RT Journal Article
SR Electronic
T1 An Investigation into the Prediction of the Plasma Concentration-Time Profile and Its Interindividual Variability for a Range of Flavin-Containing Monooxygenase Substrates Using a Physiologically Based Pharmacokinetic Modeling Approach
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP 1259
OP 1267
DO 10.1124/dmd.118.080648
VO 46
IS 9
A1 Venkatesh Pilla Reddy
A1 Barry C. Jones
A1 Nicola Colclough
A1 Abhishek Srivastava
A1 Joanne Wilson
A1 Danxi Li
YR 2018
UL http://dmd.aspetjournals.org/content/46/9/1259.abstract
AB Our recent paper demonstrated the ability to predict in vivo clearance of flavin-containing monooxygenase (FMO) drug substrates using in vitro human hepatocyte and human liver microsomal intrinsic clearance with standard scaling approaches. In this paper, we apply a physiologically based pharmacokinetic (PBPK) modeling and simulation approach (M&S) to predict the clearance, area under the curve (AUC), and Cmax values together with the plasma profile of a range of drugs from the original study. The human physiologic parameters for FMO, such as enzyme abundance in liver, kidney, and gut, were derived from in vitro data and clinical pharmacogenetics studies. The drugs investigated include itopride, benzydamine, tozasertib, tamoxifen, moclobemide, imipramine, clozapine, ranitidine, and olanzapine. The fraction metabolized by FMO for these drugs ranged from 21% to 96%. The developed PBPK models were verified with data from multiple clinical studies. An attempt was made to estimate the scaling factor for recombinant FMO (rFMO) using a parameter estimation approach and automated sensitivity analysis within the PBPK platform. Simulated oral clearance using in vitro hepatocyte data and associated extrahepatic FMO data predicts the observed in vivo plasma concentration profile reasonably well and predicts the AUC for all of the FMO substrates within 2-fold of the observed clinical data; seven of the nine compounds fell within 2-fold when human liver microsomal data were used. rFMO overpredicted the AUC by approximately 2.5-fold for three of the nine compounds. Applying a calculated intersystem extrapolation scalar or tissue-specific scalar for the rFMO data resulted in better prediction of clinical data. The PBPK M&S results from this study demonstrate that human hepatocytes and human liver microsomes can be used along with our standard scaling approaches to predict human in vivo pharmacokinetic parameters for FMO substrates.