A comparative evaluation of models to predict human intestinal metabolism from nonclinical data. (18th April 2017)

• Record Type: Journal Article
• Authors: Yau, Estelle; Petersson, Carl; Dolgos, Hugues; Peters, Sheila Annie
• Other Names: Rostami‐Hodjegan Amin guestEditor.; Tamai Ikumi guestEditor.; Pang K. Sandy guestEditor.
• Is Part Of: Biopharmaceutics & drug disposition. Volume 38:Number 3(2017:Apr.)
• Page Start: 163
• Page End: 186
• Publication Date: 2017-04-18
• Subjects: intestinal metabolism -- HLM -- ADAM -- Qgut -- competing rates -- rat; Biopharmaceutics -- Periodicals; Drugs -- Metabolism -- Periodicals; Pharmacology -- Periodicals; Biopharmaceutics -- Periodicals; Pharmaceutical Preparations -- metabolism -- Periodicals; 615.19
• DOI: 10.1002/bdd.2068
• Languages: English
• ISSNs: 0142-2782

Abstract

Abstract: Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter‐mediated secretion ( F g ) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F g and, based on the outcome, to provide recommendations for the prediction of human F g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models – the ADAM, Q gut and Competing Rates – was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F g was also explored. The ADAM, Q gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE = 1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE = 0.44) as it seems systematically to under‐predict human F g . Hence, we would recommend the use of rat to identify the need for F g assessment, followed by the use of HLM in simple models to predict human F g . © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd.