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Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities
PURPOSE OF REVIEW: This review gives a perspective on the current “state of the art” in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full conf...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5315728/ https://www.ncbi.nlm.nih.gov/pubmed/28261547 http://dx.doi.org/10.1007/s40495-017-0082-5 |
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author | Fowler, Stephen Morcos, Peter N. Cleary, Yumi Martin-Facklam, Meret Parrott, Neil Gertz, Michael Yu, Li |
author_facet | Fowler, Stephen Morcos, Peter N. Cleary, Yumi Martin-Facklam, Meret Parrott, Neil Gertz, Michael Yu, Li |
author_sort | Fowler, Stephen |
collection | PubMed |
description | PURPOSE OF REVIEW: This review gives a perspective on the current “state of the art” in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence. RECENT FINDINGS: Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where “unusual” cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement. SUMMARY: During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40495-017-0082-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5315728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-53157282017-03-02 Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities Fowler, Stephen Morcos, Peter N. Cleary, Yumi Martin-Facklam, Meret Parrott, Neil Gertz, Michael Yu, Li Curr Pharmacol Rep Molecular Drug Disposition (M Hu, Section Editor) PURPOSE OF REVIEW: This review gives a perspective on the current “state of the art” in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence. RECENT FINDINGS: Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where “unusual” cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement. SUMMARY: During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40495-017-0082-5) contains supplementary material, which is available to authorized users. Springer International Publishing 2017-02-01 2017 /pmc/articles/PMC5315728/ /pubmed/28261547 http://dx.doi.org/10.1007/s40495-017-0082-5 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Molecular Drug Disposition (M Hu, Section Editor) Fowler, Stephen Morcos, Peter N. Cleary, Yumi Martin-Facklam, Meret Parrott, Neil Gertz, Michael Yu, Li Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities |
title | Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities |
title_full | Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities |
title_fullStr | Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities |
title_full_unstemmed | Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities |
title_short | Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities |
title_sort | progress in prediction and interpretation of clinically relevant metabolic drug-drug interactions: a minireview illustrating recent developments and current opportunities |
topic | Molecular Drug Disposition (M Hu, Section Editor) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5315728/ https://www.ncbi.nlm.nih.gov/pubmed/28261547 http://dx.doi.org/10.1007/s40495-017-0082-5 |
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