Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles.
Journal
CPT: pharmacometrics & systems pharmacology
ISSN: 2163-8306
Titre abrégé: CPT Pharmacometrics Syst Pharmacol
Pays: United States
ID NLM: 101580011
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
revised:
26
04
2022
received:
11
02
2022
accepted:
28
04
2022
pubmed:
24
5
2022
medline:
19
8
2022
entrez:
23
5
2022
Statut:
ppublish
Résumé
Everolimus is an immunosuppressant with a small therapeutic index and large between-patient variability. The area under the concentration versus time curve (AUC) is the best marker of exposure but measuring it requires collecting many blood samples. The objective of this study was to train machine learning (ML) algorithms using pharmacokinetic (PK) profiles from kidney transplant recipients, simulated profiles, or both types, and compare their performance for everolimus AUC
Identifiants
pubmed: 35599364
doi: 10.1002/psp4.12810
pmc: PMC9381914
doi:
Substances chimiques
Immunosuppressive Agents
0
Everolimus
9HW64Q8G6G
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1018-1028Informations de copyright
© 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.
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