Application of Physiologically Based Pharmacokinetic Modeling to Characterize the Effects of Age and Obesity on the Disposition of Levetiracetam in the Pediatric Population.

BACKGROUND: Levetiracetam is an antiseizure medication used for several seizure types in adults and children aged 1 month and older; however, due to a lack of data, pharmacokinetic (PK) variability of levetiracetam is not adequately characterized in certain populations, particularly neonates, children younger than 2 years of age, and children older than 2 years of age with obesity. OBJECTIVE: This study aimed to address the gap by leveraging PK data from two prospective standard-of-care pediatric trials (n = 88) covering an age range from 1 month to 19 years, including those with obesity (64%), and applying a physiologically based PK (PBPK) modeling framework. METHODS: A published PBPK model of levetiracetam for children aged 2 years and older was extended to pediatric patients younger than 2 years of age and patients older than 2 years of age with obesity by accounting for the obesity and age-related changes in PK using PK-Sim® software. The prospective pediatric data, along with the literature data for neonates and children younger than 2 years of age, were used to evaluate the extended PBPK models. RESULTS: Overall, 82.4% of data fell within the 90% interval of model-predicted concentrations, with an average fold error within twofold of the accepted criteria. PBPK modeling revealed that children with obesity had lower weight-normalized clearances (0.053 L/h/kg) on average than children without obesity (0.063 L/h/kg). The effect of maturation was well-characterized, resulting in comparable PBPK-simulated, weight-normalized clearances for neonates and children younger than 2 years of age reported from the literature. CONCLUSIONS: PBPK modeling simulations revealed that the current US FDA-labeled pediatric dosing regimen listed in the prescribing information can produce the required exposure of levetiracetam in these target populations with dose adjustments for children with obesity aged 4 years to younger than 16 years.

Population Pharmacokinetics of Posaconazole in Immune-Compromised Children and Assessment of Target Attainment in Invasive Fungal Disease.

BACKGROUND AND OBJECTIVE: Posaconazole (PSZ) is a triazole antifungal for the management of invasive fungal disease (IFD) in adults and children. Although PSZ is available as an intravenous (IV) solution, oral suspension (OS) and delayed-release tablets (DRTs), OS is the preferred formulation for pediatric use because of potential safety concerns associated with an excipient in the IV formulation and difficulty in swallowing intact tablets by children. However, poor biopharmaceutical characteristics of the OS formulation leads to an unpredictable dose-exposure profile of PSZ in children, potentially risking therapeutic failure. The goal of this study was to characterize the population pharmacokinetics (PK) of PSZ in immunocompromised children and assess therapeutic target attainment. METHODS: Serum concentrations of PSZ were collected retrospectively from records of hospitalized patients. A population PK analysis was performed in a nonlinear mixed-effects modeling framework with NONMEM (v7.4). The PK parameters were scaled to body weight, then potential covariate effects were assessed. The final PK model was used to evaluate recommended dosing schemes through simulation of target attainment (as a percentage of the population having steady-state trough concentrations above the recommended target) using Simulx (v2021R1). RESULTS: Repeated measurement data of 202 serum concentrations of total PSZ were acquired from 47 immunocompromised patients between 1 and 21 years of age receiving PSZ either intravenously or orally, or both. A one-compartment PK model with first-order absorption and linear elimination best fit the data. The estimated absolute bioavailability (95% confidence interval) for suspension (Fs) was 16% (8-27%), which was significantly lower than the reported tablet bioavailability (Ft) [67%]. Fs was reduced by 62% and 75% upon concomitant administration with pantoprazole (PAN) and omeprazole (OME), respectively. Famotidine resulted in a reduction of Fs by only 22%. Both fixed dosing and weight-based adaptive dosing provided adequate target attainment when PAN or OME were not coadministered with the suspension. CONCLUSIONS: The results of this study revealed that both fixed and weight-based adaptive dosing schemes can be appropriate for target attainment across all PSZ formulations, including suspension. Additionally, covariate analysis suggests that concomitant proton pump inhibitors should be contraindicated during PSZ suspension dosing.

Use of Real-World Data and Physiologically-Based Pharmacokinetic Modeling to Characterize Enoxaparin Disposition in Children With Obesity.

Dosing guidance for children with obesity is often unknown despite the fact that nearly 20% of US 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-activated factor X (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 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.

