Abstract

METHODS

RESULTS

Baseline comparisons and regression model development

As expected, weight, BMI, BMI percentile, and extended BMI were significantly higher among children with vs. without obesity receiving enoxaparin for either treatment or prophylaxis (Table  1 ). Race significantly differed between children with and without obesity receiving enoxaparin for treatment, whereas ethnicity significantly differed across groups in children receiving enoxaparin for prophylaxis. Estimated creatinine clearance via the Bedside Schwartz equation was significantly lower in children with obesity receiving enoxaparin for both treatment and prophylaxis.

There was substantial variability in concentration across dose and time in the RWD (Figure  1 ; Figure  S1 ). Children with obesity had statistically significantly higher absolute doses and anti‐Xa 4‐hour concentrations compared to those without obesity (Table  1 ). For treatment dosing, this observation was maintained even when dose‐normalizing the concentrations, indicating that the statistically significantly higher concentration is attributable to factors beyond only dosing differences between children with and without obesity.

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Figure 1

Box plots of (a, c) observed anti‐Xa concentrations and (b, d) dose‐normalized 4‐hour anti‐Xa concentrations for children with vs. without obesity receiving enoxaparin for (a, b) treatment and (c, d) prophylaxis. Dashed lines represent the target anti‐Xa concentration range for treatment and prophylaxis. The P values for comparing the median concentration in children with vs. without obesity are (a) < 0.001, (b) 0.004, (c) 0.12, and (d) 0.12. Conc, concentration.

The linear mixed‐effects regression model indicated that absolute dose, extended BMI, and the interaction between the two were significantly associated with anti‐Xa 4‐hour concentrations for participants receiving enoxaparin for treatment (Table  2 ; Figure  S2 ) and prophylaxis (Table  S5 ; Figure  S3 ). The negative interaction term between dose and extended BMI suggested that if we compared one participant with a higher extended BMI and one with a lower extended BMI who both received the same dose, the participant with a higher extended BMI had a lower expected 4‐hour concentration. Age was statistically significant in the model for participants receiving enoxaparin for prophylaxis. Race, ethnicity, and estimated creatinine clearance were not statistically significant in either model.

Table 2

Linear mixed‐effects regression model parameters for enoxaparin treatment dosing

Parameter	Estimatea	95% CI
Intercept (IU/mL)	0.64	(0.57, 0.72)*
Absolute dose (mg)	0.08	(0.06, 0.10)*
Extended BMI (%)	0.06	(0.03, 0.07)*
Race – White American	−0.01	(−0.05, 0.03)
Race – Other Classification	−0.01	(−0.06, 0.04)
CLcreatinine (mL/minute/1.73 m2)	−0.01	(−0.03, 0.004)
Absolute dose * Extended BMI	−0.02	(−0.03, −0.01)*

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Parameters for a linear mixed‐effects regression model regressing anti‐Xa 4‐hour concentration onto key variables for children receiving enoxaparin for treatment.

BMI, body mass index; CI, confidence interval; CL_creatinine, creatinine clearance.

^aVariables were centered on the median value and scaled by the standard deviation. A random slope was fitted for each participant and site. Missing CL_creatinine values were imputed using a predictive mean matching multiple imputation method. *Statistically significant at the α = 0.05 level.

Dosing simulations

Simulated children with obesity demonstrated higher median absolute clearance (13.9%, 18.5%, and 14.3% for children 2 to < 6, 6 to < 12, and 12–18 years, respectively) and volume of distribution (19.7%, 23.7%, and 24.4% for children 2 to < 6, 6 to < 12, and 12–18 years, respectively) as compared to children without obesity (Figure  2 ). Conversely, median weight‐normalized clearance and volume of distribution was lower with obesity by a similar magnitude. Increases in absolute clearance attributable to increased kidney volume (and consequently GFR) in children with obesity did not increase to the same degree as body weight; therefore, weight‐normalized clearance decreased, as previously suggested. 7 , 26

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Figure 2

Changes in simulated enoxaparin absolute and weight‐normalized (a, c) CL and (b, d) V _d with obesity status for children ages 2 to < 6 years, 6 to < 12 years, and 12–18 years with and without obesity. Simulated children (n = 1,000 per group) received 1 mg/kg subcutaneous doses twice‐daily of enoxaparin. Boxes represent the median and IQR, and whiskers extend to 1.5 × IQR. CL, clearance; IQR, interquartile range; V _d, volume of distribution.

