Pediatric Drug–Drug Interaction Studies: Barriers and Opportunities

Salerno, Sara; Burckart, Gilbert J.; Huang, Shiew Mei; González, Daniel

doi:10.1002/cpt.1234

Cited by 38 publications

(45 citation statements)

References 9 publications

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“…Clinical drug-drug interaction (DDI) data in adults may not be suitable for extrapolation across all paediatric age bands (Salem et al, 2013b;Salerno et al, 2019). The magnitudes of enzyme-based DDI are dictated by the level of contribution (f m ) and maturational rates of corresponding CYP enzymes (Salem et al, 2013a).…”

Section: Discussionmentioning

confidence: 99%

Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics

2020

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Long-term use of imatinib is effective and well-tolerated in children with chronic myeloid leukaemia (CML) yet defining an optimal dosing regimen for imatinib in younger patients is a challenge. The potential interactions between imatinib and coadministered drugs in this "special" population also remains largely unexplored. This study implements a physiologically based pharmacokinetic (PBPK) modeling approach to investigate optimal dosing regimens and potential drug interactions with imatinib in the paediatric population. A PBPK model for imatinib was developed in the Simcyp Simulator (version 17) utilizing in silico, in vitro drug metabolism, and in vivo pharmacokinetic data and verified using an independent set of published clinical pharmacokinetic data. The model was then extrapolated to children and adolescents (aged 2-18 years) by incorporating developmental changes in organ size and maturation of drug-metabolising enzymes and plasma protein responsible for imatinib disposition. The PBPK model described imatinib pharmacokinetics in adult and paediatric populations and predicted drug interaction with carbamazepine, a cytochrome P450 (CYP)3A4 and 2C8 inducer, with a good accuracy (evaluated by visual inspections of the simulation results and predicted pharmacokinetic parameters that were within 1.25-fold of the clinically observed values). The PBPK simulation suggests that the optimal dosing regimen range for imatinib is 230-340 mg/m 2 /d in paediatrics, which is supported by the recommended initial dose for treatment of childhood CML. The simulations also highlighted that children and adults being treated with imatinib have similar vulnerability to CYP modulations. A PBPK model for imatinib was successfully developed with an excellent performance in predicting imatinib pharmacokinetics across age groups. This PBPK model is beneficial to guide optimal dosing regimens for imatinib and predict drug interactions with CYP modulators in the paediatric population.

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Section: Discussionmentioning

confidence: 99%

Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics

2020

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“…The workflow of pediatric PBPK model development has been previously described (see Figure ) . In most cases an adult PBPK model is first established, verified, and refined.…”

Section: Application Of Ontogeny Of Transporters To Midd In Childrenmentioning

confidence: 99%

“…The workflow of pediatric PBPK model development has been previously described (see Figure 2). [52][53][54][55] In most cases an adult PBPK model is first established, verified, and refined. This model is An adult PBPK model was first established and verified by comparing the output from the simulations to that in observed data.…”

Section: Application Of Ontogeny Of Transporters To Midd In Childrenmentioning

confidence: 99%

Incorporating Ontogeny in Physiologically Based Pharmacokinetic Modeling to Improve Pediatric Drug Development: What We Know About Developmental Changes in Membrane Transporters

Cheung

Groen

Burckart

et al. 2019

The Journal of Clinical Pharma

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Developmental changes in the biological processes involved in the disposition of drugs, such as membrane transporter expression and activity, may alter the drug exposure and clearance in pediatric patients. Physiologically based pharmacokinetic (PBPK) models take these age‐dependent changes into account and may be used to predict drug exposure in children. As a result, this mechanistic‐based tool has increasingly been applied to improve pediatric drug development. Under the Prescription Drug User Fee Act VI, the US Food and Drug Administration has committed to facilitate the advancement of PBPK modeling in the drug application review process. Yet, significant knowledge gaps on developmental biology still exist, which must be addressed to increase the confidence of prediction. Recently, more data on ontogeny of transporters have emerged and supplied a missing piece of the puzzle. This article highlights the recent findings on the ontogeny of transporters specifically in the intestine, liver, and kidney. It also provides a case study that illustrates the utility of incorporating this information in predicting drug exposure in children using a PBPK approach. Collaborative work has greatly improved the understanding of the interplay between developmental physiology and drug disposition. Such efforts will continue to be needed to address the remaining knowledge gaps to enhance the application of PBPK modeling in drug development for children.

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“…Like adults, children are also exposed to several drugs that may lead to DDIs; however, the magnitude of the drug interaction ratio (ratio with or without two drugs) may be age-dependent [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Pharmacokinetic DDI studies are conducted in adults during drug development but there are limited studies that have characterized pharmacokinetic DDI studies in pediatrics [3]. Due to the difficulties in conducting a pediatric DDI PK study, a general and logical view may be that drug interaction studies may not be needed in children because one can extrapolate DDI information from adult data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Clearance in Children from Adults Following Drug–Drug Interaction Studies: Application of Age-Dependent Exponent Model

Mahmood

2020

Drugs R D

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Background and Objective Pharmacokinetic drug-drug interaction (DDI) studies are conducted in adult subjects during drug development but there are limited studies that have characterized pharmacokinetic DDI studies in children. The objective of this study was to evaluate if the DDI clearance values from adults can be allometrically extrapolated from adults to children. Methods Fifteen drugs were included in this study and the age of the children ranged from premature neonates to adolescents (30 observations across the age groups). The age-dependent exponent (ADE) model was used to predict the clearance of drugs in children from adults following DDI studies. Results The prediction error of drug clearances following DDIs in children ranged from 4 to 67%. Of 30 observations, 17 (57%) and 27 (90%) observations had a prediction error ≤ 30% and ≤ 50%, respectively. Conclusion This study indicates that it is possible to predict the clearance of drugs with reasonable accuracy in children from adults following DDI studies using an ADE model. The method is simple, robust, and reliable and can replace other complex empirical models.

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Pediatric Drug–Drug Interaction Studies: Barriers and Opportunities

Cited by 38 publications

References 9 publications

Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics

Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics

Incorporating Ontogeny in Physiologically Based Pharmacokinetic Modeling to Improve Pediatric Drug Development: What We Know About Developmental Changes in Membrane Transporters

Prediction of Clearance in Children from Adults Following Drug–Drug Interaction Studies: Application of Age-Dependent Exponent Model

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