Characterizing Pharmacokinetics in Children With Obesity-Physiological, Drug, Patient, and Methodological Considerations.

Childhood obesity is an alarming public health problem. The pediatric obesity rate has quadrupled in the past 30 years, and currently nearly 20% of United States children and 9% of children worldwide are classified as obese. Drug distribution and elimination processes, which determine drug exposure (and thus dosing), can vary significantly between patients with and without obesity. Obesity-related physiological changes, such as increased tissue volume and perfusion, altered blood protein concentrations, and tissue composition can greatly affect a drug's volume of distribution, which might necessitate adjustment in loading doses. Obesity-related changes in the drug eliminating organs, such as altered enzyme activity in the liver and glomerular filtration rate, can affect the rate of drug elimination, which may warrant an adjustment in the maintenance dosing rate. Although weight-based dosing (i.e., in mg/kg) is commonly practiced in pediatrics, choice of the right body size metric (e.g., total body weight, lean body weight, body surface area, etc.) for dosing children with obesity still remains a question. To address this gap, the interplay between obesity-related physiological changes (e.g., altered organ size, composition, and function), and drug-specific properties (e.g., lipophilicity and elimination pathway) needs to be characterized in a quantitative framework. Additionally, methodological considerations, such as adequate sample size and optimal sampling scheme, should also be considered to ensure accurate and precise top-down covariate selection, particularly when designing opportunistic studies in pediatric drug development. Further factors affecting dosing, including existing dosing recommendations, target therapeutic ranges, dose capping, and formulations constraints, are also important to consider when undergoing dose selection for children with obesity. Opportunities to bridge the dosing knowledge gap in children with obesity include modeling and simulating techniques (i.e., population pharmacokinetic and physiologically-based pharmacokinetic [PBPK] modeling), opportunistic clinical data, and real world data. In this review, key considerations related to physiology, drug parameters, patient factors, and methodology that need to be accounted for while studying the influence of obesity on pharmacokinetics in children are highlighted and discussed. Future studies will need to leverage these modeling opportunities to better describe drug exposure in children with obesity as the childhood obesity epidemic continues.

Development and Evaluation of a Virtual Population of Children with Obesity for Physiologically Based Pharmacokinetic Modeling.

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 drug distribution and clearance, changes with obesity by incorporating known obesity-related physiological changes in children. The objective of this study was to develop a virtual population of children with obesity to enable physiologically based pharmacokinetic modeling, then use the novel virtual population in conjunction with previously developed models of clindamycin and trimethoprim/sulfamethoxazole to better understand dosing of these drugs in children with obesity. METHODS: To enable physiologically based pharmacokinetic modeling, a virtual population of children with obesity was developed using national survey, electronic health record, and clinical trial data, as well as data extracted from the literature. The virtual population accounts for key obesity-related changes in physiology relevant to pharmacokinetics, including increased body size, body composition, organ size and blood flow, plasma protein concentrations, and glomerular filtration rate. The virtual population was then used to predict the pharmacokinetics of clindamycin and trimethoprim/sulfamethoxazole in children with obesity using previously developed physiologically based pharmacokinetic models. RESULTS: Model simulations predicted observed concentrations well, with an overall average fold error of 1.09, 1.24, and 1.53 for clindamycin, trimethoprim, and sulfamethoxazole, respectively. Relative to children without obesity, children with obesity experienced decreased clindamycin and trimethoprim/sulfamethoxazole weight-normalized clearance and volume of distribution, and higher absolute doses under recommended pediatric weight-based dosing regimens. CONCLUSIONS: Model simulations support current recommended weight-based dosing in children with obesity for clindamycin and trimethoprim/sulfamethoxazole, as they met target exposure despite these changes in clearance and volume of distribution.

Physiologically-based pharmacokinetic modeling of oxcarbazepine and levetiracetam during adjunctive antiepileptic therapy in children and adolescents.

Oxcarbazepine (OXZ) and levetiracetam (LEV) are two new generation anti-epileptic drugs, often co-administered in children with enzyme-inducing antiepileptic drugs (EIAEDs). The anti-epileptic effect of OXZ and LEV are linked to the exposure of OXZ's active metabolite 10-monohydroxy derivative (MHD) and (the parent) LEV, respectively. However, little is known about the confounding effect of age and EIAEDs on the pharmacokinetics (PKs) of MHD and LEV. To address this knowledge gap, physiologically-based pharmacokinetic (PBPK) modeling was performed in the PK-Sim software using literature data from children greater than or equal to 2 years of age. Age-related changes in clearance (CL) of MHD and LEV were characterized, both in the presence (group 1) and absence (group 2) of concomitant EIAEDs. The drug-drug interaction effect of EIAEDs was estimated as the difference in CL estimates between groups 1 and 2. PBPK modeling suggests that bodyweight normalized CL (ml/min/kg) is higher in younger children than their older counterparts (i.e., due to an influence of age). Concomitant EIAEDs further increase MHD's CL to a fixed extent of 25% at any age, but EIAEDs' effect on LEV's CL increases with age from 20% (at 2 years) to 30% (at adolescence). Simulations with the maximum recommended doses (MRDs) revealed that children between 2 and 4 years and greater than 4 years, who are not on EIAEDs, are at risk of exceeding the reference exposure range for OXZ and LEV, respectively. This analysis demonstrates the use of PBPK modeling in understanding the confounding effect of age and comedications on PKs in children and adolescents.

Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations.

Hospitalized pediatric patients and those with complex or chronic conditions treated on an outpatient basis are commonly prescribed multiple drugs, resulting in increased risk for drug-drug interactions (DDIs). Although dedicated DDI evaluations are routinely performed in healthy adult volunteers during drug development, they are rarely performed in pediatric patients because of ethical, logistical, and methodological challenges. In the absence of pediatric DDI evaluations, adult DDI data are often extrapolated to pediatric patients. However, the magnitude of a DDI in pediatric patients may differ from adults because of age-dependent physiological changes that can impact drug disposition or response and because of other factors related to the drug (eg, dose, formulation) and the patient population (eg, disease state, obesity). Therefore, the DDI magnitude needs to be assessed in children separately from adults, although a lack of clinical DDI data in pediatric populations makes this evaluation challenging. As a result, pediatric DDI assessment relies on the predictive performance of the pharmacometric approaches used, such as population and physiologically based pharmacokinetic modeling. Therefore, careful consideration needs to be given to adequately account for the age-dependent physiological changes in these models to build high confidence for such untested DDI scenarios. This review article summarizes the key considerations related to the drug, patient population, and methodology, and how they can impact DDI evaluation in the pediatric population.

Evaluating Lean Liver Volume as a Potential Scaler for In Vitro-In Vivo Extrapolation of Drug Clearance in Obesity Using the Model Drug Antipyrine.

BACKGROUND: In vitro-in vivo extrapolation (IVIVE) of hepatic drug clearance (CL) involves the scaling of hepatic intrinsic clearance (CLint,uH) by functional liver size, which is approximated by total liver volume (LV) as per the convention. However, in most overweight and obese patients, LV includes abnormal liver fat, which is not thought to contribute to drug elimination, thus overestimating drug CL. Therefore, lean liver volume (LLV) might be a more appropriate scaler of CLint,uH. OBJECTIVE: The objective of this work was to assess the application of LLV in CL extrapolation in overweight and obese patients (BMI >25 kg/m2) using a model drug antipyrine. METHODS: Recently, a model to predict LLV from patient sex, weight, and height was developed and evaluated. In order to assess the LLV model's use in IVIVE, a correlation-based analysis was conducted using antipyrine as an example drug. RESULTS: In the overweight group (BMI >25 kg/m2), LLV could describe 36% of the variation in antipyrine CL (R2 = 0.36), which was >2-fold higher than that was explained by LV (R2 = 0.17). In the normal-weight group (BMI ≤25 kg/m2), the coefficients of determination were 58% (R2 = 0.58) and 43% (R2= 0.43) for LLV and LV, respectively. CONCLUSION: The analysis indicates that LLV is potentially a more appropriate descriptor of functional liver size than LV, particularly in overweight individuals. Therefore, LLV has a potential application in IVIVE of CL in obesity.

An Extension of Janmahasatian's Fat-Free Mass Model for Universal Application Across Populations of Different Ethnicities.

