Population pharmacokinetics of piperacillin using scavenged samples from preterm infants

Abstract

Objectives: Piperacillin is often used in preterm infants for intra-abdominal infections; however, dosing has been derived from small single-center studies excluding extremely preterm infants at a highest risk for these infections. We evaluated the population pharmacokinetics (PK) of piperacillin using targeted sparse sampling and scavenged samples obtained from preterm infants ≤ 32 weeks of gestational age at birth and <120 postnatal days.

Materials and methods: A 5-center study was performed. A population PK model using nonlinear mixed effect modeling was developed. Covariate effects were evaluated based on the estimated precision and clinical significance.

Results: Fifty-six preterm infants were evaluated and had a median (range) gestational age at birth of 25 (22-32) weeks, a postnatal age of 17 (1-77) days, a postmenstrual age of 29 (23-40) weeks, and a weight of 867 (400-2580) g. The final PK data set contained 211 samples; 202/211 (96%) were scavenged from the discarded clinical specimens. Piperacillin population PK was best described by a 1-compartment model. The population mean clearance (CL) was derived by the equation CL (L/h) = 0.479 × (weight)(0.75) × 0.5/serum creatinine and using a volume of distribution (V) (L) of 2.91 × (weight). The relative standard errors around parameter estimates ranged from 13.7% to 32.2%. A trend toward increased CL was observed with increasing gestational age at birth; infants with serum creatinine ≥ 1.2 mg/dL had a 60% reduction in piperacillin CL. The majority (>70%) of infants did not meet predefined pharmacodynamic efficacy targets.

Conclusions: Scavenged PK sampling is a minimal-risk approach that can provide meaningful information related to the development of PK models but not dosing recommendations for piperacillin. The utility of scavenged sampling in providing definitive dosing recommendations may be drug dependent and needs to be further explored.

Trial registration: ClinicalTrials.gov NCT00873327.

Conflict of interest statement

Sources of funding and conflicts of interest: This study was sponsored by the National Institute of Health through the Pediatric Pharmacology Research Unit. Dr. Cohen-Wolkowiez receives support from NICHD 1K23HD064814-01 and the non-profit organization Thrasher Research Foundation for his work in pediatric clinical pharmacology and from industry for neonatal and pediatric drug development (http://www.dcri.duke.edu/research/coi.jsp). Dr. Benjamin receives support from the U.S. government for his work in pediatric and neonatal clinical pharmacology (1R01HD057956-02, 1R01FD003519-01, 1U10-HD45962-06, 1K24HD058735-01, and Government Contract HHSN267200700051C), the non-profit organization Thrasher Research Foundation for his work in neonatal candidiasis, and from industry for neonatal and pediatric drug development (http://www.dcri.duke.edu/research/coi.jsp). Dr. Ross has no conflicts to report. Dr. James receives research support from the National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK075936, 1R01DK081406, 1R01 G1-31616-99 [PI Hinson]); NIH/National Center for Research Resources (1UL1RR029884, PI Lowery); Center for Clinical and Translational Research (2289-2); NIH (1R01HD060543-01A1, PI van den Anker); and the Arkansas Children’s Hospital Research Institute (10469370, PI Stroud). Dr. Walsh has no conflicts to report. Dr. Sullivan received support from the U.S. government for her work in pediatric pharmacology (U10 HD045934 05) and from industry for neonatal and pediatric drug development. Ms. Zadell has no conflicts to report. Ms. Newman has no conflicts to report. Ms. White has no conflicts to report. Dr. Kashuba receives support from the U.S. government for work in clinical pharmacology (1U01AI095031, P30AI37260) and from industry for clinical pharmacology (ViiV, Abbott, Tibotec, and Merck). Dr. Ouellet is an employee of GSK and has no conflict of interest.

Figures

FIGURE 1

Base model scatter plots of CL ETA1 estimates and the following: BGA (A), PNA (B), PMA (C), and SCR (D).

FIGURE 2

Final population pharmacokinetic model diagnostic plots: observed vs. population prediction (A) and individual prediction (B), weighted residuals vs. population predictions (C) and time (D). For A and B, the line of identity is included as a reference. For weighted residuals, a solid line at y=0 is included as a reference.

FIGURE 3

Standardized visual predictive check of piperacillin observation percentiles versus time after last dose. Solid black circles represent calculated percentiles. Dashed lines represent the 5th, 50th, and 95th percentiles (bottom, middle, and top, respectively) of model predicted data.

FIGURE 4

Weight-normalized piperacillin clearance versus serum creatinine (A) and body weight (B).

FIGURE 5

Pharmacodynamic target attainment rates by gestational age group. A: Proportion of patients who met PD target of desired concentrations for 50% of the dosing interval (black bar: 16 mg/L, gray bar: 64 mg/L); B: Proportion of patients who met PD target of desired concentrations for 75% of the dosing interval (black bar: 16 mg/L, gray bar: 64 mg/L); C: Proportion of simulated patients who met PD target of desired concentrations for 50% of the dosing interval (dark blue bar: Neofax 16 mg/L, light blue bar: Neofax 64 mg/L, dark gray bar: The Harriet Lane Handbook 16 mg/L, light gray bar: The Harriet Lane Handbook 64 mg/L); D: Proportion of simulated patients who met PD target of desired concentrations for 75% of the dosing interval (dark blue bar: Neofax 16 mg/L, light blue bar: Neofax 64 mg/L, dark gray bar: The Harriet Lane Handbook 16 mg/L, light gray bar: The Harriet Lane Handbook 64 mg/L).