RT Journal Article
SR Electronic
T1 Accurate estimation of in vivo inhibition constants of inhibitors and fraction metabolized of substrates with physiologically-based pharmacokinetic drug-drug interaction models incorporating parent drugs and metabolites of substrates with cluster Newton method
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP dmd.118.081828
DO 10.1124/dmd.118.081828
A1 Kenta Yoshida
A1 Kazuya Maeda
A1 Akihiko Konagaya
A1 Hiroyuki Kusuhara
YR 2018
UL http://dmd.aspetjournals.org/content/early/2018/08/22/dmd.118.081828.abstract
AB Accurate estimation of "in vivo" inhibition constants (Ki) of inhibitors and fraction metabolized (fm) of substrates is of high importance for drug-drug interaction (DDI) prediction based on physiologically-based pharmacokinetic (PBPK) models. We hypothesized that analysis of the pharmacokinetic alterations of substrate metabolites in addition to parent drug would enable accurate estimation of in vivo Ki and fm. Twenty four pharmacokinetic DDIs caused by CYP inhibition were analyzed with PBPK models using an emerging parameter estimation method, Cluster Newton Method, which enables efficient estimation of a large number of parameters to describe pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data) and parameter estimates were compared to each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of both Ki and fm. Importantly, estimated Ki for the same inhibitor from different DDI studies were generally consistent, suggesting that the estimated Ki from one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, large discrepancy was observed between reported in vitro Ki and in vitro estimates for some inhibitors, and the current in vivo Ki estimates might be used as reference values when optimizing IVIVE strategies. These results demonstrated that better utilization of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.