For total body weight–based dosing, there were observable differences in the simulated 4‐hour concentration, particularly between simulated children with and without obesity (Table  4 ). We observed that simulated children 12–18 years of age demonstrated the highest anti‐Xa 4‐hour concentration increase with obesity, with median 4‐hour concentrations ~ 20% higher in children with vs. without obesity using recommended treatment dosing (1.0 mg/kg). To further explore possible dosing regimens for this age group (12–18 years) using total body weight as the body composition metric, a full range of prophylactic and treatment dosing was simulated (Figure  3 ). From these simulations, simulated children with obesity in this age group required 0.3 mg/kg less dose (administered twice‐daily) than children without obesity to achieve target therapeutic concentrations. Across all dosing regimens, younger children demonstrated lower simulated 4‐hour concentrations compared with older age groups (Figure  3 ; Figures  S6 and S7 ). In contrast, using fat‐free mass rather than total body weight for calculating treatment and prophylaxis doses resulted in simulated anti‐Xa 4‐hour concentration ranges that were very similar across obesity status and age subgroups of simulated children.

Table 4

Summary of PBPK model‐simulated anti‐Xa concentrations

Simulated 4‐hour anti‐Xa concentration (IU/mL) following recommended dosing for treatment (1.0 mg/kg twice‐daily) 5
	2 to < 6 years	6 to < 12 years	12–18 years
TBW‐based dosing
Children without obesity	0.52 (0.39, 0.65)	0.59 (0.46, 0.76)	0.71 (0.55, 0.88)
Children with obesity	0.60 (0.47, 0.75)	0.70 (0.54, 0.88)	0.86 (0.66, 1.08)
FFM‐based dosing
Children without Obesity	0.41 (0.31, 0.52)	0.46 (0.36, 0.58)	0.50 (0.37, 0.62)
Children with obesity	0.40 (0.31, 0.61)	0.48 (0.37, 0.61)	0.56 (0.42, 0.72)

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Simulated 4‐hour anti‐Xa concentration (IU/mL) following recommended dosing for prophylaxis (0.5 mg/kg twice‐daily) 5
	2 to < 6 years	6 < 12 years	12–18 years
TBW‐based dosing
Children without obesity	0.27 (0.20, 0.33)	0.30 (0.24, 0.39)	0.36 (0.28, 0.45)
Children with obesity	0.30 (0.24, 0.45)	0.36 (0.28, 0.45)	0.44 (0.34, 0.56)
FFM‐based dosing
Children without obesity	0.21 (0.16, 0.26)	0.23 (0.18, 0.30)	0.27 (0.20, 0.33)
Children with obesity	0.21 (0.16, 0.27)	0.24 (0.19, 0.31)	0.28 (0.21, 0.37)

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Summary of PBPK model‐simulated anti‐Xa concentrations following twice‐daily recommended dosing for enoxaparin for treatment and prophylaxis. Values shown as median (IQR).

FFM, fat‐free mass; IQR, interquartile range; IU, international unit; PBPK, physiologically‐based pharmacokinetic modeling; TBW, total body weight.

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Figure 3

PBPK model‐simulated anti‐Xa 4‐hour concentrations following twice‐daily subcutaneous dosing of 0.2–1.5 mg/kg using (a, b) TBW or (c, d) FFM for children ages 12–18 years (a, c) without and (b, d) with obesity (n = 1,000 children per group). A full range of dosing was simulated to assess both treatment and prophylaxis target therapeutic concentration ranges. Boxes represent the median and IQR, and whiskers extend to 1.5 × IQR. Red and black dashed lines represent the target ranges for treatment (0.6–1.0 IU/mL) and prophylaxis (0.1–0.3 IU/mL) dosing, respectively. 3 , 4 Similar plots for children 2 to < 6 and 6 to < 12 years are presented in Figures  S6 and S7 . FFM, fat‐free mass; IQR, interquartile range; IU, international unit; PBPK, physiologically‐based pharmacokinetic; TBW, total body weight.

