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Pharmaceutical Research

, Volume 31, Issue 3, pp 754–767 | Cite as

A Neonatal Amikacin Covariate Model Can Be Used to Predict Ontogeny of Other Drugs Eliminated Through Glomerular Filtration in Neonates

  • Roosmarijn F. W. De Cock
  • Karel Allegaert
  • Catherine M. T. Sherwin
  • Elisabet I. Nielsen
  • Matthijs de Hoog
  • Johannes N. van den Anker
  • Meindert Danhof
  • Catherijne A. J. Knibbe
Research Paper

ABSTRACT

Purpose

Recently, a covariate model characterizing developmental changes in clearance of amikacin in neonates has been developed using birth bodyweight and postnatal age. The aim of this study was to evaluate whether this covariate model can be used to predict maturation in clearance of other renally excreted drugs.

Methods

Five different neonatal datasets were available on netilmicin, vancomycin, tobramycin and gentamicin. The extensively validated covariate model for amikacin clearance was used to predict clearance of these drugs. In addition, independent reference models were developed based on a systematic covariate analysis.

Results

The descriptive and predictive properties of the models developed using the amikacin covariate model were good, and fairly similar to the independent reference models (goodness-of-fit plots, NPDE). Moreover, similar clearance values were obtained for both approaches. Finally, the same covariates as in the covariate model of amikacin, i.e. birth bodyweight and postnatal age, were identified on clearance in the independent reference models.

Conclusions

This study shows that pediatric covariate models may contain physiological information since information derived from one drug can be used to describe other drugs. This semi-physiological approach may be used to optimize sparse data analysis and to derive individualized dosing algorithms for drugs in children.

KEY WORDS

developmental changes extrapolation glomerular filtration rate neonates pharmacokinetics 

ABBREVIATIONS

bBW

Birth bodyweight

cBW

Current bodyweight

GFR

Glomerular filtration rate

NPDE

Normalized prediction distribution error method

PD

Pharmacodynamics

PK

Pharmacokinetics

PNA

Postnatal age

Notes

ACKNOWLEDGMENTS AND DISCLOSURES

This study was performed within the framework of Top Institute Pharma project number D2-104. The clinical research of Karel Allegaert is supported by the Fund for Scientific Research, Flanders (Clinical Fellowship 1800214N) and has been supported by an IWT-SBO project (130033). The clinical research of J. van den Anker is supported by NIH grants (R01HD060543, K24DA027992, R01HD048689, U54HD071601) and FP7 grants TINN (223614), TINN2 (260908), NEUROSIS (223060), and GRIP (261060). The authors also would like to thank LAP&P Consultants for their technical support with NONMEM.

Supplementary material

11095_2013_1197_MOESM1_ESM.doc (49 kb)
Supplement I (DOC 49 kb)
11095_2013_1197_MOESM2_ESM.doc (52 kb)
Supplement II (DOC 52 kb)

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Roosmarijn F. W. De Cock
    • 1
  • Karel Allegaert
    • 2
  • Catherine M. T. Sherwin
    • 3
  • Elisabet I. Nielsen
    • 4
  • Matthijs de Hoog
    • 5
  • Johannes N. van den Anker
    • 5
    • 6
  • Meindert Danhof
    • 1
  • Catherijne A. J. Knibbe
    • 1
    • 7
  1. 1.Division of Pharmacology, LACDRLeiden UniversityLeidenThe Netherlands
  2. 2.Neonatal Intensive Care UnitUniversity Hospital LeuvenLeuvenBelgium
  3. 3.Division of Clinical Pharmacology & Clinical Trials Office Department of PediatricsUniversity of Utah School of MedicineSalt Lake CityUSA
  4. 4.Department of Pharmaceutical BiosciencesUppsala UniversityUppsalaSweden
  5. 5.Intensive Care and Department of Pediatric SurgeryErasmus MC - Sophia Children’s HospitalRotterdamThe Netherlands
  6. 6.Division of Pediatric Clinical PharmacologyChildren’s National Medical CenterWashingtonUSA
  7. 7.Department of Clinical PharmacySt. Antonius HospitalNieuwegeinThe Netherlands

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