A Retrospective Evaluation of Allometry, Population<scp>Pharmacokinetics,</scp>and<scp>Physiologically‐Based Pharmacokinetics</scp>for Pediatric Dosing Using Clearance as a Surrogate

Wu, Qier; Peters, Sheila Annie

doi:10.1002/psp4.12385

Cited by 29 publications

(39 citation statements)

References 42 publications

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“…Recently, Mahmood and Tegenge [16] noted that the PBPK model was comparable with the ADE model for the prediction of drug clearance in children aged < 2 years. Two other studies [30,31] reached the same conclusions.…”

Section: Discussionsupporting

confidence: 62%

Extrapolation of Drug Clearance in Children ≤ 2 Years of Age from Empirical Models Using Data from Children (> 2 Years) and Adults

Mahmood

2019

Drugs R D

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Background The application of modeling and simulation approaches in clinical pharmacology studies has gained momentum over the last 20 years. Objectives The objective of this study was to develop six empirical models from clearance data obtained from children aged > 2 years and adults to evaluate the suitability of the models to predict drug clearance in children aged ≤ 2 years (preterm, term, and infants). Methods Ten drugs were included in this study and administered intravenously: alfentanil, amikacin, busulfan, cefetamet, meperidine, oxycodone, propofol, sufentanil, theophylline, and tobramycin. These drugs were selected according to the availability of individual subjects' weight, age, and clearance data (concentration-time data for these drugs were not available to the author). The chosen drugs are eliminated by extensive metabolism by either the renal route or both the renal and hepatic routes. The six empirical models were (1) age and body weight-dependent sigmoidal maximum possible effect (E max) maturation model, (2) body weight-dependent sigmoidal E max model, (3) uridine 5′-diphospho [body weight-dependent allometric exponent model (BDE)], (4) age-dependent allometric exponent model (ADE), (5) a semi-physiological model, and (6) an allometric model developed from children aged > 2 years to adults. The model-predicted clearance values were compared with observed clearance values in an individual child. In this analysis, a prediction error of ≤ 50% for mean or individual clearance values was considered acceptable. Results Across all age groups and the ten drugs, data for 282 children were compared between observed and model-predicted clearance values. The validation data consisted of 33 observations (sum of different age groups for ten drugs). Only three of the six models (body weight-dependent sigmoidal E max model, ADE, and semi-physiological model) provided reasonably accurate predictions of clearance (> 80% observation with ≤ 50% prediction error) in children aged ≤ 2 years. In most instances, individual predicted clearance values were erratic (as indicated by % error) and were not in agreement with the observed clearance values. Conclusions The study indicated that simple empirical models can provide more accurate results than complex empirical models.

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Section: Discussionsupporting

confidence: 62%

Extrapolation of Drug Clearance in Children ≤ 2 Years of Age from Empirical Models Using Data from Children (> 2 Years) and Adults

Mahmood

2019

Drugs R D

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“…Immunogenicity is a key determinant of infliximab elimination. Results are conflicting due to inconsistency in ADA assays, although up to 60% of patients 22 Induction with 3 or 6 then 3 or 6 q8w 122 11.2 [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] NA Data presented as mean or median and [range], (interquartile range), or ± SD. "Induction" refers to intensive dosing at weeks 0, 2, and 6 before regular maintenance dosing.…”

Section: Methodsmentioning

confidence: 99%

“…The practice of allometry applies an empiric and inherently simple method to perform body‐weight‐based scaling of adult PK parameters that have been derived from population pharmacokinetic (PopPK) studies (i.e., clearance and volume of distribution) . The accuracies of allometric models have been demonstrated with numerous small molecule drugs for children older than 2 years of age, when distribution and clearance processes have achieved adult performance. Below this threshold, empirical adjustments for maturation and ontogeny are required and the resulting functions are not translatable between molecules .…”

mentioning

confidence: 99%

Physiologically‐Based Pharmacokinetic Modeling vs. Allometric Scaling for the Prediction of Infliximab Pharmacokinetics in Pediatric Patients

