease in OFV was less than with LBW, and their addition to covariate models that contained LBW during the forward stepwise process produced no further improvement in the model fitting. They therefore were not retained in the final covariate models. Interestingly, in contrast to the linear relationship between LBW and CL/F or Vss/F, a power function better described the relationship of TBW with CL/F or Vss/F. The differences in the form of the relationships with TBW, compared with LBW, are apparent in Figure 1 . As can be seen in Figure 1 , the individual Bayesian estimates of CL/F and Vss/F that were normalized by LBW remained constant over the range of observed body sizes, represented by BMI. On the other hand, TBW normalized CL/F and Vss decrease with increasing BMI. The final population pharmacokinetic model was validated from 1000 bootstrap replicates. The bootstrapped medians for the fixed and random effect parameters are provided in Table 2 . The mean estimates for the parameters from the final model were within 15% of the bootstrapped medians, which supported the stability of the population model and accuracy of the parameter estimates. Additionally, the small relative standard errors and narrow bootstrapped 95% confidence intervals ( Table 2 ) confirm the precision of the population parameters. Multiples gestation was not identified by the population analysis as significantly influencing the pharmacokinetics of betamethasone in pregnant women. Box plots of the individual CL/F, Vss/F, and elimination half-life grouped by number of offspring (single vs twin or triplet) are shown in Figure 2 . No statistically significant differences ( P > .1, Student t test) were observed for parameters between single and multiple gestations. The power for detection of a statistically significant difference ( P < .01) in CL/F between singleton and multifetal pregnancies was 65% when CL/F was assigned a value 25% higher in women with twin or triplet pregnancies, 78% when CL/F was 30% higher in women with multifetal pregnancies, and 86% when CL/F was 35% higher in women with multifetal pregnancies.
Figure 3 summarizes the betamethasone AUCs from the simulated datasets. The 12 mg (standard), 12 mg per 45 kg LBW (LBW-adjusted), and 12 mg per 70 kg TBW (TBW-adjusted) doses produced comparable median betamethasone AUCs ( Figure 3 , A). This finding reflects the similarity between median LBW and TBW in this study and typical values in women of this age. Variability in betamethasone AUC is less with the LBW-adjusted dose. The reason for the differing variability among dose groups is illustrated by the box plots in Figure 3 , B and D. For the LBW-adjusted dose, median betamethasone AUC is equivalent among the 4 BMI groups ( Figure 3 , C). On the other hand, the median AUC for the other 2 dose groups varies with BMI ( Figure 3 , B and D). For example, because BMI increases from <25 to > 40 kg/m 2 , median AUC with the administration of the standard dose decreases by approximately 35% ( Figure 3 , B) and, with administration of TBW-adjusted dose, increases by approximately 30% ( Figure 3 , D).
Comment
A 2-compartment model best described the disposition of betamethasone after intramuscular administration in pregnant women at 21-34 weeks’ gestation. The CL/F and Vss/F for the typical woman in the study of 30 weeks’ gestation and 48 kg LBW were 18.4 L/hr and 202 L, respectively. These values are comparable with estimates that derived from the study reported by Peterson et al after intramuscular administration of the same formulation to pregnant women. Conversely, the betamethasone CL/F and distribution volume found by Ballabh et al in pregnant women after an intramuscular injection of the same formulation were lower. Potential factors that could explain the discrepancy among Ballabh et al, our investigation, and Peterson et al include their use of an immunoassay vs a chemical assay by Peterson et al and us for measuring betamethasone plasma concentrations, the inability of their analysis to characterize the absorption and distribution phases of the betamethasone plasma concentration-time curves, and their failure to stabilize the betamethasone esters in the plasma samples before storage. Any of these factors may contribute to an artifactual elevation of AUC and, as a result, may explain the lower CL/F and volume of distribution described by Ballabh et al.
The betamethasone CL/F and Vss/F in our study are higher than observed in studies of nonpregnant women. Even after adjusting for a bioavailability of approximately 70% for the betamethasone phosphate/acetate suspension in pregnant women, our CL/F and Vss/F remain approximately 1.2- to 1.6-fold higher than values reported in nonpregnant women. Betamethasone is a low extraction ratio drug that was almost entirely eliminated by hepatic metabolism. Factors that explain a higher CL/F therefore include a decrease in the extent of absorption, a reduction in plasma protein binding, or enhanced hepatic metabolism. Observations of the same relative difference as we observed in CL/F and Vss/F between pregnant and nonpregnant women after intravenous administration argues against a change in extent of absorption. An alteration in plasma protein binding is also an unlikely explanation because the fraction of betamethasone that is bound to plasma proteins is equivalent in pregnant and nonpregnant women. Consequently, enhanced hepatic metabolism represents the most probable explanation for a pregnancy-related increase in CL/F. Although pathways that mediate the hepatic metabolism of betamethasone are not established, studies with other corticosteroids suggest a primary role for the cytochrome P450 isoenzyme 3A4.
