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A comparative evaluation of models to predict human intestinal metabolism from nonclinical data
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5412686/ https://www.ncbi.nlm.nih.gov/pubmed/28152562 http://dx.doi.org/10.1002/bdd.2068 |
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author | Yau, Estelle Petersson, Carl Dolgos, Hugues Peters, Sheila Annie |
author_facet | Yau, Estelle Petersson, Carl Dolgos, Hugues Peters, Sheila Annie |
author_sort | Yau, Estelle |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5412686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54126862017-05-19 A comparative evaluation of models to predict human intestinal metabolism from nonclinical data Yau, Estelle Petersson, Carl Dolgos, Hugues Peters, Sheila Annie Biopharm Drug Dispos Special Issue Articles 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. John Wiley and Sons Inc. 2017-04-18 2017-04 /pmc/articles/PMC5412686/ /pubmed/28152562 http://dx.doi.org/10.1002/bdd.2068 Text en © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Special Issue Articles Yau, Estelle Petersson, Carl Dolgos, Hugues Peters, Sheila Annie A comparative evaluation of models to predict human intestinal metabolism from nonclinical data |
title | A comparative evaluation of models to predict human intestinal metabolism from nonclinical data |
title_full | A comparative evaluation of models to predict human intestinal metabolism from nonclinical data |
title_fullStr | A comparative evaluation of models to predict human intestinal metabolism from nonclinical data |
title_full_unstemmed | A comparative evaluation of models to predict human intestinal metabolism from nonclinical data |
title_short | A comparative evaluation of models to predict human intestinal metabolism from nonclinical data |
title_sort | comparative evaluation of models to predict human intestinal metabolism from nonclinical data |
topic | Special Issue Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5412686/ https://www.ncbi.nlm.nih.gov/pubmed/28152562 http://dx.doi.org/10.1002/bdd.2068 |
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