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  • Title: 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.
    Author: Yoshida K, Maeda K, Konagaya A, Kusuhara H.
    Journal: Drug Metab Dispos; 2018 Nov; 46(11):1805-1816. PubMed ID: 30135241.
    Abstract:
    The accurate estimation of "in vivo" inhibition constants (K i) of inhibitors and fraction metabolized (f m) of substrates is highly important 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 the parent drug would enable accurate estimation of in vivo K i and f m Twenty-four pharmacokinetic DDIs caused by P450 inhibition were analyzed with PBPK models using an emerging parameter estimation method, the cluster Newton method, which enables efficient estimation of a large number of parameters to describe the pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data), and the parameter estimates were compared with each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of both K i and f m Importantly, the estimated K i for the same inhibitor from different DDI studies was generally consistent, suggesting that the estimated K i from one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, a large discrepancy was observed between the reported in vitro K i and the in vitro estimates for some inhibitors, and the current in vivo K i estimates might be used as reference values when optimizing in vitro-in vivo extrapolation strategies. These results demonstrated that better use of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.
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