PBPK and its Virtual Populations: the Impact of Physiology on Pediatric Pharmacokinetic Predictions of Tramadol

Abstract

In pediatric PBPK models, age-related changes in the body are known to occur. Given the sparsity of and the variability associated with relevant physiological parameters, different PBPK software providers may vary in their system's data. In this work, three commercially available PBPK software packages (PK-Sim®, Simcyp®, and Gastroplus®) were investigated regarding their differences in system-related information, possibly affecting clearance prediction. Three retrograde PBPK clearance models were set up to enable prediction of pediatric tramadol clearance. These models were qualified in terms of total, CYP2D6, and renal clearance in adults. Tramadol pediatric clearance predictions from PBPK were compared with a pooled popPK model covering clearance ranging from neonates to adults. Fold prediction errors were used to evaluate the results. Marked differences in liver clearance prediction between PBPK models were observed. In general, the prediction bias of total clearance was greatest at the youngest population and decreased with age. Regarding CYP2D6 and renal clearance, important differences exist between PBPK software tools. Interestingly, the PBPK model with the shortest CYP2D6 maturation half-life (PK-Sim) agreed best with the in vivo CYP2D6 maturation model. Marked differences in physiological data explain the observed differences in hepatic clearance prediction in early life between the various PBPK software providers tested. Consensus on the most suited pediatric data to use should harmonize and optimize pediatric clearance predictions. Moreover, the combination of bottom-up and top-down approaches, using a convenient probe substrate, has the potential to update system-related parameters in order to better represent pediatric physiology.

Physiologically based pharmacokinetic modeling of tramadol to inform dose adjustment and drug‐drug interactions according to CYP2D6 phenotypes

“…After intravenous administration, tramadol is rapidly distributed and metabolized. Approximately 20% of tramadol is cleared by the kidney, and the rest is cleared by liver metabolism 10 . Previous studies have demonstrated that tramadol is metabolized in the liver mainly by CYP2D6 (O‐desmethyl tramadol—M1), CYP3A4 (N‐desmethyl tramadol—M2), and CYP2B6 (N‐desmethyl tramadol—M2).…”

“…The CYP2D6 contribution represented 43% of the hepatic clearance as previously described, the CYP2B6 contribution was determined as 10% of the hepatic clearance, and the remaining CYP3A4 contribution was 47% 10,11 . Therefore, we set f m, CYP2D6 as 0.34, f m, CYP3A4 as 0.37, and f m, CYP2B6 as 0.08 after intravenous administration of tramadol to optimize the clearance parameters.…”

Development and Evaluation of a Physiologically Based Pharmacokinetic Drug-Disease Model of Propranolol for Suggesting Model Informed Dosing in Liver Cirrhosis Patients

“…A two-fold error range was considered acceptable for model verification as reported previously. 13,14,23,43 The average fold error (AFE) and root mean square error (RMSE) were calculated for further verification of the developed PBPK model. 14,44 The box-whisker plots were used to express the drug dosing recommendations in cirrhosis patients (CP-A-C).…”

Making Medicines Baby Size: The Challenges in Bridging the Formulation Gap in Neonatal Medicine

“…Comprehensive physiologically based pharmacokinetic modelling (PBPK) systems have been introduced that replicate the known parameters of the paediatric GI tract to better predict oral dosing [174,175,176,177,178,179,180,181,182]. However, the major limitation in both in vitro and in silico models is the lack of accurate knowledge about the neonatal and infant intestinal parameters [178,183,184].…”