A Preterm Physiologically Based Pharmacokinetic Model. Part I: Physiological Parameters and Model Building

Abduljalil, Khaled; Pan, Xian; Pansari, Amita; Jamei, Masoud; Johnson, Trevor N.

doi:10.1007/s40262-019-00825-6

Cited by 42 publications

(35 citation statements)

References 94 publications

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“…All the ontogeny equations used in the current study were driven by postnatal age. Postmenstrual age is more useful if preterm neonates are included in PBPK simulations (Abduljalil et al, 2019;Germovsek et al, 2019).…”

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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“…Full‐term height and BW relationships were based on our in‐house analysis based on data from UK 1996 growth charts (equations and ). For preterm neonates, equations from Abduljalil et al 30 were used (equations and ).

\begin{matrix} true \\ right H e i g h t & = & left 0.0000176179 \times A g e^{7} - 0.00119874 \times A g e^{6} \\ left + 0.0323848 \times A g e^{5} - 0.444112 \times A g e^{4} \\ left + 3.2946 \times A g e^{3} - 13.2191 \\ left \times A g e^{2} + 33.75 \times A g e + 52.62152 \end{matrix}

\begin{matrix} true \\ right B o d y w e i g h t & = & left 7.826 \times ((), 1.0 - e x p ((), A g e \times - 1.2)) \\ left + e x p ((), ((), H e i g h t \times 0.0209) + ((), 0.023 \times A g e)) \end{matrix}

where Age is in years, BW is in kilograms, and height is in centimeters.…”

Section: Methodsmentioning

confidence: 99%

“…

\begin{matrix} true \\ right H e i g h t & = & left 0.0000176179 \times A g e^{7} - 0.00119874 \times A g e^{6} \\ left + 0.0323848 \times A g e^{5} - 0.444112 \times A g e^{4} \\ left + 3.2946 \times A g e^{3} - 13.2191 \\ left \times A g e^{2} + 33.75 \times A g e + 52.62152 \end{matrix}

\begin{matrix} true \\ right B o d y w e i g h t & = & left 7.826 \times ((), 1.0 - e x p ((), A g e \times - 1.2)) \\ left + e x p ((), ((), H e i g h t \times 0.0209) + ((), 0.023 \times A g e)) \end{matrix}

where Age is in years, BW is in kilograms, and height is in centimeters.

\begin{matrix} true \\ right H e i g h t & = & left - 43.205 \times P M A^{2} \\ left + 111.84 \times P M A - 9.4871 \end{matrix}

W e i g h t = P M A \times {((), 0.0373 \times H e i g h t)}^{2.36}

where postmenstrual age is measured in years 30 …”

Section: Methodsmentioning

confidence: 99%

“…Full-term height and BW relationships were based on our in-house analysis based on data from UK 1996 growth charts (equations 4 and 5). For preterm neonates, equations from Abduljalil et al 30…”

Section: Literature Search and Compilation Of Databasementioning

confidence: 99%

“…where postmenstrual age is measured in years. 30 Where BW and height were calculated from the above equations, an additional fixed CV (coefficient of variation) = 15% for BW and CV = 6% for height based on in-house analysis were applied to these parameters before being used in BSA calculations. In full-term neonates, the BSA calculation was done using Haycock et al 31 for BW below 15 kg and Du Bois and Du Bois 32 for BW over 15 kg, the modified Meban equation was used in preterm neonates.…”

Section: Literature Search and Compilation Of Databasementioning

confidence: 99%

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Does “Birth” as an Event Impact Maturation Trajectory of Renal Clearance via Glomerular Filtration? Reexamining Data in Preterm and Full‐Term Neonates by Avoiding the Creatinine Bias

Salem

Johnson

Hodgkinson

et al. 2020

The Journal of Clinical Pharma

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Glomerular filtration rate (GFR) is an important measure of renal function. Various models for its maturation have recently been compared; however, these have used markers, which are subject to different renal elimination processes. Inulin clearance data (a purer probe of GFR) collected from the literature were used to determine age-related changes in GFR aspects of renal drug excretion in pediatrics. An ontogeny model was derived using a best-fit model with various combinations of covariates such as postnatal age, gestational age at birth, and body weight. The model was applied to the prediction of systemic clearance of amikacin, gentamicin, vancomycin, and gadobutrol. During neonatal life, GFR increased as a function of both gestational age at birth and postnatal age, hence implying an impact of birth and a discrepancy in GFR for neonates with the same postmenstrual age depending on gestational age at birth (ie, neonates who were outside the womb longer had higher GFR, on average). The difference in GFR between pre-term and full-term neonates with the same postmenstrual age was negligible from beyond 1.25 years. Considering both postnatal age and gestational age at birth in GFR ontogeny models is important because postmenstrual age alone ignores the impact of birth. Most GFR models use covariates of body size in addition to age. Therefore, prediction from these models will also depend on the change in anthropometric characteristics with age. The latter may not be similar in various ethnic groups, and this makes the head-to-head comparison of models very challenging.

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Physiologically based pharmacokinetic modelling of acetaminophen in preterm neonates—The impact of metabolising enzyme ontogeny and reduced cardiac output

Olafuyi

Abbasi

Allegaert

2021

Biopharm & Drug Disp

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In preterm neonates, physiologically based pharmacokinetic (PBPK) models are suited for studying the effects of maturational and non-maturational factors on the pharmacokinetics of drugs with complex age-dependent metabolic pathways like acetaminophen (APAP). The aim of this study was to determine the impact of drug metabolising enzymes ontogeny on the pharmacokinetics of APAP in preterm neonates and to study the effect of reduced cardiac output (CO) on its PK using PBPK modelling. A PBPK model for APAP was first developed and validated in adults and then scaled to paediatric age groups to account for the effect of enzyme ontogeny. In preterm neonates, CO was reduced by 10%, 20%, and 30% to determine how this might affect APAP PK in preterm neonates. In all age groups, the predicted concentration-time profiles of APAP were within 5th and 95th percentile of the clinically observed concentration-time profiles and the predicted Cmax and AUC were within 2-folds of the reported parameters in clinical studies. Sulfation accounted for most of APAP metabolism in children, with the highest contribution of 68% in preterm neonates. A reduction in CO by up to 30% did not significantly alter the clearance of APAP in preterm neonates. The model successfully incorporated the ontogeny of drug metabolising enzymes involved in APAP metabolism and adequately predicted the PK of APAP in preterm neonates. A reduction in hepatic perfusion as a result of up to 30% reduction in CO has no effect on the PK of APAP in preterm neonates.

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A Preterm Physiologically Based Pharmacokinetic Model. Part I: Physiological Parameters and Model Building

Cited by 42 publications

References 94 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

Does “Birth” as an Event Impact Maturation Trajectory of Renal Clearance via Glomerular Filtration? Reexamining Data in Preterm and Full‐Term Neonates by Avoiding the Creatinine Bias

Physiologically based pharmacokinetic modelling of acetaminophen in preterm neonates—The impact of metabolising enzyme ontogeny and reduced cardiac output

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