A physiologically-based pharmacokinetic modeling approach for dosing amiodarone in children on ECMO.
Venkata Kashyap YellepeddiJohn Porter HuntDanielle J GreenAutumn M McKniteAviva J WhelanKevin WattPublished in: CPT: pharmacometrics & systems pharmacology (2024)
Extracorporeal membrane oxygenation (ECMO) is a cardiopulmonary bypass device commonly used to treat cardiac arrest in children. The American Heart Association guidelines for cardiopulmonary resuscitation (CPR) and emergency cardiovascular care recommend using amiodarone as a first-line agent to treat ventricular arrhythmias in children with cardiac arrest. However, there are no dosing recommendations for amiodarone to treat ventricular arrhythmias in pediatric patients on ECMO. Amiodarone has a high propensity for adsorption to the ECMO components due to its physicochemical properties leading to altered pharmacokinetics (PK) in ECMO patients. The change in amiodarone PK due to interaction with ECMO components may result in a difference in optimal dosing in patients on ECMO when compared with non-ECMO patients. To address this clinical knowledge gap, a physiologically-based pharmacokinetic model of amiodarone was developed in adults and scaled to children, followed by the addition of an ECMO compartment. The pediatric model included ontogeny functions of cytochrome P450 (CYP450) enzyme maturation across various age groups. The ECMO compartment was parameterized using the adsorption data of amiodarone obtained from ex vivo studies. Model predictions captured observed concentrations of amiodarone in pediatric patients with ECMO well with an average fold error between 0.5 and 2. Model simulations support an amiodarone intravenous (i.v) bolus dose of 22 mg/kg (neonates), 13 mg/kg (infants), 8 mg/kg (children), and 6 mg/kg (adolescents). This PBPK modeling approach can be applied to explore the dosing of other drugs used in children on ECMO.
Keyphrases
- extracorporeal membrane oxygenation
- acute respiratory distress syndrome
- cardiac arrest
- respiratory failure
- cardiopulmonary resuscitation
- young adults
- end stage renal disease
- mechanical ventilation
- newly diagnosed
- ejection fraction
- chronic kidney disease
- healthcare
- prognostic factors
- emergency department
- palliative care
- intensive care unit
- machine learning
- physical activity
- quality improvement
- preterm birth
- low dose
- deep learning
- childhood cancer
- low birth weight