Pregnancy-related increases in clearances of other 3A4 substrates have been observed. An increase in tissue binding of betamethasone provides the most apt explanation for the higher Vss/F in pregnancy. Among the demographic and clinical variables that were evaluated, the covariates that affected betamethasone CL/F and Vss/F the most were descriptors of body size, which included LBW, TBW, BSA, and BMI. Scaling of CL/F and Vss/F by LBW produced the greatest reduction in the OFV and interindividual variability. After LBW was incorporated into the population of pharmacokinetic models for CL/F and Vss/F, the addition of other body size measures yielded no further improvement in the model fitting. The form of the relationship with betamethasone CL/F or Vss/F also differed between LBW and TBW and other body size indicators. The CL/F and Vss/F increased linearly with increasing LBW, but nonlinearly with increasing TBW. Accordingly, betamethasone CL/F that was scaled to LBW remained constant and allowed drug exposure to be satisfactorily estimated over a wide range of body compositions. On the other hand, CL/F that was scaled to TBW varies with changing body size and, when directly extrapolated from normal to obese individuals, underpredicts drug exposure in subjects with large BMIs. Figure 3 , B-D, illustrates how these relationships impact the administration of betamethasone doses. Compared with the use of a standard (unadjusted) or TBW-adjusted dose, a milligram per kilogram–LBW approach for dosing betamethasone offers the advantage of yielding consistent plasma concentrations across individuals of varying body compositions, which is represented by BMI.
Doubts about the clinical benefit of adjusting betamethasone doses for LBW were raised by recent retrospective analyses that failed to show any relationship between neonatal outcomes and maternal body size after standard courses of antenatal betamethasone. However, the assertion of Jobe and Soll that standard regimens deliver too high a dose suggests an alternative reason for the lack of differences in response between body composition groups, namely that everyone received a supratherapeutic dose of betamethasone. The standard betamethasone regimen for treatment of prematurity derives from a 1995 National Institutes of Health Consensus Panel, with the dose, timing, and frequency selected from empiric rather than scientifically derived evidence. Support for a lower dose is provided by a recent comparison of 4 different betamethasone regimens for the induction of fetal lung maturation in sheep. Induction of fetal lung maturation was comparable among the 4 treatments, including a single dose regimen of intramuscular betamethasone acetate. Notably, the single-dose betamethasone acetate regimen produced no detectable betamethasone plasma concentrations in 2 of 3 fetuses and maternal betamethasone plasma concentrations were approximately one-tenth of those seen after the standard regimen. The results of Jobe and Soll suggest the potential for unnecessary drug exposure during pregnancy with the current dosing regimen and emphasize the need to better understand the pharmacodynamic properties of betamethasone in the mother and fetus, particularly with respect to lung maturation and adverse effects on fetal growth. Whether individualized doses by LBW offer any clinical advantages is uncertain. Once the range of effective betamethasone doses and plasma concentrations are identified, studies such as ours provide a framework for individualization of doses.
Studies that involve other drugs substantiate the superiority of LBW compared with TBW for describing the effect of size on drug clearance across a broad range of body compositions. From a biologic perspective, these findings suggest that LBW more directly mirrors the functional capacity of the liver than TBW. In contrast to our results, other studies generally have found TBW to be the best body size descriptor for volume of distribution of lipophilic drugs like betamethasone. We are uncertain of the reason for this discrepancy, but the lack of pregnant subjects in the other studies may offer an explanation. Finally, although we demonstrated that LBW was the best body size indicator among those evaluated in the current study, none of the body size indicators, including LBW, were developed specifically for pregnancy. Development and evaluation of pregnancy-specific measures of body composition are indicated.
Gestational age also significantly contributed to variability in Vss/F, with Vss/F increasing as a power function of gestational age. The time profile of this change and relatively minor impact on Vss/F, approximately 18% from 24-34 weeks’ gestation, suggest pregnancy-induced increases in extracellular fluid as a possible factor.
Higher order of pregnancy was not found to alter the maternal pharmacokinetics of betamethasone. The study had sufficient power to detect at least a 30% difference in CL/F. The only other study to compare pharmacokinetics in singleton and twin pregnancies found a significantly faster elimination half-life in women with twin pregnancies. Comparable with our findings, neither clearance nor volume of distribution significantly differed for the 2 groups. The more rapid elimination was interpreted by the investigators as having the potential to lower betamethasone plasma concentrations and thus to explain the poorer response to antenatal betamethasone in twin pregnancies. This interpretation ignores that clearance and volume of distribution, not elimination half-life, are the primary determinants of the plasma concentration-time profile. Future investigations should focus on alternative explanations for decreased efficacy of betamethasone in twin pregnancies and should include reduced fetal bioavailability because of enhanced activity of placental drug metabolizing enzymes, efflux transporter, or altered pharmacologic responsiveness to betamethasone in fetuses of twin or triplet pregnancies. Also, more pharmacodynamic studies of betamethasone that is given antenatally should focus on the identification of effective betamethasone maternal doses and relative therapeutic plasma concentrations in mothers and fetuses before individualization of dosing by LBW can be used clinically.