DISCUSSION

This study used RWD from children with and without obesity receiving enoxaparin and found that obesity status was a significant predictor of anti‐Xa 4‐hour concentration. To better characterize this effect mechanistically, we developed a novel enoxaparin PBPK model in adults, scaled and evaluated the model in children, and then used the model to simulate appropriate dosing in children with and without obesity. While the impact of obesity on enoxaparin PK and dosing has been evaluated in adults, there have been limited studies to date in children, and none with data this extensive in the full pediatric age range in children both with and without obesity. 8 , 9 , 21 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 To date, this is the first PBPK model of enoxaparin, and, to our knowledge, the first PBPK model evaluated using EHR data from a national multicenter data repository, which could enable a new data source for this vital modeling tool.

Previous studies evaluating the potentially increased risk of supratherapeutic exposure in patients with obesity are conflicting for both adults and children. Some studies in adults support the recommended weight‐based dosing, suggesting that a minority of adults with obesity receive reduced healthcare provider‐adjusted dosing, and that there is no significant difference in peak concentration or bleeding events in patients with vs. without obesity. 9 , 21 , 30 , 31 , 32 , 33 Conversely, other studies support conservative dosing in adults with obesity, showing that reduced, lean body weight, and fat‐free mass dosing achieved more concentrations within the target range with reduced incidence of bleeding. 8 , 10 , 27 , 34 , 35 , 36 Although there have been limited studies in children with obesity, the results to date are similarly conflicting. One study in 30 adolescents with obesity and overweight found that reduced (< 0.9 mg/kg) and recommended (1.0 mg/kg) dosing achieved equivalent concentrations with no adverse outcomes. 28 Conversely, a study of 60 children 2–18 years of age found that the mean enoxaparin dose following routine therapeutic drug monitoring was 26% lower and concentrations were 21% higher in children with obesity, suggesting a reduced dose is more appropriate for children with obesity. 29 The results presented herein support the previous findings from the latter pediatric study.

Linear mixed‐effects regression modeling highlighted the importance of both dose and obesity status (measured by extended BMI) as significant predictors of anti‐Xa 4‐hour concentration. The observed interaction between dose and obesity status suggests that absolute doses at higher extended BMIs are associated with an increase in the anti‐Xa concentration of a lower magnitude than at lower extended BMIs. This finding makes sense within the context of known PK changes with obesity. Participants with obesity have increased kidney size and increased absolute clearance, meaning they are better able to eliminate enoxaparin renally. 7 The observed absolute creatinine clearance in the data set was ~ 10% higher for children with vs. without obesity (87.4 vs. 76.7 mL/minute for those receiving enoxaparin for treatment). However, because this increase in enoxaparin absolute clearance does not increase proportionally with body size and absolute dose, children with obesity receive higher enoxaparin doses and have higher anti‐Xa concentrations. The regression models demonstrated reduced predictive performance at the upper and lower end of observed anti‐Xa concentrations. This reduced predictive performance occurred at least partially because there were no observed samples from children without obesity at the upper end of the dose range (i.e., > 110 mg) and no children with obesity at the lower end of the absolute dose range (i.e., < 5 mg). These scenarios were not unexpected, since enoxaparin is dosed by total body weight. Therefore, these mixed‐effects model results likely only hold for the middle ~ 80% of the observed data. Other analyses, such as PBPK modeling, are needed to enable an understanding of the interaction between dosing, environmental, and demographic factors, and to extrapolate beyond this range. While a previous mixed‐effects regression analysis has not yet been conducted in children, two analyses of adults with obesity also found that body size (measured as total body weight) was a statistically significant, independent predictor of anti‐Xa 4‐hour concentration. 37 , 38