BACKGROUND: Fat-free mass (FFM)-based dose scaling is increasingly being adopted in clinical pharmacology. Given the complexities with the measurement of FFM in clinical practice, choosing an appropriate equation for FFM is critical for accurate dose scaling. Janmahasatian's FFM model (FFMJan) has largely remained the preferred choice because of its mechanistic basis and good predictive properties. This model was, however, developed from a largely European cohort and has been shown to give biased predictions of FFM in Indian people. OBJECTIVE: The objective of this work was to derive an extended version of the FFMJan model (FFMExt) that accounts for the variation in body composition due to ethnicity, and to demonstrate its application by developing an extended FFM model in an Indian population (FFMExt,Ind). METHODS: The fundamental assumption of FFMJan model development was a linear relationship between bioimpedance and body mass index. In this extension to Janmahasatian's work, this assumption was extended to allow for potential non-linear relationships. While the original ZJan model parameters were kept fixed, a set of body composition-related parameters [Formula: see text] were incorporated, where [Formula: see text] and [Formula: see text] were the ethnicity factors to the intercept and the linear coefficient, respectively, and [Formula: see text] a non-linear exponent. The model was then applied to data arising from a south Indian population and the [Formula: see text] parameters were estimated by standard non-linear regression. The data were generated from a reference model for FFM for the Indian population, which was known to provide unbiased estimates for this population. RESULTS: The parameter estimates (%RSE) of the final FFMExt,Ind model were [Formula: see text] (fixed), [Formula: see text] (3.2%) for male patients, 0.70 (3.3%) for female patients, and [Formula: see text] (12.4%). The final model predictions were in good agreement with the reference model predictions. CONCLUSIONS: An FFMExt model has been achieved by extending the original FFMJan model assumptions to account for inter-ethnic differences in body composition. The extended model can be applied to any ethnic population by estimating a set of body composition-related parameters [Formula: see text]. This can be performed using bioimpedance data without the need for formal FFM measurements.

Evaluating the Relationship Between Lean Liver Volume and Fat-Free Mass.

BACKGROUND: Fat-free mass has gained wide acceptance as a scaler of the maintenance dose rate in obese patients. The choice of fat-free mass as a size scaler for the maintenance dose rate is based on its relationship with drug clearance, on the basis that only lean tissue is sufficiently metabolically active to provide capacity for elimination. For xenobiotics, the majority of biotransformation occurs in the liver and hence fat-free mass is implied to scale linearly with the component of liver that is metabolically active. The liver, like the body, can be assumed to comprise two components, lean mass and fat mass. We expect the lean liver mass (or volume) to be the component that most closely relates to drug clearance. OBJECTIVE: The objective of this study was to investigate the relationship of lean liver volume and fat-free mass. METHODS: Total liver volume and liver fat volume were measured in 100 Indian adults by computed tomography. Lean liver volume was derived as the difference between the two measurements (as liver volume - liver fat volume). Covariate modelling to describe lean liver volume, using NONMEM version 7.3, involved testing the influence of body weight, sex, body surface area and fat-free mass with or without allometric scaling (by estimating the exponent) and the influence of clinical chemistry variables. RESULTS: The final model did not exclude a linear relationship between lean liver volume and fat-free mass, while allometric scaling by body weight0.75 was also supported by the data. While scaling by fat-free mass, the coefficient of proportionality (i.e. lean liver volume per kg fat-free mass) was higher in female (31.25 mL) than male (25.81 mL) subjects. CONCLUSIONS: A model to predict lean liver volume from readily available patient data was developed and evaluated. Fat-free mass plus sex was found to be the best body descriptor to scale lean liver volume. The utility of this model in scaling drug clearance and dose requirements of hepatically cleared drugs needs further exploration.

Design, Synthesis, and Pharmacokinetic Evaluation of Phosphate and Amino Acid Ester Prodrugs for Improving the Oral Bioavailability of the HIV-1 Protease Inhibitor Atazanavir.

Phosphate and amino acid prodrugs of the HIV-1 protease inhibitor (PI) atazanavir (1) were prepared and evaluated to address solubility and absorption limitations. While the phosphate prodrug failed to release 1 in rats, the introduction of a methylene spacer facilitated prodrug activation, but parent exposure was lower than that following direct administration of 1. Val amino acid and Val-Val dipeptides imparted low plasma exposure of the parent, although the exposure of the prodrugs was high, reflecting good absorption. Screening of additional amino acids resulted in the identification of an l-Phe ester that offered an improved exposure of 1 and reduced levels of the circulating prodrug. Further molecular editing focusing on the linker design culminated in the discovery of the self-immolative l-Phe-Sar dipeptide derivative 74 that gave four-fold improved AUC and eight-fold higher Ctrough values of 1 compared with oral administration of the drug itself, demonstrating a successful prodrug approach to the oral delivery of 1.