In summary, we estimated pharmacokinetic parameters for betamethasone for women between 21 and 34 weeks’ gestation at standard dosages. We demonstrated that, across a wide range of maternal body size, individualization of betamethasone dosage by LBW may be preferable to limit the risk of overdosing slim mothers and underdosing mothers with larger body size. However, therapeutic levels must be identified, and we must gain a better understanding of its fetal and maternal pharmacodynamics. Because we could not demonstrate any difference in maternal pharmacokinetics with multifetal pregnancies, further work should focus on alternative reasons for that betamethasone does not provide the same beneficial effects in babies who are born of multiples gestation, compared with their singleton counterparts.
Acknowledgments
We thank all the individuals who contributed to the data collection without whom the project could not have been completed: Maria Colon, MD; Cecilia Gambala, MD; Dennie Rogers, MD, and all the Obstetrics and Gynecology residents and Labor and Delivery/Mother Baby staff.
Appendix I
| Term | Definition |
|---|---|
| Pharmacokinetics | Time course of drug absorption, distribution, metabolism, and excretion in the body |
| Clinical pharmacokinetics | Application of pharmacokinetics principles to safe and effective therapeutic management of drugs in an individual |
| Pharmacodynamics | Relationship between drug concentration at the site of action and resultant effect; includes time course and intensity of therapeutic and adverse effects |
| One-compartment model | All body tissue and fluid are considered a unit; based on the assumption that, after 1 dose of a drug is administered, it distributes instantaneously to all body areas |
| Two-compartment model | At least 2 different units: a central compartment, with rapid drug distribution, usually the bloodstream and highly perfuse organs and a peripheral compartment, with slow distribution; model implies the drug moves back and forth between the 2 compartments to remain in equilibrium |
| First-order elimination | Amount of drug that is eliminated over a period of time is directly proportional to the amount of drug in the body; the total amount of drug eliminated over a set time period changes, but the fraction of the drug that is eliminated over the given time remains constant |
| Bioavailability | Fraction of a given drug dose that reaches the systemic circulation |
| Body size indicators | Total body weight (kilograms) |
| Body surface area (square meters): calculated surface of a human body (for many clinical purposes body surface area is a better indicator of metabolic mass than body weight because it is less affected by abnormal adipose mass) | |
| Body mass index (kilograms/square meter): weight (kilograms)/height (square meters) | |
| Lean body weight (kilograms) includes the weight of muscles, bones, tendons, ligaments, and water in the body; everything except fat tissue | |
| Clearance | Rate of drug removal from the plasma, expressed as volume of plasma per given unit of time |
| Volume of distribution | Extent of drug distribution into body fluids and tissues minus volume required to account for all of the drug in the body, if the concentration in all tissues is the same as plasma concentration |
| Volume of distribution at steady state | Relates total amount of drug in the body to a particular plasma concentration under steady state conditions |
| Intercompartmental clearance | Rate of drug moving back and forth between the central compartment and the tissues and fluid of the peripheral compartment, to maintain a status of pseudoequilibrium |
| Area under the plasma concentration vs time curve | Area formed under the curve when plasma concentration is plotted vs time; expressed also as total drug exposure: clearance = dose/area under the plasma concentration |
Appendix II
Betamethasone assay
Betamethasone plasma concentrations were measured with validated liquid chromatography-tandem mass spectrometry assay that was adapted from previously published procedures. The lower limit of quantitation was 1.05 ng/mL. Samples with concentrations above the upper limit of quantitation,105 ng/mL, were analyzed after being diluted 1:4 with blank plasma. The between-run precision, based on the relative standard deviation of replicate quality control (n = 7) was 1.3% at 402 ng/mL, 4.7% at 80.5 ng/mL, 4.2% at 40.2 n/mL, 5.5% at 4.02 ng/mL, and 2.9% at 1.05 ng/mL. The assay was specific for betamethasone, with resolution of betamethasone base from the acetate and phosphate esters.
Explanation of equations utilized for pharmacokinetic modeling
Betamethasone plasma concentrations were analyzed with NONMEM software (version VI, level 1.0; Globomax LLC, Hanover, MD). Model building first focused on identification of an appropriate structural (base) model, that consisted of a pharmacokinetic compartmental model and expressions for describing the inter- and intraindividual (residual) variabilities. The best model was selected based on (1) goodness of fit plots (such as those that were observed vs the model-predicted population betamethasone plasma concentrations and weighted residuals vs predicted plasma concentrations; (2) precision of the parameter estimates, as indicated by the relative standard error from the model fitting; (3) minimum OFV (OFV; ie, the minimization criteria in NONMEM), and (4) physiologic relevance of the parameter estimates. The difference in the OFV between competing models is approximately χ 2 -distributed with the degrees of freedom equal to the difference in the number of parameters between the models.
One- and two-compartment models with first order absorption and elimination were evaluated to describe the disposition of betamethasone and provide estimates of the fixed effect pharmacokinetic parameters. Models were tested with and without an absorption lag time. The first-order conditional estimation method was selected for fitting the pharmacokinetic data.
A log normal distribution was assumed for the pharmacokinetic parameters; variability among individuals (ie, interindividual variability) for the pharmacokinetic parameters was described with the use of exponential random effects. This relationship is illustrated for clearance (CL) by the following equation:
CL/F i = TVCL × e i
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