In developing the novel enoxaparin PBPK model, enoxaparin binding to antithrombin to form the anti‐Xa complex was key to evaluating the model, since enoxaparin is measured via its surrogate anti‐Xa concentration. Limited studies available in the literature reported a K _D of 0.64–177 nM, with studies varying considerably in the molecular weight and affinity of heparin used for analysis. 39 , 40 , 41 , 42 , 43 This is well below enoxaparin concentrations in plasma at therapeutic doses. However, enoxaparin is widely used clinically because it is known to have dose‐linear PK, so this binding process was forced within the linear binding range by using a K_D value of 2.5 μM, just above enoxaparin's therapeutic concentration range. Plasma antithrombin concentration was assumed in excess of this therapeutic concentration to allow for adequate enoxaparin binding, as previously assumed in a coagulation cascade systems model. 44 Additionally, no differences in the absorption rate constant were implemented with obesity. While increased fat mass may decrease the rate at which a subcutaneously dosed drug can reach the bloodstream, no differences in absorption rate were observed when attempting to optimize this parameter in children without vs. with obesity.

Under these assumptions, the enoxaparin PBPK model captured observed adult data and observed pediatric concentrations, with a bias toward underestimation. However, the majority of this underestimation occurred in samples taken after the last dose, as 74.9% of observed pediatric concentrations that were sampled at ~ 4 hours had an AFE of 0.94, with 79.8% of observed concentrations within the 90% model prediction interval. Adult simulated intravenous clearance (~ 24 mL/minute; Table  S7 ) was similar to the FDA label‐reported adult clearance following an intravenous dose (26 mL/minute). 1 Because enoxaparin’s bioavailability in the PBPK model was nearly 100% as reported by adult studies, the simulated subcutaneous adult clearance was ~ 24 mL/minute as well, though this overestimates the FDA label‐reported subcutaneous clearance of 15 mL/minute. 1 , 17 , 22 No reports of pediatric subcutaneous clearance were identified. The pediatric volume of distribution (median 2.8 L) approximated simulated pediatric participants’ plasma volume given age as expected from enoxaparin's binding and mechanism of action. 1 Peak simulated anti‐Xa concentrations were similar to a previous report in children 12–18 years (median 0.77 vs. 0.7 IU/mL) and children ~ 6–18 years (median 0.71 vs. 0.67 IU/mL) with obesity under equivalent dosing. 28 , 29

PBPK‐model simulated lower anti‐Xa 4‐hour concentrations for younger children is in line with prior population PK analyses in children without obesity, noting a trend of decreased weight‐normalized clearance with increasing age, with allometrically scaled weight as a significant covariate. 45 , 46 This makes sense, as younger children have higher weight‐normalized clearance compared with older children. Using total body weight–based dosing, model simulations show ~ 20% higher anti‐Xa exposure for older children with obesity ages 12–18 years, potentially necessitating reduced total body weight–based dosing in older children with obesity as has been previously suggested for treatment and prophylactic dosing. 29 , 47 These results suggest that to effectively select an initial total body weight–based enoxaparin dose for children, both the child's age and body size should be considered. Alternatively, fat‐free mass‐simulated dosing resulted in similar anti‐Xa 4‐hour concentrations across all age and obesity status subgroups. Since fat‐free mass already incorporates age and body size, a fat‐free mass dosing strategy could offer a simplistic and more precise method of dose selection for physicians, provided the fat‐free mass metric can be calculated.

