The Influence of Haemostatic System Maturation on the Dose–Response Relationship of Unfractionated Heparin

Derbalah, Abdallah; Duffull, Stephen B.; Moynihan, Katie; Al‐Sallami, Hesham S.

doi:10.1007/s40262-020-00949-0

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…The results from the study are consistent with a previously published population PK/PD model in pediatrics indicating that body weight was the only significant covariate for clearance 27 . However, a recent report showed that including maturation function and body weight on clearance improved model fitting, compared with a model omitting these factors 33 . But, it was not clear if the improvement was solely the result of including body weight or arose from the addition of maturation function as well as body weight.…”

Section: Discussionsupporting

confidence: 89%

“…27 However, a recent report showed that including maturation function and body weight on clearance improved model fitting, compared with a model omitting these factors. 33 But, it was not clear if the improvement was solely the result of including body weight or arose from the addition of maturation function as well as body weight. The current analysis demonstrated that the addition of random betweenoccasion variability led to significant improvement in PK model fitting.…”

Section: Discussionmentioning

confidence: 99%

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Reassessing the Pediatric Dosing Recommendations for Unfractionated Heparin Using Real‐World Data: A Pharmacokinetic–Pharmacodynamic Modeling Approach

Salem

Niu

Li³

et al. 2022

The Journal of Clinical Pharma

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Optimal pediatric dosing of unfractionated heparin (UFH) is challenging because of the paucity of clinical outcome and pharmacokineticpharmacodynamic (PK/PD) studies in pediatrics. This study aimed to: (i) develop a PK/PD model for UFH, quantified by anti-factor Xa assay, and the UFH effect, measured by activated partial thromboplastin time (aPTT); and (ii) use simulations to evaluate pediatric UFH infusions for achieving the anti-factor Xa (0.3-0.7 IU/mL) therapeutic target. Electronic health record data were retrospectively collected from 633 patients aged <19 years admitted to Texas Children's Hospital. The PK/PD model was developed using a 70% (training)/30% (testing) split-sample approach. A 1-compartment PK model with linear elimination adequately described the UFH PK. An allometrically scaled body weight on clearance (CL) and volume of distribution (Vd) with an age-dependent maturation function of extracellular water on Vd were the covariates identified. Comparable with literature, the typical values for CL and Vd were 3.28 L/(h•50 kg) and 8.83 L/50 kg, respectively. A linear model adequately described the UFH-aPTT relationship with an estimated slope of 150 seconds/(IU/mL). Simulations of the currently recommended starting infusions (28 IU/h/kg for pediatrics <1 year old or 20 IU/h/kg for pediatrics >1 year old) showed that the anti-factor Xa therapeutic target was achieved only in 15.3%, 14.6%, 36.9%, and 45.11% of subjects in the age groups of <1 year, 1-6 years, 6-12 years, and 12-19 years, respectively. In conclusion, the UFH anti-factor Xa target is not achieved initially, especially in young pediatrics, suggesting the need to optimize UFH dosing to achieve higher therapeutic success.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Reassessing the Pediatric Dosing Recommendations for Unfractionated Heparin Using Real‐World Data: A Pharmacokinetic–Pharmacodynamic Modeling Approach

Salem

Niu

Li³

et al. 2022

The Journal of Clinical Pharma

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“…The results match with the physicochemical properties of UFH, which is considered a hydrophilic drug, hence distributing only in the intravascular space indicating the 1‐compartment PK model is a reasonable structural PK model for UFH 24 . Additionally, the results are consistent with the previous PK modeling efforts on UFH indicating that a 1‐compartment PK model adequately describes the UFH concentration–time profile 25‐29 . Adding a time‐varying component to CL significantly improved the PK model in terms of the statistical criteria and diagnostic plots.…”

Section: Discussionsupporting

confidence: 82%

“…20 The estimated typical steady-state CL value was consistent between ECMO and non-ECMO settings. 25,26,29,30 However, the consistency between CL under ECMO and non-ECMO conditions contradicts with a prior ECMO study in infants (n = 5), where UFH CL was approximately doubled during the ECMO session compared to after the decannulation procedure. 12 Considering the physicochemical properties, heparin is a hydrophilic drug that reduces the susceptibility of sequestration by ECMO circuits.…”

Section: Discussionmentioning

confidence: 68%

“…24 Additionally, the results are consistent with the previous PK modeling efforts on UFH indicating that a 1-compartment PK model adequately describes the UFH concentration-time profile. [25][26][27][28][29] Adding a time-varying component to CL significantly improved the PK model in terms of the statistical criteria and diagnostic plots. The time-varying CL can be considered biologically plausible given that the pediatric patients with severe respiratory and/or cardiac dysfunction require ECMO for oxygenation and circulation support while allowing the heart and lungs to rest and recover.…”

