Fig 1 - uploaded by Daniel Gonzalez
Content may be subject to copyright.
Updated growth curves based on National Health and Nutrition Examination Survey (NHANES) pooled data for male and female groups. Key body mass index (BMI) percentiles are highlighted in blue (5th percentile), black (50th percentile), dark red (85th percentile), and red (95th percentile). The BMI cut-off for obesity as defined by the Centers for Disease Control and Prevention (CDC)
Source publication
Background and objective:
While one in five children in the USA are now obese, and more than three-quarters receive at least one drug during childhood, there is limited dosing guidance for this vulnerable patient population. Physiologically based pharmacokinetic modeling can bridge the gap in the understanding of how pharmacokinetics, including dr...
Contexts in source publication
Context 1
... the 2000 CDC growth charts were collected. There is an upward shift in BMI for current children such that a greater number of children are above the CDC-defined obesity cut-off. To create a virtual population of children with obesity that reflects today's higher BMI ranges, the growth curves were updated using more recent data reported in NHANES (Fig. 1). These growth curves were generated using the same lambda-mu-sigma parameter method used for the 2000 CDC growth charts [31]. Separate male and female growth curves were generated for AsianAmerican, Black-American, Mexican-American, and WhiteAmerican children (Fig. 1 of the ESM). BMI-for-age data for all three racial groups included ...
Context 2
... the growth curves were updated using more recent data reported in NHANES (Fig. 1). These growth curves were generated using the same lambda-mu-sigma parameter method used for the 2000 CDC growth charts [31]. Separate male and female growth curves were generated for AsianAmerican, Black-American, Mexican-American, and WhiteAmerican children (Fig. 1 of the ESM). BMI-for-age data for all three racial groups included in the PTN Data Repository were used to validate the growth curves (Fig. 2 of the ESM). Additional details and final lambda-mu-sigma parameter files can be found in Sect. 1.3 of the ...
Context 3
... obesity well in the secondary validation dataset, with 82% of observed concentrations falling within the 90% model prediction interval (18% above), and an AFE of 1.29 ( Fig. 9 of the ESM). Ninety percent of sulfamethoxazole concentrations from children without obesity fell within the 90% model prediction interval (10% above), with an AFE of 1.44 (Fig. 10 of the ESM). Of 50 children with obesity (n = 87 trimethoprim and 89 sulfamethoxazole samples), 75% of observed trimethoprim concentrations fell within the 90% model prediction interval (3% above, 22% below), with an AFE of 1.24 (Figs. 11 and 12 of the ESM). Sulfamethoxazole observed concentrations in children with obesity were ...
Context 4
... from children without obesity fell within the 90% model prediction interval (10% above), with an AFE of 1.44 (Fig. 10 of the ESM). Of 50 children with obesity (n = 87 trimethoprim and 89 sulfamethoxazole samples), 75% of observed trimethoprim concentrations fell within the 90% model prediction interval (3% above, 22% below), with an AFE of 1.24 (Figs. 11 and 12 of the ESM). Sulfamethoxazole observed concentrations in children with obesity were moderately well captured, with 54% falling with the 90% model Table 2 Simulated vs reported glomerular filtration rate (GFR) values for children aged 8-9 years with and without obesity Values reported as mean (standard deviation). The ratio of ...
Context 5
... equation [48,49]. Simulated GFR is estimated based on each virtual subject's kidney size. Further description and equations for each method are provided in Sect. 1 prediction interval (6% above, 40% below) and an AFE of 1.53 (Figs. 12 and 13 of the ESM). No trends were identified that explained model overestimation of observed concentrations (Fig. 12 of the ...
Context 6
... trimethoprim, and sulfamethoxazole weightnormalized clearance decreased with obesity, as increases in absolute clearance attributable to increased kidney volume (thus GFR) in the virtual population of children with obesity did not increase to the same degree as body weight with obesity (Fig. 3, Figs. 14-18 of the ESM). This decreasing trend in weight-normalized clearance was more profound with increasing percent renal elimination. Decreased clearance coupled with higher absolute doses under weight-based dosing resulted in higher AUC ss in virtual children with vs without obesity for all drugs (Figs. 19 and 20 of the ...
Context 7
... as BMI divided by the 95th BMI percentile for a subject's age and sex, where children with an extended BMI percentile ≥ 100% are considered obese. Clearance and volume of distribution were calculated from virtual children aged 12-18 years with and without obesity (similar plots for virtual children aged 2-6 years and 6-12 years are presented in Figs. 14 and 15 of the ESM). Virtual children received single doses of 600 mg intravenous (IV) infusion (30 min) clindamycin (CLIN), 160 mg oral (PO) trimethoprim (TMP), and 800 mg PO sulfamethoxazole (SMX). The shaded regions denote the 90% (95th and 5th percentiles), 80% (90th and 10th percentiles), and 50% (75th and 25th percentiles) prediction ...
Context 8
... one study of caffeine clearance mediated by NAT2 metabolism in children with obesity found that NAT2 activity was five-fold higher in these children compared with normal weight controls [44]. Incorporating this increase in NAT2 clearance into the PBPK model resulted in substantially better model performance (AFE of 0.93 for sulfamethoxazole, Fig. 21 of the ESM), further supporting the theory that NAT2 activity is increased in children with obesity. Further studies of drugs metabolized by NAT2 in patients with obesity should explore this potential increase in clearance. Dosing simulations revealed that all children with obesity aged < 12 years met the efficacy and safety targets ...
Context 9
... the 2000 CDC growth charts were collected. There is an upward shift in BMI for current children such that a greater number of children are above the CDC-defined obesity cut-off. To create a virtual population of children with obesity that reflects today's higher BMI ranges, the growth curves were updated using more recent data reported in NHANES (Fig. 1). These growth curves were generated using the same lambda-mu-sigma parameter method used for the 2000 CDC growth charts [31]. Separate male and female growth curves were generated for AsianAmerican, Black-American, Mexican-American, and WhiteAmerican children (Fig. 1 of the ESM). BMI-for-age data for all three racial groups included ...
Context 10
... the growth curves were updated using more recent data reported in NHANES (Fig. 1). These growth curves were generated using the same lambda-mu-sigma parameter method used for the 2000 CDC growth charts [31]. Separate male and female growth curves were generated for AsianAmerican, Black-American, Mexican-American, and WhiteAmerican children (Fig. 1 of the ESM). BMI-for-age data for all three racial groups included in the PTN Data Repository were used to validate the growth curves (Fig. 2 of the ESM). Additional details and final lambda-mu-sigma parameter files can be found in Sect. 1.3 of the ...
Context 11
... obesity well in the secondary validation dataset, with 82% of observed concentrations falling within the 90% model prediction interval (18% above), and an AFE of 1.29 ( Fig. 9 of the ESM). Ninety percent of sulfamethoxazole concentrations from children without obesity fell within the 90% model prediction interval (10% above), with an AFE of 1.44 (Fig. 10 of the ESM). Of 50 children with obesity (n = 87 trimethoprim and 89 sulfamethoxazole samples), 75% of observed trimethoprim concentrations fell within the 90% model prediction interval (3% above, 22% below), with an AFE of 1.24 (Figs. 11 and 12 of the ESM). Sulfamethoxazole observed concentrations in children with obesity were ...
Context 12
... from children without obesity fell within the 90% model prediction interval (10% above), with an AFE of 1.44 (Fig. 10 of the ESM). Of 50 children with obesity (n = 87 trimethoprim and 89 sulfamethoxazole samples), 75% of observed trimethoprim concentrations fell within the 90% model prediction interval (3% above, 22% below), with an AFE of 1.24 (Figs. 11 and 12 of the ESM). Sulfamethoxazole observed concentrations in children with obesity were moderately well captured, with 54% falling with the 90% model Table 2 Simulated vs reported glomerular filtration rate (GFR) values for children aged 8-9 years with and without obesity Values reported as mean (standard deviation). The ratio of ...
Context 13
... equation [48,49]. Simulated GFR is estimated based on each virtual subject's kidney size. Further description and equations for each method are provided in Sect. 1 prediction interval (6% above, 40% below) and an AFE of 1.53 (Figs. 12 and 13 of the ESM). No trends were identified that explained model overestimation of observed concentrations (Fig. 12 of the ...
Context 14
... trimethoprim, and sulfamethoxazole weightnormalized clearance decreased with obesity, as increases in absolute clearance attributable to increased kidney volume (thus GFR) in the virtual population of children with obesity did not increase to the same degree as body weight with obesity (Fig. 3, Figs. 14-18 of the ESM). This decreasing trend in weight-normalized clearance was more profound with increasing percent renal elimination. Decreased clearance coupled with higher absolute doses under weight-based dosing resulted in higher AUC ss in virtual children with vs without obesity for all drugs (Figs. 19 and 20 of the ...
Context 15
... as BMI divided by the 95th BMI percentile for a subject's age and sex, where children with an extended BMI percentile ≥ 100% are considered obese. Clearance and volume of distribution were calculated from virtual children aged 12-18 years with and without obesity (similar plots for virtual children aged 2-6 years and 6-12 years are presented in Figs. 14 and 15 of the ESM). Virtual children received single doses of 600 mg intravenous (IV) infusion (30 min) clindamycin (CLIN), 160 mg oral (PO) trimethoprim (TMP), and 800 mg PO sulfamethoxazole (SMX). The shaded regions denote the 90% (95th and 5th percentiles), 80% (90th and 10th percentiles), and 50% (75th and 25th percentiles) prediction ...
Context 16
... one study of caffeine clearance mediated by NAT2 metabolism in children with obesity found that NAT2 activity was five-fold higher in these children compared with normal weight controls [44]. Incorporating this increase in NAT2 clearance into the PBPK model resulted in substantially better model performance (AFE of 0.93 for sulfamethoxazole, Fig. 21 of the ESM), further supporting the theory that NAT2 activity is increased in children with obesity. Further studies of drugs metabolized by NAT2 in patients with obesity should explore this potential increase in clearance. Dosing simulations revealed that all children with obesity aged < 12 years met the efficacy and safety targets ...
Similar publications
Most conventional sewage treatment systems are not able to fully remove micropollutants found in sewage. Thus, the simultaneous degradation of 11 pharmaceuticals identified in the effluent of a sewage treatment plant in Southern Brazil was herein investigated through advanced oxidation processes based on ozonation, ultraviolet radiation and hydroge...
Citations
... The pediatric PBPK model was expanded to children and adolescents with obesity using a previously published virtual population of children and adolescents with obesity that incorporated obesity-associated physiological changes, including changes in organ volumes. 25 Then model simulation was compared with plasma pantoprazole concentration-time profiles from 40 children and adolescents with obesity aged 6-17 years enrolled in the PK Study with Pantoprazole in Obese Children and Adolescents (PAN01; NCT02186652). 19,20 This study ...
... 11 Virtual populations of 1000 children and adolescents with obesity (6-11 years and 12-17 years, respectively) for CYP2C19 EM and IM phenotypes were generated by incorporating PK-Sim® predicted ontogeny factor distributions of CYP2C19 and CYP3A4 to a published virtual population of children and adolescents with obesity. 25 To compare the dosing simulation results in the children and adolescents with obesity with their non-obese peers, virtual populations (n = 1500) of children with the same age groups and CYP2C19 EM and IM phenotypes were generated in PK-Sim®. To avoid the inclusion of children with obesity randomly generated in PK-Sim®, 1000 individuals were randomly selected from the 1500 individuals, based on the criteria of a BMI percentile of less than 95th for age ( Figure S2). ...
... We used a previously developed virtual population of children with obesity that incorporated key physiological changes associated with obesity. 25 Furthermore, by accounting for CYP2C19*2 allele-associated decreases in total pantoprazole clearance, we were able to simultaneously assess obesity-and CYP2C19 phenotypeassociated changes in pantoprazole PK. Our PBPK model leveraged a virtual population of children with obesity previously developed by Gerhart et al. 25 This model incorporates updated weight and BMI distributions and provides organ scaling factors to reflect increased organ size and blood flow in children with obesity. ...
Pantoprazole is a proton pump inhibitor indicated for the treatment of gastroesophageal reflux disease, a condition that disproportionately affects children with obesity. Appropriately dosing pantoprazole in children with obesity requires understanding the body size metric that best guides dosing, but pharmacokinetic (PK) trials using traditional techniques are limited by the need for larger sample sizes and frequent blood sampling. Physiologically‐based PK (PBPK) models are an attractive alternative that can account for physiologic‐, genetic‐, and drug‐specific changes without the need for extensive clinical trial data. In this study, we explored the effect of obesity on pantoprazole PK and evaluated label‐suggested dosing in this population. An adult PBPK model for pantoprazole was developed using data from the literature and accounting for genetic variation in CYP2C19. The adult PBPK model was scaled to children without obesity using age‐associated changes in anatomical and physiological parameters. Lastly, the pediatric PBPK model was expanded to children with obesity. Three pantoprazole dosing strategies were evaluated: 1 mg/kg total body weight, 1.2 mg/kg lean body weight, and US Food and Drug Administration‐recommended weight‐tiered dosing. Simulated concentration–time profiles from our model were compared with data from a prospective cohort study (PAN01; NCT02186652). Weight‐tiered dosing resulted in the most (>90%) children with pantoprazole exposures in the reference range, regardless of obesity status or CYP2C19 phenotype, confirming results from previously published population PK models. PBPK models may allow for the efficient study of physiologic and developmental effects of obesity on PK in special populations where clinical trial data may be limited.
... Nonetheless, simulations still supported the recommended weight-based dosing in children with obesity. Last, other PBPK models supported the use of adult metformin doses in older children and adolescents with obesity, offering valuable insights into potential drug labeling for this unique population [78,79]. ...
The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method (‘top-down’ approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters (‘bottom-up approach’), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug–drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.
... There is evidence that children with obesity display obesity-related physiological changes that affect drug disposition, such as increased body size, organ volume and blood flow, and glomerular filtration rate (GFR). 17 Children with obesity may have increased kidney volume, which could lead to increased GFR and a potential increase in absolute clearance. 17 Using a virtual population of children with obesity that accounts for key physiologically-related changes in this patient population with clindamycin and sulfamethoxazole/trimethoprim as model drugs, the authors found children with obesity have decreased weightnormalized clearance of the drug compared to children without obesity. ...
... 17 Children with obesity may have increased kidney volume, which could lead to increased GFR and a potential increase in absolute clearance. 17 Using a virtual population of children with obesity that accounts for key physiologically-related changes in this patient population with clindamycin and sulfamethoxazole/trimethoprim as model drugs, the authors found children with obesity have decreased weightnormalized clearance of the drug compared to children without obesity. 17 Various dosing considerations in children, such as weight-based versus fixed dosing, body size metric, and dose capping (i.e., administering a maximum total dose), may complicate dosing in children with obesity. ...
... 17 Using a virtual population of children with obesity that accounts for key physiologically-related changes in this patient population with clindamycin and sulfamethoxazole/trimethoprim as model drugs, the authors found children with obesity have decreased weightnormalized clearance of the drug compared to children without obesity. 17 Various dosing considerations in children, such as weight-based versus fixed dosing, body size metric, and dose capping (i.e., administering a maximum total dose), may complicate dosing in children with obesity. 18 Weightbased dosing is commonly used, and for many drugs, it is not generally known whether the same FDA-dosing label recommendations can be used for children with obesity. ...
Multiple treatment options exist for children with epilepsy, including surgery, dietary therapies, neurostimulation, and antiseizure medications (ASMs). ASMs are the first line of therapy, and more than 30 ASMs have U.S. Food and Drug Administration (FDA) approval for the treatment of various epilepsy and seizure types in children. Given the extensive FDA approval of ASMs in children, it is crucial to consider how the physiological and developmental changes throughout childhood may impact drug disposition. Various sources of pharmacokinetic (PK) variability from different extrinsic and intrinsic factors such as patients' size, age, drug–drug interactions, and drug formulation could result in suboptimal dosing of ASMs. Barriers exist to conducting clinical pharmacological studies in neonates, infants, and children due to ethical and practical reasons, limiting available data to fully characterize these drugs' disposition and better elucidate sources of PK variability. Modeling and simulation offer ways to circumvent traditional and intensive clinical pharmacology methods to address gaps in epilepsy and seizure management in children. This review discusses various physiological and developmental changes that influence the PK and pharmacodynamic (PD) variability of ASMs in children, and several key ASMs will be discussed in detail. We will also review novel trial designs in younger pediatric populations, highlight the role of extrapolation of efficacy in epilepsy, and the use of physiologically based PK modeling as a tool to investigate sources of PK/PD variability in children. Finally, we will conclude with current challenges and future directions for optimizing the efficacy and safety of these drugs across the pediatric age spectrum.
... those with normal body weights, simulations were performed using the default virtual individuals and populations embedded in the PK-Sim (Ford et al., 2022). For those with obese body weights, the validated virtual population (Gerhart et al., 2022b) accounted for key obesity-related physiological changes relevant to PK (i.e., body weight, body composition, organ size, blood flow and glomerular filtration rate (GFR)), was adopted. The characteristics of the virtual pediatric population used for the model development were shown in Supplementary Table S1 of the Electronic supplementary material (ESM). ...
Tirzepatide is an emerging hypoglycemic agent that has been increasing used in adults, yet its pharmacokinetic (PK) behavior and dosing regimen in pediatric population remain unclear. This study aimed to employ the physiologically based pharmacokinetic (PBPK) model to predict changes of tirzepatide exposure in pediatric population and to provide recommendations for its dose adjustments. A PBPK model of tirzepatide in adults was developed and verified by comparing the simulated plasma exposure with the observed data using PK-Sim&MoBi software. This model was then extrapolated to three specific age subgroups, i.e., children (10–12 years), early adolescents (12–15 years), and adolescents (15–18 years). Each subgroup included healthy and obese population, respectively. All known age-related physiological changes were incorporated into the pediatric model. To identify an appropriate dosing regimen that yielded PK parameters which were comparable to those in adults, the PK parameters for each aforementioned subgroup were predicted at pediatric doses corresponding to 87.5%, 75%, 62.5%, and 50% of the adult reference dose. According to the results of simulation, dose adjustments of tirzepatide are necessary for the individuals aged 10–12 years, as well as those aged 12–15 years with healthy body weights. In conclusion, the adult PBPK model of tirzepatide was successfully developed and validated for the first time, and the extrapolated pediatric model could be used to predict pediatric dosing regimen of tirzepatide, which will provide invaluable references for the design of future clinical trials and its rational use in the pediatric population.
... Physiological models are capable of using existing information about obesity-related physiological changes (e.g., altered organ size, composition, and function), and drug-specific properties (e.g., lipophilicity and elimination pathways) [33]. This type of modelling has been used successfully to investigate clindamycin, trimethoprim/sulfamethoxazole, and metformin to better understand the dosing of these drugs in children with obesity [34,35]. ...
Acetaminophen is a commonly used perioperative analgesic drug in children. The use of a preoperative loading dose achieves a target concentration of 10 mg/L associated with a target analgesic effect that is 2.6 pain units (visual analogue scale 1–10). Postoperative maintenance dosing is used to keep this effect at a steady-state concentration. The loading dose in children is commonly prescribed per kilogram. That dose is consistent with the linear relationship between the volume of distribution and total body weight. Total body weight is made up of both fat and fat-free mass. The fat mass has little influence on the volume of distribution of acetaminophen but fat mass should be considered for maintenance dosing that is determined by clearance. The relationship between the pharmacokinetic parameter, clearance, and size is not linear. A number of size metrics (e.g., fat-free and normal fat mass, ideal body weight and lean body weight) have been proposed to scale clearance and all consequent dosing schedules recognize curvilinear relationships between clearance and size. This relationship can be described using allometric theory. Fat mass also has an indirect influence on clearance that is independent of its effects due to increased body mass. Normal fat mass, used in conjunction with allometry, has proven a useful size metric for acetaminophen; it is calculated using fat-free mass and a fraction (Ffat) of the additional mass contributing to total body weight. However, the Ffat for acetaminophen is large (Ffat = 0.82), pharmacokinetic and pharmacodynamic parameter variability high, and the concentration–response slope gentle at the target concentration. Consequently, total body weight with allometry is acceptable for the calculation of maintenance dose. The dose of acetaminophen is tempered by concerns about adverse effects, notably hepatotoxicity associated with use after 2–3 days at doses greater than 90 mg/kg/day.
... COVID-19, coronavirus disease 2019; P-PBPK, pediatric physiologically-based pharmacokinetic the area of obesity, people preferred to use PK-Sim; however, this does not necessarily mean that the software was only used for the obesity area of applications, but because obesity research is completed by the same team. [57][58][59][60] These findings can be used to improve the design of the current P-PBPK model and highlight the need for childhood obesity dosing research. The COVID-19 infection in children has attracted attention from experts in pharmaceutics since 2020 (Figure 4). ...
The pharmacokinetics (PK) and safety of medications in particular groups can be predicted using the physiologically based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P‐PBPK) modeling software in dose prediction over the past six years and briefly introduces the process of general P‐PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.
... Obesity can affect kidney enzyme functions [20] Clindamycin (lincosamide) 20-40 mg/kg/day in three or four equal doses. Recommended weight-based dosing in children with obesity [21,22]. ...
... According to the model used, these patients experience decreased weight-normalized clearance and volume of distribution of both drugs, so that these patients should require higher absolute doses under recommended pediatric weight-based dosing regimens. These data on one side fill the existing gap for TMP/SMZ [24] and on the other support Clindamycin's current recommended TBW-based dosing [2,21,22,24]. ...
Childhood obesity and its associated comorbidities are highly prevalent diseases that may add to any other possible health problem commonly affecting the pediatric age. Uncertainties may arise concerning drug dosing when children with obesity need pharmacologic therapies. In general, in pediatric practice, there is a tendency to adapt drug doses to a child’s total body weight. However, this method does not consider the pharmacological impact that a specific drug can have under a two-fold point of view, that is, across various age and size groups as well. Moreover, there is a need for a therapeutic approach, as much as possible tailored considering relevant interacting aspects, such as modification in metabolomic profile, drug pharmacokinetics and pharmacodynamics. Taking into account the peculiar differences between children with overweight/obesity and those who are normal weight, the drug dosage in the case of obesity, cannot be empirically determined solely by the per kg criterion. In this narrative review, we examine the pros and cons of several drug dosing methods used when dealing with children who are affected also by obesity, focusing on specific aspects of some of the drugs most frequently prescribed in real-world practice by general pediatricians and pediatric subspecialists.
... The application of PBPK modeling has increased over the past decade to improve the mechanistic understanding of drug PK and support dosing recommendations [55]. PBPK models can also incorporate relevant disease-specific changes in the physiology, allowing the prediction of drug PK under different chronic conditions, as for example renal or hepatic disease, heart failure, or obesity [56][57][58]. The results of Zimmermann et al. strongly support the role of efflux transp the prostatic tissue penetration of LEV [53], but not of CIP [54]. ...
Fluoroquinolones (FQs) are a critical group of antimicrobials prescribed in urological infections as they have a broad antimicrobial spectrum of activity and a favorable tissue penetration at the site of infection. However, their clinical practice is not problem-free of treatment failure, risk of emergence of resistance, and rare but important adverse effects. Due to their critical role in clinical improvement, understanding the dose-response relation is necessary to optimize the effectiveness of FQs therapy, as it is essential to select the right antibiotic at the right dose for the right duration in urological infections. The aim of this study was to review the published literature about inter-individual variability in pharmacological processes that can be responsible for the clinical response after empiric dose for the most commonly prescribed urological FQs: ciprofloxacin, levofloxacin, and moxifloxacin. Interindividual pharmacokinetic (PK) variability, particularly in elimination, may contribute to treatment failure. Clearance related to creatinine clearance should be specifically considered for ciprofloxacin and levofloxacin. Likewise, today, undesired interregional variability in FQs antimicrobial activity against certain microorganisms exists. FQs pharmacology, patient-specific characteristics, and the identity of the local infecting organism are key factors in determining clinical outcomes in FQs use.
... 11 In addition, a recently developed virtual population of children with obesity incorporating key obesity-induced physiological changes (including increased organ size, blood flow, and elimination processes) was used to describe the PKs of trimethoprim/sulfamethoxazole and clindamycin. 12 However, this approach has not been systematically applied to evaluate dosing of other drugs commonly used in children with obesity, including opioid analgesics. ...
... To expand the pediatric PBPK models to include children with obesity, a published virtual population of children with obesity that incorporates known pediatric obesityinduced physiological changes was used. 12 The models were again evaluated using digitized or individual-level concentration data for children with obesity. Last, the final pediatric PBPK models were used to simulate exposure in children with versus without obesity to evaluate dosing regimens in these children under various clinical scenarios. ...
... Virtual children with obesity had increased overall body weight as determined by updated BMI-forage growth curves and increased lean body weight, organ volume, blood flow, and corresponding effects on clearance processes as previously described. 12 Detailed drugspecific information for each model, including model parameter tables, is described in Sections S2 and S3 of the Supporting Information. In addition, project files for the methadone and fentanyl PBPK models are available as Supporting Information. ...
Obesity is an increasingly alarming public health threat, with nearly 20% of children classified as obese in the United States today. Children with obesity are commonly prescribed the opioids fentanyl and methadone, and accurate dosing is critical to reducing the risk of serious adverse events associated with overexposure. However, pharmacokinetic studies in children with obesity are challenging to conduct, so there is limited information to guide fentanyl and methadone dosing in these children. To address this clinical knowledge gap, physiologically‐based pharmacokinetic models of fentanyl and methadone were developed in adults and scaled to children with and without obesity to explore the interplay of obesity, age, and pharmacogenomics. These models included key obesity‐induced changes in physiology and pharmacogenomic effects. Model predictions captured observed concentrations in children with obesity well, with an overall average fold error of 0.72 and 1.08 for fentanyl and methadone, respectively. Model simulations support a reduced fentanyl dose (1 vs. 2 μg/kg/h) starting at an earlier age (6 years) in virtual children with obesity, highlighting the importance of considering both age and obesity status when selecting an infusion rate most likely to achieve steady‐state concentrations within the target range. Methadone dosing simulations highlight the importance of considering genotype in addition to obesity status when possible, as cytochrome P450 (CYP)2B6*6/*6 virtual children with obesity required half the dose to match the exposure of wildtype children without obesity. This physiologically‐based pharmacokinetic modeling approach can be applied to explore dosing of other critical drugs in children with obesity.
... Enoxaparin exhibits significant renal elimination (with ~ 40% of the dose excreted unchanged in urine in adults), and renal clearance is often altered with obesity due to increases in kidney size and function with obesity. 1,6,7 This increase in clearance does not increase proportionally with overall increases in total body size and absolute dose, leading to potentially higher anti-Xa exposure. [8][9][10][11] Additionally, enoxaparin's volume of distribution is approximately equal to 4 L in adults, indicating that its distribution is restricted to the vasculature. ...
... Virtual populations including children with obesity had increased overall body weight as determined by updated BMI-forage growth curves and increased lean body weight, organ volume, blood flow, and corresponding effects on clearance processes as previously described. 7 PBPK model evaluation For each reported adult study, virtual populations of 500 virtual participants were generated based on patient demographics from the respective study to evaluate the adult model. The number of observed concentrations falling within the 90% model prediction interval was calculated, as well as the average fold error (AFE) of the median simulated concentration for all observations using Eq. ...
... First, to evaluate how absolute and weight-normalized clearance and volume of distribution change with increasing extent of obesity, the currently recommended enoxaparin treatment dose of 1 mg/kg total body weight administered twice-daily was evaluated using simulated populations (n = 1,000) of children with and without obesity stratified by ages 2 to < 6 years, 6 to < 12 years, and 12-18 years (Table S4). 7 Next, a range of enoxaparin dosing for prophylaxis (0.2-0.6 mg/kg) and for treatment (0.7-1.5 mg/kg) administered subcutaneously twice-daily was simulated for each group using total body weight and fat-free mass as calculated by Eqs. (6a) and (6b): ...
Dosing guidance for children with obesity is often unknown despite the fact that nearly 20% of United States children are classified as obese. Enoxaparin, a commonly prescribed low‐molecular‐weight heparin, is dosed based on body weight irrespective of obesity status to achieve maximum concentration within a narrow therapeutic or prophylactic target range. However, whether children with and without obesity experience equivalent enoxaparin exposure remains unclear. To address this clinical question, 2,825 anti‐Xa surrogate concentrations were collected from the electronic health records of 596 children, including those with obesity. Using linear mixed‐effects regression models, we observed that 4‐hour anti‐Xa concentrations were statistically significantly different in children with and without obesity, even for children with the same absolute dose (p=0.004). To further mechanistically explore obesity‐associated differences in anti‐Xa concentration, a pediatric physiologically‐based pharmacokinetic (PBPK) model was developed in adults, and then scaled to children with and without obesity. This PBPK model incorporated binding of enoxaparin to antithrombin (AT‐III) to form anti‐Xa and elimination via heparinase‐mediated metabolism and glomerular filtration. Following scaling, the PBPK model predicted real‐world pediatric concentrations well, with an average fold error (standard deviation of the fold error) of 0.82 (0.23) and 0.87 (0.26) in children with and without obesity, respectively. PBPK model simulations revealed that children with obesity have at most 20% higher 4‐hour anti‐Xa concentrations under recommended, total body weight‐based dosing compared to children without obesity owing to reduced weight‐normalized clearance. Enoxaparin exposure was better matched across age groups and obesity status using fat‐free mass weight‐based dosing.