While this study uses extensive EHR data to address a key clinical question in children with obesity, some limitations exist. EHR data served as a valuable, extensive data source, but inherently had a large amount of variability that translated to wide variability in model simulations (due to variability in data recording, assay variability between sites, etc.). There were missing and incomplete data present within the EHR. ICD‐10 codes were used to extract diagnoses, and information on organ dysfunction may be biased, as these codes are intended primarily for billing vs. research purposes. These codes are inconsistently recorded and might be incomplete or inaccurate. Additionally, measurement techniques and accuracy of equipment for measuring weight and height cannot be standardized. The ICD‐10 codes cannot delineate with certainty which participants received enoxaparin for treatment vs. prophylaxis, so this was assumed based on initial dosing. Note that there could be other reasons for a participant to receive a low initial dose of enoxaparin apart from prophylaxis (e.g., high bleeding risk). While the data are extensive, in many cases there was only one anti‐Xa sample available per dose. EHR data collection aimed to exclude children who were critically ill to avoid confounding factors on PK comparison (e.g., children on dialysis or with severe renal and hepatic dysfunction), but observed concentrations were obtained from hospitalized children who may have other disease state factors not accounted for within the PBPK model. The observed concentrations were mostly (74.9%) within 3–5 hours post dosing, and PBPK model predictions may slightly underestimate observed concentrations. Nevertheless, this study sheds further light on the appropriate dosing of enoxaparin, a commonly used drug, in an underserved patient population of children with obesity. This study also demonstrates the utility of EHR data for PBPK model analysis, provided the sample size is large enough to overcome inherent data noise.

In conclusion, extensive RWD were used to demonstrate that obesity is a significant predictor of anti‐Xa 4‐hour concentration in children. A novel PBPK model of enoxaparin was developed to further mechanistically explore the role of obesity on anti‐Xa exposure in children. The model was able to capture observed anti‐Xa concentrations from children with and without obesity. PBPK model simulations suggest higher absolute clearance and volume of distribution in children with vs. without obesity, leading to ~ 20% higher anti‐Xa exposure in children with obesity aged 12–18 years compared with those without obesity using recommended weight‐based dosing. However, using fat‐free mass instead of total body weight under recommended weight‐based dosing resulted in more comparable 4‐hour anti‐Xa exposure across all ages and obesity classes.

FUNDING

This research was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under award 5R01HD096435. The enoxaparin pediatric data were collected by the Pediatric Trials Network (PTN) through NICHD contract HHSN275201000003I (Principal Investigator (PI): Daniel K. Benjamin, Jr.). J.G.G. received research support from a National Institute of General Medical Sciences (NIGMS) funded T32 program (T32GM122741), a Fred Eshelman Pre‐Doctoral Fellowship in Pharmaceutical Sciences from the American Foundation for Pharmaceutical Education (AFPE), and a PharmAlliance travel award. F.O.C. was funded through a UNC/GSK Pharmacokinetics/Pharmacodynamics Post‐Doctoral Fellowship. C.P.H. receives salary support for research from the NICHD (R13HD102136; RL1HD107784), the National Heart Lung and Blood Institute (NHLBI) (R61/R33HL147833), the US Food and Drug Administration (R01‐FD006099, PI Laughon; and U18‐FD006298), the US government for his work in pediatric clinical pharmacology (Government Contract HHSN275201800003I, PI: Benjamin under the Best Pharmaceuticals for Children Act), the non‐profit Burroughs Wellcome Fund, and other sponsors for drug development in adults and children (https://dcri.org/about‐us/conflict‐of‐interest/). D.G. received research support from the NICHD (5R01HD096435‐04 and 1R01HD102949‐01A1). The content is solely the authors' responsibility and does not necessarily represent the official views of the National Institutes of Health.

CONFLICTS OF INTEREST

D.G. receives research support from Nabriva Therapeutics through a contract with the University of North Carolina at Chapel Hill. In addition, D.G. serves as a consultant for Tellus Therapeutics, focusing on neonatal drug development. All other coauthors declared no competing interests for this work.

AUTHOR CONTRIBUTIONS

J.G.G., F.O.C., M.S.L., C.R.L., A.N.E., J.S., K.R.K., C.M.K., C.P.H., and D.G. wrote the manuscript. J.G.G., F.O.C., M.S.L., A.N.E., J.S., and D.G. designed the research. J.G.G., F.O.C., M.S.L., C.R.L., A.N.E., J.S., K.R.K., C.M.K., C.P.H., and D.G. performed the research. J.G.G. and F.O.C. analyzed the data.

Supporting information

ACKNOWLEDGMENTS

Contributor Information

the Best Pharmaceuticals for Children Act – Pediatric Trials Network Steering Committee: