OBJECTIVE: To assess the inter-individual variability in phenytoin (PHT) clearance predicted from in vitro kinetic data. METHODS: The Simcyp Population-based ADME Simulator was used to predict the clearance of PHT from reported in vitro kinetic data generated in the absence or presence of albumin. Liver intrinsic clearance (CLint.liver) was calculated from pharmacokinetic studies and compared to the time-averaged liver intrinsic clearance (CLint.liver.0–96 hr) of the simulations. Trough PHT concentrations at steady-state were collated from a therapeutic drug monitoring service, and data from the patient cohort were compared with the results from the virtual population. RESULTS: Simulations of PHT clearance were most accurate when based on ‘albumin-adjusted’ in vitro kinetic data, and they identified inter-individual variability outside the reported range of pharmacokinetic studies in healthy volunteers (CLint.liver = 4.6–22.5 L h−1 vs. CLint.liver.0–96 hr = 2.4–40.1 L h−1). Cumulative frequency plots of trough PHT concentration at steady-state were comparable between the patient cohort and the virtual population. CONCLUSIONS: Mechanistic modelling and simulation allow inter-individual variability in clearance to be considered during in vitro–in vivo extrapolation, and this approach may offer a superior indication of variability and covariates in the clinic than that provided by small pharmacokinetic studies.
OBJECTIVE: To assess the inter-individual variability in phenytoin (PHT) clearance predicted from in vitro kinetic data. METHODS: The Simcyp Population-based ADME Simulator was used to predict the clearance of PHT from reported in vitro kinetic data generated in the absence or presence of albumin. Liver intrinsic clearance (CLint.liver) was calculated from pharmacokinetic studies and compared to the time-averaged liver intrinsic clearance (CLint.liver.0–96 hr) of the simulations. Trough PHT concentrations at steady-state were collated from a therapeutic drug monitoring service, and data from the patient cohort were compared with the results from the virtual population. RESULTS: Simulations of PHT clearance were most accurate when based on ‘albumin-adjusted’ in vitro kinetic data, and they identified inter-individual variability outside the reported range of pharmacokinetic studies in healthy volunteers (CLint.liver = 4.6–22.5 L h−1 vs. CLint.liver.0–96 hr = 2.4–40.1 L h−1). Cumulative frequency plots of trough PHT concentration at steady-state were comparable between the patient cohort and the virtual population. CONCLUSIONS: Mechanistic modelling and simulation allow inter-individual variability in clearance to be considered during in vitro–in vivo extrapolation, and this approach may offer a superior indication of variability and covariates in the clinic than that provided by small pharmacokinetic studies.
Authors: Thomas M Polasek; Farhaan Patel; Berit P Jensen; Michael J Sorich; Michael D Wiese; Matthew P Doogue Journal: Br J Clin Pharmacol Date: 2013-04 Impact factor: 4.335
Authors: Thomas M Polasek; Geoffrey T Tucker; Michael J Sorich; Michael D Wiese; Titus Mohan; Amin Rostami-Hodjegan; Porntipa Korprasertthaworn; Vidya Perera; Andrew Rowland Journal: Br J Clin Pharmacol Date: 2018-01-11 Impact factor: 4.335
Authors: Kenneth H Wills; Stephen J Behan; Michael J Nance; Jessica L Dawson; Thomas M Polasek; Ashley M Hopkins; Madelé van Dyk; Andrew Rowland Journal: Pharmaceutics Date: 2021-12-27 Impact factor: 6.321