Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level

Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (...

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Detalles Bibliográficos
Autores principales: Hutchinson, Lucy, Steiert, Bernhard, Soubret, Antoine, Wagg, Jonathan, Phipps, Alex, Peck, Richard, Charoin, Jean‐Eric, Ribba, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Perspectives
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430152/
https://www.ncbi.nlm.nih.gov/pubmed/30549240
http://dx.doi.org/10.1002/psp4.12377
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author Hutchinson, Lucy
Steiert, Bernhard
Soubret, Antoine
Wagg, Jonathan
Phipps, Alex
Peck, Richard
Charoin, Jean‐Eric
Ribba, Benjamin
author_facet Hutchinson, Lucy
Steiert, Bernhard
Soubret, Antoine
Wagg, Jonathan
Phipps, Alex
Peck, Richard
Charoin, Jean‐Eric
Ribba, Benjamin
author_sort Hutchinson, Lucy
collection PubMed
description Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow.
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spelling pubmed-64301522019-04-01 Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level Hutchinson, Lucy Steiert, Bernhard Soubret, Antoine Wagg, Jonathan Phipps, Alex Peck, Richard Charoin, Jean‐Eric Ribba, Benjamin CPT Pharmacometrics Syst Pharmacol Perspectives Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow. John Wiley and Sons Inc. 2019-02-03 2019-03 /pmc/articles/PMC6430152/ /pubmed/30549240 http://dx.doi.org/10.1002/psp4.12377 Text en © 2019 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the 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 Perspectives
Hutchinson, Lucy
Steiert, Bernhard
Soubret, Antoine
Wagg, Jonathan
Phipps, Alex
Peck, Richard
Charoin, Jean‐Eric
Ribba, Benjamin
Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
title Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
title_full Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
title_fullStr Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
title_full_unstemmed Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
title_short Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
title_sort models and machines: how deep learning will take clinical pharmacology to the next level
topic Perspectives
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430152/
https://www.ncbi.nlm.nih.gov/pubmed/30549240
http://dx.doi.org/10.1002/psp4.12377
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