Agathe Béranger1,2, Sihem Benaboud2,3, Saïk Urien1,2, Florence Moulin4, Emmanuelle Bille5, Mathieu Genuini4, Fabrice Lesage4, Déborah Hirt2,3, Sylvain Renolleau4, Jean-Marc Tréluyer1,2,3, Mehdi Oualha4
1 Unité de Recherche Clinique, hôpital Tarnier, Université Paris Descartes, Sorbonne-Paris Cité, 89 rue d’Assas, 75006, Paris, France. 2 EA7323, Evaluation des thérapeutiques et pharmacologie périnatale et pédiatrique, Université Paris Descartes, Paris, France. 3 Service de pharmacologie clinique, hôpital Cochin, Paris Descartes University, Sorbonne-Paris Cité, 27 rue du Faubourg Saint-Jacques, 75014, Paris, France. 4 Service de réanimation et surveillance continue médico-chirurgicales, hôpital Necker Enfants-Malades, Université Paris Descartes, Sorbonne-Paris Cité, 149 rue de Sèvres, 75015, Paris, France. 5 Service de microbiologie, hôpital Necker Enfants-Malades, Université Paris Descartes, Sorbonne-Paris Cité, 149 rue de Sèvres, 75015, Paris, France.
Objectives: Pharmacokinetic parameters are altered in critically ill patients, leading to a reduction of the beta-lactam concentrations. For instance, in adult patients, it has been well demonstrated that augmented renal clearance results in subtherapeutic antibiotic concentrations. Moreover, there is a large between subject variability in children. Our objectives were to build a pediatric population pharmacokinetic model for Piperacillin, in order to optimize individual dosing regimen.
Methods: All children admitted in pediatric intensive care unit, aged less than 18 years, weighing more than 2.5 kg, and receiving intermittent Piperacillin infusions were included. Blood samples (1 mL on heparin tube) were collected during routine laboratory tests, part of patient clinical routine care. Piperacillin was quantified by high performance liquid chromatography. To create a population pharmacokinetics model, Piperacillin data was analyzed using non-linear mixed effect modelling software (MONOLIX, version 2016R1), along with the SAEM algorithm. Models were coded with differential equations in a MLXTRAN script file. Monte Carlo simulations were used to optimize dosing regimen, in order to maintain Piperacillin plasma concentration above the minimum inhibitory concentration (16 mg.L-1 for Pseudomonas aeruginosa) throughout the dosing interval (100% fT>MIC).
Results: We included 50 children with a median (range) post natal age of 2.3 (0.1-18) years, body weight of 11.9 (2.7-50) kg, PELOD-2 severity score of 4 (0-16), and estimated creatinine clearance (eCCL) of 142 (29-675) mL.min-1.1.73m-2. A one-compartment model with first-order elimination adequately described the data. Median (range) values for piperacillin clearance and volume of distribution were respectively 3 (0.71-10) L.h-1 and 0.33 (0.21-0.86) L.kg-1. Body weight was integrated with the allometric relationship. eCCL and PELOD-2 severity score were the covariates explaining between subject variability on clearance and volume, respectively. A third of the cohort attained the target, according to our dosing regimen and to the European and American guidelines. Monte Carlo simulations were conducted with two daily dosing regimens: 300 mg.kg-1.day-1 for normal clearance (40-130 mL.min-1.1.73m-2), and 400 mg.kg-1.day-1 for eCCL > 130 mL.min-1.1.73m-2. According to the simulations, for children with normal and augmented renal clearance, continuous infusion provided the highest probability to reach the target, with dosing regimen of 300 and 400 mg.kg-1.day-1 respectively.
Conclusions: To reach the target of 100% fT>MIC, standard intermittent Piperacillin dosing regimen in critically ill children is not appropriate. In addition to body weight, dosing regimens should take into account the creatinine clearance and the PELOD-2 severity score. Continuous infusion is the most adequate dosing regimen for children with augmented renal clearance. Piperacillin individualized dosing regimens and therapeutic drug monitoring are mandatory in pediatric intensive care unit.
References:
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Reference: PAGE 27 (2018) Abstr 8418 [www.page-meeting.org/?abstract=8418]
Poster: Drug/Disease Modelling - Paediatrics