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Development and Verification of a Japanese Pediatric Physiologically based Pharmacokinetic Model with Emphasis on Drugs Eliminated by Cytochrome P450 or Renal Excretion.

Trevor N JohnsonKhaled AbduljalilXian PanChie Emoto
Published in: Journal of clinical pharmacology (2023)
Physiologically based pharmacokinetic (PBPK) models are useful in bridging between different ethnic groups and there is increasing regulatory application of this approach in adults. Reported pediatric PBPK models tend to focus on the North European population with few examples in other ethnic groups. This study describes the development and verification of a Japanese Pediatric PBPK population. Development of the model was based on the existing North European pediatric population. Japanese systems and clinical data were collated from public databases and the literature, underlying demographics and equations were optimized so that physiological outputs represented the Japanese pediatric population. The model was tested using 14 different small molecule drugs, eliminated by a variety of pathways including CYP3A4 metabolism and renal excretion. Given the limitations of the clinical data, overall performance of the model was good with 44/62 predictions for PK parameters (AUC, C max , clearance) being within 0.8 to 1.25-fold, 56/62 within 0.67 to 1.5-fold and 61/62 within 0.5 to 2-fold of observed. Specific results for the five CYP3A4 substrates showed 20/31 cases were predicted within 0.8 to 1.25-fold, 27/31 within 0.67 to 1.5-fold and all were within 0.5 to 2-fold. Given increased regulatory use of pediatric PBPK in drug development expanding these models to other ethnic groups are important. Considering qualifying these models based on context of use, there is a need to expand on the current research to include a larger range of drugs having different elimination pathways. Collaboration between academic, industry, model providers and regulators will facilitate further development. This article is protected by copyright. All rights reserved.
Keyphrases
  • small molecule
  • systematic review
  • transcription factor
  • big data
  • emergency department
  • artificial intelligence
  • young adults
  • deep learning
  • protein protein