Malik

Edginton

2019

CPT Pharmacom & Syst Pharma

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The comparative performances of physiologically‐based pharmacokinetic (PBPK) modeling and allometric scaling for predicting the pharmacokinetics (PKs) of large molecules in pediatrics are unknown. Therefore, both methods were evaluated for accuracy in translating knowledge of infliximab PKs from adults to children. PBPK modeling was performed using the base model for large molecules in PK‐Sim version 7.4 with modifications in Mobi. Eight population PK models from literature were reconstructed and scaled by allometry to pediatrics. Evaluation data included seven pediatric studies (~4–18 years). Both methods performed comparably with 66.7% and 68.6% of model‐predicted concentrations falling within twofold of the observed concentrations for PBPK modeling and allometry, respectively. Considerable variability was noted among the allometric models. Therefore, pediatric clinical trial planning would benefit from using approaches that require predictions depending on the specific question i.e., PBPK modeling and allometry.

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“…Our recent work addresses the comparative performances of both methods for predicting infliximab PK in children between 4 and 18 years of age . In the case of small‐molecule drugs, allometric scaling and PBPK modeling often achieve predictions within 2‐fold prediction error >90% of the time for children over 2 years of age, when maturation and ontogeny do not critically influence drug disposition . Below the age of 2 years there is more uncertainty.…”

mentioning

confidence: 99%

“…7 In the case of small-molecule drugs, allometric scaling and PBPK modeling often achieve predictions within 2-fold prediction error >90% of the time for children over 2 years of age, when maturation and ontogeny do not critically influence drug disposition. 8,9 Below the age of 2 years there is more uncertainty. Allometric scaling with age-dependent exponents can match PBPK modeling in pediatric PK prediction for very young children, but the age-dependent exponents have to be learned with similar compounds as prior information.…”

mentioning

confidence: 99%

Integration of Ontogeny Into a Physiologically Based Pharmacokinetic Model for Monoclonal Antibodies in Premature Infants

Malik

Edginton

2019

The Journal of Clinical Pharma

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An understanding of pediatric pharmacokinetics (PK) is essential for first‐in‐pediatric dose selection and clinical trial design. At present, there is no reliable way to scale the PK of monoclonal antibodies and immunoglobulin G drug products from adults to young children or to premature infants—a vulnerable population with a rapidly growing drug development pipeline. In this work, pediatric physiologically based PK models are constructed in PK‐Sim and Mobi to explore the PK of pagibaximab, palivizumab, MEDI8897, and intravenous immunoglobulin in preterm infants. In addition to considering ontogeny in pediatric organ volumes, organ composition, blood flow rates, and hematocrit, advanced ontogeny is applied for 3 key parameters: capillary surface area, hematopoietic cell concentration, and lymph flow rate. The role and importance of each parameter for determining pediatric clearance (CL) and volume of distribution at steady state (VSS) are quantitatively assessed with a local sensitivity analysis. In addition, the uncertainty around parameters with limited information in pediatrics is addressed (eg, free neonatal Fc receptor concentration). The full ontogeny parameterization yields pediatric PK predictions that are within 1.5‐fold prediction error >90% of the time for preterm infants, with an absolute average fold error of 1.05. This result suggests that many of the key factors related to ontogeny are appropriately addressed. Overall, this study makes a first step toward developing a platform pediatric physiologically based PK model for monoclonal antibodies and immunoglobulin G drug products by solidifying existing parameterizations, integrating new concepts, and drawing attention to unmet needs for physiologic knowledge in children.

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A Retrospective Evaluation of Allometry, PopulationPharmacokinetics,andPhysiologically‐Based Pharmacokineticsfor Pediatric Dosing Using Clearance as a Surrogate

Cited by 29 publications

References 42 publications

Extrapolation of Drug Clearance in Children ≤ 2 Years of Age from Empirical Models Using Data from Children (> 2 Years) and Adults

Extrapolation of Drug Clearance in Children ≤ 2 Years of Age from Empirical Models Using Data from Children (> 2 Years) and Adults

Physiologically‐Based Pharmacokinetic Modeling vs. Allometric Scaling for the Prediction of Infliximab Pharmacokinetics in Pediatric Patients

Integration of Ontogeny Into a Physiologically Based Pharmacokinetic Model for Monoclonal Antibodies in Premature Infants

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