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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...

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Autores principales: Fowler, Stephen, Morcos, Peter N., Cleary, Yumi, Martin-Facklam, Meret, Parrott, Neil, Gertz, Michael, Yu, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
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.
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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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