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Using ontogeny information to build predictive models for drug elimination

Jane Alcorn1, Patrick J McNamara

  • 1College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada S7N 5C9. jane.alcorn@usask.ca

Drug Discovery Today
|June 14, 2008
PubMed

Insights

Understanding drug elimination in children is crucial for safe dosing. Predictive models using ontogeny data can help design paediatric drug regimens and assess risks when specific data is lacking.

Area of Science:

  • Pharmacology and Toxicology
  • Paediatric Drug Development

Background:

  • Incomplete understanding of developmental maturation of drug elimination mechanisms challenges paediatric dosage regimen design and toxicological risk assessment.
  • Dynamic and variable maturation limits acquiring pharmacokinetic data in all paediatric populations.

Purpose of the Study:

  • To review principal approaches for estimating paediatric systemic clearance in the absence of comprehensive age-group-specific data.
  • To highlight the promise of predictive models using human ontogeny data for paediatric drug dosage and risk assessment.

Main Methods:

  • Review of population pharmacokinetic models.
  • Application of allometric scaling principles.
  • Utilization of physiologically based clearance scaling models.

Main Results:

  • Identified key modelling approaches for estimating paediatric systemic clearance.
  • Demonstrated the potential of predictive models to overcome data limitations.

Conclusions:

  • Population pharmacokinetic, allometric scaling, and physiologically based clearance scaling models are principal methods for estimating paediatric systemic clearance.
  • Predictive models utilizing ontogeny data offer a promising solution for paediatric dosage regimen design and risk assessment.

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