Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry.
computational psychiatry
digital health
pharmacometrics
precision dosing
reinforcement learning
Journal
Frontiers in pharmacology
ISSN: 1663-9812
Titre abrégé: Front Pharmacol
Pays: Switzerland
ID NLM: 101548923
Informations de publication
Date de publication:
2022
2022
Historique:
received:
09
11
2022
accepted:
12
12
2022
entrez:
6
3
2023
pubmed:
7
3
2023
medline:
7
3
2023
Statut:
epublish
Résumé
Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)-a set of computational methods addressing optimization problems as a continuous learning process-shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry-a way to characterize mental dysfunctions in terms of aberrant brain computations-and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities.
Identifiants
pubmed: 36873047
doi: 10.3389/fphar.2022.1094281
pii: 1094281
pmc: PMC9981647
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1094281Informations de copyright
Copyright © 2023 Ribba.
Déclaration de conflit d'intérêts
The author is employed by F. Hoffmann La Roche Ltd.
Références
Trends Cogn Sci. 2012 Jan;16(1):72-80
pubmed: 22177032
Breast Cancer Res Treat. 2016 Apr;156(2):331-41
pubmed: 27002506
CPT Pharmacometrics Syst Pharmacol. 2022 Nov;11(11):1497-1510
pubmed: 36177959
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020 Mar;4(1):
pubmed: 34527853
Front Hum Neurosci. 2013 Jun 11;7:261
pubmed: 23781184
Biol Mood Anxiety Disord. 2013 Jun 19;3(1):12
pubmed: 23782813
CPT Pharmacometrics Syst Pharmacol. 2021 Mar;10(3):241-254
pubmed: 33470053
Clin Pharmacol Ther. 2020 Apr;107(4):796-805
pubmed: 31955409
Ann Oncol. 2022 May;33(5):556-560
pubmed: 35189267
CPT Pharmacometrics Syst Pharmacol. 2020 Jan;9(1):5-20
pubmed: 31674729
Clin Pharmacol Ther. 2018 Jul;104(1):72-80
pubmed: 29377057
Health Psychol. 2015 Dec;34S:1220-8
pubmed: 26651463
JAMA. 2021 Apr 20;325(15):1505-1506
pubmed: 33760028
CPT Pharmacometrics Syst Pharmacol. 2022 Jun;11(6):745-754
pubmed: 35582964
Psychol Methods. 2022 Oct;27(5):874-894
pubmed: 35025583
Stat Sci. 2020;35(3):375-390
pubmed: 33132496
Am J Psychiatry. 2010 Jul;167(7):748-51
pubmed: 20595427
Science. 2004 Dec 10;306(5703):1944-7
pubmed: 15591205
Lancet. 2012 Jul 21;380(9838):219-29
pubmed: 22818936
Clin Cancer Res. 2012 Sep 15;18(18):5071-80
pubmed: 22761472
Ann Behav Med. 2018 May 18;52(6):446-462
pubmed: 27663578
Clin Pharmacol Ther. 2021 Jan;109(1):47-50
pubmed: 33107023
J Clin Oncol. 2002 Dec 15;20(24):4713-21
pubmed: 12488418
CPT Pharmacometrics Syst Pharmacol. 2021 May;10(5):412-419
pubmed: 33719204
iScience. 2021 Jun 30;24(7):102804
pubmed: 34308294
Science. 2015 May 1;348(6234):499-500
pubmed: 25931539
Medicine (Baltimore). 2021 Jan 29;100(4):e23930
pubmed: 33530193