Choosing the Allometric Exponent in Covariate Model Building.

BACKGROUND: Allometric scaling is often used to describe the covariate model linking total body weight (WT) to clearance (CL); however, there is no consensus on how to select its value. OBJECTIVES: The aims of this study were to assess the influence of between-subject variability (BSV) and study design on (1) the power to correctly select the exponent from a priori choices, and (2) the power to obtain unbiased exponent estimates. METHODS: The influence of WT distribution range (randomly sampled from the Third National Health and Nutrition Examination Survey, 1988-1994 [NHANES III] database), sample size (N = 10, 20, 50, 100, 200, 500, 1000 subjects), and BSV on CL (low 20%, normal 40%, high 60%) were assessed using stochastic simulation estimation. A priori exponent values used for the simulations were 0.67, 0.75, and 1, respectively. RESULTS: For normal to high BSV drugs, it is almost impossible to correctly select the exponent from an a priori set of exponents, i.e. 1 vs. 0.75, 1 vs. 0.67, or 0.75 vs. 0.67 in regular studies involving < 200 adult participants. On the other hand, such regular study designs are sufficient to appropriately estimate the exponent. However, regular studies with < 100 patients risk potential bias in estimating the exponent. CONCLUSION: Those study designs with limited sample size and narrow range of WT (e.g. < 100 adult participants) potentially risk either selection of a false value or yielding a biased estimate of the allometric exponent; however, such bias is only relevant in cases of extrapolating the value of CL outside the studied population, e.g. analysis of a study of adults that is used to extrapolate to children.

A Review of the Methods and Associated Mathematical Models Used in the Measurement of Fat-Free Mass.

Fat-free mass (FFM) represents the lean component of the body devoid of fat. It has been shown to be a useful predictor of drug dose requirements, particularly in obesity where the excess fat mass does not contribute to drug clearance. However, measuring FFM involves complex and/or expensive experimental methodologies that preclude their use in routine clinical practice. Thus, models to predict FFM from readily measurable variables, such as body weight and height, have been developed and are used in both population pharmacokinetic modelling and clinical practice. In this review, methods used to measure FFM are explained and compared in terms of their assumptions, precision, and limitations. These methods are broadly classified into six different principles: densitometry, hydrometry, bioimpedance, whole-body counting, dual energy X-ray absorptiometry, and medical imaging. They vary in their processes and key biological assumptions that are often not applicable in certain populations (e.g. children, elderly, and certain disease states). This review provides a summary of the various methods of FFM measurement and estimation, and links these methods to a scientific framework to help clinicians and researchers understand the usefulness and potential limitations of these methods.

Absorption and cleavage of enalapril, a carboxyl ester prodrug, in the rat intestine: in vitro, in situ intestinal perfusion and portal vein cannulation models.

In recent years prodrug strategy has been used extensively to improve the pharmacokinetic properties of compounds exhibiting poor bioavailability. Mechanistic understanding of the absorption and the role of intestine and liver in the activation of oral prodrugs is crucial. Enalapril, a carboxyl ester prodrug, is reported to be metabolized by human carboxylesterase-1 (CES1) but not by carboxylesterase-2 (CES2) to its active metabolite enalaprilat. Further, it has been reported that the small intestines of both rat and human contain mainly CES2. The objective of this work was to understand whether enalapril remains unchanged as it is absorbed through the intestine into the portal circulation. This was evaluated using different intestinal preparations, an in situ intestinal perfusion experiment and a portal vein cannulated rat model. No turnover of enalapril was seen with commercial rat intestinal S9 and microsomes, but reasonable turnover was observed with freshly prepared rat intestinal and mucosal homogenate and S9. In the intestinal perfusion study, both enalapril and enalaprilat were observed in the mesenteric plasma with the data suggesting 32% hydrolysis of enalapril in the intestine. In the portal vein cannulated rat, about 51% of enalapril absorbed into intestine was converted to enalaprilat. Overall, it was demonstrated that even though enalapril has been shown to be a specific substrate for CES1, it is converted to enalaprilat to a significant extent in the intestine. Such experimental techniques can be applied by other scientific groups who are working on prodrugs to determine the region and extent of activation. Copyright © 2015 John Wiley & Sons, Ltd.