Open AccessModels and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
Author(s) -
Hutchinson Lucy,
Steiert Bernhard,
Soubret Antoine,
Wagg Jonathan,
Phipps Alex,
Peck Richard,
Charoin JeanEric,
Ribba Benjamin
Publication year - 2019
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12377
Subject(s) - machine learning , computer science , workflow , artificial intelligence , context (archaeology) , biopharmaceutical , pipeline (software) , big data , data science , pharmaceutical industry , deep learning , data mining , medicine , pharmacology , paleontology , genetics , database , biology , programming language
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.