Open AccessA Flexible Approach for Context‐Dependent Assessment of Quantitative Systems Pharmacology Models
Author(s) -
Ramanujan Saroja,
Chan Jason R.,
Friedrich Christina M.,
Thalhauser Craig J.
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/psp4.12409
Subject(s) - computer science , context (archaeology) , robustness (evolution) , systems pharmacology , premise , machine learning , data mining , data science , pharmacology , biology , paleontology , drug , biochemistry , linguistics , philosophy , gene
Systems pharmacology models are having an increasing impact on pharmaceutical research and development, from preclinical through post-approval phases, including use in regulatory interactions. Given the wide diversity among the models and the contexts of use, a common but flexible strategy for model assessment is needed to enable the appropriate interpretation of model-based results. We present an approach to evaluate these models and discuss how it can be customized to available data and intended application. This article is protected by copyright. All rights reserved.