Section: Discussionmentioning

confidence: 99%

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Pharmacokinetic Modeling Using Real‐World Data to Optimize Unfractionated Heparin Dosing in Pediatric Patients on Extracorporeal Membrane Oxygenation and Evaluate Target Achievement–Clinical Outcomes Relationship

Salem

Smith

Wilkes

et al. 2023

The Journal of Clinical Pharma

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Unfractionated heparin (UFH) is a commonly used anticoagulant for pediatric patients undergoing extracorporeal membrane oxygenation (ECMO) but evidence is lacking on the ideal dosing. We aimed to: (i) develop a population pharmacokinetic (PK) model for UFH, measured through anti‐factor Xa assay, (ii) optimize UFH starting infusions and dose titrations through simulations, and (iii) explore UFH exposure‐clinical outcomes relationship. Data from 218 patients admitted to Utah's Primary Children's Hospital were retrospectively collected. A one‐compartment PK model with time‐varying clearance (CL) adequately described UFH PK. Weight on CL and volume of distribution and, ECMO circuit change on CL were significant covariates. The typical estimates for initial CL and first‐order rate constant to reach steady‐state CL were 0.57 L/(h·10 kg) and 0.02 h−1. Comparable to non‐ECMO patients, the typical steady‐state CL was 0.81 L/(h·10 kg). Simulations showed that a 75 IU/kg UFH bolus dose followed by starting infusions of 25 & 20 IU/h/kg for patients < 6 & ≥ 6 years, respectively, achieved the therapeutic target in 56.6% of all patients while only 3.1% exceeded the target. The proposed UFH titration schemes achieved the target in > 90% of patients while < 0.63% were above the target after 24 and 48 hours of treatment. The median intensive care unit survival time in patients within and below the target at 24 hours was 136 and 66 hours, respectively. In conclusion, PK model of UFH was developed for pediatric patients on ECMO. The proposed UFH dosing scheme attained the anti‐factor Xa target rapidly and safely.This article is protected by copyright. All rights reserved

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Reduction of quantitative systems pharmacology models using artificial neural networks

Derbalah

Al‐Sallami

Duffull

2021

J Pharmacokinet Pharmacodyn

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cuyas enseñanzas de vida me han motivado para persistir. IX ResumenFarmacocinética poblacional en el manejo empírico de infecciones en pacientes con neoplasias hematológicas y neutropenia febril pos-quimioterapia Los pacientes con neoplasias hematológicas y neutropenia febril postquimioterapia podrían presentar alteraciones fisiológicas que lleven a cambios en la farmacocinética (PK) de los fármacos comparado a individuos sin cáncer o neutropenia. Estos cambios, en la PK de antibióticos, podrían resultar en fallos terapéuticos y esto a su vez en hospitalizaciones prolongadas, empeoramiento en la severidad de la infección e inclusive en la muerte. En este estudio, se desarrollaron modelos de PK poblacional para cefepime (FEP) y vancomicina (VAN) en el tratamiento empírico de infecciones en pacientes con neutropenia post-quimioterapia. La farmacocinética de FEP fue descrita por un modelo de dos compartimentos con aclaramiento dependiente del nivel de creatinina sérica (S CR ), variabilidad interindividual en todos los parámetros y variabilidad residual con una función aditiva. Por otra parte, la farmacocinética de VAN fue descrita con un modelo de dos compartimentos con aclaramiento dependiente del aclaramiento renal de creatinina (ClCr), variabilidad interindividual en todos los parámetros, correlación entre los parámetros V 1 y V 2 y una variabilidad residual con funciones aditivas dependientes del método de determinación de VAN. Mediante simulaciones de Monte Carlo se encontró que para FEP, el alcance de los objetivos PK/PD (60 % f T >MIC y 100 % f T >MIC ) es muy dependiente de la duración de infusión, así como del efecto de S CR . Para VAN se encuentra que el alcance del indicador AUC/MIC ≥ 400 se ve afectado por cambios en la dosis diaria total y la prolongación de la duración de infusión no afecta el PTA. Se realizó una comparación entre objetivos dependientes de AUC vs C min , y se encuentró que el último no es un predictor adecuado del primero.

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The Influence of Haemostatic System Maturation on the Dose–Response Relationship of Unfractionated Heparin

Cited by 6 publications

References 20 publications

Reassessing the Pediatric Dosing Recommendations for Unfractionated Heparin Using Real‐World Data: A Pharmacokinetic–Pharmacodynamic Modeling Approach

Reassessing the Pediatric Dosing Recommendations for Unfractionated Heparin Using Real‐World Data: A Pharmacokinetic–Pharmacodynamic Modeling Approach

Pharmacokinetic Modeling Using Real‐World Data to Optimize Unfractionated Heparin Dosing in Pediatric Patients on Extracorporeal Membrane Oxygenation and Evaluate Target Achievement–Clinical Outcomes Relationship

Reduction of quantitative systems pharmacology models using artificial neural networks

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