Login / Signup

Development and application of a pediatric mechanistic kidney model.

Farzaneh SalemBen G SmallTrevor N Johnson
Published in: CPT: pharmacometrics & systems pharmacology (2022)
Pediatric physiologically-based pharmacokinetic (P-PBPK) models have been used to predict age related changes in the pharmacokinetics (PKs) of renally cleared drugs mainly in relation to changes in glomerular filtration rate. With emerging data on ontogeny of renal transporters, mechanistic models of renal clearance accounting for the role of active and passive secretion should be developed and evaluated. Data on age-related physiological changes and ontogeny of renal transporters were applied into a mechanistic kidney within a P-PBPK model. Plasma concentration-time profile and PK parameters of cimetidine, ciprofloxacin, metformin, tenofovir, and zidovudine were predicted in subjects aged 1 day to 18 years. The predicted and observed plasma concentration-time profiles and PK parameters were compared. The predicted concentration-time profile means and 5th and 95th percent intervals generally captured the observed data and variability in various studies. Overall, based on drugs and age bands, predicted to observed clearance were all within two-fold and in 11 of 16 cases within 1.5-fold. Predicted to observed area under the curve (AUC) and maximum plasma concentration (C max ) were within two-fold in 12 of 14 and 12 of 15 cases, respectively. Predictions in neonates and early infants (up to 14 weeks postnatal age) were reasonable with 15-20 predicted PK parameters within two-fold of the observed. ciprofloxacin but not zidovudine PK predictions were sensitive to basal kidney uptake transporter ontogeny. The results indicate that a mechanistic kidney model accounting for physiology and ontogeny of renal processes and transporters can predict the PK of renally excreted drugs in children. Further data especially in neonates are required to verify the model and ontogeny profiles.
Keyphrases
  • electronic health record
  • big data
  • pseudomonas aeruginosa
  • young adults
  • low birth weight
  • artificial intelligence
  • drug induced
  • childhood cancer