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Suggestions for Model-Informed Precision Dosing to Optimize Neonatal Drug Therapy.

Joshua C EuteneuerSuyog KamatkarTsuyoshi FukudaAlexander A VinksHenry T Akinbi
Published in: Journal of clinical pharmacology (2018)
Evidence for dosing, efficacy, and safety of most medications used to treat neonates is sparse. Thus, dosing is usually derived by extrapolation from adult and pediatric pharmacologic data with scaling by body weight or body surface area. This may lead to drug dosing that is unsafe or ineffective. However, new strategies are being developed and studied to dose medications in critically ill neonates. Mass spectroscopy technology capable of quickly analyzing drug levels is readily available. Software that integrates population pharmacokinetics and pharmacodynamics with data from sparse samples from neonates allows for timely adjustments of dosing to achieve the desired effect while minimizing adverse outcomes. Some genetic polymorphisms that affect drug response in neonates have also been reported. This review highlights aspects of drug response and how it is impacted by prematurity, assesses pharmacogenomic studies in neonates, and offers suggestions for innovative pharmacokinetic/pharmacodynamic model-based approaches that combine population- or physiology-based pharmacology data, Bayesian analysis, and electronic decision support tools for precision dosing in neonates while illustrating examples where this approach can be used to optimize medical therapy in neonates. Barriers to implementing precision dosing in neonates and how to overcome them are also discussed.
Keyphrases
  • low birth weight
  • preterm infants
  • body weight
  • electronic health record
  • preterm birth
  • big data
  • adverse drug
  • emergency department
  • machine learning
  • artificial intelligence
  • bone marrow